UAV Path Planning Optimization Based on Extended Kalman Filter

In the study of distributed UAV path planning, this paper proposes a path planning method based on the Extended Kalman Filter (EKF) algorithm. A system model integrating UAV trajectory, communication link, and sensing accuracy is constructed to leverage the advantages of Integrated Sensing and Communication (ISAC) technology and provide accurate data for path planning. In order to accurately adapt the UAV dynamics and realize the target tracking, the Extended Kalman Filter algorithm is used to combine the real-time environmental information to establish the state and observation model. Meanwhile, for distributed cooperative optimization, the Sequential Least Squares Quadratic Programming (SLSQP) algorithm and the Hungarian algorithm are fused to optimize the trajectory optimization and dynamic correlation of distributed UAVs alternatively, with the goal of simultaneously improving the communication rate and the sensing accuracy as the optimization problem. The scheme improves the real-time and accuracy of path planning, enhances the adaptability and synergy of UAVs in complex scenarios, and provides an innovative and practical solution for distributed UAV communication-sensing integrated path planning.