Dynamic-Occlusion Likelihood Incorporation in a PHD Filter Based Range-Only Tracking System

In multiple target tracking target occlusion or shadowing is a common occurrence. A target may be occluded by an existing structure, or in many cases, by another moving target in the environment. In this paper we consider a UWB-based range-only person tracking system. Occlusion regions induced by moving targets in the scenario are defined followed by a derivation of an occlusion likelihood function. The occlusion likelihood is then incorporated within a person localization and tracking framework based on the PHD filter by influencing the probability of target detection. Numerical and experimental results demonstrate that the incorporation of 'negative information' leads to a more complete belief of the scenario and can result in use of fewer sensors for covering the area of interest.