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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.
In order to solve the problem of sensor divergence caused by target tracking or the filtering accuracy is not high, an adaptive unscented Kalman filter (UKF) algorithm is proposed in this paper. In the filtering process, adaptive matrix factor is introduced according to adaptive estimation principle, Adjust the covariance of the system state vector and the measurement interest vector in real time to meet the optimal conditions of the unscented Kalman filter and take measures to judge and suppress the filtering divergence.Compared with the traditional UKF and existing adaptive UKF algorithm, the adaptive UKF algorithm significantly improves the filtering accuracy and numerical stability, and has adaptive ability to deal with sensor failures.The simulation results show the effectiveness of the proposed algorithm.