A Gaussian Mixture PHD Filter for Nonlinear Jump Markov Models

Abstract

The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown, and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and missdetection. The PHD filter has a closed form solution under linear Gaussian assumptions on the target dynamics and births. However, the linear Gaussian multi-target model is not general enough to accommodate maneuvering targets, since these targets follow jump Markov system models. In this paper, we propose an analytic implementation of the PHD filter for jump Markov system (JMS) multi-target model. Our approach is based on a closed form solution to the PHD filter for linear Gaussian JMS multi-target model and the unscented transform. Using simulations, we demonstrate that the proposed PHD filtering algorithm is effective in tracking multiple maneuvering targets

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