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.

Kalman filter tracking of sequence spot centroid ablated by femtosecond laser

“…The centroid coordinates of sequential spot image extracted at time intervals are actually the sampling data of discrete time points, Therefore, in this paper, the discrete linear Kalman filter algorithm is used and its parameters are optimized to track the spot centroid. The traditional Kalman filter has the characteristics of low accuracy in the estimation of targets, and the state estimation mainly faces three problems 11 : one is the inaccuracy in the establishment of motion model, the other is the inaccuracy in the description of process noise, and the third is the too large influence of observation noise, which leads to the reduction of estimation accuracy and filter divergence. In order to solve the above problems, the square root filtering (SRF), 12 fuzzy fading memory technology filtering (FFM), 13 adaptive Kalman filtering (AKF) [14][15][16] and other optimization methods gradually emerge.…”