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首页> 美国政府科技报告 >Detection Algorithms and Track Before Detect Architecture Based on Nonlinear Filtering for Infrared Search and Track Systems
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Detection Algorithms and Track Before Detect Architecture Based on Nonlinear Filtering for Infrared Search and Track Systems

机译：基于非线性滤波的红外搜索跟踪系统检测算法及跟踪检测

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In this report, we describe the developed computationally efficient algorithms and adaptive architecture with optimized overall performance (statistical and computational) for real-time reliable detection and tracking of low-observable targets in IRST systems. Despite the fact that we focus on an IRST against cruise missiles over land and sea cluttered backgrounds, the results are equally applicable to other sensors (e.g., Radar, Lidar) and other kind of targets (e.g., ballistic missiles). We concentrated on the three interrelated problems: (1) efficient clutter suppression; (2) development of the adaptive track-before-detect architecture based on optimal nonlinear filtering; (3) development of efficient algorithms for detection of a priori unknown number of targets that appear and disappear at unknown points in time. The detection algorithms are adaptive and use the estimates of the target location. These estimates are the results of target tracking (before detection) by the optimal spatio-temporal nonlinear filters. The corresponding nonlinear filtering algorithm is based on the spectral separation scheme and allows for the real time computation of the whole joint posterior distribution of the target location. The algorithms are tested for real heavy cluttered IR background with artificially inserted dim maneuvering targets. The developed algorithms show high performance even in very low SNR situations (up to 7dB alter preprocessing and clutter removal).

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