Tracking and Identifying a Magnetic Spheroid Target Using Unscented Particle Filter

In this paper we use the recursive Bayesian estimation method to solve the tracking and identification problem of a target modeled by an equivalent magnetic spheroid. Target positions, velocity, heading, magnetic moments and size are defined as the state vector, which is estimated from noisy magnetic field measurements by a sequential Monte Carlo based method known as particle filter. In order to improve the performance of the filter, the unscented Kalman filter is applied to generate the transition prior as the proposal distribution. A simulated experiment is given to test the performance of the unscented particle filter, and the results show that the filter is suitable for magnetic target's track and identification.