扩展容积卡尔曼滤波定位技术研究

为提高被动定位技术的精度与环境适应性,本文提出运用一种新的非线性滤波方法—扩展容积卡尔曼滤波算法进行多角度传感器目标定位;它首先利用EMD(经验模态分解)算法对目标的量测噪声协方差矩阵进行估计;然后,将过程噪声协方差和量测噪声协方差融入循环过程;同时,为保持算法的稳定性和正定性,利用求平方根的形式对算法改进。通过对扩展容积卡尔曼滤波与UKF(不敏卡尔曼滤波)算法跟踪目标的结果进行比较,在运算复杂度与UKF相当的前提下,扩展容积卡尔曼滤波算法不仅可以对未知量测噪声情况下的目标进行跟踪,而且显著提高了被动定位的精度。

Location Technology Based on the Extend Cubature Kalman Filter

To improve the accuracy of passive positioning technology and its environmental adaptability, a new nonlinear filter method Extend Square-root Cubature Kalman Filter(SCKF) is offered for multi-station passive location with three moving angle-measured sensors’ measurements. Firstly, Empirical Mode Decomposition (EMD) algorithm is used to estimate measurement noise covariance. And then the covariance of the procession noise and measurement noise is brought into the circle procession; At the same time, Cubature Kalman filter (CKF) is improved by the way of square root to keep the stability and positivity, and the results of tracking by Extend SCKF are compared with the results by Unscented Kalman Filter (UKF). By the tracking results to the velocity of the target, Extend SCKF algorithm can not only track the target with unknown measurement noise, but also improve the passive position precision remarkably with the same complexity to UKF.