Feedback particle filter-based multiple target tracking using bearing-only measurements

This paper describes the joint probabilistic data association-feedback particle filter (JPDA-FPF) introduced in our earlier paper [1]. The JPDA-FPF is based on the feedback particle filter concept (see [2],[3]). A remarkable feature of the JPDA-FPF algorithm is its innovation error-based feedback structure, even with data association uncertainty in the general nonlinear case. The classical Kalman filter-based joint probabilistic data association filter (JPDAF) is shown to be a special case of the JPDA-FPF. A multiple target tracking application is presented: In the application, bearing only measurements with multiple sensors are used to track targets in the presence of data association uncertainty. It is shown that the algorithm is successfully able to track targets with significant uncertainty in initial estimate, and even in the presence of certain "track coalescence" scenarios.