UUV Cluster Distributed Navigation Fusion Positioning Method with Information Geometry

The development and utilization of marine resources by humanity are increasing rapidly, and a single unmanned underwater vehicle (UUV) is insufficient to meet the demands of ocean exploitation. Large-scale UUV swarms present a primary solution; however, challenges such as underwater mountain ranges and signal attenuation critically impact the real-time collaborative positioning and autonomous clustering abilities of these swarms, posing major issues for their practical application. To address these challenges, this paper proposes a UUV cluster distributed navigation fusion positioning method with information geometry (UCDFP). This method transforms the navigation data of individual UUVs into an information geometric probability model, thereby reducing the impact of temporal asynchrony-induced positioning errors. By integrating factor graph theory and utilizing ranging information between UUVs, a distributed collaborative fusion positioning architecture for UUV swarms is established, enabling seamless dispersion and regrouping. In experimental evaluations, the proposed method is compared with existing techniques concerning convergence speed and the capability of UUV swarms for autonomous dispersion and regrouping. The results indicate that the method proposed in this paper achieves faster convergence and higher positioning stability during the autonomous clustering of UUV swarms, marking a notable advancement in underwater vehicular technology.