再次，针对由马尔可夫转移概率矩阵固定而导致交互多模型算法的误差问题，本

Again, for the Markov transition probability matrix fixed error which led to problems interacting multiple model algorithm, this paper presents a volume Kalman filter algorithm (AIMMA5CKF) Markov parameters of the adaptive interacting multiple model adaptive five bands, which It is a correction method based on a posteriori information. The algorithm uses the error ratio is defined as the compression ratio adjustment adaptive Markov probability transition matrix, causes an increase in the model information matching the model handover process information while reducing the non-matching model, thereby reducing the error tracking . In order to verify the good performance of the proposed algorithm will be applied to "snake" movement of anti-ship missile tracking model, by contrast IMM5CKF algorithm and IMM-A5CKF algorithm to verify the good performance of the algorithm.

Third, in view of the error problem of the interactive multi-model algorithm caused by the fixed probability matrix of Markov transfer, a five-order multi-model adaptive five-order volume Kalman filter algorithm (AIMMA5CKF) based on The Accov parameter adaptation is proposed, which is a method based on post-mortem information correction. The algorithm makes use of the ratio of the defined error compression rate to realize the adaptive adjustment of the Markov probability transfer matrix, which promotes the addition of matching model information while reducing the information of the non-matching model, thus reducing the tracking error. In order to verify the good performance of the proposed algorithm, it is applied to the anti-ship missile tracking model of the "snake type" movement, and the good performance of the algorithm is verified by comparison with the IMM5CKF algorithm and the IMM-A5CKF algorithm.

Thirdly, in order to solve the problem of the error of interacting multiple model algorithm caused by the fixed Markov transition probability matrix, this paper proposes an interacting multiple model adaptive fifth order volume Kalman filter algorithm (aimma5ckf), which is based on the posteriori information correction. In this algorithm, the ratio of the defined error compression rate is used to realize the adaptive adjustment of the Markov probability transfer matrix, which makes the matching model information increase and the unmatched model information decrease in the process of model switching, so as to reduce the tracking error. In order to verify the good performance of the proposed algorithm, it is applied to the tracking model of "snake" anti-ship missile. By comparing with imm5ckf algorithm and imm-a5ckf algorithm, the good performance of the algorithm is verified.<br>