Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

In this paper, an improved interactive multiple model adaptive strong tracking random weighted cubature Kalman filter (IIMM-ASTRWCKF) algorithm is developed to overcome the low tracking accuracy and easy divergence when dealing with complex maneuvering situations. The algorithm is improved in two aspects: On the one hand, ASTRWCKF is used as the sub filter of IMM algorithm to filter different motion models. By introducing the random weight factor to replace the original weight factor, the accuracy of the algorithm is improved. At the same time, the adaptive strong tracking filter is added to update the prediction covariance matrix and noise covariance matrix for the stability of the algorithm. On the other hand, this algorithm proposes a new method to improve the probability conversion accuracy of IMM by adding time-varying factor to adjust Markov probability transfer matrix. Compared with the performance of IMM-CKF and in dealing with maneuvering problems, IIMM-ASTRWCKF algorithm has better tracking accuracy in solving maneuvering problem.