基于单极点反馈积累的三种目标跟踪算法

摘要

The state covariance matrix and state estimate directly affects KF filtering system. In order to decrease error, three improved algorithms are proposed based on single-pole-filtering-feedback-accumulation (SPFA) theory. In this paper, SPFA processing is used to approach the estimated parameters by current and the passed information and to update the state error covariance matrix and state estimate. The theoretical analysis and simulation results show that the proposed algorithms are valid.%状态误差协方差矩阵和状态估计的精度直接影响卡尔曼滤波(KF)体系的滤波性能.为减小滤波误差,提高滤波精度,基于单极点反馈积累理论,提出了改进的三种滤波算法.该算法利用单极点反馈积累思想,通过综合当前时刻和过去时刻的信息来实现对待估计参数的良好逼近,更新卡尔曼滤波中的状态误差协方差矩阵和状态估计,来提高它们的估计精度,从而达到提高卡尔曼滤波总体性能的目的.理论分析和仿真结果验证了该算法的有效性.