时变转移概率IMM-SRCKF机动目标跟踪算法  

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

作　　者：郭志[1] 董春云 蔡远利[1] 于振华[2] 

机构地区：[1]西安交通大学电子与信息工程学院,陕西西安710049 [2]空军工程大学信息与导航学院,陕西西安710077

出　　处：《系统工程与电子技术》2015年第1期24-30,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金(61202128);宇航动力学国家重点实验室开放基金(2011ADL-JD0202)资助课题

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。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.

关 键 词：机动目标跟踪 交互多模型 平方根容积卡尔曼滤波 Markov转移概率 

分 类 号：TN953[电子电信—信号与信息处理]