A Cooperative Navigation Algorithm Based on WGBP for Master–Slave UAV Formations

To address severe measurement error fluctuations and heterogeneous information source uncertainties in master–slave unmanned aerial vehicle (UAV) formations, a high-precision cooperative navigation method is proposed. Integrating inertial navigation, satellite positioning, and inter-UAV relative distance, the method innovatively introduces three key components: a multi-source information fusion-based cooperative navigation framework for accurate formation state estimation, a cooperative geometric dilution of precision (CGDOP) model based on hybrid observation configurations for positioning accuracy evaluation, and a dynamic-weight Gaussian belief propagation (WGBP) algorithm for adaptive measurement weight adjustment to suppress low-quality observation interference. Experiments demonstrate that WGBP achieves the lowest mean error in 22 out of 24 cases and the smallest standard deviation in 21 cases compared with EKF, WGP, HRGBP, and WGBP. Empirical field experiments further demonstrate consistent superiority of WGBP in dynamic environments.