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Distributed Estimation of Fields Using a Sensor Network with Quantized Measurements

Title: Distributed Estimation of Fields Using a Sensor Network with Quantized Measurements
Authors: Chethaka Jayasekaramudeli; Alex S. Leong; Alexei T. Skvortsov; David J. Nielsen; Omar Ilaya
Source: Sensors, Vol 24, Iss 16, p 5299 (2024)
Publisher Information: MDPI AG
Publication Year: 2024
Collection: Directory of Open Access Journals: DOAJ Articles
Subject Terms: distributed estimation; field estimation; quantized measurements; sensor networks; time-varying systems; Chemical technology; TP1-1185
Description: In this paper, the problem of estimating a scalar field (e.g., the spatial distribution of contaminants in an area) using a sensor network is considered. The sensors are assumed to have quantized measurements. We consider distributed estimation algorithms where each sensor forms its own estimate of the field, with sensors able to share information locally with its neighbours. Two schemes are proposed, called, respectively, measurement diffusion and estimate diffusion. In the measurement diffusion scheme, each sensor broadcasts to its neighbours the latest received measurements of every sensor in the network, while in the estimate diffusion scheme, each sensor will broadcast local estimates and Hessians to its neighbours. Information received from its neighbours will then be iteratively combined at each sensor to form the field estimates. Time-varying scalar fields can also be estimated using both the measurement diffusion and estimate diffusion schemes. Numerical studies illustrate the performance of the proposed algorithms, in particular demonstrating steady state performance close to that of centralized estimation.
Document Type: article in journal/newspaper
Language: English
Relation: https://www.mdpi.com/1424-8220/24/16/5299; https://doaj.org/toc/1424-8220; https://doaj.org/article/be477059058b46b48f7d89b7d2d74e97
DOI: 10.3390/s24165299
Availability: https://doi.org/10.3390/s24165299; https://doaj.org/article/be477059058b46b48f7d89b7d2d74e97
Accession Number: edsbas.B76D666B
Database: BASE