Random Finite Sets and Sequential Monte Carlo Methods in Multi-Target Tracking
MELBOURNE UNIV VICTORIA (AUSTRALIA)
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Random finite sets provide a rigorous foundation for optimal Bayes multi-target filtering. The major hurdle faced in Bayes multi-target filtering is the inherent computational intractability of the method. Even the Probability Hypothesis Density PHD filter, which propagates only the first moment or PHD instead of the full multi-target posterior, still involves multiple integrals with no closed forms. In this paper, the authors highlight the relationship between the Radon-Nikodym derivative and set derivative of random finite sets that enable a Sequential Monte Carlo SMC implementation of the optimal multi-target filter. In addition, a generalized SMC method to implement the PHD filter also is presented. the SMC PHD filter has an attractive feature -- its computational complexity is independent of the time-varying number of targets.
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