Owing to the flexible and lightweight characteristics, unmanned aerial vehicles (UAVs) are widely used in the field of cooperative navigation and positioning technology. Furthermore, wireless sensor networks (WSN) provide a bridge for information exchange and fusion between the cooperative UAV nodes. Focusing on the demand of the lightweight and real-time dynamic requirements in the UAV-WSN, this paper proposes a cooperative positioning algorithm based on manifold gradient filtering assisted by Geometric Dilution of Precision (GDOP). In this algorithm, the measurement model between the cooperative sensors nodes in UAV-WSN is utilized to construct the Riemannian manifold. By deriving the gradient on the manifold, the fastest descent direction in iteration can be determined. The GDOP corresponding to the geometric configuration of the cooperative UAV nodes is derived, which is applied to modifying the iterative descent rate and making the algorithm converge quickly. The simulation results show that the algorithm converges fast and has good performance in accuracy. Moreover, the algorithm also exhibits a certain level of robustness in extremely harsh environments.

This letter discusses the improvement of cooperative positioning method for distributed system under the condition of nonlinear measurement. In order to improve the accuracy and convergence speed of the cooperative positioning algorithm based on Bayesian filtering, a cooperative positioning algorithm utilizing Manifold Gradient Filtering (MGF) is proposed. The Information Geometry (IG) theory is applied to derive the manifold gradient in distributed cooperative filtering, which can analysis the geometric structure inherent in the distribution information of nonlinear measurement and make the fusion result more accurate. In addition, the proposed algorithm has fast convergence performance in the iterations. The simulation results demonstrate the accuracy and great performance of the proposed method.

This letter discusses the improvement of cooperative positioning frameworks for wide-area unmanned aerial vehicle (UAV) networks under the ranging measurement alone. The cooperative nodes are far apart in the wide-area UAV networks, which makes the angle measurement methods result in significant 3-D positioning errors. Therefore, only the ranging information between nodes is utilized to optimize the positioning results. To improve the accuracy and computation speed of the cooperative positioning algorithm, the ranging measurement-based manifold gradient fusion (RM-MGF) method is proposed. An error propagation model for cooperative nodes based on dilution of precision (DOP) is derived, making the error estimation more accurate. Furthermore, the Riemannian manifold gradient corresponding to the measurement network is applied to the improvement of positioning accuracy. The experiment results verify that the proposed algorithm has the best positioning accuracy and lower computational complexity.