传感器故障条件下的自适应UKF 算法

针对目标跟踪中传感器故障导致滤波发散或者滤波精度不高的问题, 提出一种自适应无迹卡尔曼滤波(UKF) 算法. 该算法在滤波过程中, 根据自适应估计原理引入自适应矩阵因子, 实时调整系统状态向量和量测新息向量的协方差, 以满足无迹卡尔曼滤波算法的最优性条件, 并采取措施对滤波发散的情况进行判断和抑制. 与传统UKF和已有自适应UKF算法相比, 该自适应UKF算法显著提高了滤波精度和数值稳定性, 且具有应对传感器故障的自适应能力. 仿真实验结果表明了所提出算法的有效性.

Adaptive UKF algorithm under condition of sensor faults

In order to improve low filtering precision and divergence caused by sensor faults in target tracking, an adaptive unscented Kalman filter(UKF) is proposed. In the filtering process, by applying an adaptive matrix gene for the UKF according to the adaptive estimation principle, the algorithm can adjust the covariance matrixes of the state vector and innovation vector in real time, which meets the optimal conditions of the UKF algorithm. Then, the filtering divergence is judged and restrained by taking some measures. Compared with the traditional and existing adaptive UKF algorithm, the filter accuracy and numerical stability are remarkably improved in this adaptive UKF filter algorithm, and an adaptive capability to deal with sensor faults is performed. Simulation results show the effectiveness of the proposed algorithm.