Notice Type: Award Notice
Contract Award Date: September 17, 2015
Contract Award Number: N00014-15-C-5100
Contract Award Dollar Amount: $497,134.00
Contractor Awarded DUNS: 611827812
Contractor Awardee: RDRTEC INCORPORATED
3737 ATWELL ST. SUITE 208, DALLAS, TX 75209
Synopsis: Added: Sep 17, 2015 4:30 pm IGF::CT::IGF
Adaptive Radar Detection Approaches for Low-RCS Maritime Vessels in Highly Variable Clutter Condition.
Related/Background:
(SBIR) Navy - Enhanced Small-Target Detection and Tracking Using a Mode-Adaptive Constant False Alarm Rate (CFAR) Detector
- OBJECTIVE: Develop adaptive Constant False Alarm Rate (CFAR) Detectors that support rapid mode interleaving under resource management control that allows simultaneous support for range dependent multi-resolution processing.
- DESCRIPTION: Advancements in sensor resource management are needed that would utilize rapid mode interleaving on dynamic irregular time scales, capable of multi-resolution processing and data rate management. Current fielded radar systems offer only the most rudimentary resource management approaches to support detection and tracking. In a coherent radar system the dynamics of the target plays a critical part in determining if the target is detected against either a clutter or noise like background. Optimal detection of targets with different dynamics requires different Constant False Alarm Rate (CFAR) schemes and parameters. Optimal performance therefore requires utilizing variant CFAR’s in a hierarchal manner as well as making the threshold parameters of the CFARs adaptive. The CFAR processing must be capable of maintaining consistent performance across multiple waveform resolutions at the pulse repetition interval (PRI) level. In a maritime environment an objective of this approach is to provide small target detection and false alarm performance in sea-state four equal to that provided by conventional approaches in sea-state two or three. The desired deliverable is an adaptive real-time CFAR software application suitable for demonstration with a candidate Navy radar system.
- REFERENCES:
- 1. Weinberg, G.V. (2012). Suboptimal Coherent Radar Detection in a KK-Distributed ...d Clutter Environment. International Scholarly Research Network, ISRN Signal Processing, Volume 2012, Article ID 614653, 8 pages
- 2. Rosenberg, L., Crisp, D.J., & Stacy, N.J. (2010). Analysis of the KK-distribution with medium grazing angle sea-clutter, IET Radar Sonar Navig., Vol. 4, Iss. 2, pp. 209–222
Awards
RDRtec proposes Compound Weibull - Enhanced Small-Target Detection and Tracking Using a Mode-Adaptive Constant False Alarm Rate (CFAR) Detector
Sol No.:
|
Navy SBIR FY2014.1 |
| Topic No.: | N141-020 |
| Topic Title: | Enhanced Small-Target Detection and Tracking Using a Mode-Adaptive Constant False Alarm Rate (CFAR) Detector |
Proposal No.:
|
N141-020-0367 |
| Firm: | RDRTec Inc.3737 Atwell St. Suite 208 Dallas, Texas 75209 |
Contact:
|
Sidney Theis |
Phone:
|
(214) 353-8755 |
Abstract:
|
The propose effort will determine the feasibility of and continue development of innovative feature-based adaptive constant false alarm rate (FA-CFAR) techniques to detect slow moving and low radar cross section (RCS) maritime targets in challenging clutter environments. A first order approach employed by conventional techniques uses a whitener to notch out the clutter entirely and thus fails to detect endo-clutter targets moving too slowly relative to the radar sensor. Without a good model for sea clutter, CFAR estimates are unreliable in the maritime environment and as a result, saturation can occur. Our work has shown that sea clutter can be approximated by a piecewise linear compound Weibull distribution that independently accounts for Bragg scattering and sea spikes. Our analysis indicates that reliable estimation of the CFAR threshold based on the compound Weibull distribution shows up to 25 percent improvement in probability of detection (Pd) at a constant probability of false alarms (Pfa). |
Benefits:
|
The anticipated benefit of the developed algorithms that provide additional information needed by the war fighter to have heightened awareness of hostile intent of small boats in a dense traffic environment. |
Lambda Sciences - Enhanced Small-Target Detection and Tracking Using a Mode-Adaptive Constant False Alarm Rate (CFAR) Detector
Sol No.:
|
Navy SBIR FY2014.1
|
| Topic No.: | N141-020 |
| Topic Title: | Enhanced Small-Target Detection and Tracking Using a Mode-Adaptive Constant False Alarm Rate (CFAR) Detector |
Proposal No.:
|
N141-020-0165 |
| Firm: | Lambda Science, Inc. P.O. Box 238 Wayne, Pennsylvania 19087 |
Contact:
|
Joseph Teti |
| Phone: | (610) 581-7940 |
| Web Site: | www.lamsci.com |
| Abstract: | Conventional CFAR detection processing has difficulty in heterogeneous clutter (i.e., maritime clutter) because the fixed data size windowing is not well matched to the spatial scales of the wave field over a wide range of sea states. The non-Gaussian nature of heterogeneous clutter gives rise to significant spatially varying CFAR loss to maintain low false alarms that desensitize the radar in stressing clutter conditions. Furthermore, conventional CFAR detection processing will have difficulty maintaining consistent performance with varying range resolution and fixed window data sizes at the PRI level. Recent advances in radar resource management algorithms make use of rapid mode interleaving at the PRI level on dynamic irregular time scales with different range resolution and associated data rates. Optimal detection of targets requires adaptive CFAR processing over a wide range of maritime clutter conditions (e.g., up to and including sea state 5). LSI is currently working on an advanced adaptive clutter removal processing technique that shows promising performance with conventional CFAR processing, and the development of mode adaptive CFAR detection processing algorithms is a natural extension to this effort. |
Benefits:
|
Resolution and sea state adaptive CFAR detection algorithms for maritime surface search radars.
|
- Efficient 3-D Imaging of Vessels for Improved Classification and Persistent Tracking
- Navy chooses RDRTec to develop common sense-and-avoid radar for Fire Scout and Triton UAVs
- Slow and small target detection in high sea states References
- CiteSeerX — SMALL TARGET DETECTION IN HEAVY SEA
- Watts, S., "Radar Sea Clutter Modelling and Simulation - Recent Progress and Future Challenges," in Radar Clutter Modelling, 2008 IET Seminar on , vol., no., pp.1-7, 19-19 Feb. 2008
Abstract: This paper reviews current developments in sea clutter modelling, covering both statistical models, mainly based on empirical observations, and modelling of electromagnetic scattering from simulated sea surfaces. The emphasis throughout the paper is on the modelling of low grazing angle microwave radar backscatter in relation to airborne and surface maritime surveillance radar. As well as reviewing current knowledge in these areas the paper also highlights areas where further research is needed.
keywords: {electromagnetic wave scattering;marine radar;radar clutter;search radar;airborne maritime surveillance radar;electromagnetic scattering;grazing angle;microwave radar backscatter;radar sea clutter modelling;radar sea clutter simulation;surface maritime surveillance radar},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4476395&isnumber=4476385 - Kemkemian, Stephane; Lupinski, Ludovic; Nouvel, Myriam; Corretja,
Vincent; Cottron, Rodolphe, "Slow and small target detection in high sea
states," in Radar Symposium (IRS), 2015 16th International , vol., no., pp.828-833, 24-26 June 2015
doi: 10.1109/IRS.2015.7226300
Abstract: This paper addresses the detection of small and slow craft with Maritime Radar. Fast target detection can be carried out using standard processing such as MMTI (Maritime Moving Target Indicator) regardless of the sea state. However, in case of slow moving targets in high sea state, these methods no longer work because of the intrinsic spreading of the sea clutter. After having summarized the statistical modelling of the sea clutter, in order to highlight its particularity especially when observed with high range resolution waveforms, the paper explains the interest of Track-Before-Detect (TBD) detectors for taking benefits of both high-resolution waveforms and sea clutter properties. Two TBD detectors are compared to standard detectors working on a single dwell.
keywords: {Antennas;Clutter;Correlation;Detectors;Noise;Sea surface;Surface waves},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7226300&isnumber=7226207 - Carretero-Moya,
J.; De Maio, A.; Gismero-Menoyo, J.; Asensio-Lopez, A., "Experimental
Performance Analysis of Distributed Target Coherent Radar Detectors," in
Aerospace and Electronic Systems, IEEE Transactions on , vol.48, no.3, pp.2216-2238, JULY 2012
doi: 10.1109/TAES.2012.6237589
Abstract: In this paper a performance analysis on recorded live data of some detectors for range-spread targets is developed. To this end, real target and sea-clutter data collected by a fully coherent Ka-band radar system, featuring submeter range resolution, are used. The study is of particular interest for homeland security radar applications where a careful coastal control is necessary to prevent the arrival of nonauthorized small boats. The performance of both rank-one and subspace range-spread target detection strategies is analyzed, both in terms of constant false alarm rate (CFAR) behavior and in terms of detection capabilities. With reference to the former issue, clutter-only datafiles are used whereas, concerning the latter data containing both real target and clutter are used. The targets returns come from typical small boats (such as inflatable, wooden, and patrol boats) appearing range distributed at the resolution of the exploited radar system. Range-time detection maps are shown, assessing the capability of the analyzed processors to detect the aforementioned targets of great interest for homeland coastal security. Finally, the performance improvements achievable by over-resolving the target is quantified.
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 - Sungho
Kim; Yukyung Yang; Joohyoung Lee, "Horizontal small target detection
with cooperative background estimation and removal filters," in Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on , vol., no., pp.1761-1764, 22-27 May 2011
doi: 10.1109/ICASSP.2011.5946843
Abstract: Detecting small targets is essential for mitigating the sea based Infrared search and track (IRST) problem. It is easy to detect small targets in homogeneous backgrounds such as the sky. When targets are on the border line of heterogeneous backgrounds such as the horizon in the sky and sea surface, solving the problem of detection becomes difficult. This pa per presents a novel spatial filtering method, called Double Layered-Background Removal Filter (DL-BRF), for achieving high detection rates and low false alarm rates. DL-BRF consists of a Modified-Mean Subtraction Filter (M-MSF) and a consecutive Local-Directional Background Removal Filter (L-DBRF). M-MSF enhances the target signal and reduces background noise. L-DBRF removes horizontal structures, which upgrade the signal-to-clutter ratio and background suppression factor. L-DBRF used after M-MSF enhances the synergistic performance of horizontal target detection. We validate the superior performance of the proposed method via real evaluation tests.
keywords: {infrared detectors;object detection;spatial filters;L-DBRF;M-MSF;cooperative background estimation;double layered-background removal filter;infrared search and track;local directional background removal filter;modified-mean subtraction filter;spatial filtering method;target detection;Noise;Background estimation;Detection;Heterogeneous background;Horizon;Infrared target},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5946843&isnumber=5946226 - Kabakchiev,
C.; Garvanov, I.; Behar, V.; Kabakchiev, A.; Gashinova, M.; Cherniakov,
M., "CFAR detection and parameter estimation of moving marine targets
using forward scatter radar," in Radar Symposium (IRS), 2011 Proceedings International , vol., no., pp.85-90, 7-9 Sept. 2011
Abstract: In this paper we research one original structure of the CFAR detector and made a parameter estimation of the moving marine targets at the background of a sea clutter using Bistatic Forward Scatter Radar (FSR) system. The specific two pulse MTI CFAR processor with K/M-L batch processor and parameter estimator for a marine target with unknown size are investigated on the base of real data records. The data itself t have been gathered by the team of the Birmingham University using in-house developed FSR.
keywords: {parameter estimation;radar tracking;target tracking;CFAR detection;FSR system;bistatic forward scatter radar;moving marine targets;parameter estimation;sea clutter;Clutter;Detectors;Educational institutions;Estimation;Frequency domain analysis;Radar;Radar scattering},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6042096&isnumber=6041611 - Hughes,
E.J.; Lewis, M.B., "A multiple intelligent software agent based
technique for improving radar detection of low observable small craft in
sea clutter," in Signal Processing Solutions for Homeland Security, 2005. The IEE Seminar on (Ref. No. 2005/11108) , vol., no., pp.11 pp.-, 11 Oct. 2005
Abstract: The current method of detection of a radar target is based on the setting of a threshold determined by the average of the background returns in the region of interest. Problems arise with this method when attempting to detect small targets in littoral waters since in designing the detector it is necessary to make assumptions concerning the statistical behaviour of the background clutter. Since only long term data is available and short term prediction is required there is an inevitable missed detection/false alarm problem. The problems associated with detecting low observable targets using track-before-detect systems based on Hough transform or dynamic programming techniques are reviewed. An alternative self-adaptive spatio-temporal CFAR system and a multiple hypothesis tracker based on multiple intelligent software agents are described. The process is not perfect but, by assuming that there will be too few data measurements to establish the clutter statistics accurately, a robust sub-optimal solution is formed. The process reported is not restricted to radar returns but has potential applications in infra-red and electro-optical systems, and for processing images in particle physics and astronomy.
keywords: {Hough transforms;dynamic programming;military computing;military radar;radar clutter;radar computing;radar detection;software agents;Hough transform;clutter statistics;dynamic programming techniques;false alarm problem;inevitable missed detection;low observable small craft;multiple hypothesis tracker;multiple intelligent software agent;radar target detection;sea clutter;self-adaptive spatio-temporal CFAR system;track-before-detect systems},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1542909&isnumber=32953 - Lopez-Risueno, G.; Grajal, J.; Diaz-Oliver, R., "Target detection in sea clutter using convolutional neural networks," in Radar Conference, 2003. Proceedings of the 2003 IEEE , vol., no., pp.321-328, 5-8 May 2003
doi: 10.1109/NRC.2003.1203421
Abstract: A detector based on convolutional neural networks is proposed for radar detection of floating targets in highly complex and nonstationary cluttered environments. This detector is coherent and monocell, i.e. it works with the complex envelope of the echoes from the same range cell. It includes a pre-processing time-frequency block implemented by the Wigner-Ville distribution, which provides a constant false alarm rate (CFAR) behavior regarding the clutter power when normalization is utilized. Simple theoretical models for the clutter and targets were allowed to study the impact of the correlation and Doppler of both target and clutter on its performance. This detector has also been tested with real-life sea clutter with an improved performance compared to classic detectors.
keywords: {Doppler effect;Wigner distribution;convolution;marine radar;neural nets;radar clutter;radar detection;radar tracking;target tracking;Wigner-Ville distribution;clutters Doppler;coherent detector;constant false alarm rate;convolutional neural networks;correlation impact;monocell;nonstationary cluttered environments;preprocessing time-frequency block;radar detection;sea clutter;target detection;targets Doppler;Convolution;Detectors;Intelligent networks;Neural networks;Object detection;Radar clutter;Radar detection;Radar signal processing;Testing;Time frequency analysis},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1203421&isnumber=27105 - Hennessey, G.; Leung, H.; Drosopoulos, A.; Yip, P.C., "Sea-clutter modeling using a radial-basis-function neural network," in Oceanic Engineering, IEEE Journal of , vol.26, no.3, pp.358-372, Jul 2001
doi: 10.1109/48.946510
Abstract: Recently, neural networks have been proposed for radar clutter modeling because of the inherent nonlinearity of clutter signals. This paper performs an analysis of the practicality of using a radial basis function (RBF) neural network to model sea clutter and to detect small target embedded in sea clutter. An experiment using an instrumental quality radar was carried out on the eastcoast of Canada to create a rich sea clutter and small surface target database. This database contains both staring and scanning data under various environmental conditions. Using data-sets with different characteristics, we investigate the effects of quantization error, measurement noise, generalization of the neural net over ranges and sampling rate on the RBF clutter model. Despite these physical limitations, the RBF model was shown to approach an optimal predictive performance. The RBF predictor was also applied to detect various small targets in this database based on the constant false alarm rate (CFAR) principle. This RBF-CFAR detector was demonstrated to be able to detect small floating targets even in rough sea conditions
keywords: {backscatter;chaos;quantisation (signal);radar clutter;radar signal processing;radial basis function networks;Canada;constant false alarm rate;instrumental quality radar;measurement noise;optimal predictive performance;quantization error;radial-basis-function neural network;rough sea conditions;sampling rate;scanning data;sea-clutter modeling;small floating targets;staring data;Databases;Instruments;Neural networks;Noise measurement;Performance analysis;Quantization;Radar clutter;Radar detection;Sea measurements;Sea surface},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=946510&isnumber=20487 - Wang Guoyou; Zhang tianxu; Wei Luogang; Sang Nong, "A multifeature-based algorithm for small target detection," in Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on , vol.5, no., pp.4085-4088 vol.5, 22-25 Oct 1995
doi: 10.1109/ICSMC.1995.538430
Abstract: In this paper, an efficient algorithm of small target detection has been developed which combines the geometric properties of low-order moments of specific targets with the signature of discontinuity not widely used in current methods between small targets and its neighbor regions. This algorithm includes two steps: 1) to construct an average grey absolute difference maximum map (AGADMM), eliminate the background clutter and then enhance the target; and 2) to use the geometric properties of low-order moments of a specified target to discriminate between the targets and other normal objects. Experiments with visual and infrared images has shown that this algorithm can provide satisfactory detection performance for exploiting many features of a specified target in detecting a small target
keywords: {clutter;computational geometry;image recognition;object detection;target tracking;average grey absolute difference maximum map;discontinuity signature;geometric properties;ground clutter;infrared images;low-order moments;multifeatures-based algorithm;small target detection;visual images;Fractals;Infrared detectors;Infrared imaging;Jitter;Object detection;Pixel;Q measurement;Sea measurements;Shape;Size measurement},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=538430&isnumber=11543
