Abstract:

A novel iterated unscented Kalman filter (UKF) is proposed to realize fast and highprecision passive tracking using multi-plane. The proposed algorithm gets the likelihood function directly with respect to the target state by the probability density function transformation of the random variable and the state is iteratively solved using the maximum likelihood estimator based on it. The convergence criterion which can reach the maximum likelihood surface is derived and a novel iterated UKF algorithm is gotten by combining the criterion with the UKF algorithm. The multi-plane bearingonly tracking is used as an example to analyze the performance of the proposed algorithm. Simulation results indicate that the proposed algorithm has better tracking performance compared with existing iterated UKF algorithms, which indicate its good application prospect.