WSN Mobile Target Tracking Based on Improved Snake-Extended Kalman Filtering Algorithm

A wireless sensor network mobile target tracking algorithm (ISO-EKF) based on improved snake optimization algorithm (ISO) is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking. First, the steps of extended Kalman filtering (EKF) are introduced. Second, the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target. Finally, the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model (CM). Under the specified conditions, the position and velocity mean square error curves are compared among the snake optimizer (SO)-EKF algorithm, EKF algorithm, and the proposed algorithm. The comparison shows that the proposed algorithm reduces the root mean square error of position by 52% and 41% compared to the SO-EKF algorithm and EKF algorithm, respectively.