A WSN (Wireless Sensor Network) mobile target tracking algorithm based on the golden section grey wolf particle filter (PF) is proposed to address the problem of low tracking accuracy caused by sample impoverishment. This algorithm guides the particles as a whole to move towards randomly selected regions with higher weights, effectively adjusting the global exploration and local exploration capabilities of the particle swarm, and improving the problems of sample impoverishment and local extreme values. Additionally, this method increases the diversity of the particle swarm, further improving the tracking performance. The filtering tracking is performed using a constant velocity circular motion model (CM). The Extended Kalman Filter (EKF) algorithm and PF algorithm are compared with this algorithm using the root mean square error as the evaluation metric. The results of the simulation experiments show that the proposed algorithm reduces the root mean square error by 57% and 37% compared to the above-mentioned algorithms, respectively.