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13 June 2014 Tracking low SNR targets using particle filter with flow control
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Abstract
In this work we study the problem of detecting and tracking challenging targets that exhibit low signal-to-noise ratios (SNR). We have developed a particle filter-based track-before-detect (TBD) algorithm for tracking such dim targets. The approach incorporates the most recent state estimates to control the particle flow accounting for target dynamics. The flow control enables accumulation of signal information over time to compensate for target motion. The performance of this approach is evaluated using a sensitivity analysis based on varying target speed and SNR values. This analysis was conducted using high-fidelity sensor and target modeling in realistic scenarios. Our results show that the proposed TBD algorithm is capable of tracking targets in cluttered images with SNR values much less than one.
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Nima Moshtagh, Paul M. Romberg, and Moses W. Chan "Tracking low SNR targets using particle filter with flow control", Proc. SPIE 9092, Signal and Data Processing of Small Targets 2014, 90920A (13 June 2014); https://doi.org/10.1117/12.2050523
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