一种基于距离参数化CKF的单站无源定位方法

A single-station passive location method based on range-parameterised Cubature Kalman Filter

针对单站无源定位系统中,观测站机动导致的滤波精度降低问题,提出了一种基于距离参数化CKF的单站无源定位方法(range-parameterised cubature kalman filter smoothing,RPCKFS)。引入距离参数化的思想,结合观测站观测范围作为先验值,将观测范围划分为若干个区间,并赋予初始权重,在各个区间引入后向平滑容积卡尔曼滤波(cubature kalman filter smoothing,CKFS),利用各时刻的预测值与观测值的比值来更新区间权重,最后对各区间状态信息加权融合来实现目标状态的获取。仿真结果表明:该方法能够有效降低滤波全局对观测站机动的敏感性,提高滤波稳定性与定位精度。

Aiming at the problem that the filtering accuracy decreases due to the maneuvering of the observation station,this paper proposes a single-station passive positioning method based on the Range-Parameterized Cubature Kalman Filter Smoothing(PRCKFS).The idea of distance parameterization is introduced,combined with the observation range of the observation station as the prior value.The observation range is divided into several intervals,and the initial weights are given.Backward Cubature Kalman Filter Smoothing(CKFS)is introduced into each interval,whose weight is updated by the ratio of the predicted value and the observed value,and finally the target state is obtained through a weighted fusion of the state information of each interval.The simulation results show that the method can effectively reduce the sensitivity of the global filtering to the maneuvering of the observation station,and improve filtering stability and positioning accuracy.