Adaptive IMM-Based Smoothing Probabilistic Data Association for Maneuvering Target Tracking in Cluttered Environments

Abstract

Modern radar systems face many challenges, including complex nonlinear motion modelling, real-time changes of target motion model and difficult target trajectory estimation under low signal noise ratio when tracking high maneuvering targets in a cluttered environment. Therefore, an improved probabilistic data association tracking algorithm, termed adaptive transition probability matrix and improved smoothing integrated probabilistic data association (ATPM-ISIPDA) by embedding adaptive interactive multiple models (IMM) and parallel cubature information filter (PCIF) is proposed. Based on the fixed-lag smoothing integrated probabilistic data association (FLSIPDA) and IMM framework, the proposed algorithm uses the model posterior information to adaptively adjust the model transition probability, thereby enhancing model matching accuracy. Additionally, the parallel cubature information filter (PCIF) is utilized in each IMM sub-filter to suppress the state estimation error of nonlinear systems. In the fusion stage, the multi-branch cubature Kalman filter (CKF) prediction results are fused by the weighted accumulation method of the information matrix and vector, and the optimized smoothed state predictions and covariance matrices are generated. Then, the smoothed component data association probability is calculated to obtain the final state estimate, enhancing the fusion and smoothing performance of forward and backward tracks. The simulation results show that compared to the traditional IMM-IPDA algorithm, the average position RMSE is reduced by 31.1%, the FTD accuracy is improved by 15%-20%, and it still maintains a good tracking confirmation rate in cluttered environments.