Accession Number:

ADA445311

Title:

Random Finite Sets and Sequential Monte Carlo Methods in Multi-Target Tracking

Descriptive Note:

Conference paper

Corporate Author:

MELBOURNE UNIV VICTORIA (AUSTRALIA)

Report Date:

2005-04-14

Pagination or Media Count:

7.0

Abstract:

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.

Subject Categories:

  • Statistics and Probability
  • Computer Programming and Software
  • Active and Passive Radar Detection and Equipment

Distribution Statement:

APPROVED FOR PUBLIC RELEASE