A Cooperative Positioning Algorithm via Manifold Gradient for Distributed Systems

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.