基于多重渐消因子的强跟踪SVDCKF组合导航算法

针对标准容积卡尔曼滤波(cubature Kalman filter,CKF)在载体状态突变时滤波精度下降的问题,提出了一种基于多重渐消因子的强跟踪SVDCKF组合导航(strong tracking SVDCKF integrated navigation algorithm based on multiple fa...

Strong tracking SVDCKF integrated navigation algorithm based on multiple fading factors

When the carrier is in a state of sudden change, the filtering accuracy of the standard CKF will decrease. To solve this problem, a strong tracking SVDCKF integrated navigation algorithm based on multiple fading factors (MST-SVDCKF) is proposed. The singular value decomposition (SVD) is introduced to replace the original Cholesky decomposition in standard CKF, which improves the numerical stability of the state covariance matrix decomposition iteration. Then, the state of the system is evaluated by the chi-square test, and if the system has a sudden change in state, the multi-fade factor is employed to adjust the prediction state covariance matrix, which enables different filter channels to obtain different fade-out capabilities. In this way the strong tracking of the true state of carrier can be achieved. The simulation results show that the proposed algorithm has stronger adjustment ability and higher filtering precision than CKF and traditional STCKF.