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  Rao-Blackwellized Monte Carlo data association for multiple target tracking (2004) [7 citations — 1 self]

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by Simo Särkkä, Aki Vehtari, Jouko Lampinen
In Proceedings of the Seventh International Conference on Information Fusion
http://www.fusion2004.foi.se/papers/IF04-0583.pdf
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Abstract:

Abstract – We propose a new Rao-Blackwellized sequential Monte Carlo method for tracking multiple targets in presence of clutter and false alarm measurements. The advantage of the new approach is that Rao-Blackwellization allows the estimation algorithm to be partitioned into single target tracking and data association sub-problems, where the single target tracking sub-problem can be solved by Kalman filters or extended Kalman filters, and the data association by sequential importance resampling. Because the sampled sub-space is finite, it is possible to use the optimal importance distribution explicitly, which significantly reduces the required number of Monte Carlo samples.

Citations

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34 A hybrid bootstrap filter for target tracking in clutter – Gordon - 1997
17 Monte Carlo data association for multiple target tracking – Karlsson, Gustafsson - 2001
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4 The (MR)MTPF: particle filters to track multiple targets using multiple receivers – Hue, Cadre, et al. - 2001
3 Xiao-Rong Li, and Thiagalingam Kirubarajan. Estimation with Applications to Tracking and Navigation – Bar-Shalom - 2001
1 Bayesian adaptive Kalman filtering and smoothing by separable approximations of posterior distributions – Särkkä, Vehtari, et al.
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