Multi-Bernoulli Filtering with Unknown Clutter Intensity and Sensor Field-of-View

In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and sensor field-of-view are of critical importance. Significant mismatches in clutter and sensor field of view model parameters results in biased estimates. In this paper we propose a multi-target filtering solution that can accommodate non-linear target model and unknown non-homogeneous clutter intensity and sensor field-of-view. Our solution is based on the multi-target multi-Bernoulli filter that adaptively learns non-homogeneous clutter intensity and sensor field-of-view while filtering.