平方根容积卡尔曼滤波在角测量跟踪中的应用

为解决扩展卡尔曼滤波算法(EKF)在处理角测量跟踪问题时对复杂非线性状态估计收敛速度慢、估计精度低的问题,引入一种平方根容积卡尔曼滤波算法(SRCKF)。SRCKF是一类sigma点滤波方法,基于容积原则的数值积分方法计算非线性随机函数的均值与协方差,避免了EKF中Jacobian矩阵的计算,有效提高了计算效率。另外,与一般容积卡尔曼滤波算法相比,SRCKF确保了状态协方差矩阵的对称性与半正定性,有效改进了数值精度和鲁棒性。将SRCKF应用于角测量跟踪系统中,仿真结果表明,SRCKF、Unscented卡尔曼滤波(UKF)滤波精度较传统EKF有较大提高,同时,与UKF相比,SRCKF能以较快的运行效率获得较好的滤波效果。

Applications of Square Root Cubature Kalman Filtering to Bearing Only Tracking

To solve the problems of slow convergence speed and low estimation accuracy of extended Kalman filter (EKF) for the complex nonlinear state estimation in bearing only tracking system, the square root cubature Kalman filter (SRCKF) is introduced. The SRCKF is a kind of sigma points filtering method. The numerical integration method based on cubature rule is used directly to calculate the mean and covariance of the nonlinear random function. The work of computing Jacobian matrix in EKF can be avoided and the computational efficiency is enhanced. Comparing with the traditional CKF, the symmetry and positive semi definiteness of the covariance matrix of state are guaranteed using the SRCKF and the numerical accuracy are improved as well as the robustness. Simulation results show that in the bearing only tracking system, compared with the traditional EKF, the estimation accuracy of SRCKF and UKF are improved. Meanwhile, the run efficiency of SRCKF is faster than UKF.