Abstract: Aiming at the problem that the square root cubature Kalman filter Gaussian mixture probability hypothesis density (SRCKF-GM-PHD) algorithm is not strong in tracking non-linear targets under the condition of the high clutter, in this paper firstly the improved grey wolf optimizer is infused to adjust the process noise Q and measurement noise R in real time. Secondly, the gain matrix of SRCKF-GM-PHD algorithm is adjusted to enhance the tracking accuracy of the target with the idea of improved fading factors. In addition, in order to avoid discontinuing the algorithm, an improved measure of dynamic weight adjustment strategy is proposed to adjust the covariance of the actual output residual sequence in the algorithm. Finally, a novel SRCKF-GM-PHD algorithm combining the improved grey wolf optimizer and the improved fading factors is formed. The performances of the four algorithms are compared by simulation analysis, and the validity and superiority of the proposed algorithm in tracking accuracy are demonstrated.