基 于 路 径 和 算 法 的 飞 行 器 集 群 分 布 式 相 对 定 位

According to the inertial measurement of each node in the aircraft group and the relative distance measurement given by the data link between nodes，the relative positioning including the estimation of the relative position and relative attitude between node pairs can be realized by using the centralized Kalman filter. The centralized algorithm gathers all the data in the group into the central node for centralized processing，and the scalability and reliability of the system are restricted. The distributed relative positioning algorithm such as belief propagation can realize global estimation of the relative position and relative attitude through the processing of local information by each node of the cluster and the information interaction between adjacent nodes. However，in the problem of group relative positioning，the data link topology contains a large number of ring structures，and the belief propagation algorithm has a convergence problem in such case. This paper proposes a distributed exact inference method for relative positioning，which can be applied to any group topology. Under the Gaussian standard and canonical descriptions，the distributed time update and measurement update algorithms for the error states of each node are designed. The distributed algorithms for standard and normative parameters conversion are given based on paths-sum principles. Under the linear Gaussian model assumption，the method in this paper is equivalent to the centralized Kalman filter，which can achieve the optimal estimation of relative positioning. A distributed approximate inference algorithm is proposed based on group decomposition to improve the speed of the algorithm. Results on the six-degree-of-freedom long-duration simulation data show that the relative positioning accuracy of the distributed approximate inference based on the path-sum is close to that of the centralized Kalman filter，and significantly outperforms the existing distributed Gaussian belief propagation algorithm.