{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:05:50Z","timestamp":1760241950367,"version":"build-2065373602"},"reference-count":18,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,20]],"date-time":"2018-10-20T00:00:00Z","timestamp":1539993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603072, 61673084"],"award-info":[{"award-number":["61603072, 61673084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper is concerned with the distributed field estimation problem using a sensor network, and the main purpose is to design a local filter for each sensor node to estimate a spatially-distributed physical process using the measurements of the whole network. The finite element method is employed to discretize the infinite dimensional process, which is described by a partial differential equation, and an approximate finite dimensional linear system is established. Due to the sparsity on the spatial distribution of the source function, the     \u2113 1    -regularized     H \u221e     filtering is introduced to solve the estimation problem, which attempts to provide better performance than the classical centralized Kalman filtering. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed method.<\/jats:p>","DOI":"10.3390\/s18103557","type":"journal-article","created":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T08:43:36Z","timestamp":1540284216000},"page":"3557","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Distributed Field Estimation Using Sensor Networks Based on H\u221e Consensus Filtering"],"prefix":"10.3390","volume":"18","author":[{"given":"Haiyang","family":"Yu","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission, Dalian Minzu University, Dalian 116600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rubo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission, Dalian Minzu University, Dalian 116600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junwei","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission, Dalian Minzu University, Dalian 116600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuwen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Science, Dalian Minzu University, Dalian 116600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1049\/iet-cta.2012.0732","article-title":"Distributed Kalman filtering: A Bibliographic Review","volume":"7","author":"Mahmoud","year":"2013","journal-title":"IET Control Theory Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"858624","DOI":"10.1155\/2014\/858624","article-title":"A Survey on Distributed Filtering and Fault Detection for Sensor Networks","volume":"2014","author":"Dong","year":"2014","journal-title":"Math. 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