Improved CKF Collaborative Positioning Algorithm Between UAVs Based on UWB/IMU

Abstract Addressing the challenges of position information solution among multiple Unmanned Aerial Vehicles (UAVs) and the issue of low relative navigation accuracy, an improved collaborative localization algorithm based on the Cubature Kalman Filter (CKF) is proposed. It utilizes a tightly coupled approach to construct a relative ranging model equipped with Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) sensors, enabling the correction of self-navigation information. The cosine similarity function is utilized to determine the reliability of the observations, and the observation noise matrix is adaptively adjusted based on the new interest vector from each iteration, thereby refining the positioning trajectory for collaborative navigation. Simulation experiments results demonstrate that the improved CKF collaborative localization algorithm significantly enhances the relative positioning accuracy between UAVs, effectively reduces the maximum relative positioning error, and exhibits strong anti-interference capabilities. It is more suitable for indoor multi-UAV and multi-sensor collaborative localization.