The cardinality balanced multi-target multi-Bernoulli filter and its implementations

  • Authors:

  • Ba-Tuong Vo;Ba-Ngu Vo;Antonio Cantoni

  • Affiliations:

  • School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, WA, Australia;Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia;School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, WA, Australia

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2009

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Abstract

It is shown analytically that the multi-target multi-Bernoulli (MeMBer) recursion, proposed by Mahler, has a significant bias in the number of targets. To reduce the cardinality bias, a novel multi-Bernoulli approximation to the multi-target Bayes recursion is derived. Under the same assumptions as the MeMBer recursion, the proposed recursion is unbiased. In addition, a sequential Monte Carlo (SMC) implementation (for generic models) and a Gaussian mixture (GM) implementation (for linear Gaussian models) are proposed. The latter is also extended to accommodate mildly nonlinear models by linearization and the unscented transform.