Multi-target multi-Bernoulli Filter Research Articles

Couple-sensor CBMeMBer filter

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter shows excellent performance compared with the probability hypothesis density (PHD) and cardinality PHD (CPHD) filters. However, the algorithm addressed only single-sensor scenarios. To resolve the problem of multisensor multi-target tracking with the CBMeMBer filter, an ad hoc approach is proposed. Simulation results verify the proposed solution.

Improved multi-target multi-Bernoulli filter

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.