3 September 2009 Rao-Blackwellised approximate conditional mean probability hypothesis density filtering
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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.
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N. Nandakumaran, S. Sutharsan, R. Tharmarasa, T. Lang, and T. Kirubarajan "Rao-Blackwellised approximate conditional mean probability hypothesis density filtering", Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 74450J (3 September 2009); doi: 10.1117/12.826423; https://doi.org/10.1117/12.826423
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