CAJ | 학술논문

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。
급출료일충교호다모형（interacting multiple model，IMM）산법중 Markov 전이개솔구진재선수정적방법，병장평방근용적잡이만려파기（square-root cubature Kalman filter，SRCKF）인입도 IMM 산법중，제출일충시변전이개솔적궤동목표근종 IMM-SRCKF 산법。해산법이용당전량측중포함적모식신식，대 IMM 산법중적전이개솔구진진행실시체추고계，피면료상규 IMM 산법중전이개솔선험학정적곤난，제고료모형절환속도화근종정도；동시，SRCKF 이목표상태협방차적평방근진행질대경신，학보료려파과정중협방차구진적대칭성화반정정성，개선료수치정도화은정성。방진실험결과표명，해산법대궤동목표적근종성능우우상규적 IMM 급 IMM-CKF산법。
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.