An Improved Probability Hypothesis Density Filter Based on Variational Bayes for Closely Spaced Multi-Target Tracking

An improved probability hypothesis density filter based on variational bayes is proposed in closely spaced multi-target tracking scenario with unknown measurement noise in complex environment. The proposed algorithm combines adjacent Gaussian Inverse Wishart components representing the same target as much as possible, so as to minimize the number of components in the subsequent filtering iteration process, ensure the efficient operation of the multi-target tracking algorithm, and better adapt to the tracking scenarios of neighboring targets. The simulation results show that the improved algorithm can not only avoid the wrong merging of different target components, but also effectively improve the precision of target state and number estimation when the measurement noise is unknown and the target is in close proximity.