Hierarchical Extended Kalman Filter Cooperative Positioning Algorithm for UAV Swarm

To enhance the operational range of the unmanned aerial vehicle (UAV) swarm and optimize the positioning accuracy and stability of the UAV positioning system, this study introduces a Hierarchical Extended Kalman Filter (HEKF) algorithm. Collaborative positioning is achieved through the utilization of high-precision guidance layer information in conjunction with low-precision information from the following layer. To enhance positioning accuracy and convergence speed, the integration of the consistency principle with HEKF is employed to promote state consistency among individual nodes within the UAV system. Considering that the excessive number of leaders in the guidance layer leads to information redundancy, an optimization strategy is used to optimize the leaders by using the average distance measurement and its standard deviation, and the measurement information is weighted before collaborative positioning. The experimental findings demonstrate a notable enhancement in both the accuracy and stability of the algorithm's positioning capability.