Convergence analysis of distributed Kalman filtering for relative sensing networks

作     者：Che LIN Rong-hao ZHENG Gang-feng YAN Shi-yuan LU Che LIN;Rong-hao ZHENG;Gang-feng YAN;Shi-yuan LU

作者机构：College of Electrical EngineeringZhejiang Universit Zhejiang Province Marine Renewable Energy Electrical Equipment and System Technology Research Laborator 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2018年第19卷第9期

页      面：1063-1075页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 080902[工学-电路与系统] 0839[工学-网络空间安全] 080202[工学-机械电子工程] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(No.61503335) the Key Laboratory of System Control and Information Processing,China(No.Scip201504) 

主　　题：Relative sensing network Distributed Kalman filter Schur stable Linear matrix inequality 

摘      要：We study the distributed Kalman filtering problem in relative sensing networks with rigorous *** relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous dynamical models. The sufficient and necessary condition for the observability of the whole system is given with detailed proof. By local information and measurement communication, we design a novel distributed suboptimal estimator based on the Kalman filtering technique for comparison with a centralized optimal estimator. We present sufficient conditions for its convergence with respect to the topology of the network and the numerical solutions of n linear matrix inequality（LMI） equations combining system parameters. Finally, we perform several numerical simulations to verify the effectiveness of the given algorithms.