A Parameter Adaptive Gaussian Mixture CQKF Algorithm Under Non-Gaussian Noise

A Gaussian mixture filtering method under non-Gaussian noise environment was studied, and the target tracking of pure azimuth tracking system was carried out. Firstly, a modified parameter adaptive method was used to adjust the size of the displacement parameter, so the Gaussian mixture model could be modified. The parameter adaptive Gaussian mixture CQKF algorithm (PGM-ACQKF) under non-Gaussian noise was proposed. Then based on the discrete system model under non-Gaussian noise, the limitations of the modeling process in the Gaussian mixture CQKF (GM-CQKF) was analyzed. Combining with the initial optimization method, a method to modify the Gaussian mixture model was proposed based on parameter adaptive method. Thus the limitations of GM-CQKF could be overcome and the filtering accuracy could be improved. The simulation results show the effectiveness of the proposed algorithm, which proves that the PGM-ACQKF has higher filtering accuracy than the original algorithm under non-Gaussian noise.