Improved adaptive unscented Kalman filter algorithm for target tracking

Abstract

An adaptive unscented Kalman filter (AUKF) algorithm is proposed to solve the problem that the statistical characteristics of the process noise are unknown in the target tracking, which leads to filter divergence or low filtering precision. The improved Sage-Husa estimator is used to estimate the statistical characteristics of the unknown process noise in the filtering process, and to judge and suppress the filtering divergence, which effectively improves the numerical stability of the filtering and reduces the error of the state estimation. The simulation results show that the improved AUKF algorithm not only keeps convergence but also improves the accuracy and stability of the target tracking under the condition of unknown time-varying process noise statistic, compared with the standard UKF algorithm.