Multi-target tracking based on improved probability hypothesis density filter

Due to the most recent observational data being unused, the particles in sequential Mote Carlo probability hypothesis density (SMC-PHD) filter which are drawn from prior transition is far away from the real states and may seriously degenerate. Aiming at these problems, we propose a method named square-rooted cubature Kalman sequential Mote Carlo PHD (SCK-SMC-PHD) filter which uses square-rooted cubature Kalman filter to generate the proposal density function and obtains the present particles states by sampling from the proposal density function. The proposed method which can alleviate particles degradation effectively has rigorous mathematical theoretical basis and strong adaptability. Simulation compares the proposed method with C-SMC-PHD filter and the SMC-PHD based on unscented Kalman filter. The results show that the proposed SCK-SMC-PHD filter has a higher accuracy in estimation of both individual state and target number than the two methods mentioned above.