15 September 2005 A multiple model SNR/RCS likelihood ratio score for radar-based feature-aided tracking
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Most approaches to data association in target tracking use a likelihood-ratio based score for measurement-to-track and track-to-track matching. The classical approach uses a likelihood ratio based on kinematic data. Feature-aided tracking uses non-kinematic data to produce an "auxiliary score" that augments the kinematic score. This paper develops a nonkinematic likelihood ratio score based on statistical models for the signal-to-noise (SNR) and radar cross section (RCS) for use in narrowband radar tracking. The formulation requires an estimate of the target mean RCS, and a key challenge is the tracking of the mean RCS through significant "jumps" due to aspect dependencies. A novel multiple model approach is used track through the RCS jumps. Three solution are developed: one based on an α-filter, a second based on the median filter, and the third based on an IMM filter with a median pre-filter. Simulation results are presented that show the effectiveness of the multiple model approach for tracking through RCS transitions due to aspect-angle changes.
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Benjamin J. Slocumb, Benjamin J. Slocumb, Michael E. Klusman, Michael E. Klusman, "A multiple model SNR/RCS likelihood ratio score for radar-based feature-aided tracking", Proc. SPIE 5913, Signal and Data Processing of Small Targets 2005, 59131N (15 September 2005); doi: 10.1117/12.615288; https://doi.org/10.1117/12.615288

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