UAV Networks Geometry Configuration Aided Cooperative Positioning Algorithm

Due to their flexible and lightweight nature, unmanned aerial vehicles (UAVs) find extensive application in cooperative navigation and positioning technology. Additionally, wireless sensor networks (WSNs) serve as a conduit for exchanging and merging information among cooperative UAV nodes. Addressing the need for lightweight design and real-time adaptability in UAV networks, this study introduces a cooperative positioning algorithm employing manifold gradient filtering assisted by Geometric Dilution of Precision (GDOP). The algorithm leverages the measurement model among cooperative sensor nodes within the UAV network to construct a Riemannian manifold. By computing the gradient on this manifold, the algorithm identifies the steepest descent direction during iteration. Furthermore, it derives the GDOP corresponding to the geometric configuration of cooperative UAV nodes, thereby adjusting the iterative descent rate to expedite convergence. Simulation results demonstrate the algorithm's rapid convergence and high accuracy.