Visibility informed Bernoulli filter for target tracking in cluttered environments

Incorporating prior environmental information to the diversely applied field of target tracking is becoming more beneficial in order to leverage maximum sensing performance. Urban scenarios in particular present numerous large obstacles that will block the field of view of most sensors along with many potential sources of measurement clutter. Handling these complications appropriately is imperative to ensure improved target tracking performance. This paper presents a computationally efficient method of integrating visibility information within the Bernoulli particle filter. Through estimation of target visibility with ray casting, the probability of target detection, birth density and spatial target density are modified. Numerical results demonstrate significantly more gradual degradation in target state estimation performance and improved estimation of target existence in the occlusion situation. Faster tracking recovery when emerging from occluded regions is also demonstrated.