An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering

We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli (SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets. Since most clutter measurements do not participate in the update step, the computing time is reduced significantly. Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.