基于强跟踪容积卡尔曼滤波的单站无源跟踪算法

A Single Observer Passive Tracking Algorithm Based on Strong Tracking Cubature Kalman Filter

系统模型和滤波算法是机动目标单站无源定位跟踪要解决的核心问题。文中采用截断正态概率模型和一种新型的滤波算法--容积卡尔曼滤波,对机动目标进行单站无源定位跟踪。针对目标突发机动的情况,借鉴强跟踪滤波器的思想,在滤波过程中引入时变渐消因子,提出了一种强跟踪容积卡尔曼滤波算法(Strong Tracking Cubature Kalman Filter,STCKF)。该算法利用容积数值积分原则直接计算非线性随机函数的均值和方差,实现简单,估计精度高,并通过渐消因子自适应在线调节增益矩阵,增强了系统对突发机动的跟踪能力。结合空频域单站无源定位模型进行仿真实验表明,STCKF算法在对一般机动目标进行跟踪时,性能与CKF算法相当,并优于传统的EKF算法。当目标突变大机动时,STCKF算法的滤波性能要高于EKF以及CKF算法。

System model and filtering algorithm are the core problem to maneuvering target single observer passive location and tracking. Truncation Gaussian probability model and a new filtering algorithm-cubature Kalman filter（CKF） are applied to maneuvering target single observer passive location and tracking in this paper. With reference to the strong tracking filter（ STF）, a strong tracking cubature Kalman filter （STCKF）is proposed by introducing a time-varying fading factor to filtering process for the sudden maneuver case. The cubature rule based numerical integration method is directly used to calculate the mean and covariance of the nonlinear random function in this algorithm and the implementation of the method is simple and higher accuracy of state estimate is achieved. By adjusting the gain matrix on-line with the fading factor, this al. gorithm improves the adaptive tracking performance when there is a sudden maneuver. Combining with the spatial-frequency domain model, simulation results show that,when there is only common maneuver the performance of STCKF and CKF are nearly same and better than EKF. When there is a sudden maneuver, the performance of STCKF is much better than EKF and CKF.