Consensus-based distributed receding horizon estimation?

被引：4

作者：

Huang, Zenghong ^{[1
]}

Lv, Weijun ^{[1
]}

Chen, Hui ^{[1
]}

Rao, Hongxia ^{[1
]}

Xu, Yong ^{[1
]}

机构：

[1] Guangdong Univ Technol, Sch Automat, Guangdong Prov Key Lab Intelligent Decis & Coopera, Guangzhou 510006, Peoples R China

来源：

ISA TRANSACTIONS | 2022年 / 128卷

关键词：

Distributed estimation; Receding horizon estimation; Consensus; Sensor networks; STATE ESTIMATION; SYSTEMS; CONVERGENCE; DISTURBANCE;

D O I：

10.1016/j.isatra.2021.10.015

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper studies the distributed state estimation over sensor networks based on receding horizon estimation (RHE). Firstly, a new scheme of centralized RHE is introduced, which gathers the decom-position terms instead of collecting the measurements of each node. Then, we present a distributed estimate algorithm based on the centralized RHE. To avoid the quadratic programming (QP) problem, the proposed algorithm takes advantage of the analytical solution of the centralized RHE and performs consensus steps to generalize the distributed estimation for each node, which greatly reduces each node's computation. Under the assumption of collective observability over networks, the proposed algorithm can guarantee the stability of estimation error in the case of enough consensus steps. Finally, the simulation results verify the effectiveness of the proposed method.(c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：106 / 114

页数：9

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