Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 57, Issue 2, Pages 409-423Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2008.2007924
Keywords
Estimation; finite set statistics; multi-Bernoulli; point processes; random sets; tracking
Categories
Funding
- Australian Research Council [DP0989007, DP0878158]
- Whitteld Fellowship of The University of Western Australia
- Australian Research Council [DP0878158, DP0989007] Funding Source: Australian Research Council
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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.
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