转换观测卡尔曼滤波算法中转换统计量的一种改进计算方法(英文)

An Improved Evaluation Method for Converted Statistics in Converted Measurement Kalman Filtering Algorithm

为了改进转换观测卡尔曼滤波算法的性能,提出了一种计算转换观测误差统计量(均值和协方差)的改进方法。有两组信息可用于计算这些统计量,即观测值和滤波器的一步预测值。首先,推导了滤波器球坐标状态一步预测误差的均值和协方差。然后,利用滤波器球坐标状态一步预测值更精确地计算了转换观测误差的统计量。最后,将改进的转换观测误差统计量用于目标跟踪问题中的转换观测卡尔曼滤波算法。仿真结果表明,本文提出的方法能够改善转换观测卡尔曼滤波算法的收敛性和估计精度,这种改善在观测噪声较大时尤为显著。

To improve the performance of Converted Measurement Kalman Filter （CMKF）,a more accurate evaluation method for converted measurement error statistics （means and covariance） was presented.There were two sets of information that could be used to evaluate the converted measurement error statistics,i.e.,measurements of the target and a priori state estimate of the filter.Firstly,the proposed procedure in this work derived the means and covariance of the filter＇s a priori spherical state estimation errors.Secondly,a more accurate evaluation of the converted measurement error statistics was obtained according to the a priori spherical state estimate of the filter instead of measurements.Finally,the improved evaluation method for converted measurement error statistics was utilized to implement the CMKF algorithm for a target tracking scenario.The simulation results show that the proposed method can provide superior performance in terms of convergence and estimation accuracy,especially in the case of significant measurement noises.