Pose angle aided target tracking algorithm based on Gaussian sum square-root cubature Kalman filter

Based on the relationship between the velocities of 2D motion and the pose angle, a pose angle aided target tracking algorithm is proposed. In terms of target kinematics, the tracking models, in which the state vector includes pose information, are constructed to realize the aiding for target tracking. In order to improve the filtering ability of nonlinear non-Gaussian systems, the Gaussian Sum Square-root Cubature Kalman Filter (GSSCKF) algorithm is proposed by introducing Square-root Cubature Kalman Filter (SCKF) into the framework of Gaussian Sum Filter (GSF), due to the non-Gaussian pose measurement noise obtained by model matching. Moreover, tracking models with different pose components are established by exploiting the pose variation law in targets motion, and maneuvering pose is estimated by model switching. The proposed algorithm is able not only to filter the pose measurement, but also to fuse the pose information and the position information effectively. The simulation results show the validity and the correctnesss of the proposed algorithm.