Accurate multi-source fusion is based on the relia-bility,quantity,and fusion mode of the sources.The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modu-lar nor super-modular.Furthermore,in the case of the Kalman filter(KF)fusion algorithm,accurate statistical characteristics of noise are difficult to obtain,and this leads to an unsatisfactory fusion result.To settle the referred cases,a distributed and adap-tive weighted fusion algorithm based on KF has been proposed in this paper.In this method,on the basis of the pseudo prior prob-ability of the estimated state of each source,the reliability of the sources is evaluated and the optimal set is selected on a certain threshold.Experiments were performed on multi-source pedes-trian dead reckoning for verifying the proposed algorithm.The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation,and the fusion error is reduced by 16.6％as compared to the correspond-ing value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evalu-ation is effective against the varied-noise multi-source fusion sys-tem,and the fusion error caused by inaccurate statistical charac-teristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness,adaptability,and value on applications.

Yuanxun Zheng;Qinghua Li;Changhong Wang;Xiaoguang Wang;Lifeng Hu

NORINCO GROUP Air Ammunition Research Institute,Harbin,and also with the Research Center of Space Control and Inertial Technology,Harbin Institute of Technology,Harbin 150001,China;Research Center of Space Control and Inertial Technology,Harbin Institute of Technology,Harbin 150001,China;NORINCO GROUP Air Ammunition Research Institute,Harbin 150030,China

自动化学报（英文版）

[1]Yuanxun Zheng;Qinghua Li;Changhong Wang;Xiaoguang Wang;Lifeng Hu-.Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning)[J].自动化学报（英文版）,2022(12):2174-2185

nondeterministic,pedes

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