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30 September 2013 Particle filter tracking for the banana problem
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In this paper we present an approach for tracking with a high-bandwidth active sensor in very long range scenarios. We show that in these scenarios the extended Kalman filter is not desirable as it suffers from major consistency problems; and most flavors of particle filter suffer from a loss of diversity among particles after resampling. This leads to sample impoverishment and the divergence of the filter. In the scenarios studied, this loss of diversity can be attributed to the very low process noise. However, a regularized particle filter is shown to avoid this diversity problem while producing consistent results. The regularization is accomplished using a modified version of the Epanechnikov kernel.
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Kevin Romeo, Peter Willett, and Yaakov Bar-Shalom "Particle filter tracking for the banana problem", Proc. SPIE 8857, Signal and Data Processing of Small Targets 2013, 885709 (30 September 2013);

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