Consensus-Based Distributed State Estimation Over Sensor Networks With Encoding-Decoding Scheme: Accommodating Bandwidth Constraints

In this article, the consensus-based distributed estimation problem is investigated for linear time-invariant systems over sensor networks, where the sensors are required to estimate the system states in a cooperative manner through communication. A novel encoding-decoding scheme (EDS), which consis...

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Bibliographic Details
Published in:IEEE transactions on network science and engineering Vol. 9; no. 6; pp. 4051 - 4064
Main Authors: Gao, Chen, Wang, Zidong, Hu, Jun, Liu, Yang, He, Xiao
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bandwidths
Coders
Consensus estimation
Constraints
Convergence
data size
Decoding
distributed state estimation
Encoders-Decoders
Encoding
Encoding-Decoding
encoding-decoding scheme
Estimation
Monitoring
Optimization
Quantization (signal)
sensor network
Sensors
State estimation
Technological innovation
Time invariant systems
Topology
ISSN:2327-4697, 2334-329X
Online Access:Get full text
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Summary:In this article, the consensus-based distributed estimation problem is investigated for linear time-invariant systems over sensor networks, where the sensors are required to estimate the system states in a cooperative manner through communication. A novel encoding-decoding scheme (EDS), which consists of two pairs of an innovation encoder/decoder and an estimation encoder/decoder, is proposed on each sensor to compress the data in order to accommodate the bandwidth-constrained network. An EDS-based consensus estimator is designed whose estimation performance is thoroughly discussed. Specially, a necessary and sufficient condition is established to ensure the convergence of the error dynamics of the state estimates, and then the boundedness issue of the size of the transmitted data is examined. Three optimization algorithms are provided for, respectively, the fastest convergence of the error dynamics, the minimization of the estimator gains, as well as the tradeoff between the convergence rate and the estimation deviation. The effectiveness of the developed distributed estimators is finally illustrated by a series of numerical examples.
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ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2022.3195283