文献类型：期刊文献

英文题名：Consensus-based cubature information filtering for sensor networks with incomplete measurements

作者：Liu, Ji[1];Shao, Qing[1];Hua, Chenchao[2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]AVIC Aeronaut Radio Elect Res Inst, Shanghai 200233, Peoples R China

年份：2019

卷号：364

起止页码：49

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20193007239459);WOS:【SCI-EXPANDED(收录号:WOS:000484070700004)】；

基金：This research was supported by the Foundation of Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai, 200240 and Chinese National Natural Science Foundation (Nos. 61573144 and 61673175) and Fundamental Research Funds for the Central Universities, 222201917006

语种：英文

外文关键词：Sensor network; Incomplete measurement; Consensus weight design; Distributed filtering algorithm

摘要：Consensus-based distributed filtering is an effective technique for state estimation or data fusion over sensor networks. However, nonlinearity of systems as well as network-induced incomplete information problems are the main obstacles on the way. In this paper, we deal with the distributed filtering of nonlinear systems over sensor networks with incomplete measurements. A perception of credibility evaluation on nodes' estimations is presented, followed by a novel credibility weight design method. The weights are redistributed by nodes' estimations in a normal fashion at each time step. The convergence of data fusion with the weights is proved. Further the credibility weight method is integrated into the general consensus-based cubature information filtering algorithm to handle various incomplete measurement problems. Numerical experiments, in the case of unknown noise statistics, measurement interference and measurement missing, demonstrate the effectiveness and robustness of the proposed algorithm. (C) 2019 Elsevier B.V. All rights reserved.