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29 May 2012 Simultaneous tracking and recognition performance model
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High value target tracking and identification (ID) performance is impacted by sensor, target, and environmental conditions. Radar sensors are preferred since they provide sensor capabilities over a wide range of weather conditions. Sensor management provides some control, such as adjustment of the collection geometry. However, ground target dynamics and the collection environment can't be controlled and degrade tracking and identification performance. Some examples are when the target maneuvers into dense traffic, stops at intersections, or travels in a cluttered environment and is obscured by vegetation or buildings. Target identification algorithms using high range resolution (HRR) profiles formed from moving target data and range profiles formed from synthetic aperture radar (SAR) data have been demonstrated. Feature aided tracking (FAT) exploits the features derived from HRR data to improve target tracking. Identifying the dominant features which can be reliably exploited when a target is either moving or stationary that can then be used to maintain track and ID the target is expected to enhance algorithm performance in realistic scenarios. A simultaneous tracking and recognition (STAR) performance model is developed and applied to realistic scenarios to provide performance gain estimates based on the number of exploited features and operating conditions. This paper presents performance results for simultaneous target tracking and identification using HRR and SAR sensor data.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bart Kahler "Simultaneous tracking and recognition performance model", Proc. SPIE 8394, Algorithms for Synthetic Aperture Radar Imagery XIX, 83940Q (29 May 2012);


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