BAE Systems has developed a baseline real-time selected vehicle (SV) radar tracking capability that successfully tracked multiple civilian vehicles in real-world traffic conditions within challenging semi-urban clutter. This real-time tracking capability was demonstrated in laboratory setting. Recent enhancements to the baseline capability include multiple detection modes, improvements to the system-level design, and a wide-area tracking mode. The multiple detection modes support two tracking regimes; wide-area and localized selected vehicle tracking. These two tracking regimes have distinct challenges that may be suited to different trackers. Incorporation of a wide-area tracking mode provides both situational awareness and the potential for enhancing SV track initiation. Improvements to the system-level design simplify the integration of multiple detection modes and more realistic SV track initiation capabilities. Improvements are designed to contribute to a comprehensive tracking capability that exploits a continuous stare paradigm. In this paper, focus will be on the challenges, design considerations, and integration of selected vehicle tracking.
Proc. SPIE. 9093, Algorithms for Synthetic Aperture Radar Imagery XXI
KEYWORDS: Synthetic aperture radar, Target detection, Radar, Doppler effect, Antennas, Global Positioning System, Digital filtering, Signal to noise ratio, Device simulation, Detection and tracking algorithms
We describe techniques for improving ground moving target indication (GMTI) performance in multi-channel synthetic aperture radar (SAR) systems. Our approach employs a combination of moving reference processing (MRP) to compensate for defocus of moving target SAR responses and space-time adaptive processing (STAP) to mitigate the effects of strong clutter interference. Using simulated moving target and clutter returns, we demonstrate focusing of the target return using MRP, and discuss the effect of MRP on the clutter response. We also describe formation of adaptive degrees of freedom (DOFs) for STAP filtering of MRP processed data. For the simulated moving target in clutter example, we demonstrate improvement in the signal to interference plus noise (SINR) loss compared to more standard algorithm configurations. In addition to MRP and STAP, the use of tracker feedback, false alarm mitigation, and parameter estimation techniques are also described. A change detection approach for reducing false alarms from clutter discretes is outlined, and processing of a measured data coherent processing interval (CPI) from a continuously orbiting platform is described. The results demonstrate detection and geolocation of a high-value target under track. The endoclutter target is not clearly visible in single-channel SAR chips centered on the GMTI track prediction. Detections are compared to truth data before and after geolocation using measured angle of arrival (AOA).
This paper examines the theory, application, and results of using single-channel synthetic aperture radar (SAR) data with Moving Reference Processing (MRP) to focus and geolocate moving targets. Moving targets within a standard SAR imaging scene are defocused, displaced, or completely missing in the final image. Building on previous research at AFRL, the SAR-MRP method focuses and geolocates moving targets by reprocessing the SAR data to focus the movers rather than the stationary clutter. SAR change detection is used so that target detection and focusing is performed more robustly. In the cases where moving target returns possess the same range versus slow-time histories, a geolocation ambiguity results. This ambiguity can be resolved in a number of ways. This paper concludes by applying the SAR-MRP method to high-frequency radar measurements from persistent continuous-dwell SAR observations of a moving target.
The Lincoln Laboratory baseline ATR system for synthetic aperture radar (SAR) data applies a super-resolution technique known as high-definition vector imaging (HDVI) before the input image is passed through the final target classification subsystem. In previous studies, it has been demonstrated that HDVI improves target recognition performance significantly. Recently, however, several other viable SAR image enhancement techniques have been proposed and discussed in the literature which could be used in place of (or perhaps in conjunction with) the HDVI technique. This paper compares the performance achieved by the Lincoln Laboratory template-based classification subsystem when these alternative image enhancement techniques are used instead of the HDVI technique. In addition, empirical evidence is presented suggesting that target recognition performance could be further improved by fusing the classifier outputs generated by the best image enhancement techniques.
MIT Lincoln Laboratory is responsible for developing the ATR system for the DARPA/DARO/NIMA/OSD-sponsored SAIP program; the baseline ATR system recognizes 10 GOB targets; the enhanced version of SAIP requires the ATR system to recognize 20 GOB targets. This paper compares ATR performance results for 10- and 20-target MSE classifiers using high-resolution SAR imagery.
MIT Lincoln Laboratory has developed a complete, end-to-end, automatic target detection/recognition system for synthetic aperture radar data. The system uses resolution enhancement (super-resolution) techniques to improve the performance of the automatic target recognition stage. This paper presents a new multi-resolution classification scheme that greatly improves the computational efficiency of the classifier with only a slight loss in classification performance.
MIT Lincoln Laboratory has developed a compete, end-to-end, automatic target detection/recognition (ATD/R) system for synthetic aperture radar (SAR) data. A data-adaptive approach has been developed to enhance SAR image resolution based on super-resolution techniques; this approach is called high-definition imaging. This paper quantifies the improvement in ATR performance from enhanced resolution SAR imagery in the Lincoln Laboratory ATD/R system.
A new technique, developed at Lincoln Laboratory, utilizes algorithms developed for two-pass change detection to exploit the differences in aspect angle dependency between target scatterers and clutter scatterers. This technique, referred to as split- aperture detection, involves forming several aspect-angle-diverse SAR images from a single flight pass over a given area by separating the synthetic aperture into sub-apertures during image formation. Change detection algorithms are then applied to these aspect-angle-diverse looks, in the hopes that clutter returns will be nearly isotropic over small variations of aspect angle and will look similar in each image, while man-made objects will provide anisotropic returns over the same angular variation and, therefore, will appear brighter in one image. In this case, the change detection algorithms will significantly suppress the background clutter (thereby significantly reducing the number of false alarms) while enhancing target detectability.
This paper presents performance results for a new, fully polarimetric SAR ATR algorithm suite. The new algorithm suite is a modified version of the current Lincoln Laboratory baseline ATR algorithm suite. The detection (plus clustering) portion of the ATR system remains unchanged; however, the new ATR system uses a larger, optimized feature set for discrimination, and a multi- layered scheme for target classification. The new ATR system was found to provide improved performance over the baseline system when tested on a large data set of tactical targets (both bare and netted) and a large set of ground clutter (which contained numerous man-made objects).
A new “product” clutter model is proposed for fitting the cumulative distribution function of high- resolution SAR data. Results shown in this paper indicate that this new clutter model fits the measured clutter data more accurately than the traditional product clutter models.
The Advanced Detection Technology Program has as one objective the application of fully polarimetric, high-resolution radar data to the detection, discrimination, and classification of stationary targets. In support of this program, the Advanced Detection Technology Sensor (ADTS), a fully polarimetric, 35-GHz SAR with 1 ft by 1 ft resolution was developed. In April of 1989, the ADTS gathered target and clutter data near Stockbridge, NY. Data from this collection is being used to investigate optimal polarimetric processing techniques. This paper summarizes the results of a recent study of an optimal polarimetric method for reducing speckle in SAR imagery.