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Proceedings Paper

A comparative study of multiple-model algorithms for maneuvering target tracking
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Paper Abstract

Many multiple-model (MM) algorithms for tracking maneuvering targets are available, but there are few comparative studies of their performance. This work compares seven MM algorithms for maneuvering target tracking in terms of tracking performance and computational complexity. Six of them are well known and widely used. They are the autonomous multiple-model algorithm, generalized pseudo-Bayesian algorithm of first order (GPB1), and of second order (GPB2), interacting multiple-model (IMM) algorithm, B-best based MM algorithm, and Viterbi-based MM algorithm. Also considered is the reweighted interacting multiple-model algorithm, which was developed recently. The algorithms were compared using three scenarios. The first scenario consists of two segments of tangential acceleration while the second scenario consists of two segments of normal acceleration. Both of these scenarios have maneuvers that are represented by one of the models in the model set. The third scenario, however, has a single maneuver that consists of a tangential and a normal acceleration. This type of maneuver is not overed by the model set and is used to see how the algorithms react to a maneuver outside of the model set. Based on the study, there is no clear-cut best algorithm but the IMM algorithm has the best computational complexity among the algorithms that have acceptable tracking errors. It also showed a remarkable robustness to model mismatching, and appears to be the top choice if the computational cost is of concern.

Paper Details

Date Published: 25 May 2005
PDF: 12 pages
Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); doi: 10.1117/12.609681
Show Author Affiliations
Ryan R. Pitre, Univ. of New Orleans (United States)
Vesselin P. Jilkov, Univ. of New Orleans (United States)
X. Rong Li, Univ. of New Orleans (United States)


Published in SPIE Proceedings Vol. 5809:
Signal Processing, Sensor Fusion, and Target Recognition XIV
Ivan Kadar, Editor(s)

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