The cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter is a promising algorithm for multi-target tracking. However, there exists a problem that when the legacy tracks cardinality is big enough, the effect of measurement innovation will be negligible, even if the measurement is close to the target state prediction. Such a problem is shown analytically in this study, and then an improved MeMBer filter has been proposed, which balances the posterior cardinality by modifying the legacy rather than the measurement-updated tracks parameters. The sequential Monte Carlo (SMC) implementation of the proposed algorithm is developed and its performance has been verified by simulation experiments.
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Ouyang et al. (2012) studied this question.
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