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1 September 2009 Interacting multiple model forward filtering and backward smoothing for maneuvering target tracking
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The Interacting Multiple Model (IMM) estimator has been proven to be effective in tracking agile targets. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimates of target states. Various methods have been proposed for multiple model smoothing in the literature. In this paper, a new smoothing method, which involves forward filtering followed by backward smoothing while maintaining the fundamental spirit of the IMM, is proposed. The forward filtering is performed using the standard IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned smoother uses standard Kalman smoothing recursion. Resulting algorithm provides improved but delayed estimates of target states. Simulation studies are performed to demonstrate the improved performance with a maneuvering target scenario. The comparison with existing methods confirms the improved smoothing accuracy. This improvement results from avoiding the augmented state vector used by other algorithms. In addition, the new technique to account for model switching in smoothing is a key in improving the performance.
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N. Nandakumaran, S. Sutharsan, R. Tharmarasa, Tom Lang, Mike McDonald, and T. Kirubarajan "Interacting multiple model forward filtering and backward smoothing for maneuvering target tracking", Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 744503 (1 September 2009);


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