Abstract
This paper deals with the problem of time-varying multitarget track-before-detect (TBD) using image observation model. The multitarget state is formulated as random finite set (RFS) and its posterior distribution is approximated by multi-Bernoulli parameters, which are recursively evaluated using sequential Monte Carlo approach. The state estimates are first extracted from the updated Bernoulli components with moderate existence probabilities, allowing for all the true targets and false alarms. The extracted target states are then distilled using track consistency test strategy to remain only the true tracks. Simulation results show the improved performance of the proposed method over the traditional multitarget multi-Bernoulli (MeMBer) filter as well as its capability to provide the identity of individual target.
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