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Proceedings Paper

Rao-Blackwellised approximate conditional mean probability hypothesis density filtering
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Paper Abstract

In this paper, a new state estimation algorithm for estimating the states of targets that are separable into linear and nonlinear subsets with non-Gaussian observation noise distributed according to a mixture of Gaussian functions is proposed. The approach involves modeling the collection of targets and measurements as random finite sets and applying a new Rao-Blackwellised Approximate Conditional Mean Probability Hypothesis Density (RB-ACM-PHD) recursion to propagate the posterior density. The RB-ACM-PHD filter jointly estimates the time-varying number of targets and the observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms. The proposed algorithm approximates a mixture Gaussian distribution with a moment-matched Gaussian in the weight update phase of the filtering recursion. A two dimensional maneuvering target tracking example is used to evaluate the merits of the proposed algorithm. The RB-ACM-PHD filter results in a significant reduction in computation time while maintaining filter accuracies similar to the standard sequential Monte Carlo PHD implementation.

Paper Details

Date Published: 3 September 2009
PDF: 11 pages
Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 74450J (3 September 2009); doi: 10.1117/12.826423
Show Author Affiliations
N. Nandakumaran, McMaster Univ. (Canada)
S. Sutharsan, McMaster Univ. (Canada)
R. Tharmarasa, McMaster Univ. (Canada)
T. Lang, General Dynamics Canada Ltd. (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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