时变转移概率IMM-SRCKF机动目标跟踪算法

给出了一种交互多模型(interacting multiple model,IMM)算法中Markov转移概率矩阵在线修正的方法,并将平方根容积卡尔曼滤波器(square-root cubature Kalman filter,SRCKF)引入到IMM算法中,提出一种时变转移概率的机动目标跟踪IMM-SRCKF算法。该算法利用当前量测中包含的模式信息,对IMM算法中的转移概率矩阵进行实时递推估计,避免了常规IMM算法中转移概率先验确定的困难,提高了模型切换速度和跟踪精度;同时,SRCKF以目标状态协方差的平方根进行迭代更新,确保了滤波过程中协方差矩阵的对称性和半正定性,改善了数值精度和稳定性。仿真实验结果表明,该算法对机动目标的跟踪性能优于常规的IMM及IMM-CKF算法。

Time-varying transition probability based IMM-SRCKF algorithm for maneuvering target tracking

An on-line updating method of Markov transition probability for the interacting multiple model(IMM)algorithm is proposed, and the square-root cubature Kalman filter(SRCKF)is introduced into IMM, so a novel time-varying Markov transition IMM-SRCKF algorithm is obtained.Using real-time recursive estimation method based on the system mode information implicit in the current measurements, the proposed algorithm effectively avoids the problem of prior determination of the Markov transition probability matrix in traditional IMM. Furthermore, SRCKF propagates the square root of the covariance in filter interaction so that it guarantees the symmetry and positive semi-definiteness of the covariance matrix and greatly improves the numerical stability and numerical accuracy. Simulation results show that the proposed algorithm has better tracking performance and higher efficiency compared with the conventional IMM and IMM CKF.