混合噪声条件下的目标被动定位算法

为提高被动传感器观测噪声为含时变有色噪声、跳变噪声的混合噪声时容积卡尔曼滤波（CKF）算法的滤波精度和稳定性，提出一种自适应容积卡尔曼滤波（ACKF）算法。在ACKF算法中，在基本CKF算法基础上，采用观测重构、待定系数去相关方法，推导得到有色噪声条件下的容积卡尔曼滤波算法。针对时变有色噪声和跳变噪声导致滤波精度受损的问题，引入噪声方差在线修正及有害观测剔除的思想，进行了ACKF算法设计。仿真结果表明，与基本CKF算法相比，ACKF算法在x轴、y轴、z轴3个方向得到的被动定位精度分别提升了24.75％、32.57％和28.48％，具有更高的滤波稳定性和精度。

Passive Target Positioning Algorithm for Mixed Noise

An adaptive cubature Kalman filter(ACKF) algorithm is proposed to improve the filtering accuracy and stability of the cubature Kalman filter(CKF) algorithm when the passive sensor measurement noise is a mixed noise containing time-varying colored noise and jumping noise. Based on the basic CKF algorithm, the measurement reconstruction and undetermined coefficient decorrelation methods are used to derive the cubature Kalman filter algorithm with colored measurement noise (CKF-CMN). For the impaired filtering accuracy caused by time-varying colored noise and jumping noise, the idea of online correction of noise variance and removal of harmful measurement is introduced, and the ACKF algorithm is designed. The simulated results show that, compared with the basic CKF algorithm, the passive positioning accuracies of ACKF algorithm on x, y, and z axes are increased by 24.75%, 32.57% and 28.48%, respectively. The ACKF algorithm has higher filtering stability and accuracy.