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Proceedings Paper

Interacting multiple model forward filtering and backward smoothing for maneuvering target tracking
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Paper Abstract

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.

Paper Details

Date Published: 1 September 2009
PDF: 10 pages
Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 744503 (1 September 2009); doi: 10.1117/12.826549
Show Author Affiliations
N. Nandakumaran, McMaster Univ. (Canada)
S. Sutharsan, McMaster Univ. (Canada)
R. Tharmarasa, McMaster Univ. (Canada)
Tom Lang, General Dynamics Canada Ltd. (Canada)
Mike McDonald, Defence Research and Development Canada (Canada)
T. Kirubarajan, McMaster Univ. (Canada)

Published in SPIE Proceedings Vol. 7445:
Signal and Data Processing of Small Targets 2009
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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