Evidence theory-based mixture particle filter for joint detection and tracking of multiple targets

In this study, a novel algorithm named evidence theory-based mixture particle filter is proposed for joint detection and tracking for a varying number of targets in a cluttered environment. The posterior distribution of multiple target state considered in single target state space is a multi-modal distribution with each mode corresponding to either a target or clutter. A general global posterior distribution is adopted, which consists of existing components propagated from the previous time step, and new components generated at the current time step to capture the newly appeared targets. An evidence theory-based framework is utilised to determine the structure of the global posterior distribution. A set of masses are used to describe the possible kinds of nature for each mixture component (e.g. it is from a target, clutter or undetermined at the current time step). The masses are then transformed to a set of Pignistic probabilities, based on which a decision process is utilised to determine the nature for each mixture component. The decision on the nature for each component (target or clutter) is made until sufficient information arrives, which avoids the misjudgement because of insufficient information efficiently.