A nonlinear prediction filter algorithm based on the adaptive tracking theory

Abstract

The tracking and orientation of optoelectronic targets must obtain the data of target's velocity and angle by prediction algorithm. But the state and measurement equations are usually nonlinear and uncoupled models, so the tracking problem often connects with nonlinear estimation. The commonly classical extended Kalman filter (EKF) algorithm suffers from a lot of defects. There are those problems such as easy to diverge and the convergence rate is slow and the tracking accuracy is low. In this paper, a new nonlinear adaptive Kalman filter (AEKF) algorithm based on the adaptive tracking theory in current statistical model is presented. It expresses variation of acceleration with the information of position and angle to carry out self adaptation of noise variance in on-line mode, and to compensate the linear errors of model in dynamic mode. Analytic results of Monte Carlo simulation prove the AEKF algorithm is right and feasible, and the accuracy and the convergence rate are both improved. It has better performance than the EKF algorithm and modified variance EKF (MVEKF) algorithm in the tracking and orientation of optoelectronic maneuvering target. The simulation results and new method will been widely and directly applied into various engineering.