Distributed Receding Horizon Estimation for Time Invariant Discrete Time Linear Systems Based on Substate Decomposition
Date
2025
Authors
Huang, Z.
Chen, Z.
Xu, Y.
Liu, C.
Shi, P.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
IEEE Transactions on Network Science and Engineering, 2025; 1-13
Statement of Responsibility
Zenghong Huang, Zijie Chen, Yong Xu, Chang Liu, and Peng Shi
Conference Name
Abstract
This paper investigates distributed receding horizon estimation (DRHE) for time-invariant discrete-time linear systems over a sensor network. The original system is decomposed into several low-dimensional subsystems, where each sensor is capable of observing only one specific subsystem. The observable substate for each node is estimated by minimizing a local cost associated with receding horizon estimation (RHE), while the prediction of unobservable substates is updated through onestep weighted fusion. A maximal directed acyclic graph (MDAG) is introduced to facilitate the construction of the weight values for fusing these predictions, which is a more general method compared to directed spanning trees. Additionally, we propose a novel algorithm for identifying an MDAG across the network. We establish sufficient stability conditions for the proposed estimator under the assumption of collective observability. Finally, a numerical example of temperature monitoring is presented to demonstrate the effectiveness of the developed method.
School/Discipline
Dissertation Note
Provenance
Description
OnlinePubl.
Available online 5 August 2025
Access Status
Rights
© 2025 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.