Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking

In this correspondence, we derive a forward-backward smoother for joint target detection and estimation and propose a sequential Monte Carlo implementation. We model the target by a Bernoulli random finite set since the target can be in one of two "present" or "absent" modes. Finite set statistics is used to derive the smoothing recursion. Our results indicate that smoothing has two distinct advantages over just using filtering: First, we are able to more accurately identify the appearance and disappearance of a target in the scene, and second, we can provide improved state estimates when the target exists.