Passive tracking of maneuvering target in water based on bearings of two sonar sensors is a nonlinear state estimating issue; the nonlinear filtering algorithm is inevitably applied to the filtering part because of the linearity of observed equation. The ordinary method to solve this issue is that extended Kalman filtering ( EKF) algorithm is applied to the filtering part of interactive multiple models ( IMM ) algorithm, but when calculating the error covariance matrix, the real-time observed information is not considered, so in this paper, the EKF algorithm is replaced by the modified algorithm I. E. Modified covariance extended Kalman filtering ( MCEKF) in order to improve the tracking performance, then a new method is acquired. The correctness as well as validity is verified by simulations.%利用2部被动声呐基阵获取的目标方位信息对水中机动目标的跟踪实质是一个非线性状态估计问题,由于观测方程的非线性性,滤波环节不可避免地要用到非线性滤波算法.以往解决此问题的方法是在基于交互多模型(IMM)算法并在其滤波环节应用扩展卡尔曼滤波(EKF)算法.然而,EKF算法在计算滤波误差协方差阵时没有融入当前观测信息.为此提出在原方法的基础上用其改进算法即修正协方差扩展卡尔曼滤波(MCEKF)算法取代EKF算法,以改善跟踪性能,从而得到一种新的方法.经仿真验证了所提方法的正确性和有效性.
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