Geometric-Manifold-Assisted Distributed Navigation Probabilistic Information Fusion Cooperative Positioning Algorithm

Abstract

Positioning information is the cornerstone of a new generation of electronic information technology applications represented by the Internet of Things and smart city. However, due to various environmental electromagnetic interference, building shielding, and other factors, the positioning source can fail. Cooperative positioning technology can realize the sharing of positioning information and make up for the invalid positioning source. When one node in the cooperative positioning network has error, the positioning stability of all nodes in the whole cooperative network will be significantly reduced, but the positioning probability information technology can effectively reduce the impact of mutation error. Based on this idea, this paper proposes an information-geometry-assisted distributed algorithm for probabilistic cooperative fusion positioning (IG-CP) of navigation information. The position information of different types of navigation sources is utilized to establish a probability density model, which effectively reduces the influence of a single position error on the whole cooperative position network. Combined with the nonlinear fitting characteristics of the information geometric manifold, mapping and fusion of the ranging information between cooperative nodes on the geometric manifold surface are conducted to achieve cooperative positioning, which can effectively improve the stability of the positioning results. The proposed algorithm is simulated and analyzed in terms of the node positioning error, ranging error, convergence speed, and distribution of the cooperative positioning network. The simulation results show that our proposed cooperative positioning algorithm can effectively improve the positioning stability and display better positioning performance.

Height information aided 3D real‐time large‐scale underground user positioning

“…The simulation here still takes the root mean square error (RMSE) as the evaluation index and considers the positioning situation when the number of underground positioning users is 1000. The simulation is also compared with the weighted least squares method (WLS) [15], the factor graph three‐dimensional colocation method (3DFG) [23] and the generalised regression neural network method (GRNN) [29].…”

“…simulation is also compared with the weighted least squares method (WLS) [15], the factor graph three-dimensional colocation method (3DFG) [23] and the generalised regression neural network method (GRNN) [29].…”

“…Bayesian estimation transforms the problem of cooperative location into the problem of variable a posteriori probability distribution and introduces the concepts of factor graph and confidence degree. Qiu Yixi and others introduced confidence into trajectory tracking to realise data fusion, which has considerable complexity, and its accuracy still lags behind that of the Kalman filter method [23]. Tang Chengkai's team proposed the cooperative positioning algorithm of factor graph in three‐dimensional space, which realised the three‐dimensional application of factor graphs.…”

Distributed massive UAV jamming optimization algorithm with artificial bee colony

“…In recent years, UAVs have developed rapidly due to the advantages of low cost, small volume, strong stealth and high flexibility [1][2][3]. At present, UAVs have been applied in many fields such as agriculture, personal aerial photography, early warning of natural disasters, power security, and investigation information [2,3].…”