An Improved State-Independent Fusion Algorithm Based on the Federated Kalman Filters
Xuan Xiao, Jiaxin Liu, Chao Xu, Chen Wang · 2020
In this paper, an improved optimal fusion algorithm based on independent fusion of states is proposed for federal filter, which can mainly solve the problem that the state with poor estimation accuracy pollutes other states during fusion, which causes the estimation accuracy of the federated filter system to decrease and the convergence rate to slow down. This improves the stability of the federated filter, thereby improving the robustness of the federal filter. Meanwhile, for the problem of complex fusion weighting matrix, large amount of calculation and poor stability in the fusion algorithm proposed by Carlson, the improved fusion algorithm, the improved fusion algorithm can reduce the complexity of fusion, reduce the operation time of the filtering system, and improve the stability of the filtering system.