中文摘要：

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。

英文摘要：

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 ef-fectively 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 guaran-tees 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 per-formance and higher efficiency compared with the conventional IMM and IMM-CKF.