Aiming at the problem of degenerative performance of single-sensor tracking method under complicated environment and the unknown detection probability

an improved Gaussian mixture probability hypothesis density（ GM-PHD） multi-target tracking algorithm based on camera and radar fusion is proposed. Through preassociation

the target set is divided into both camera and radar matched targets

only-camera-matched targets

only-radar-matched targets and non-matched targets. According to that different degrees of confidence are applied to update target states. Integrating the distance measured by radar and the azimuth angle measured by camera

the position estimation of target is more accurate. Taking camera measurements as a priori condition

the merging and pruning process is simplified and improved. Simulation results indicate that the proposed algorithm is more accurate and robust.