: Added: Dec 03, 2014 3:29 pm
Special Notice DARPA-SN-15-13 for DARPA-BAA-15-09 Proposers' Day WorkshopDARPA-BAA-15-09 PROPOSERS' DAY WORKSHOP:
DARPA will host a Proposers' Day Workshop in support of
DARPA-BAA-15-09, Target Recognition and Adaption in Contested
Environment (TRACE) on 16 December 2014 at the George Mason University
Conference Center in Arlington, VA from 8:30 AM to 6:00 PM. The purpose
of this workshop is to provide information on the TRACE program, promote
additional discussions on this topic, address questions from potential
proposers, and provide a forum for potential proposers to discuss
teaming opportunities.
PROGRAM OBJECTIVE AND DESCRIPTION:
The Target Recognition and Adaption in Contested Environments (TRACE)
program seeks to develop algorithms and techniques that rapidly and
accurately identify military targets using radar sensors on manned and
unmanned tactical platforms. TRACE will develop accurate, real-time,
low-power target recognition systems that can be co-located with the
radar to provide responsive long-range targeting for tactical airborne
surveillance and strike applications. In addition, TRACE will deliver a
flight demonstration of radar target recognition operating in tactically
relevant air-to-ground counter air defense scenarios.
DARPA TRACE Proposers’ Day Workshop
Agenda Key Elements
| 0920 - 1020 | Program Overview Dr. John Gorman |
| 1020 - 1040 | BAA Submission |
| 1050 - 1130 | AFRL Wright Patterson Agent Review |
| 1230 - 1330 | Data Sets, Modeling & Simulation: AFRL |
DARPA-BAA-15-09: Target Recognition and Adaption in Contested Environments, Response Date 1/29/2015
DARPA is soliciting innovative research proposals in the area of tactical automatic target recognition. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice.
Related Previous Developments
SPIE | Optical Engineering | Review of current aided/automatic target acquisition technology for military target acquisition tasks U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, Maryland 20817Aided and automatic target recognition (Ai/ATR) capability is a critical technology needed by the military services for modern combat. However, the current level of performance that is available is largely deficient compared to the requirements. This is largely due to the difficulty of acquiring targets in realistic environments but has also been due to the difficulty in getting new concepts from, for example, the academicA compact, low-cost, wide-angle radar test bed | (2006) | Gorman | Publications | Spie
community, due to limitations for distribution of classified data. The difficulty of the performance required has limited the fulfillment of the promise that is so anticipated by the war fighter. We review the metrics, imagery data bases, and sensors associated with Ai/ATR performance and suggest possible technical approaches that could enable new advancements in military-relevant performance.
Author(s): John D. Gorman; Uttam Majumder; John C. Reed; Ronald L. Dilsavor; Michael Minardi; Edmund G. Zelnio
Recent technology developments in digital radio, low-cost inertial navigation systems and unmanned air vehicle design are converging to enable and make practical several new radar sensing modes such as simultaneous SAR/GMTI from persistent staring-mode radar, 3D SAR from a single-pass, single phase center radar and wide-angle radar tracking of dismounts. One of the challenges for algorithm developers is a lack of
high-quality target and clutter signature data from the new radar modes. AFRL's Sensor Directorate and SET Corporation are developing a compact, low-cost wide-angle radar test bed capable of simulating a variety of radar modes, including 3D SAR, SAR/GMTI from staring-mode radar and ultra-fine resolution range-Doppler. We provide an overview of the wide-angle radar test bed architecture, its modular design and our
implementation approach. We then describe several non-conventional wide-angle radar sensor modes and outline a corresponding series of test bed data collection experiments that could be used to support the development of new tracking and recognition algorithms.
SET-205 Active Electro-optic Sensing for Target identification and Tactical Applications
I. BACKGROUND AND JUSTIFICATION (Relevance to NATO):
Under several preceding RTGs, NATO has advanced the State of the Art for 2D and 3D Active Imaging. These groups have contributed to NATO advances in active sensors, and the development of advanced discrminants for targeting and fire control. Over these many years, a wide variety of active imaging techniques and systems have beenAUTOMATIC TARGET RECOGNITION ON LAND USING THREE DIMENSIONAL (3D) LASER RADAR AND ARTIFICIAL NEURAL NETWORKS | Kerim Goztepe - Academia.edu
developed, such as scanning laser radar, 3D flash laser radar, 2D gated SWIR imaging, digital holography systems and vibration sensing. Additionally, these techniques or systems are applied to a wide variety of tactical applications, such as targeting and fire control, as input to ATR algorithms, and long range target ID. The recommendation of a
2012 ET (SET-ET-079) was to form an RTG to investigate these active-imaging challenges.
During combat, measuring the dimensions of targets is extremely important for knowing when to fire on the enemy. The importance of identifying a known target on land emphasizes the importance of techniques devoted to automatic target recognition.Although a number of object-recognition techniques have been developed in the past, none of them have provided the desired specifics for unidentified target recognition. Studies on target recognition are largely based on images that assume that images of a known targetcan be readily viewed under any circumstance. But this is not true for military operations conducted on various terrains under specific circumstances. Usually it is not possible to capture images of unidentified objects because of weather, inadequate equipment, or concealment. In this study, a new approach that integrates neural networks and laser rada rhas been developed for automatic target recognition inLeveraging the Infosphere
order to reduce the above-mentioned problems. Unlike current studies, the proposed model uses the geometric dimensions of unidentified targets in order to detect and recognise them under severe weather conditions.
Automatic Target Recognition using High-Range Resolution data - a343438.pdf
A new algorithm is presented for Automatic Target Recognition (ATR) using High Range Resolution (HRR) profiles as opposed to traditional Synthetic Aperture Radar (SAR) images. ATR performance using SAR images degrades considerably in case of moving targets
due to blurring caused in the cross-range domain. ATR based on HRR profiles, which are formed without Fourier transform in the cross-range, is expected to have superior performance for moving targets with the proposed method. One of the major contributions of this project so far has been the utilization of Eigen-templates as ATR features that are obtained via Singular Value Decomposition (SVD) of HRR profiles. SVD analysis of a large class of HRR data revealed that the Range-space eigenvectors corresponding to the largest
singular value accounted for more than 90% of target energy. Hence, it has been proposed that the Range-space Eigen-vectors be used as templates for classification. The effectiveness of data normalization and Gaussianization of profile data in improving classification performance is also studied. With extensive simulation studies it is shown that the proposed Eigen-template based ATR approach provides consistent superior performance with recognition rate reaching 99.5% for the four class XPATCH database.
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