Particle Swarm Optimization Based Tuning of Unscented Kalman Filter for Bearings Only Tracking

Abstract

Kalman filter is a well known adaptive filtering algorithm, widely used for target tracking applications. When the system model and measurements are non linear, variation of Kalman filter like extended Kalman filter (EKF) and unscented Kalman filters (UKF) are used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation (off line). Tuning an UKF is the process of estimation of the noise covariance matrices from process data. In practical applications, due to unavailable measurements of the process noise and high dimensionality of the problem tuning of the filter is left for engineering intuition. In this paper, tuning of the UKF is investigated using particle swarm optimization (PSO). The simulation results show the superiority of the PSO tuned UKF over the conventional tuned UKF.