Preparation of IPCSG Newsletter

Subject: IPCSG Newsletters

Programs required: Have a Windows PC with internet access and have knowledge of the following Programs Microsoft Word,   Microsoft Publisher [4 Alternatives to Microsoft Publisher] and   Microsoft PowerPoint.

files used to create the newsletter.  

1. The Word file “Newsletter Notes July.docx” is where I begin gathering data to drop into 
2. the Publisher file “IPCSG 2016 July Newsletter Final.pub” (This starts out as a Draft file).  

Production Files

1. distribute draft.pub and .pdf files to Lyle, Gene Van Vleet and John Tassi for comment---probably now to me as well.  
2. create Final .pub and .pdf files. 

newsletter process steps:

1. start by calling up the previous Final.pub file and edit it. 
2. create a recap of the previous month’s meeting.  
1. The tools are a rough-cut video provided by Bill Manning, our videographer, usually only a few days after the meeting.  It comes from the 2^nd camera that you may have noticed on the right side of the auditorium and is unedited.  
2. get a copy of each speaker’s PowerPoint file.  I work with a note pad to drop in significant data by viewing the video and pulling up the slides from PowerPoint as an aid. 
3. create the recap from the notes in a draft .pub file---this could be done in word first if preferred. 
3. start developing the data for the succeeding pages. 
1. “Future Meetings” (Page 3 of the attached Newsletter) is updated by communications with George Johnson and/or Gene Van Vleet .  
2. The FYI is sourced by me, Lyle and George, primarily. 
3. “On The Lighter Side” section is on page 4.  
1. I google funny cartoons and drop a few I like on the Word document.  
2. I google things like “About Humor” and “Aha Jokes” to develop the humor stuff.  
4. develop the Prostate Cancer News articles to include in the newsletter.  
1. google Prostate Cancer News and find the best sources are
   Medical News Today,
   Science Daily,
   Prostate Cancer News Today and
   Prostate Cancer Foundation
   to name a few.  You will find that many have the same stories.  
2. try to focus on the most recent.  Try to fit to our general audience, I try not to pick highly technical articles.  PCRI puts out some good info.  You should get on their distribution. I drop articles I like on the Word document for starters, because they have to be edited for the Newsletter by sizing and often converting to the format used in the newsletter—Gill Sans MT font size 12. 
4. Prepare final draft. 
1. sort through the stuff developed on the word document and start dropping it in the .pub draft.  This requires text box sizing and page adds or deletions to get to the final product.  
2. The text boxes on page 9 of the July sample are standard as is the last page which is the map and directions. 
3. There is no “copyright” in how I have developed it.  I had to learn all the programs when I took over.  IPCSG will pay for any needed programs. 
5. Timing of the newsletter is to get the final version to John Tassi, our webmaster, on Thursday of the week prior to the next meeting.  
1. It is necessary to print about 80 copies, about 
2. 50 get mailed to members who don’t do e-mail and about 
3. 30 to put on the meeting freebie table. 

SACHI uses Soli in RadarCat to Identify Real-World Objects

SACHI | RadarCat for object recognition

RadarCat Doesn't Purr, But It Can Identify Real-World Objects | Digital Trends
Researchers at the University of St Andrews in Scotland recently figured out a way for a computer to recognize different types of materials and objects ranging from glass bottles to computer keyboards to human body parts. They call the resulting device RadarCat, which is short for Radar Categorization for Input and Interaction. As the name implies, this device uses radar to identify objects.
RadarCat was created within the university’s Computer Human Interaction research group. The radar-based sensor used in RadarCat stems from the Project Soli alpha developer kit provided by the Google Advanced Technology and Projects (ATAP) program. This sensor was originally created to detect the slightest of finger movements, but the RadarCat team saw even greater potential.

Read more: http://www.digitaltrends.com/computing/radarcat-machine-learning-radar-sensor-google-soli/#ixzz4MdFkaKWL
Follow us: @digitaltrends on Twitter | digitaltrendsftw on Facebook

New technology using radar can identify materials and objects in real timeProfessor Aaron Quigley, Chair of Human Computer Interaction at the University, explained, "The Soli miniature radar opens up a wide-range of new forms of touchless interaction. Once Soli is deployed in products, our RadarCat solution can revolutionise how people interact with a computer, using everyday objects that can be found in the office or home, for new applications and novel types of interaction."
The system could be used in conjunction with a mobile phone, for example it could be trained to open a recipe app when you hold a phone to your stomach, or change its settings when operating with a gloved hand.
A team of undergraduates and postgraduate students at the University's School of Computer Science was selected to show the project to Google in Mountain View in the United States earlier this year. A snippet of the video was also shown on stage during the Google's annual conference (I/O).
Professor Quigley continued, "Our future work will explore object and wearable interaction, new features and fewer sample points to explore the limits of object discrimination.
"Beyond human computer interaction, we can also envisage a wide range of potential applications ranging from navigation and world knowledge to industrial or laboratory process control."

Related/Background:

• Yeo, H.-S., Flamich, G., Schrempf, P., Harris-Birtill, D., and Quigley, A. (2016) RadarCat: Radar Categorization for Input & Interaction. In Proceedings of the 29th Annual ACM Symposium on User Interface Software and Technology New York, NY, USA: ACM UIST ’16.
  We will update with a link to this paper when it becomes live in the ACM digital library
• SACHI May 2016 blog post on work at Google I/O ’16
• Video shown by ATAP at Google I/O 16
• ATAP's Project Soli Gesture Tracking System Is Smaller And Sleeker In Version 2.0, With New Concept Hardware From LG And Harman 
•  SoliType prototype lets you type in the air through radar tech - Android Community
•  spendergast: Google’s Project Soli demo's mmw micro-radar wearable HMI

US and 44 Nations Declare Armed Drone Export/Use Limits

Global Drone Trade: World's Largest Importing And Exporting Countries
» CEOWORLD magazine

Joint Declaration for the Export and Subsequent Use of Armed or Strike-Enabled 

Unmanned Aerial Vehicles (UAVs)

An increasing number of States are acquiring and employing Unmanned Aerial Vehicles (UAVs) to support a range of missions, including military missions that promote peace and security. Individual States may already have laws and policies in place to ensure the responsible export and use of UAVs that are armed, or that include equipment related uniquely to the deployment or delivery of weapons. However, recognizing that misuse of armed or strike-enabled UAVs could fuel conflict and instability, and facilitate terrorism and organized crime, the international community must take appropriate transparency measures to ensure the responsible export and subsequent use of these systems. In this context, we continue to recognize the following principles, none of which should be construed to undermine the legitimate interest of any State to indigenously produce, export, or acquire such systems for legitimate purposes:

• A. The applicability of international law, including both the law of armed conflict and international human rights law, as applicable, to the use of armed or strike-enabled UAVs, as with other weapon systems;
• B. The importance of engaging in the responsible export of armed or strike-enabled UAVs in line with existing relevant international arms control and disarmament norms that help build confidence as to the peaceful intention of States;
• C. That the export of armed or strike-enabled UAVs should be done consistent with the principles of existing multilateral export control and nonproliferation regimes, taking into account the potential recipient country’s history regarding adherence to its relevant international obligations and commitments;
• D. The importance of appropriate voluntary transparency measures on the export of armed or strike-enabled UAVs including reporting of military exports through existing mechanisms, where appropriate, and with due regard to national security considerations; and
• E. That in light of the rapid development of UAV technology and the benefit of setting international standards for the export and subsequent use of such systems, we are resolved to continue discussions on how these capabilities are transferred and used responsibly by all States.

We call upon other governments to support this declaration.

The declaration was issued by the United States and the governments of

1. Argentina, 
2. Australia, 
3. Austria, 
4. Belgium, 
5. Bulgaria, 
6. Canada, 
7. Chile, 
8. Colombia, 
9. Czech Republic, 
10. Estonia, 
11. Finland, 
12. Georgia, 
13. Germany, 
14. Hungary, 
15. Ireland, 
16. Italy, 
17. Japan, 
18. Latvia, 
19. Lithuania, 
20. Luxembourg, 
21. Malawi, 
22. Malta, 
23. Montenegro, 
24. Netherlands, 
25. New Zealand, 
26. Nigeria, 
27. Paraguay, 
28. Philippines, 
29. Poland, 
30. Portugal, 
31. Republic of Korea, 
32. Romania, 
33. Serbia, 
34. Seychelles, 
35. Singapore, 
36. Slovakia, 
37. Slovenia, 
38. South Africa, 
39. Spain, 
40. Sri Lanka, 
41. Sweden, 
42. Ukraine, 
43. United Kingdom
44. Uruguay.

World of Drones | The International Security Program
According to data collected by New America, there are 86 countries that have some sort of drone capability, both armed and unarmed. So far, eight countries have used armed drones in combat: the United States, Israel, the United Kingdom, Pakistan, Iraq, Nigeria, Iran, and Turkey. One non-state actor, Hezbollah, has also used armed drones in combat. But many other countries are arming drones and it’s only a matter of time before they deploy them in combat. According to New America’s research, 19 countries have armed drones or are acquiring armed drone technology.

All of These Countries Now Have Weaponized Drones
these are just the ones we know about.
Last week Nigeria joined a dubious international clique when it bombed a logistics base used by the militant group Boko Haram in the country’s northeast. Though the airstrike itself was unremarkable—the Nigerian Air Force has conducted hundreds of strikes against Boko Haram in recent months—it was the first Nigeria has delivered via an unmanned drone.

For many, the news wasn’t that Nigeria had used a weaponized drone in combat for the first time, but that the Nigerian military has weaponized drones at all. While it’s well-understood that military powers like the U.S., U.K., and China possess armed drones, it’s less well-known that Nigeria, South Africa, and Somalia (most likely) have them as well. Pakistan and Iraq have both used weaponized drones in combat inside their own borders. At least a dozen other nations have publicly declared they are pursuing armed drone technologies, and countless others seek to discreetly build or buy them as well.
In the past 18 months the weaponized drone club has quietly grown to double-digit membership, largely thanks to Chinese technology that is both less expensive and easier to obtain than U.S. drone technology.

So how many countries now possess armed drones? The long answer is nuanced, depending on what exactly constitutes a “weaponized drone.” The short answer is at least 10, and soon it will be a far larger club than that.

White House Rolls Out Armed Drone Declaration
Notably missing from the list of countries are Russia, China, India and Israel, seen as current or future exporters of armed drones. Israel, in particular, had previously expressed great skepticism about the deal.


For years, experts like Horowitz have warned that countries that may look to operate armed UAVs without regard for US norms would turn to those nations, a concern that still exists.

“One challenge for the United States and its allies and partners will be getting China, Russia and other actors on board with any joint declaration,” he said. “China, in particular, may view reluctance on the part of the US to export UAVs as a market opportunity.”

"Nilsson said there was “extensive engagement with both China and Israel,” and also had talks with Russia despite them being “not so much a producer or exporter.”

As to concerns that without big exporters of armed UAVs on board, the group has less impact than it otherwise might, Nilsson called it “a fair criticism — just as it’s fair to point out most governments don’t belong to [the informal, non-treaty Missile Technology Control Regime]. But there is merit to maintaining those export control regimes, even if the memberships are small.”

Israel’s Growing Arms Export - Languages Of The World

Israel is considered to be the world’s leading exporter of unmanned aerial vehicle (UAV). Missile-armed drones were developed by Israel to assassinate key militant leaders; the US has been using such drones against al-Qaeda, its first such ‘targeted killing’ mission being carried out in Yemen in November 2002. In 2001‑2011, Israeli companies were behind 41% of all UAVs exported to 24 countries, including the US, India, Russia, Nigeria, and Mexico.

Read more: http://www.languagesoftheworld.info/uncategorized/israels-growing-arms-export.html#ixzz4Mc5CdCVv

Fact Sheet: Joint Declaration for the Export and Subsequent Use of Armed or Strike-Enabled Unmanned Aerial Vehicles (UAVs)

This Joint Declaration will serve as the basis for discussions on a more detailed set of international standards for the export and subsequent use of armed or strike-enabled UAVs, which the United States and its partners will convene in Spring 2017. These discussions will be open to all countries, even if they choose not to join the Joint Declaration.

For further information, please contact the U.S. Department of State’s Bureau of Political-Military Affairs, Office of Congressional and Public Affairs at pm-cpa@state.gov, visit our website, and follow us on Twitter @StateDeptPM. 

Related/Background:

• These 5 Facts Explain the Rise of the Drone | TIME 
• Sixty percent of global drone exports come from Israel -- new data | The Electronic Intifada 
• Why Israel dominates global drone exports — Quartz 
• The Consequences of Global Armed Drone Proliferation 
• The numbers behind the worldwide trade in drones | News | The Guardian 
• U.S. Preparing to Export Armed Military Drones to the World -- The Motley Fool 
• 45 nations sign declaration on export, use of armed and strike-enabled drones
• U.S., other countries issue declaration on export, use of armed drones | INFORUM
   45 nations sign declaration on drone use, exports - UPI.com

Drone safety: User-centric control software improves pilot performance and safety

User-Centric Control Reduces Drone Accidents
| iHLS Israel Homeland Security

Drone safety: User-centric control software improves pilot performance and safety
The study was comprised of two experiments wherein participants used flight simulators to compare the ease and safety of user-centric controls (such as those used by an airplane pilot) with external piloting (remote control methods ranging from joysticks to smartphone apps).
The 30 participants had no prior experience driving drones or remote control cars, and were tasked with guiding their drone through an obstacle course. The study measured obstacle avoidance response time as well as avoidance success rate, and shows the user-centric interface improving performance on both counts.
Findings support the conclusion that a user-centric interface design significantly improves performance of drone pilots, and resolves some of the user control issues undermining drone safety.
Professor Kwangsu Cho explains "despite increases in drone-crashes, research and development on user-centered control interfaces has been limited. The user interface of drones is critical to safety, quality piloting, and satisfaction. We are developing other user-centered drone interfaces especially for non-experts, and are eager to collaborate with manufacturers to improve safety".

Fly a Drone Safely: Evaluation of an Embodied Egocentric Drone Controller Interface

Abstract - As the activity of flying drones becomes increasingly widespread, drone accidents are also increasing. Many of these accidents are related to operational issues and, therefore, it is urgent to improve drone controls. The purpose of this study was to evaluate the usability of a drone controller interface supporting an external pilot's egocentric perspective. The interface tried to solve the problem of misaligned perspectives that arise from controllers constructed from a drone-centric or allocentric perspective. To achieve this, two experiments were performed to identify the differences in control performance depending on embodied mental rotation. The results of the experiments revealed that the controller designed from the pilot's egocentric point of view had a stronger performance regardless of the direction of drone flight. Based on the results, implications are discussed.

• External piloting is considered as one of the fundamental causes of frequent drone crashes.
• External piloting causes a misalignment problem due to the difference between the drone-centric or allocentric perspective and an external pilot's egocentric perspective.
• The egocentric drone control interface outperforms the traditional, drone-centric control interface by removing the cognitive load of mental rotation generated by the process of aligning two different perspectives.

Related/Background:

• CogEngSquare - UX Lab Cognitive Engineering, Yonsei University Square
• Allocentric vs. Egocentric Spatial Processing 
• x

GA-ASI funded to join Boeing and LM in next phase of MQ-25 Stinger Carrier Based UAV design

Concept for MQ-25 Stingray

Navy Awards Another Contract For MQ-25 Carrier-Based Drone | Defense Daily Network
General Atomics Aeronautical Systems Inc. (GA-ASI) has become the third company to receive a concept refinement contract for the U.S. Navy's new MQ-25 program, which aims to develop a carrier-based unmanned aircraft.GA-ASI, whose one-year, $43.7 million contract.

Navy Taps Boeing, Lockheed for MQ-25 Carrier UAV Risk Reduction Contracts
The U.S. Navy has awarded a $43.4 million contract to Boeing (NYSE: BA) and a $43.6 million contract to Lockheed Martin (NYSE: LMT) to provide risk reduction measures for the military branch’s first carrier unmanned aerial vehicle program.
Boeing and Lockheed will also refine concepts and develop trade space models to support requirements generation ahead of the MQ-25 program’s engineering and manufacturing development phase, the Defense Department said Friday.


General Atomics joins Boeing and LM in de-risking MQ-25 unmanned tanker | IHS Jane's 360
The contract, announced by the Department of Defense (DoD) on 4 October, follows previous awards to Boeing and Lockheed Martin to refine the MQ-25 concept ahead of the engineering and manufacturing development (EMD) phase of the programme. A fourth contract for Northrop Grumman is expected to be awarded shortly.

Alongside Boeing and Lockheed Martin, GA-ASI now has through to the end of October 2017 to conduct its de-risking activities before release of a draft request for proposals and the commencement of EMD.

Lockheed Martin and Boeing score contracts for unmanned Navy tanker
Lockheed Martin and Boeing each received $43 million risk reduction contracts on 23 September from the US Navy. Two more bidders, Northrop Grumman and General Atomics Aeronautical Systems, are waiting for their awards.

The new round of contracts pays the contractors to convert their preliminary designs, which were tuned to support the navy's original requirement for a stealthy, carrier-launched surveillance and strike aircraft (UCLASS).

A four-way competition to build the US Navy's next carrier-based unmanned air system (UAS) expect to begin revising year-old, preliminary designs that were submitted before the mission changed.

Lockheed Martin and Boeing each received $43 million risk reduction contracts on 23 September from the US Navy. Two more bidders, Northrop Grumman and General Atomics Aeronautical Systems, are waiting for their awards.

The new round of contracts pays the contractors to convert their preliminary designs, which were tuned to support the navy's original requirement for a stealthy, carrier-launched surveillance and strike aircraft (UCLASS).

The navy has since converted the MQ-25 programme into the carrier-based airborne refueling system (CBARS). Rather than penetrating into defended airspace to detect and attack targets, the MQ-25s will mostly serve as an escort or "buddy" tanker for manned strike aircraft. The MQ-25 also would be equipped with a 19-23in-diameter forward looking infrared sensor turret for a surveillance mission in permissive airspace.

Related/Background:

• UCLASS to be Descoped for CBARS Conversion AKA MQ-25 Stingray
• spendergast: Can Stingray give the Carrier Air Wing the reach to do its job 
• spendergast: Shoemaker evolving requirements and mission for MQ-25 UAS Carrier Fit

USS Princeton longest range SM-6 air intercept in naval history

US Navy boasts longest range anti-air warfare intercept in Navy history

Navy Conducts Longest Range AAW Intercept from USS Princeton
WASHINGTON (NNS) -- During the Naval Integrated Fire Control - Counter Air (NIFC-CA) test, USS Princeton (CG 59), equipped with the latest Aegis Baseline 9, successfully processed data from a remote airborne sensor to engage and destroy an over-the-horizon threat representative target using Standard Missile-6 (SM-6).

This is not the first time that SM-6 has shattered its own distance record. The missile broke the previous long-range intercept record in January of this year onboard USS John Paul Jones at Pacific Missile Range Facility, a milestone it originally set in June of 2014.

This NIFC-CA test was the tenth consecutive successful live-fire test to demonstrate an over-the- horizon, engage-on-remote capability. This particular test also successfully validated the NIFC-CA from the sea kill chain concept.

US Navy achieves longest range surface-to-air intercept in naval history - FreshNews :FreshNews
POINT MUGU NAVAL STATION, Calif., Sept. 30, 2016 /PRNewswire/ — In the longest range surface-to-air intercept of its kind in naval history, a Raytheon Company (NYSE: RTN) Standard Missile-6 successfully destroyed an over-the-horizon, threat target.

The mission also demonstrated the combat capabilities that SM-6 brings to Naval Integrated Fire Control-Counter Air, an effort designed to link U.S. Navy ships and airborne sensors into a single network via Cooperative Engagement Capability. The SM-6 was fired from the USS Princeton (CG59), a U.S. Navy Cruiser equipped with the latest Aegis baseline 9 combat system.

The active radar and extended range of the 'smart missile' allow it to track and destroy over-the-horizon targets, out of sight of operators on deck.

“The multi-mission SM-6 is in a class of its own as it demonstrates its ability to go further, faster and counter more threats to offer maximum mission flexibility,” said Mike Campisi, Standard Missile-6

senior program director. “The missile's ability to defend against so many different threats makes it the go-to solution to meet modern fleet defense needs across the globe.”

This is not the first time that SM-6 has shattered its own distance record. The missile broke the previous long-range intercept record in January of this year, a milestone it set itself in June of 2014.

RIM-174 SM-6 Extended Range Active Missile (ERAM)
The Aegis/SM-2 is limited in handling saturation air attacks by the radar's horizon - low flying aircraft and cruise missiles could approach within dozens of miles of an AEGIS vessel before the SM-2 could conduct an intercept. With the SM-6, an AEGIS ship can target aircraft upwards of 200 miles away, before they can launch their Anti-Shipping Missiles.
The US Navy is seeking to adapt the Standard 6 (SM-6) missile for use against ships. If this modification is successful, the range of the SM-6 will be increased from 250 km to 370 km. This is a new anti-ship mode that can shoot down airborne threats, and now the same missile can attack and destroy a ship at long range. The Navy wanted to spend $2.9 billion over the five years FY17-FY22on the modified SM-6 as part of its “distributed lethality” initiative. This new anti-ship mode makes the SM-6 highly lethal due to its speed and agility and nearly overnight doubles the purpose of every such missile used across our fleet of Aegis destroyers. Boeing’s (BA) Harpoon anti-ship missile has a range of about 67 nautical miles, less than 130 km.

Related/Background:

• Talos Missile History
• USS Oklahoma City CLG-5 was commissioned 7 September 1960 and decommissioned 15 December 1979, after serving the longest of any Cleveland class ship.
  The ship had fleet flagship accommodations and served as flagship for the First and Seventh Fleets. In February 1972 the Oklahoma City destroyed a North
  Vietnamese mobile radar site with a Talos ARM missile. It was
  the first successful combat surface to surface missile operation
  in US Navy history. On 6 November 1979 the USS Oklahoma City fired the last Talos missile launched from a ship.
  
• USS Long Beach CGN-9 was the first nuclear powered guided missile cruiser. Commissioned 9 September 1961, the ship carried Talos and Terrier missile systems. In May 1968 the Long Beach scored the first recorded US Navy combat kill with a surface to air missile, downing a North Vietnamese MiG aircraft with a Talos at a range of 65 miles. In September 1968 the Long Beach downed another MiG with a Talos at a
  range of 61 miles. The Talos missiles were off loaded in 1979 and the spaces were converted for other uses until the ship was decommissioned on 1 May 1995. 
• Lockheed reports successful F-35 integration with Aegis system - UPI.com 
• The Aegis Combat System’s continuous modernization - Military Embedded Systems
• The Navy's Air Defense Missile Will Become a Supersonic Ship Killer
• spendergast: USN and MDA ok Aegis BL 9.C1 for BMD 
•  spendergast: LMCO MS&T Contract for up to $420M for Aegis Weapon System ACB12
•  spendergast: Navy’s Next Air War Strategy depends on Data Link Advances

International Consortium offers Re-leased Do328 solution to the South African AF for maritime patrol

AeroRescue operates a fleet of five Dornier 328 turboprops with one aircraft each based at Perth, Darwin, Cairns, Brisbane and Melbourne. The aircraft are modified for SAR (Search And Rescue) and operate under a 10-year contract to the Australian Maritime Safety Authority (AMSA), using RESCUE callsigns – VH-PPF is ‘RESCUE 461’. When not used for SAR duties, the aircraft are also used for pollution detection and by Customs / Border Protection for maritime suveillance duties, using CUSTOMS callsigns.
Photo © David Eyre

Atlantis Aviation leads consortium at AAD | Atlantis Corporation

Atlantis demonstrates interim maritime surveillance solution to the SAAF | defenceWeb
Atlantis Aviation leads consortium at AAD | Atlantis Corporation
The aircraft offered by the Atlantis consortium is the Dornier 328-100 (Mod 20) operated by AeroRescue of Australia on behalf of the Australian Maritime Safety Authority (AMSA). The ten year contract with AMSA for search and rescue services is progressively tailing off as the new tenderer is utilising the longer ranged Challenger 604.

Apart from Atlantis, which act as Project Managers, the consortium includes South Africa’s Avex Air (providing maintenance), AeroData of Germany (sensors and equipment) and AeroRescue (the current aircraft owners, providing search and rescue services for Australia). The South African companies would own 51% of the new company which will take over ownership of the aircraft from AeroRescue.

IAI-Elta EL/M-2022A Radar

FLIR Systems
Star Safire III E/O Ball

The Dorniers are currently equipped with a second-generation STAR Safire III IR/digital TV FLIR electro-optical ball and the  IAI-Elta EL/M-2022(V)3 multi-mode search radar. This radar, which is rumoured to have been fitted to the SAAF’s Cheetah C fighter prior to being replaced by the Gripen, has been optimised for small contacts in high sea states. Besides various search, navigation, weather, air-to-air and moving target modes, it is capable of Spot-SAR (Synthetic Aperture Radar) and Strip map SAR mapping capability. The ISAR (Inverse Synthetic Aperture Radar) and Circular Synthetic Aperture Radar (CSAR) capabilities allow crew members to view a 3D picture at long distances from the target.

Other systems include digital satellite communication, enhanced navigation and AIS transponders fully integrated into an AeroData Mission Management System that allows for data transfer and full digital recording of all mission data, voice, video and data for a comprehensive evidence trail.

The airframe itself has been modified to incorporate large observer windows, an operator console and an airdrop capability. The modified inflight opening door can dispatch life rafts, self-locating datum marker buoys (SLDMB) and a variety of other containers stocked with food, water, medical supplies and communications equipment.

Gordon Blackbeard, Chairman of Atlantis, said that they were originally “looking at a very cheap and cheerful interim solution” based on the King Air 350ER. Despite the SAAF already operating earlier versions of the King Air, Atlantis felt that the good range of the King Air was offset by the lack of crew comfort and limited stores carriage capability.

Antonio De Maio Publications

A. De Maio and D. Orlando, "Adaptive Radar Detection of a Subspace Signal Embedded in Subspace Structured Plus Gaussian Interference Via Invariance," in IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 2156-2167, April15, 2016.
doi: 10.1109/TSP.2015.2507544
keywords: {Gaussian processes;adaptive radar;covariance matrices;radar clutter;radar detection;Gaussian interference;adaptive radar detection;clutter;covariance matrix;hypothesis testing problem invariant;optimum invariant detector;principle of invariance;radar antenna;subspace signal;subspace structure;thermal noise;Clutter;Detectors;Jamming;Radar antennas;Radar detection;Adaptive radar detection;coherent interference;constant false alarm rate;invariance;maximal invariants;subspace model},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7352359&isnumber=7422173

A. Aubry, A. De Maio, Y. Huang and M. Piezzo, "Robust Design of Radar Doppler Filters," in IEEE Transactions on Signal Processing, vol. 64, no. 22, pp. 5848-5860, Nov.15, 15 2016.
doi: 10.1109/TSP.2016.2576423
keywords: {Covariance matrices;Doppler effect;Doppler radar;Interference;Robustness;Signal to noise ratio;Uncertainty;Robust filter design;covariance matrix uncertainity;doppler processing;radar signal processing;steering vector uncertainity},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7491258&isnumber=7573028

A. De Maio, D. Orlando, C. Hao and G. Foglia, "Adaptive Detection of Point-Like Targets in Spectrally Symmetric Interference," in IEEE Transactions on Signal Processing, vol. 64, no. 12, pp. 3207-3220, June15, 2016.
doi: 10.1109/TSP.2016.2539140
keywords: {Clutter;Covariance matrices;Detectors;Electronic mail;Radar;Symmetric matrices;Adaptive radar detection;constant false alarm rate;generalized likelihood ratio test;recursive estimation;symmetric spectra},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7426844&isnumber=7456375

A. Aubry, A. De Maio and Y. Huang, "MIMO Radar Beampattern Design Via PSL/ISL Optimization," in IEEE Transactions on Signal Processing, vol. 64, no. 15, pp. 3955-3967, Aug.1, 2016.
doi: 10.1109/TSP.2016.2543207
keywords: {MIMO radar;computational complexity;concave programming;covariance matrices;quadratic programming;radar signal processing;MIMO radar beampattern design;PSL-ISL optimization;double-sided potentially nonconvex quadratic constraints;figure of merit;integrated sidelobe level;multiple-input multiple-output waveform covariance matrices;nonconvex optimization problems;optimization variable;peak sidelobe level;polynomial time procedures;relaxed elemental power requirement;steering vector;transmit beampattern;uncertainty set;Covariance matrices;Linear matrix inequalities;MIMO;MIMO radar;Optimization;Radar antennas;Robustness;MIMO radar;robust design;steering vector mismatches;transmit beampattern;waveform covariance matrix},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7435338&isnumber=7480962

A. Aubry, A. D. Maio, V. Carotenuto and A. Farina, "Radar Phase Noise Modeling and Effects-Part I : MTI Filters," in IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 2, pp. 698-711, April 2016.
doi: 10.1109/TAES.2015.140549
keywords: {filters;phase noise;radar signal processing;MTI filters;characteristic function;clutter cancellation;composite power-law model;fast-time data matrix;improvement factor;moving target indication algorithms;multivariate circular distributions;power spectral density;radar phase noise modeling;radar signal representation;slow-time data matrix;undesired phase fluctuations;Bandwidth;Clutter;Noise measurement;Phase measurement;Phase noise;Radar},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7472965&isnumber=7472948

X. Cheng, A. Aubry, D. Ciuonzo, A. De Maio, Y. Li and X. Wang, "Optimizing polarimetrie radar waveform and filter bank for extended targets in clutter," 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Rio de Janerio, Brazil, 2016, pp. 1-5.
doi: 10.1109/SAM.2016.7569683
keywords: {Clutter;Optimization;Radar polarimetry;Scattering;Signal to noise ratio},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7569683&isnumber=7569603

A. Aubry, V. Carotenuto and A. De Maio, "Radar waveform design with multiple spectral compatibility constraints," 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, 2016, pp. 1-6.
doi: 10.1109/RADAR.2016.7485190
keywords: {computational complexity;mathematical programming;polynomials;radar signal processing;radiofrequency interference;telecommunication control;NP-hard optimization problem;SDR;interference energy;multiple spectral compatibility constraints;polynomial computational complexity procedure;radar signal design;radar waveform design;semidefinite relaxation;Algorithm design and analysis;Bandwidth;Interference;NP-hard problem;Optimization;Radar;Signal to noise ratio},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7485190&isnumber=7485053

V. Carotenuto, A. De Maio, C. Clemente, J. J. Soraghan and G. Alfano, "Forcing Scale Invariance in Multipolarization SAR Change Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 36-50, Jan. 2016.
doi: 10.1109/TGRS.2015.2449332
keywords: {decision theory;matrix algebra;radar detection;radar imaging;radar polarimetry;reliability;synthetic aperture radar;constant false alarm rate property;data-dependent matrix;data-parameter domain compression;forcing scale invariance;generalized likelihood ratio testing;image pair availability;maximal invariant statistics;multipolarization SAR change detection;polarimetric return;power mismatch-miscalibration;scale-invariant decision rule;synthetic aperture radar;three-polarimetric channel;two-polarimetric channel;Detectors;Linear matrix inequalities;Orbits;Receivers;Space vehicles;Synthetic aperture radar;Testing;Coherent change detection;maximal invariant;multipolarization;scale invariance},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7210163&isnumber=7312390

D. Ciuonzo, A. De Maio and D. Orlando, "A Unifying Framework for Adaptive Radar Detection in Homogeneous Plus Structured Interference— Part II: Detectors Design," in IEEE Transactions on Signal Processing, vol. 64, no. 11, pp. 2907-2919, June1, 2016.
doi: 10.1109/TSP.2016.2519005
keywords: {Gaussian processes;adaptive signal detection;covariance matrices;multidimensional signal processing;radar detection;radar interference;statistical analysis;CFAR property;CFARness property;GMANOVA;MIS;adaptive multidimensional-multichannel signal detection problem;adaptive radar detection;constant false alarm rate property;detector design;generalized multivariate analysis of variance;homogeneous Gaussian disturbance;homogeneous plus structured interference;maximal invariant statistics;statistical equivalence;structured deterministic interference;unifying framework;unknown covariance matrix;Adaptation models;Analytical models;Covariance matrices;Detectors;Interference;Radar detection;Testing;Adaptive radar detection;CFAR;GMANOVA;coherent interference;double-subspace model;invariance theory;maximal invariants},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7384511&isnumber=7453252

A. R. Persico, C. Clemente, L. Pallotta, A. De Maio and J. Soraghan, "Micro-Doppler classification of ballistic threats using Krawtchouk moments," 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, 2016, pp. 1-6.
doi: 10.1109/RADAR.2016.7485086
keywords: {Doppler shift;missiles;signal classification;2-dimensional Gabor filter based approach;Krawtchouk moments;antimissile defence systems;ballistic missile classification;ballistic threats;interception success ratio;microDoppler based classification technique;real radar data;Feature extraction;Missiles;Radar;Shape;Spectrogram;Target recognition;Time-frequency analysis},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7485086&isnumber=7485053

A. De Maio; D. Orlando; I. Soloveychik; A. Wiesel, "Invariance Theory for Adaptive Detection in Interference with Group Symmetric Covariance Matrix," in IEEE Transactions on Signal Processing , vol.PP, no.99, pp.1-1
doi: 10.1109/TSP.2016.2591502
keywords: {Covariance matrices;Electronic mail;Interference;Maximum likelihood estimation;Radar detection;Sensor arrays;Symmetric matrices;Adaptive detection;GLRT;Group Symmetric Structures;Invariance;Rao test;Wald test},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7513456&isnumber=4359509

C. Hao, A. De Maio, D. Orlando, S. Iommelli and C. Hou, "Adaptive radar detection in the presence of Gaussian clutter with symmetric spectrum," 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 2016, pp. 3091-3095.
doi: 10.1109/ICASSP.2016.7472246
keywords: {adaptive radar;radar clutter;radar detection;radar signal processing;spectral analysis;GLRT procedure;Gaussian clutter;adaptive radar detection;binary hypothesis test problem;performance assessment;spectral properties;spectrum symmetry;symmetric spectrum;two-step generalized likelihood ratio test design procedure;Bayes methods;Clutter;Covariance matrices;Detectors;Gain;Signal to noise ratio;Thyristors;Adaptive Detection;Generalized Likelihood Ratio Test (GLRT);Ground Clutter;Symmetric Spectrum},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7472246&isnumber=7471614

A. De Maio, D. Orlando and S. Iommelli, "An invariant approach to adaptive radar detection under covariance persymmetry," 2015 IEEE Radar Conference (RadarCon), Arlington, VA, 2015, pp. 0455-0460.
doi: 10.1109/RADAR.2015.7131042
keywords: {Gaussian processes;covariance analysis;decision theory;radar detection;radar interference;radar receivers;statistical analysis;vectors;Gaussian interference;adaptive radar detection;benchmark invariant testing;four dimensional vector;hypothesis testing problem;maximal invariant statistic approach;persymmetric covariance structure;signal-to-interference-plus-noise ratio regime;suboptimum decision rule;two-step generalized likelihood ratio test decision statistics;Arrays;Covariance matrices;Detectors;Interference;Radar detection;Receivers;Signal to noise ratio},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7131042&isnumber=7130933

V. Carotenuto, A. De Maio, C. Clemente and J. J. Soraghan, "Invariant Rules for Multipolarization SAR Change Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 6, pp. 3294-3311, June 2015.
doi: 10.1109/TGRS.2014.2372900
keywords: {remote sensing by radar;synthetic aperture radar;aforementioned class;analysis stage;binary hypothesis testing problem;change detection problem;constant false alarm rate property;generalized likelihood ratio test;high-resolution SAR data;invariance principle;maximal invariant statistic;multiple polarimetric channels;multipolarization SAR change detection;optimum clairvoyant invariant detector;powerful invariant detector;reference image;suboptimum invariant receivers;synthetic aperture radar;test image;Detectors;Linear matrix inequalities;Orbits;Receivers;Space vehicles;Synthetic aperture radar;Vectors;Coherent change detection;invariant rules;maximal invariant;multipolarization},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6995998&isnumber=7032018

A. De Maio and D. Orlando, "An Invariant Approach to Adaptive Radar Detection Under Covariance Persymmetry," in IEEE Transactions on Signal Processing, vol. 63, no. 5, pp. 1297-1309, March1, 2015.
doi: 10.1109/TSP.2014.2388441
keywords: {adaptive radar;covariance matrices;radar detection;radar signal processing;4D vector;Gaussian interference;adaptive radar detection;covariance persymmetry;decision statistics;generalized likelihood ratio test;invariant receivers;maximal invariant statistic;persymmetric covariance structure;signal-to-interference-plus-noise ratio regime;sub-optimum decision rules;Arrays;Benchmark testing;Covariance matrices;Detectors;Interference;Radar detection;Vectors;Adaptive radar detection;constant false alarm rate;invariance;maximal invariants;persymmetry},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7001660&isnumber=7027925

S. M. Karbasi, A. Aubry, A. De Maio and M. H. Bastani, "Robust design of transmit code and receive filter for extended targets in clutter," 2015 23rd Iranian Conference on Electrical Engineering, Tehran, 2015, pp. 257-262.
doi: 10.1109/IranianCEE.2015.7146220
keywords: {filtering theory;mathematical programming;radar clutter;radar transmitters;transient response;PAR constraint;SDP problems;SINR;TIR;clutter;figure of merit;peak-to-average power ratio constraint;radar transmit code;randomization steps;receive filter;semidefinite programming problems;sequential optimization procedure;signal-to-interference-plus-noise ratio;target impulse response;worst-case optimization approach;Electrical engineering;Frequency modulation;PAR constraint;Robust design;extended target model;semi-definite programming;signal-dependent interference},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7146220&isnumber=7146167

V. Carotenuto, A. De Maio, C. Clemente and J. Soraghan, "Unstructured Versus Structured GLRT for Multipolarization SAR Change Detection," in IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 8, pp. 1665-1669, Aug. 2015.
doi: 10.1109/LGRS.2015.2418575
keywords: {covariance matrices;radar detection;radar polarimetry;radar resolution;synthetic aperture radar;N multiple polarimetric channel;binary hypothesis testing problem;block-diagonal structure;coherent multipolarization synthetic aperture radar;constant false alarm rate property;generalized likelihood ratio test;multipolarization SAR change detection;polarimetric covariance matrix;real high-resolution SAR data;structured GLRT;structured decision rule;unknown disturbance covariance;unstructured GLRT;Covariance matrices;Detectors;Performance analysis;Receivers;Remote sensing;Synthetic aperture radar;Testing;Change detection;multipolarization;structured generalized likelihood ratio test (GLRT)},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7095522&isnumber=7123712

D. Ciuonzo, A. De Maio, G. Foglia and M. Piezzo, "Intrapulse radar-embedded communications via multiobjective optimization," in IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 4, pp. 2960-2974, Oct. 2015.
doi: 10.1109/TAES.2015.140821
keywords: {Pareto optimisation;concave programming;correlation methods;error statistics;minimisation;radar interference;vectors;Pareto weight;constrained maximization;constrained minimization;correlation index;intrapulse radar-embedded communication;multiobjective optimization paradigm;nonconvex multiobjective optimization;quadratic constraint;scalarization technique;signal-to-interference ratio;symbol error rate;vectorial problem;waveform interception;Backscatter;Correlation;Manganese;Optimization;Radar;Radar scattering;Receivers},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7376230&isnumber=7376194

A. Aubry, A. De Maio, G. Foglia and D. Orlando, "Diffuse Multipath Exploitation for Adaptive Radar Detection," in IEEE Transactions on Signal Processing, vol. 63, no. 5, pp. 1268-1281, March1, 2015.
doi: 10.1109/TSP.2014.2388439
keywords: {Gaussian distribution;adaptive radar;covariance matrices;echo;multipath channels;object detection;radar detection;radiofrequency interference;adaptive radar detection;constant false alarm rate;deterministic signal;diffuse multipath exploitation;generalized likelihood ratio test;glistening surface;hypothesis testing problem;multipath echo;point-like target detection;target echo;unknown covariance matrix;unknown interference parameters;unknown scaling factor;zero-mean complex circular symmetric Gaussian random vector;Covariance matrices;Interference;Radar;Rough surfaces;Surface roughness;Surface treatment;Vectors;Adaptive radar detection;Constant False Alarm Rate (CFAR);Generalized Likelihood Ratio Test (GLRT);constrained optimization;diffuse multipath environments},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7001702&isnumber=7027925

S. M. Karbasi, A. Aubry, A. De Maio and M. H. Bastani, "Robust Transmit Code and Receive Filter Design for Extended Targets in Clutter," in IEEE Transactions on Signal Processing, vol. 63, no. 8, pp. 1965-1976, April15, 2015.
doi: 10.1109/TSP.2015.2404301
keywords: {Interference;Optimization;Radar;Robustness;Signal to noise ratio;Uncertainty;Vectors;Extended target model;PAR constraint;robust design;semi-definite programming;signal-dependent interference},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7042797&isnumber=7058323

C. Clemente, L. Pallotta, I. Proudler, A. De Maio, J. J. Soraghan and A. Farina, "Pseudo-Zernike-based multi-pass automatic target recognition from multi-channel synthetic aperture radar," in IET Radar, Sonar & Navigation, vol. 9, no. 4, pp. 457-466, 4 2015.
doi: 10.1049/iet-rsn.2014.0296
keywords: {Zernike polynomials;feature extraction;military radar;object recognition;radar imaging;synthetic aperture radar;ATR;Gotcha dataset;battlefield scenario;channel diversity;data transfer requirements;feature extraction;limited computational complexity;multichannel synthetic aperture radar platform;pZm invariant properties;pseudoZernike-based multipass automatic target recognition;spatial diversity;target identification;uncertainty mitigation},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7070594&isnumber=7070506

A. Aubry, A. De Maio and M. M. Naghsh, "Optimizing Radar Waveform and Doppler Filter Bank via Generalized Fractional Programming," in IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 8, pp. 1387-1399, Dec. 2015.
doi: 10.1109/JSTSP.2015.2469259
keywords: {Doppler shift;channel bank filters;concave programming;iterative methods;minimax techniques;radar signal processing;SINR;generalized fractional programming;polynomial computational complexity;radar waveform optimisation;receive Doppler filter bank;signal-to-interference-plus-noise-ratio;target Doppler shift;transmit radar waveform;Cognitive radar;Doppler effect;Filter banks;Mathematical analysis;Optimization;Radar clutter;Signal to noise ratio;Cognitive radar;Dinkelbach-type algorithms;filter bank design;generalized fractional programming;receiver optimization;signal-dependent clutter;waveform design},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7202844&isnumber=7327073

A. De Maio, D. Orlando, A. Farina and G. Foglia, "Design and Analysis of Invariant Receivers for Gaussian Targets," in IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 8, pp. 1560-1569, Dec. 2015.
doi: 10.1109/JSTSP.2015.2440183
keywords: {adaptive radar;exponential distribution;radar detection;radar receivers;Gaussian targets;adaptive radar detection;covariance persymmetry;exponential distribution;invariant framework;invariant sub-optimum decision rules;locally optimum invariant receivers;maximal invariant statistic;probability density functions;radar cross section;target fluctuations;Covariance matrices;Interference;Radar cross-sections;Radar detection;Receivers;Adaptive radar detection;Swerling models;constant false alarm rate;invariance;maximal invariants;persymmetry},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7115873&isnumber=7327073

V. Carotenuto, A. Aubry, A. De Maio and A. Farina, "Phase noise modeling and its effects on the performance of some radar signal processors," 2015 IEEE Radar Conference (RadarCon), Arlington, VA, 2015, pp. 0274-0279.
doi: 10.1109/RADAR.2015.7131009
keywords: {matrix algebra;phase noise;radar clutter;radar signal processing;signal representation;spectral analysis;CF;MTI algorithm;characteristic function;closed form expression;clutter cancellation;coherent integration technique;composite power-law model;data matrix radar signal representation;ideal reference sinusoidal signal;moving target indication;multivariate circular distribution;performance degradation;phase fluctuation;phase noise PSD;phase noise modeling;power spectral density;radar signal processor;Clutter;Frequency measurement;Noise measurement;Phase measurement;Phase noise;Radar},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7131009&isnumber=7130933

M. M. Naghsh, M. Soltamalian, P. Stoica, M. Modarres-Hashemi, A. De Maio and A. Aubry, "A max-min design of transmit sequence and receive filter," 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, 2014, pp. 71-75.
doi: 10.1109/ICASSP.2014.6853560
keywords: {Doppler shift;filtering theory;minimax techniques;Doppler robust transmit sequence;Doppler shifts;SINR;active sensing system;filter output;max-min design;maxmin optimization problem;receive filter;signal dependent interference;signal-to-interference-plus-noise ratio;transmit sequence;Clutter;Doppler shift;Optimization;Radar;Robustness;Signal to noise ratio;Doppler shift;max-min;receive filter;robust design;transmit sequence},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6853560&isnumber=6853544

A. De Maio and Y. Huang, "New Results on Fractional QCQP with Applications to Radar Steering Direction Estimation," in IEEE Signal Processing Letters, vol. 21, no. 7, pp. 895-898, July 2014.
doi: 10.1109/LSP.2014.2320300
keywords: {Gaussian noise;array signal processing;maximum likelihood estimation;quadratic programming;radar signal processing;Charnes-Cooper transformation;Cramer Rao lower bound;additive Gaussian disturbance;double-sided quadratic constraints;fractional QCQP;maximum likelihood criterion;quadratically constrained quadratic program;radar steering direction estimation;rank-one decomposition;semidefinite programming relaxation;uncertainty region;Linear matrix inequalities;Maximum likelihood estimation;Radar detection;Vectors;Constrained maximum likelihood steering direction estimation;fractional QCQP with three double-sided constraints},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6805184&isnumber=6799218

A. Aubry, A. De Maio, G. Foglia and D. Orlando, "Adaptive radar detection in diffuse multipath environments," 2014 IEEE Radar Conference, Cincinnati, OH, 2014, pp. 1135-1138.
doi: 10.1109/RADAR.2014.6875766
keywords: {Gaussian processes;adaptive signal detection;covariance matrices;decision theory;probability;radar detection;radar interference;statistical testing;vectors;CFAR property;GLRT;adaptive radar detection;constant false alarm rate;constrained generalized likelihood ratio test;decision scheme;detection probability;deterministic signal superposition;diffuse multipath environments;hypothesis testing problem;multipath echoes;performance assessment;point-like target detection;primary data covariance matrix;secondary data sample covariance matrix;target echo modelling;unknown covariance matrix;unknown interference parameters;unknown scaling factor;zero-mean Gaussian random vector;Covariance matrices;Detectors;Interference;Linear matrix inequalities;Receivers;Signal to noise ratio;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6875766&isnumber=6875503

C. Clemente, L. Pallotta, I. Proudler, A. De Maio, J. J. Soraghan and A. Farina, "Multi-sensor full-polarimetric SAR Automatic Target Recognition using pseudo-Zernike moments," 2014 International Radar Conference, Lille, 2014, pp. 1-5.
doi: 10.1109/RADAR.2014.7060271
keywords: {pattern recognition;radar polarimetry;sensor fusion;synthetic aperture radar;ATR;full-polarimetric Gotcha dataset;multisensor full-polarimetric SAR automatic target recognition;pseudoZernike moment;spatial diversity;synthetic aperture radar;Feature extraction;Polynomials;Synthetic aperture radar;Target recognition;Training;Vehicles},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7060271&isnumber=7060235

L. Pallotta, C. Clemente, A. De Maio, J. J. Soraghan and A. Farina, "Pseudo-Zernike moments based radar micro-Doppler classification," 2014 IEEE Radar Conference, Cincinnati, OH, 2014, pp. 0850-0854.
doi: 10.1109/RADAR.2014.6875709
keywords: {Doppler radar;Zernike polynomials;radar signal processing;signal classification;microDoppler classification;microDoppler signature classification;pseudoZernike moments based radar;robust features;two dimensional matrices;Doppler radar;Feature extraction;Polynomials;Radar imaging;Reliability;Spectrogram},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6875709&isnumber=6875503

V. Carotenuto, C. Clemente, A. De Maio, J. Soraghan and S. Iommelli, "Multi-polarization SAR change detection: unstructured versus structured GLRT," Sensor Signal Processing for Defence (SSPD), 2014, Edinburgh, 2014, pp. 1-5.
doi: 10.1109/SSPD.2014.6943315
keywords: {covariance matrices;decision theory;radar imaging;radar polarimetry;synthetic aperture radar;binary hypothesis testing problem;block-diagonal structure;coherent multipolarization SAR change detection;decision rule;multiple polarimetric channels;polarimetric covariance matrix;structured generalized likelihood ratio test criterion;synthetic aperture radar;unstructured GLRT;Covariance matrices;Detectors;Performance analysis;Receivers;Synthetic aperture radar;Testing;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6943315&isnumber=6943293

A. Aubry, A. De Maio, G. Foglia, D. Orlando and C. Hao, "Enhanced radar detection and range estimation via oversampled data," 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, 2014, pp. 61-65.
doi: 10.1109/ICASSP.2014.6853558
keywords: {adaptive radar;radar detection;GLRT;adaptive receiver;discrete-time model;enhanced radar detection;enhanced range estimation capability;generalized likelihood ratio test;noisy return over-sampling;oversampled data;Detectors;Estimation;Interference;Radar detection;Signal to noise ratio;Vectors;Adaptive Radar Detection;Constant False Alarm Rate (CFAR);Generalized Likelihood Ratio Test (GLRT);Oversampling;Range Estimation},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6853558&isnumber=6853544

A. Pauciullo, A. De Maio, S. Perna, D. Reale and G. Fornaro, "Detection of Partially Coherent Scatterers in Multidimensional SAR Tomography: A Theoretical Study," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 12, pp. 7534-7548, Dec. 2014.
doi: 10.1109/TGRS.2014.2313703
keywords: {radar detection;radar imaging;radar interferometry;spaceborne radar;synthetic aperture radar;4D analysis;CFAR decision rule;SAR interferometry;coherent SAR data combination;constant false alarm rate decision rule;differential interferometry concept;multidimensional SAR system;multidimensional SAR tomography;partial correlation property;partially coherent scatterer detection;satellite constellation;satellite formation acquisition;space full 3-D analysis;space-deformation-velocity analysis;synthetic aperture radar imaging;temporal distribution;Clutter;Coherence;Correlation;Decorrelation;Detectors;Synthetic aperture radar;Vectors;Fully coherent;SAR tomography;multidimensional SAR imaging;partially coherent;scatterer detection;synthetic aperture radar (SAR)},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6807810&isnumber=6832760

M. M. Naghsh, M. Soltanalian, P. Stoica, M. Modarres-Hashemi, A. De Maio and A. Aubry, "A Doppler Robust Design of Transmit Sequence and Receive Filter in the Presence of Signal-Dependent Interference," in IEEE Transactions on Signal Processing, vol. 62, no. 4, pp. 772-785, Feb.15, 2014.
doi: 10.1109/TSP.2013.2288082
keywords: {Doppler shift;filtering theory;optimisation;radar clutter;radar signal processing;radiofrequency interference;DESIDE method;Doppler robust design;Doppler robust transmit sequence;Doppler shifts;NP hard problem;SINR;active sensing system;max-min optimization problem;receive filter;signal dependent interference;signal-to-noise-plus-interference;synthesis algorithm;Clutter;Doppler shift;Robustness;Signal to noise ratio;Vectors;Code design;Doppler shift;interference;receive filter;robust design;synthesis;transmit sequence},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6650043&isnumber=6716102

D. Gaglione, C. Clemente, L. Pallotta, I. Proudler, A. De Maio and J. J. Soraghan, "Krogager decomposition and Pseudo-Zernike moments for polarimetric distributed ATR," Sensor Signal Processing for Defence (SSPD), 2014, Edinburgh, 2014, pp. 1-5.
doi: 10.1109/SSPD.2014.6943309
keywords: {decomposition;image recognition;radar imaging;radar polarimetry;synthetic aperture radar;Krogager decomposition component;automatic target recognition;full-polarimetric SAR image recognition algorithm;polarimetric distributed ATR;polarization;pseudoZernike moment;radar signal processing;spatial diversity;Feature extraction;Image sensors;Scattering;Sensors;Synthetic aperture radar;Training},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6943309&isnumber=6943293

A. Aubry, A. De Maio, G. Foglia, C. Hao and D. Orlando, "A radar detector with enhanced range estimation capabilities for partially homogeneous environment," in IET Radar, Sonar & Navigation, vol. 8, no. 9, pp. 1018-1025, 12 2014.
doi: 10.1049/iet-rsn.2013.0316
keywords: {radar detection;sampling methods;adaptive decision scheme;discrete-time model;point-like targets;radar detector;range estimation capabilities;two-step generalised likelihood ratio test},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6985805&isnumber=6985776

M. Soltanalian, P. Stoica, M. M. Naghsh and A. De Maio, "Design of piecewise linear polyphase sequences with good correlation properties," 2014 22nd European Signal Processing Conference (EUSIPCO), Lisbon, 2014, pp. 1297-1301.
keywords: {codes;fast Fourier transforms;piecewise linear techniques;radar;fast Fourier transform;good correlation properties;impulse-like autocorrelation;peak-to-average power ration;piecewise linear polyphase sequences;radar codes;waveform design;Correlation;Educational institutions;Indexes;Optimization;Peak to average power ratio;Radar;Vectors;Autocorrelation;peak-to-average-power ratio (PAR);polyphase sequences;radar codes;waveform design},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6952459&isnumber=6951911

A. Aubry, V. Carotenuto, A. D. Maio and G. Foglia, "Exploiting multiple a priori spectral models for adaptive radar detection," in IET Radar, Sonar & Navigation, vol. 8, no. 7, pp. 695-707, Aug. 2014.
doi: 10.1049/iet-rsn.2013.0233
keywords: {adaptive signal detection;covariance matrices;interference suppression;matrix inversion;maximum likelihood detection;object detection;radar detection;spectral analysis;PSD;a priori spectral model;adaptive radar detection;cell under test;environmental heterogeneity;generalised likelihood ratio test-based detection algorithms;interference inverse covariance matrix;lower bound;power spectral density;target detection;white disturbance},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6867011&isnumber=6867010

A. Aubry, A. De Maio, L. Pallotta, A. Farina and C. Fantacci, "Median matrices and geometric barycenters for training data selection," 2013 14th International Radar Symposium (IRS), Dresden, 2013, pp. 331-336.
keywords: {covariance matrices;estimation theory;probability;radar signal processing;covariance matrix estimation;geometric barycenter;median matrices;median matrix;positive definite matrix space;probability distribution;radar signal processing;training data selection;Clutter;Covariance matrices;Electronic mail;Probability distribution;Silicon;Training data;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6581109&isnumber=6581048

H. T. Hayvaci, A. De Maio and D. Erricolo, "Improved detection probability of a radar target in the presence of multipath with prior knowledge of the environment," in IET Radar, Sonar & Navigation, vol. 7, no. 1, pp. 36-46, Jan. 2013.
doi: 10.1049/iet-rsn.2012.0081
keywords: {radar receivers;radar target recognition;ray tracing;detection probability;multipath environments;perfectly reflecting planar surface;radar target detection problems;ray-tracing electromagnetic modelling;receivers;signal-to-noise ratio},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6475225&isnumber=6475219

A. De Maio, "Generalized CFAR property for radar detection," 2013 IEEE Radar Conference (RadarCon13), Ottawa, ON, 2013, pp. 1-4.
doi: 10.1109/RADAR.2013.6586051
keywords: {Gaussian processes;adaptive signal detection;matrix algebra;radar detection;statistical distributions;vectors;GLRT;Gaussian assumption;SIRV;adaptive signal detection;additive disturbance;compound matrix variate model;disturbance component;generalized CFAR property;generalized constant false alarm rate;generalized likelihood ratio test;invariant decision rules;mild technical conditions;multivariate distribution;natural generalization;quoted property;radar detection;radar interference;spherically invariant random vector;statistical models;Compounds;Covariance matrices;Detectors;Radar;Receivers;Signal to noise ratio;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6586051&isnumber=6585950

A. Aubry, A. De Maio and V. Carotenuto, "Optimality Claims for the FML Covariance Estimator with respect to Two Matrix Norms," in IEEE Transactions on Aerospace and Electronic Systems, vol. 49, no. 3, pp. 2055-2057, July 2013.
doi: 10.1109/TAES.2013.6558039
keywords: {Gaussian processes;Hermitian matrices;covariance matrices;maximum likelihood estimation;radar signal processing;spectral analysis;FML covariance estimator;Frobenius norm;Hermitian matrix;cost functions;fast maximum likelihood covariance matrix estimator;matrix norms;multivariate Gaussian assumption;optimality claims;spectral norm;statistical data characterization;zero-mean complex circular Gaussian training data;Convex functions;Covariance matrices;Eigenvalues and eigenfunctions;Joints;Linear matrix inequalities;Maximum likelihood estimation;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6558039&isnumber=6557991

G. Cui, A. De Maio, M. Piezzo and A. Farina, "Sidelobe Blanking with Generalized Swerling-Chi Fluctuation Models," in IEEE Transactions on Aerospace and Electronic Systems, vol. 49, no. 2, pp. 982-1005, APRIL 2013.
doi: 10.1109/TAES.2013.6494394
keywords: {Gaussian noise;antennas;jamming;probability;signal processing;Nuttall Q-function;antenna sidelobes;blanking region;closed-form performance probabilities;detection region;generalized Marcum function;generalized Swerling-Chi fluctuation models;integrated pulses;jammer;noncoherent integration;nonfluctuating target;sidelobe blanking;white Gaussian noise},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6494394&isnumber=6494371

L. Pallotta, A. De Maio and A. Aubry, "Extended target detection in interference whose covariance matrix is defined via uncertainty convex constraints," 2013 IEEE Radar Conference (RadarCon13), Ottawa, ON, 2013, pp. 1-4.
doi: 10.1109/RADAR.2013.6586013
keywords: {Gaussian processes;interference (signal);probability;signal detection;GLRT;Gaussian interference;PD;covariance uncertainty;detection probability;extended target detection;generalized likelihood ratio tests;hypothesis test;inverse disturbance covariance matrix;scaling factor;target echo;uncertainty convex constraints;unitary invariant convex functions;Covariance matrices;Detectors;Interference;Maximum likelihood estimation;Radar;Uncertainty;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6586013&isnumber=6585950

M. Piezzo, A. De Maio, A. Aubry and A. Farina, "Cognitive radar waveform design for spectral coexistence," 2013 IEEE Radar Conference (RadarCon13), Ottawa, ON, 2013, pp. 1-4.
doi: 10.1109/RADAR.2013.6586024
keywords: {computational complexity;convex programming;electromagnetic waves;radar;ACF;REM;SINR;autocorrelation function;cognitive radar waveform design;convex optimization;electromagnetic radiators;polynomial computational complexity;radio environmental map;signal to interference plus noise ratio;spectral coexistence;spectral compatibility;Interference;Jamming;Optimization;Polynomials;Radar;Signal to noise ratio;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6586024&isnumber=6585950

A. DeMaio, "Generalized CFAR Property and UMP Invariance for Adaptive Signal Detection," in IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 2104-2115, April15, 2013.
doi: 10.1109/TSP.2013.2245662
keywords: {adaptive signal detection;radar signal processing;statistical analysis;GLRT;Gaussian assumption;SIRV;UMP invariance;UMPI detector;Wijsman theorem;adaptive signal detection;additive disturbance;compound matrix variate model;constant false alarm rate;disturbance component;generalized CFAR property;generalized likelihood ratio test;invariant decision rules;maximal invariant likelihood ratio;most powerful invariant detector;multivariate distribution;radar disturbance;receivers;spherically invariant random vector;statistical models;uniformly most powerful invariant;Compounds;Context;Covariance matrix;Detectors;Linear matrix inequalities;Receivers;Vectors;Adaptive radar detection;UMPI;constant false alarm rate (CFAR);non-Gaussian interference},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6451296&isnumber=6482682

A. Aubry, A. De Maio, B. Jiang and S. Zhang, "Ambiguity Function Shaping for Cognitive Radar Via Complex Quartic Optimization," in IEEE Transactions on Signal Processing, vol. 61, no. 22, pp. 5603-5619, Nov.15, 2013.
doi: 10.1109/TSP.2013.2273885
keywords: {Doppler effect;optimisation;polynomials;radar signal processing;tensors;waveform analysis;MBI method;ambiguity function shaping;cognitive radar;complex quartic optimization;conjugate-partial-symmetric fourth order tensor theory;conjugate-super-symmetric fourth order tensor theory;constant modulus constraint;homogeneous complex quartic order polynomial;knowledge sources;maximum block improvement;optimization variable;performance assessment;phase-only modulated waveform design;polynomial-time waveform optimization;range-Doppler bins;range-Doppler response;Doppler effect;Doppler radar;Optimization;Radar signal processing;Vectors;Cognitive radar;complex tensor optimization;maximum block improvement method;radar waveform optimization},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6563125&isnumber=6626366

A. Aubry, A. De Maio, L. Pallotta and A. Farina, "Radar Detection of Distributed Targets in Homogeneous Interference Whose Inverse Covariance Structure is Defined via Unitary Invariant Functions," in IEEE Transactions on Signal Processing, vol. 61, no. 20, pp. 4949-4961, Oct.15, 2013.
doi: 10.1109/TSP.2013.2273444
keywords: {Gaussian processes;covariance matrices;inverse problems;maximum likelihood estimation;object detection;parameter estimation;radar detection;radar interference;radar receivers;Doppler processing;GLRT;H₀ hypothesis;H₁ hypothesis;ML;bin under test;constrained maximum likelihood estimation;decision problem;distributed target detection;extended target embedded detection;generalized likelihood ratio test;homogeneous Gaussian interference;inverse noise covariance matrix structure;parameter estimation;radar detection;spatial processing;unitary invariant continuous function;Covariance matrices;Interference;Maximum likelihood estimation;Noise;Radar detection;Uncertainty;Vectors;Constrained maximum likelihood estimation;extended targets;generalized likelihood ratio test;radar signal processing;unitary invariant constraints},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6560449&isnumber=6588613

G. Cui, A. De Maio, A. Aubry, A. Farina and L. Kong, "Advanced SLB Architectures with Invariant Receivers," in IEEE Transactions on Aerospace and Electronic Systems, vol. 49, no. 2, pp. 798-818, APRIL 2013.
doi: 10.1109/TAES.2013.6494382
keywords: {Gaussian noise;antennas;clutter;radio receivers;CFAR behavior;SLB architectures;SLB system design;antenna;constant false alarm rate;homogeneous Gaussian clutter;invariant receivers;sidelobe blanking system design},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6494382&isnumber=6494371

V. Carotenuto, A. De Maio, A. Aubry and G. Foglia, "Adaptive radar detection based on multiple a-priori models," 2013 IEEE Radar Conference (RadarCon13), Ottawa, ON, 2013, pp. 1-5.
doi: 10.1109/RADAR.2013.6586054
keywords: {covariance matrices;interference;radar detection;radar receivers;GLRT;adaptive radar detection;conventional adaptive techniques;covariance matrix;detection algorithms;environmental heterogeneity;generalized likelihood ratio test;inverse covariance;multiple a-priori models;Adaptation models;Covariance matrices;Detectors;Interference;Radar;Receivers;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6586054&isnumber=6585950

A. Aubry, A. D. Maio, S. Iommelli, M. Piezzo, A. Farina and M. Wicks, "Transmitted phase code/receive filter design for high reverberating environment: A cognitive approach," Advances in Radar and Remote Sensing (TyWRRS), 2012 Tyrrhenian Workshop on, Naples, 2012, pp. 146-152.
doi: 10.1109/TyWRRS.2012.6381120
keywords: {cognitive systems;encoding;filters;radar clutter;radar computing;radar receivers;radar signal processing;cognitive approach;high reverberating environment;homogeneous clutter;knowledge aided transmit signal;optimization procedure;phase code;phase only waveform;point like target;radar waveform;receive filter design;signal to interference plus noise ratio;similarity constraint;Algorithm design and analysis;Clutter;Optimization;Radar;Signal to noise ratio;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6381120&isnumber=6381088

H. T. Hayvaci, A. De Maio and D. Erricolo, "Performance analysis of diverse GLRT detectors in the presence of multipath," 2012 IEEE Radar Conference, Atlanta, GA, 2012, pp. 0902-0906.
doi: 10.1109/RADAR.2012.6212265
keywords: {Gaussian noise;probability;radar detection;time-domain analysis;GLRT;GLRT1 receiver;GLRT2 receiver;NP1 receiver;NP2 receiver;Neyman Pearson;PSD;distinct multipath structures;diverse GLRT detectors;generalized likelihood ratio test;highly-clumped multipath returns;multipath components;optimum receivers;power spectral density;radar-target environment;suboptimum receivers;target detection probability;time-domain;transition region;zero-mean CWGN;zero-mean complex circular Gaussian noise;Correlation;Detectors;Radar;Random variables;Receivers;Signal to noise ratio},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6212265&isnumber=6212083

A. Aubry, A. De Maio, M. Piezzo, A. Farina and M. Wicks, "Cognitive design of the transmitted phase code and receive filter in reverberating environment," Waveform Diversity & Design Conference (WDD), 2012 International, Kauai, HI, 2012, pp. 085-090.
doi: 10.1109/WDD.2012.7311299
keywords: {iterative methods;optimisation;radar signal processing;computational complexity;constrained optimization procedure;high reverberating signal-dependent environment;knowledge-aided transmit signal and receive filter design;phase-only waveforms;radar waveform sharing;signal to interference plus noise ratio;Algorithm design and analysis;Clutter;Doppler effect;Optimization;Radar;Signal to noise ratio},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7311299&isnumber=7311172

L. Pallotta, A. Aubry, A. De Maio and A. Farina, "Estimation of a structured covariance matrix with a condition number constraint for radar applications," 2012 IEEE Radar Conference, Atlanta, GA, 2012, pp. 0778-0783.
doi: 10.1109/RADAR.2012.6212243
keywords: {computational complexity;covariance matrices;eigenvalues and eigenfunctions;matrix decomposition;maximum likelihood estimation;radar interference;radar signal processing;MAXDET problems;ML estimator;SINR;computational complexity;condition number constraint;constrained optimization problem;covariance structure;disturbance covariance matrix estimation;eigenvalue decomposition;maximum likelihood estimator;positive semidefinite matrix;radar applications;radar signal processing;sample covariance matrix decomposition;secondary data set;signal to interference plus noise ratio;spatial processing;structured covariance matrix estimation techniques;training data;Covariance matrix;Interference;Jamming;Maximum likelihood estimation;Signal to noise ratio;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6212243&isnumber=6212083

A. Aubry, A. De Maio, M. Piezzo, G. Cui, L. Kong and A. Farina, "A coherent SLB architecture with Kelly's receiver," Advances in Radar and Remote Sensing (TyWRRS), 2012 Tyrrhenian Workshop on, Naples, 2012, pp. 159-163.
doi: 10.1109/TyWRRS.2012.6381122
keywords: {Gaussian noise;numerical analysis;probability;radar antennas;radar clutter;radio receivers;CFAR behavior;Kelly receiver;antenna sidelobes;blanking regions;closed-form performance probabilities;coherent SLB architecture;constant false alarm rate behavior;detection regions;homogeneous Gaussian clutter plus noise;numerical simulations;sidelobe blanking system design;Barium;Doppler effect;Jamming;Thermal analysis},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6381122&isnumber=6381088

L. Pallotta, A. Aubry, A. De Maio and A. Farina, "Geometric barycenters and their application to radar training data selection/target detection," 2012 13th International Radar Symposium, Warsaw, 2012, pp. 85-90.
doi: 10.1109/IRS.2012.6233294
keywords: {covariance matrices;object detection;probability;radar signal processing;covariance matrix estimation;geometric barycenters;probability distribution;radar signal processing;radar training data selection;target detection;Clutter;Covariance matrix;Detectors;Doppler effect;Silicon;Training data;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6233294&isnumber=6233281

A. Aubry, A. De Maio, M. Piezzo, A. Farina and M. Wicks, "Quantized phase code and receive filter synthesis in reverberating environment," 2012 IEEE Radar Conference, Atlanta, GA, 2012, pp. 0790-0795.
doi: 10.1109/RADAR.2012.6212245
keywords: {phase coding;radar;actual scattering environment;ambiguity function;cognitively designing;computational complexity;dynamic environmental database;high reverberating environment;homogeneous clutter scenario;optimization procedure;point-like target;polynomial;quantized phase code;quantized phase-only condition;radar waveform;receive filter length;receive filter synthesis;signal to interference plus noise ratio;similarity constraint;transmitted signal;Algorithm design and analysis;Clutter;Optimization;Radar;Signal to noise ratio;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6212245&isnumber=6212083

A. Aubry, A. De Maio, L. Pallotta and A. Farina, "Radar covariance matrix estimation through geometric barycenters," Radar Conference (EuRAD), 2012 9th European, Amsterdam, 2012, pp. 57-62.
keywords: {adaptive filters;covariance matrices;probability;radar detection;radar signal processing;KASSPER datacube;adaptive detection structure;adaptive matched filter;data selector screening;geometric barycenters;outliers excision;positive definite matrix space;probability distribution;radar covariance matrix estimation;radar signal processing applications;target detection;training data;Clutter;Covariance matrix;Detectors;Doppler effect;Receivers;Silicon;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6450695&isnumber=6450609

A. Pauciullo, D. Reale, A. De Maio and G. Fornaro, "Detection of Double Scatterers in SAR Tomography," in IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 9, pp. 3567-3586, Sept. 2012.
doi: 10.1109/TGRS.2012.2183002
keywords: {geophysical image processing;image reconstruction;remote sensing by radar;synthetic aperture radar;3D reconstruction;SAR interferometry;SAR tomography;double scatterer detection;ground scatterers localization;ground scatterers monitoring;individual buildings;synthetic aperture radar;urban areas;Detectors;Estimation;Image resolution;Monitoring;Tomography;Vectors;Multidimensional SAR imaging;SAR tomography;scatterer detection},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6165350&isnumber=6280643

G. Cui, A. De Maio, M. Piezzo, V. Carotenuto and A. Farina, "Sidelobe blanking with correlated generalized Swerling-Chi fluctuation models," 2012 13th International Radar Symposium, Warsaw, 2012, pp. 141-144.
doi: 10.1109/IRS.2012.6233305
keywords: {AWGN;integration;jamming;probability;radar antennas;SLB system;closed-form performance probability;correlated generalized swerling-chi fluctuation model;generalized Marcum Q -function;jamming return;noncoherent integration;nonidentically distributed fluctuating target;radar antenna sidelobe;sidelobe blanking system;white Gaussian noise;Blanking;Radio access networks;Generalized Swerling-Chi model;Incoherent receiver;SLB},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6233305&isnumber=6233281

A. Aubry, V. Carotenuto, A. De Maio, L. Pallotta and A. Farina, "Detection capabilities evaluation of a constrained structured covariance matrix estimator for radar applications," Advances in Radar and Remote Sensing (TyWRRS), 2012 Tyrrhenian Workshop on, Naples, 2012, pp. 202-208.
doi: 10.1109/TyWRRS.2012.6381130
keywords: {adaptive filters;computational complexity;covariance matrices;eigenvalues and eigenfunctions;matched filters;maximum likelihood estimation;optimisation;radar detection;radar receivers;radar signal processing;MAXDET problems;adaptive matched filter receiver;computational complexity;condition number upper-bound constraint;constrained optimization problem;constrained structured covariance matrix estimator;detection capabilities evaluation;detection capability;detection probabilities;disturbance covariance matrix;eigenvalue decomposition;maximum likelihood estimator;positive semidefinite matrix;radar signal processing applications;spatial processing;special covariance structure;training data;Covariance matrix;Eigenvalues and eigenfunctions;Interference;Jamming;Maximum likelihood estimation;Radar;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6381130&isnumber=6381088

M. Piezzo, A. De Maio and G. Cui, "Performance prediction of the incoherent radar detector for Generalized Swerling-Chi fluctuating targets," 2012 IEEE Radar Conference, Atlanta, GA, 2012, pp. 0784-0789.
doi: 10.1109/RADAR.2012.6212244
keywords: {diversity reception;echo;polarisation;probability;radar detection;radar receivers;detection performance;detection probability;frequency agility;generalized Swerling-Chi distribution models;generalized Swerling-Chi fluctuating targets;incoherent radar detector;incoherent radar receiver;nonidentically distributed target backscattered echoes;performance prediction;polarization;probability density function;spatial diversity;Correlation;Fluctuations;Probability density function;Random variables;Receivers;Shape;Signal to noise ratio},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6212244&isnumber=6212083

A. Aubry, A. De Maio, B. Jiang and S. Zhang, "A cognitive approach for ambiguity function shaping," Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012 IEEE 7th, Hoboken, NJ, 2012, pp. 41-44.
doi: 10.1109/SAM.2012.6250527
keywords: {optimisation;phase modulation;polynomials;radar theory;MBI approach;ambiguity function shaping;cognitive approach;constant modulus constraint;maximum block improvement approach;phase-only modulated waveform design;quartic order polynomial optimization problem;radar waveforms;Algorithm design and analysis;Doppler effect;Optimization;Polynomials;Radar;Signal processing algorithms;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6250527&isnumber=6250436

J. Carretero-Moya, A. De Maio, J. Gismero-Menoyo and A. Asensio-Lopez, "Experimental Performance Analysis of Distributed Target Coherent Radar Detectors," in IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 3, pp. 2216-2238, JULY 2012.
doi: 10.1109/TAES.2012.6237589
keywords: {distributed tracking;millimetre wave radar;radar tracking;target tracking;Ka-band radar system;constant false alarm rate;distributed target coherent radar detectors;homeland coastal security;performance analysis;performance improvements;rank-one target detection;sea-clutter data;submeter range resolution;subspace range-spread target detection;Clutter;Covariance matrix;Detectors;Doppler effect;Radar detection;Vectors},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6237589&isnumber=6237562

A. Aubry, A. De Maio, L. Pallotta and A. Farina, "Maximum Likelihood Estimation of a Structured Covariance Matrix With a Condition Number Constraint," in IEEE Transactions on Signal Processing, vol. 60, no. 6, pp. 3004-3021, June 2012.
doi: 10.1109/TSP.2012.2190408
keywords: {computational complexity;covariance matrices;maximum likelihood estimation;optimisation;radar signal processing;Doppler processing;MAXDET problems;SINR;computational complexity;condition number upper-bound constraint;disturbance covariance matrix estimation;eigenvalue decomposition;formulated constrained optimization problem;maximum likelihood estimation;radar signal processing;signal-to-interference-plus-noise ratio;spatial processing;structured covariance matrix;Clutter;Covariance matrix;Maximum likelihood estimation;Optimization;Radar;Adaptive radar signal processing;condition number;knowledge based;shrinkage;structured covariance matrix estimation},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6166345&isnumber=6198804

A. De Maio, Y. Huang, M. Piezzo, S. Zhang and A. Farina, "Design of Radar Receive Filters Optimized According to -Norm Based Criteria," in IEEE Transactions on Signal Processing, vol. 59, no. 8, pp. 4023-4029, Aug. 2011.
doi: 10.1109/TSP.2011.2153199
keywords: {Doppler shift;computational complexity;convex programming;filters;pulse compression;radar;radar signal processing;Doppler tolerance;Doppler tolerances;Lp-norm based criteria;SOCP problem;convex optimization second-order cone programming;filter sidelobe energy;integrated sidelobe level;interior point methods;inverse signal-to-noise power ratio;peak sidelobe level;polynomial time computational complexity;pulse compression;radar receive filter design;upper-bound constraint;Convex functions;Electronic mail;Interference;Noise;Programming;Radar cross section;Mismatched filter design;radar receive filter design;second order cone programming},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5765722&isnumber=5948440

J. Carretero-Moya, A. De Maio, J. Gismero-Menoyo and A. Asensio-Lopez, "High resolution sea clutter and maritime target data: Experimental performance of distributed target coherent detectors," 2011 IEEE RadarCon (RADAR), Kansas City, MO, 2011, pp. 383-388.
doi: 10.1109/RADAR.2011.5960565
keywords: {radar clutter;radar detection;statistical analysis;CFAR behavior;coherent target data;high resolution Ka-band radar data;high resolution sea clutter;maritime target data;range distributed target coherent detectors;statistical analysis;submeter range resolution clutter data;target-free sea-clutter acquisition;Clutter;Covariance matrix;Detectors;Doppler effect;Radar detection},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5960565&isnumber=5960475

H. T. Hayvaci, A. De Maio and D. Erricolo, "Diversity in receiving strategies based on time-delay analysis in the presence of multipath," 2011 IEEE RadarCon (RADAR), Kansas City, MO, 2011, pp. 1040-1045.
doi: 10.1109/RADAR.2011.5960694
keywords: {diversity reception;electromagnetic wave propagation;radar target recognition;target tracking;electromagnetic analysis;generalized likelihood ratio test receivers;matched-filter detectors;multipath delay profile;radar-target environment;target detection;time-delay analysis;Adaptation models;Analytical models;Detectors;Geometry;Radar;Random variables;Receivers},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5960694&isnumber=5960475

A. De Maio, Y. Huang, D. P. Palomar, S. Zhang and A. Farina, "Fractional QCQP With Applications in ML Steering Direction Estimation for Radar Detection," in IEEE Transactions on Signal Processing, vol. 59, no. 1, pp. 172-185, Jan. 2011.
doi: 10.1109/TSP.2010.2087327
keywords: {mathematical programming;maximum likelihood estimation;radar detection;Charnes-Cooper transformation;Gaussian disturbance;ML criterion;ML steering direction estimation;SDP relaxation;constrained Cramer Rao lower Bound;constrained fractional semidefinite programming;fractional QCQP;fractional quadratically constrained quadratic problem;general quadratic inequality constraint;maximum likelihood criterion;radar detection;rank-one decomposition;signal component;Antenna arrays;Arrays;Maximum likelihood estimation;Radar antennas;Radar detection;Constrained maximum likelihood steering direction estimation;fractional QCQP;radar applications},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5604325&isnumber=5662471

A. De Maio, Y. Huang, M. Piezzo, S. Zhang and A. Farina, "Design of Optimized Radar Codes With a Peak to Average Power Ratio Constraint," in IEEE Transactions on Signal Processing, vol. 59, no. 6, pp. 2683-2697, June 2011.
doi: 10.1109/TSP.2011.2128313
keywords: {Doppler shift;computational complexity;concave programming;minimax techniques;quadratic programming;quantisation (signal);radar detection;Doppler frequency;Doppler shifts;NP-hard problems;PAR;SDP relaxation;SNR;colored Gaussian disturbance;detection performance;energy constraint;high-quality suboptimal solutions;max-min approach;nonconvex quadratic optimization programs;peak-to-average power ratio constraint;phase-quantized versions;polynomial time computational complexity;radar code optimization design;radar signal;radar waveform design;semidefinite programming;signal-to-noise ratio;trigonometric polynomials;waveform design algorithms;Algorithm design and analysis;Approximation algorithms;Approximation methods;Doppler effect;Peak to average power ratio;Radar;Signal to noise ratio;Approximation bound;nonconvex quadratic optimization;nonnegative trigonometric polynomials;radar waveform design;randomized algorithm;semidefinite programming relaxation;waveform diversity},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5732713&isnumber=5767741

A. De Maio and E. Conte, "Radar detection in Gaussian environment after reduction by invariance," 2011 IEEE RadarCon (RADAR), Kansas City, MO, 2011, pp. 079-084.
doi: 10.1109/RADAR.2011.5960503
keywords: {covariance matrices;radar detection;radiofrequency interference;C-UMPI test;Durbin test;GLRT;Gaussian environment;conditional uniformly most powerful invariant test;covariance matrix;generalized likelihood ratio test;homogeneous Gaussian interference;maximal invariant statistic;radar detection;Covariance matrix;Detectors;Orbits;Receivers;Signal to noise ratio;Solids;Testing},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5960503&isnumber=5960475

D. Reale, A. Pauciullo, G. Fornaro and A. De Maio, "A scatterers detection scheme in SAR Tomography for reconstruction and monitoring of individual buildings," 2011 Joint Urban Remote Sensing Event, Munich, 2011, pp. 249-252.
doi: 10.1109/JURSE.2011.5764766
keywords: {radar detection;radar imaging;radar interferometry;tomography;3D monitoring;3D reconstruction;SAR interferometry;SAR sensors;TerraSAR-X data;generalized likelihood ratio test;multiple stable scatterers;scatterers detection scheme;tomographic based SAR imaging;Buildings;Image resolution;Interferometry;Monitoring;Remote sensing;Signal to noise ratio;Tomography},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5764766&isnumber=5764698

A. Aubry, A. Bazzoni, V. Carotenuto, A. De Maio and P. Failla, "Cumulants-based Radar Specific Emitter Identification," 2011 IEEE International Workshop on Information Forensics and Security, Iguacu Falls, 2011, pp. 1-6.
doi: 10.1109/WIFS.2011.6123155
keywords: {pattern classification;radar signal processing;SEI;cumulants-based radar specific emitter identification;k-nearest neighbor classifier;radar signals;Dispersion;Electromagnetics;Feature extraction;Noise;Noise measurement;Radar;Training},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6123155&isnumber=6123119

A. De Maio and E. Conte, "Adaptive Detection in Gaussian Interference With Unknown Covariance After Reduction by Invariance," in IEEE Transactions on Signal Processing, vol. 58, no. 6, pp. 2925-2934, June 2010.
doi: 10.1109/TSP.2010.2044835
keywords: {Gaussian processes;interference (signal);signal detection;Durbin test;Rao test;generalized likelihood ratio test;homogeneous Gaussian interference adaptive detection;invariant detector;maximal invariant statistic;signal adaptive detection;Adaptive detection;Durbin test;GLRT;Rao test;UMPI;invariance theory},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5424068&isnumber=5464420

A. De Maio, A. Farina and G. Foglia, "Knowledge-Aided Bayesian Radar Detectors & Their Application to Live Data," in IEEE Transactions on Aerospace and Electronic Systems, vol. 46, no. 1, pp. 170-183, Jan. 2010.
doi: 10.1109/TAES.2010.5417154
keywords: {Bayes methods;Gaussian processes;covariance matrices;electrical engineering computing;inference mechanisms;radar clutter;radar detection;Bayesian approach;Gaussian clutter;adaptive radar detection;clutter covariance matrix;decision rules;generalized likelihood ratio test;knowledge-aided Bayesian radar detectors;knowledge-aided sensor signal processing and expert reasoning;live data application;probability density function;Bayesian methods;Covariance matrix;Detectors;Probability density function;Radar applications;Radar clutter;Radar detection;Radar signal processing;Signal analysis;Testing},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5417154&isnumber=5417139

S. De Nicola, A. De Maio, A. Farina and M. C. Wicks, "Fast code design for sensors in noncooperative networks," Wireless Conference (EW), 2010 European, Lucca, 2010, pp. 758-765.
doi: 10.1109/EW.2010.5483457
keywords: {computational complexity;mathematical programming;radar detection;waveform analysis;fast code design;noncooperative network;nondeterministic polynomial hard;polynomial time complexity;radar sensor;semidefinite programming problem;waveform design;Analytical models;Frequency diversity;Interference;MIMO;NP-hard problem;Performance analysis;Polynomials;Radar antennas;Radar detection;Wireless sensor networks},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5483457&isnumber=5483383

A. De Maio, Y. Huang and M. Piezzo, "A Doppler Robust Max-Min Approach to Radar Code Design," in IEEE Transactions on Signal Processing, vol. 58, no. 9, pp. 4943-4947, Sept. 2010.
doi: 10.1109/TSP.2010.2050317
keywords: {Doppler shift;communication complexity;concave programming;minimax techniques;quadratic programming;radar signal processing;radar theory;Doppler robust max-min approach;Doppler shifts;colored Gaussian disturbance;energy constraint;high-quality radar signal algorithm;nonconvex quadratically constrained quadratic program;optimization problem;polynomial computational complexity;radar code design;radar waveform;robust waveform design;worst case detection performance;Non-convex quadratic optimization;non-negative trigonometric polynomials;radar waveform design;semidefinite programming relaxation;waveform diversity},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5464316&isnumber=5545581

A. De Maio, S. M. Kay and A. Farina, "On the Invariance, Coincidence, and Statistical Equivalence of the GLRT, Rao Test, and Wald Test," in IEEE Transactions on Signal Processing, vol. 58, no. 4, pp. 1967-1979, April 2010.
doi: 10.1109/TSP.2009.2039728
keywords: {covariance analysis;invariance;signal detection;GLRT;Rao test;Wald test;binary hypothesis testing problem;coincidence;decision variables;generalized likelihood ratio test;invariance;monotonic transformations;statistical signal processing;Biomedical signal processing;Detectors;Radar detection;Radar remote sensing;Radar signal processing;Remote sensing;Signal detection;Statistics;Sufficient conditions;Testing;Detection;GLRT;Rao test;Wald test;invariance},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5371928&isnumber=5427215

A. De Maio, G. Foglia, A. Farina and M. Piezzo, "Estimation of the covariance matrix based on multiple a-priori models," 2010 IEEE Radar Conference, Washington, DC, 2010, pp. 1025-1029.
doi: 10.1109/RADAR.2010.5494470
keywords: {covariance matrices;maximum likelihood estimation;radar clutter;clutter power spectral density;covariance matrix;inverse covariance;maximum likelihood estimation;multiple a-priori spectral model;Clutter;Covariance matrix;Detectors;Doppler radar;Jamming;Maximum likelihood estimation;Radar detection;Statistics;Testing;Training data;Convex Optimization;Covariance Matrix Estimation;MAXDET},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5494470&isnumber=5494381

S. Buzzi, L. Venturino, A. Zappone and A. De Maio, "Blind User Detection in Doubly Dispersive DS/CDMA Fading Channels," in IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1446-1451, March 2010.
doi: 10.1109/TSP.2009.2033001
keywords: {cochannel interference;code division multiple access;fading channels;spread spectrum communication;blind user detection;cochannel interference;direct-sequence/code-division-multiple-access;doubly dispersive DS-CDMA fading channels;multiaccess interference;spreading code;time-varying channel impulse response;Cellular networks;Code standards;Data communication;Detectors;Dispersion;Fading;Frequency;Interchannel interference;Multiaccess communication;Telecommunication network reliability;Cell-search;DS/CDMA;doubly dispersive channel;neighbor discovery;user detection},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5263006&isnumber=5410627

A. De Maio, S. De Nicola, Y. Huang, D. P. Palomar, S. Zhang and A. Farina, "Code Design for Radar STAP via Optimization Theory," in IEEE Transactions on Signal Processing, vol. 58, no. 2, pp. 679-694, Feb. 2010.
doi: 10.1109/TSP.2009.2032993
keywords: {concave programming;quadratic programming;radar signal processing;space-time adaptive processing;code design algorithm;colored Gaussian disturbance;expert reasoning datacube;knowledge-aided sensor signal processing;pre-fixed radar code;quadratic optimization problem;radar STAP;radar space-time adaptive processing;rank-one decomposition;semideflnite program class;spatial Doppler estimation;temporal Doppler estimation;Algorithm design and analysis;Constraint optimization;Design optimization;Doppler radar;Optimal control;Radar detection;Radar theory;Signal analysis;Signal processing algorithms;Spaceborne radar;Nonconvex quadratic optimization;radar signal processing;semidefinite programming relaxation;space-time adaptive processing (STAP);waveform design},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5263002&isnumber=5379139

G. Alfano, A. De Maio and A. M. Tulino, "A Theoretical Framework for LMS MIMO Communication Systems Performance Analysis," in IEEE Transactions on Information Theory, vol. 56, no. 11, pp. 5614-5630, Nov. 2010.
doi: 10.1109/TIT.2010.2070230
keywords: {MIMO communication;antenna arrays;channel capacity;covariance matrices;diversity reception;land mobile radio;least mean squares methods;mobile satellite communication;probability;radio reception;radio transmitters;LMS MIMO communication systems performance analysis;capacity achieving input covariance matrix;channel matrix knowledge;diversity order;ergodic assumption;ergodic capacity;land mobile satellite channel;line-of-sight fluctuation;multiantenna LMS channel;nonergodic assumption;outage probability;receive side information;spectral statistics;Covariance matrix;Eigenvalues and eigenfunctions;Jacobian matrices;Joints;Least squares approximation;MIMO;Transmitters;Asymptotic analysis;LMS;eigenanalysis;multiple-input multiple-output (MIMO);noncentral Wishart},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5605379&isnumber=5605336

G. Alfano, A. De Maio and A. M. Tulino, "Information-theoretic performance analysis of LMS MIMO communications," Information Theory Workshop (ITW), 2010 IEEE, Dublin, 2010, pp. 1-5.
doi: 10.1109/CIG.2010.5592839
keywords: {MIMO communication;covariance matrices;information theory;MIMO communication;channel knowledge;channel matrix;covariance matrix;information theory performance analysis;land mobile satellite channel;line of sight fluctuation;multiantenna LMS channel;power control;signal to noise ratio;Covariance matrix;Least squares approximation;MIMO;Mutual information;Receiving antennas;Signal to noise ratio;Transmitters},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5592839&isnumber=5592637

A. De Maio, M. Piezzo, A. Farina and M. Wicks, "Pareto-optimal radar waveform design," 2010 International Waveform Diversity and Design Conference, Niagara Falls, ON, 2010, pp. 000224-000228.
doi: 10.1109/WDD.2010.5592547
keywords: {Doppler radar;Gaussian noise;Pareto optimisation;concave programming;estimation theory;radar detection;radar theory;CRLB;Cramer Rao lower bound;Doppler estimation accuracy;Pareto weight;Pareto-optimal radar waveform design;Pareto-optimal waveform design;colored Gaussian noise;constrained minimization;detection performance;detection probability;joint constrained maximization;nonconvex multiobjective optimization problem;optimal radar code;optimization process;quadratic constraints;quadratic forms;scalarization technique;vectorial problem;waveform design scheme;Accuracy;Doppler effect;Doppler radar;Estimation;Optimization;Radar detection},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5592547&isnumber=5591882

F. Bandiera, A. De Maio, S. De Nicola, A. Farina, D. Orlando and G. Ricci, "Adaptive strategies for discrimination between mainlobe and sidelobe signals," 2010 IEEE Radar Conference, Washington, DC, 2010, pp. 910-914.
doi: 10.1109/RADAR.2010.5494492
keywords: {matched filters;maximum likelihood detection;radar detection;GLRT implementation;Neyman-Pearson rule;adaptive detectors;generalized likelihood ratio test;mainlobe signals;matched filter;sidelobe interferer;sidelobe signals;Clutter;Detectors;Linear antenna arrays;Noise cancellation;Radar detection;Radar scattering;Signal design;Statistics;Testing;Uncertainty;Adaptive Radar Detection;Constant False Alarm Rate (CFAR);Sidelobe Signals Rejection},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5494492&isnumber=5494381

A. De Maio, S. De Nicola, A. Farina and S. Iommelli, "A robust adaptive detector for steering phase uncertainties," 2009 International Radar Conference "Surveillance for a Safer World" (RADAR 2009), Bordeaux, 2009, pp. 1-6.
keywords: {mathematical programming;polynomials;radar detection;radar receivers;radar signal processing;adaptive signal detection;concentrated likelihood function;convex optimization problem;hidden convexity property;radar signal processing;robust adaptive detector;robust receiver accounting;semidefinite programming convex optimization;steering phase uncertainties;trigonometric polynomials;Adaptive signal detection;Covariance matrix;Detectors;Performance analysis;Phase detection;Polynomials;Radar detection;Robustness;Signal detection;Uncertainty;Radar Signal Processing;Robust Detection;Semidefinite Programming;bounded-CFAR},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5438467&isnumber=5438351

A. De Maio, S. De Nicola, Y. Huang, S. Zhang and A. Farina, "Adaptive Detection and Estimation in the Presence of Useful Signal and Interference Mismatches," in IEEE Transactions on Signal Processing, vol. 57, no. 2, pp. 436-450, Feb. 2009.
doi: 10.1109/TSP.2008.2008249
keywords: {adaptive signal detection;covariance matrices;maximum likelihood estimation;signal denoising;adaptive detection;adaptive estimation;covariance matrix;generalized likelihood ratio test;interference mismatches;joint maximum likelihood estimators;primary data share;random disturbance components;secondary data share;semideflnite program;useful signal;Adaptive detection;nonconvex quadratic optimization;radar signal processing;semidefinite programming relaxation},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4668421&isnumber=4773578

A. De Maio, G. Foglia, N. Pasquino and M. Vadursi, "Measurement and analysis of clutter signal from GSM/DCS and UMTS-based passive radar," 2009 International Radar Conference "Surveillance for a Safer World" (RADAR 2009), Bordeaux, 2009, pp. 1-6.
keywords: {3G mobile communication;cellular radio;passive radar;radar clutter;radar receivers;radar transmitters;statistical analysis;GSM-DCS;UMTS-based passive radar;clutter signal analysis;clutter signal measurement;measurement system;receiver;statistical property;transmitter;Clutter;Distributed control;GSM;Passive radar;Poles and towers;Radar applications;Radar detection;Radio broadcasting;Radio transmitters;Signal analysis},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5438436&isnumber=5438351

A. De Maio, G. Fornaro, A. Pauciullo and D. Reale, "Detection of double scatterers in SAR Tomography," 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, 2009, pp. III-172-III-175.
doi: 10.1109/IGARSS.2009.5417945
keywords: {geophysical image processing;synthetic aperture radar;tomography;Bayesian Information Criterion;Generalized Likelihood Ratio Test;SAR tomography;azimuth-range pixel;differential tomography;double scatterers;ground scatterers;high resolution radar systems;multi-dimensional SAR imaging;scatterers detection;IEEE members;Interferometry;Multidimensional systems;Pixel;Radar polarimetry;Radar scattering;Signal to noise ratio;Synthetic aperture radar;Testing;Tomography;Differential Tomography;Multi-Dimensional SAR imaging;Scatterers Detection},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5417945&isnumber=5417750

S. Buzzi, L. Venturino, A. Zappone and A. De Maio, "Blind User Detection and Delay Acquisition in Doubly-Dispersive DS/CDMA Fading Channels," 2009 IEEE Wireless Communications and Networking Conference, Budapest, 2009, pp. 1-6.
doi: 10.1109/WCNC.2009.4917615
keywords: {code division multiple access;fading channels;multipath channels;blind procedure;blind user detection;bounded constant false alarm rate;code-aided detection algorithm;delay acquisition;direct-sequence/code division multiple access system;doubly-dispersive DS/CDMA fading channel;multipath channel delay estimation;multipath replicas;statistical tool;Communications Society;Delay estimation;Detection algorithms;Detectors;Fading;Multiaccess communication;Multipath channels;Multiple access interference;Power system modeling;Testing},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4917615&isnumber=4917481

A. De Maio, G. Fornaro and A. Pauciullo, "Detection of Single Scatterers in Multidimensional SAR Imaging," in IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 7, pp. 2284-2297, July 2009.
doi: 10.1109/TGRS.2008.2011632
keywords: {radar imaging;radar interferometry;synthetic aperture radar;CFAR detection scheme;Rao test;SAR imaging;SAR interferometry;Wald test;constant false alarm rate;differential interferometry;generalized likelihood ratio test;multi-interferogram complex coherence;multidimensional synthetic aperture radar;scatterer interferometry;single scatterers detection;space deformation-velocity analysis;Differential tomography;multidimensional synthetic aperture radar (SAR) imaging;scatterer detection},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4814567&isnumber=5075850

A. De Maio, S. De Nicola and A. Farina, "GLRT Versus MFLRT for Adaptive CFAR Radar Detection With Conic Uncertainty," in IEEE Signal Processing Letters, vol. 16, no. 8, pp. 707-710, Aug. 2009.
doi: 10.1109/LSP.2009.2022566
keywords: {Gaussian noise;radar detection;receivers;GLRT;MFLRT;adaptive CFAR radar detection;complex Gaussian disturbance;cone aperture parameter;conic region;conic uncertainty;constant false alarm rate;generalized likelihood ratio test;multifamily likelihood ratio test;receivers;unknown signal;Adaptive detection;conic optimization;statistical signal processing},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4912348&isnumber=4967994

A. De Maio, S. De Nicola, Y. Huang, Z. Q. Luo and S. Zhang, "Design of Phase Codes for Radar Performance Optimization With a Similarity Constraint," in IEEE Transactions on Signal Processing, vol. 57, no. 2, pp. 610-621, Feb. 2009.
doi: 10.1109/TSP.2008.2008247
keywords: {Gaussian noise;phase coding;polynomial approximation;quadratic programming;radar signal processing;NP-hard quadratic optimization problem;coded waveforms design;colored Gaussian disturbance;phase codes;prefixed radar code;radar performance optimization;radar signal processing;semidefinite program randomization;semidefinite program relaxation;Radar signal processing;nonconvex quadratic optimization;randomization;semidefinite program relaxation},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4668417&isnumber=4773578

A. De Maio, G. Foglia, N. Pasquino and M. Vadursi, "Measurement and analysis of clutter signal from GSM/DCS-based passive radar," 2008 IEEE Radar Conference, Rome, 2008, pp. 1-6.
doi: 10.1109/RADAR.2008.4721052
keywords: {FM radar;cellular radio;passive radar;radar clutter;FM modulated carrier;GSM/DCS-based passive radar;classic active radar systems;measurement system;signal clutter;statistical properties;Antenna measurements;Attenuation measurement;Clutter;Current measurement;Directive antennas;GSM;Passive radar;Radar detection;Radio frequency;Signal analysis},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4721052&isnumber=4720717

S. De Nicola, Y. Huang, A. De Maio, S. Zhang and A. Farina, "Code optimization with similarity and accuracy constraints," 2008 IEEE Radar Conference, Rome, 2008, pp. 1-6.
doi: 10.1109/RADAR.2008.4720795
keywords: {Doppler radar;Gaussian processes;concave programming;iterative methods;linear codes;quadratic programming;radar detection;radar signal processing;Doppler estimation accuracy;ambiguity function;coded waveform relaxation;colored Gaussian disturbance;linear coded pulse train;nonconvex optimization problem;pre-fixed radar code optimization;quadratic optimization problem;radar detection;radar performance optimization;radar signal processing;semidefinite program;similarity constraint;Algorithm design and analysis;Constraint optimization;Design optimization;Doppler radar;Frequency estimation;Performance analysis;Radar detection;Radar signal processing;Signal design;Signal processing algorithms;Non-Convex Quadratic Optimization;Radar Signal Processing;Semidefinite Programming Relaxation},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4720795&isnumber=4720717

A. De Maio, S. De Nicola, Y. Huang, S. Zhang and A. Farina, "Code Design to Optimize Radar Detection Performance Under Accuracy and Similarity Constraints," in IEEE Transactions on Signal Processing, vol. 56, no. 11, pp. 5618-5629, Nov. 2008.
doi: 10.1109/TSP.2008.929657
keywords: {Doppler measurement;concave programming;quadratic programming;radar detection;radar theory;Doppler estimation;coded waveforms;colored Gaussian disturbance;linearly coded pulse trains;optimum radar code;radar detection;Non-Convex Quadratic Optimization;Nonconvex quadratic optimization;Radar Signal Processing;Semidefinite Programming Relaxation;radar signal processing;semidefinite programming relaxation},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4626391&isnumber=4652320

G. Foglia, D. Marcantoni, F. Trotta and A. De Maio, "ECM counteracting SLB: Analysis and effectiveness evaluation," 2008 IEEE Radar Conference, Rome, 2008, pp. 1-6.
doi: 10.1109/RADAR.2008.4720729
keywords: {antenna arrays;electromagnetic wave polarisation;jamming;phased array radar;radar antennas;antenna array elements;co-polarized jamming signals;cross-polarized jamming signals;impulsive interference;jamming techniques;mutual coupling;phased array radar;radar systems;sidelobe blanking devices;Blanking;Electrochemical machining;Electronic countermeasures;Fluctuations;Interference;Jamming;Mutual coupling;Radar antennas;Radar detection;Receiving antennas;ECM;Polarization;SLB},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4720729&isnumber=4720717

L. Landi, A. De Maio, S. De Nicola and A. Farina, "Knowledge-aided covariance matrix estimation: A MAXDET approach," 2008 IEEE Radar Conference, Rome, 2008, pp. 1-6.
doi: 10.1109/RADAR.2008.4720823
keywords: {adaptive radar;convex programming;covariance matrices;maximum likelihood estimation;radar detection;radar signal processing;MAXDET approach;a-priori estimation;adaptive radar detection;convex optimization problem;detection probability;knowledge-aided covariance matrix estimation;maximum likelihood estimation;physical scattering model;radar signal processing;Clutter;Covariance matrix;Detectors;Jamming;Maximum likelihood estimation;Performance analysis;Radar detection;Radar scattering;Statistics;Training data;Covariance Matrix Estimation;Knowledge-Aided Radar Signal Processing;MAXDET},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4720823&isnumber=4720717

A. De Maio, M. Lops and L. Venturino, "Diversity-integration trade-offs in MIMO detection," 2008 IEEE International Symposium on Information Theory, Toronto, ON, 2008, pp. 594-598.
doi: 10.1109/ISIT.2008.4595055
keywords: {MIMO communication;correlation methods;diversity reception;encoding;matrix algebra;signal detection;MIMO detection problem;arbitrary time-correlation;average signal-to-clutter level;diversity path transmission;diversity-integration trade-off;generalized likelihood ratio test;optimized code matrix;power-unlimited system;Encoding;Matrix decomposition;Receivers;Receiving antennas;Thyristors;Transmitters;Transmitting antennas},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4595055&isnumber=4594928

A. De Maio, G. Foglia, N. Pasquino and M. Vadursi, "Experimental Verification of a Two-State Model for the Cumulative Distribution Function of GSM Passive Radar Clutter," Instrumentation and Measurement Technology Conference Proceedings, 2008. IMTC 2008. IEEE, Victoria, BC, 2008, pp. 1288-1293.
doi: 10.1109/IMTC.2008.4547241
keywords: {cellular radio;passive radar;radar clutter;statistical distributions;GSM cellular system;GSM passive radar clutter;cumulative distribution function;statistical distribution;two-state model;Distribution functions;Frequency;GSM;Passive radar;Performance evaluation;Radar clutter;Radar detection;Radar tracking;Signal analysis;Working environment noise;GSM system;clutter measurement;empirical CDF fitting;passive radar},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4547241&isnumber=4546978

A. De Maio and S. Iommelli, "Coincidence of the Rao Test, Wald Test, and GLRT in Partially Homogeneous Environment," in IEEE Signal Processing Letters, vol. 15, no. , pp. 385-388, 2008.
doi: 10.1109/LSP.2008.920016
keywords: {Gaussian processes;covariance matrices;signal detection;GLRT;Rao Test;Wald test;covariance matrix;generalized likelihood ratio test;partially homogeneous Gaussian disturbance;scaling factor;signal detection;Adaptive detection;Rao test;Wald test;generalized likelihood ratio test (GLRT)},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4481246&isnumber=4418381