未知噪声协方差的自适应容积卡尔曼滤波

针对噪声协方差不确定情况下容积卡尔曼滤波解决非线性目标跟踪中存在的问题,提出了一种优化的自适应容积卡尔曼滤波.首先根据新息序列和残差序列导出的线性矩阵方程得到噪声的协方差,基于新息序列与残差序列的相关性,推导出一种新的过程噪声协方差Q估计方法;然后采用残差序列对测量噪声协方差进行估计,利用加权因子将当前的噪声协方差矩阵与估计值组合成为新的测量噪声协方差阵R,有效避免了不准确状态估计的局限性.仿真结果表明:在时变噪声协方差的条件下,所提出的自适应容积卡尔曼算法的跟踪精度明显提高.

Adaptive Cubature Kalman Filter Based on Unknown Noise Covariance

Aimed at the problems that a lot of problems remains to be solved by cubature Kalman filter in nonlinear target tracking when the noise covariance is uncertain, an optimized adaptive cubature Kalman filter is proposed. First, the noise covariance is obtained by the linear matrix equation derived from the innovation sequence and the residual sequence, a new process noise covariance WTHX]QWT] estimation method is derived based on the correlation between the innovation sequence and the residual sequence, and then the estimation of the measured noise covariance is made by adopting the residual sequence, and the weighting factors are utilized for combining the current noise covariance matrix and the estimated value into a new measurement noise covariance matrix WTHX]RWT], thus avoiding effectively the limitations of inaccurate state estimation. The simulation results show that under conditions of time varying noise covariance, the tracking accuracy is improving obviously by the proposed adaptive algorithm cubature Kalman filter.