{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:14:22Z","timestamp":1771679662632,"version":"3.50.1"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T00:00:00Z","timestamp":1612915200000},"content-version":"vor","delay-in-days":40,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["cstc2019jcyj-msxmX0491"],"award-info":[{"award-number":["cstc2019jcyj-msxmX0491"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["cstc2018jcyjAX0464"],"award-info":[{"award-number":["cstc2018jcyjAX0464"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>This paper combines the distributed sensor fusion system with the signal detection under chaotic noise to realize the distributed sensor fusion detection from chaotic background. First, based on the short\u2010term predictability of the chaotic signal and its sensitivity to small interference, the phase space reconstruction of the observation signal of each sensor is carried out. Second, the distributed sensor local linear autoregressive (DS\u2010LLAR) model is constructed to obtain the one\u2010step prediction error of each sensor. Then, we construct a Bayesian risk model and also obtain the corresponding conditional probability density function under each sensor\u2019s hypothesis test which firstly needs to fit the distribution of prediction errors according to the parameter estimation. Finally, the fusion optimization algorithm is designed based on the Bayesian fusion criterion, and the optimal decision rule of each sensor and the optimal fusion rule of the fusion center are jointly solved to effectively detect the weak pulse signal in the observation signal. Simulation experiments show that the proposed method which used a distributed sensor combined with a local linear model can effectively detect weak pulse signals from chaotic background.<\/jats:p>","DOI":"10.1155\/2021\/6661142","type":"journal-article","created":{"date-parts":[[2021,2,11]],"date-time":"2021-02-11T14:07:23Z","timestamp":1613052443000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Distributed Sensor Local Linear Fusion Detection of Weak Pulse Signal in Chaotic Background"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7150-2161","authenticated-orcid":false,"given":"Liyun","family":"Su","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9701-0915","authenticated-orcid":false,"given":"Meini","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0879-8606","authenticated-orcid":false,"given":"Shengli","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0297-8320","authenticated-orcid":false,"given":"Ting","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,2,10]]},"reference":[{"key":"e_1_2_9_1_2","first-page":"48","article-title":"Theory and application of multi-sensor information fusion","volume":"13","author":"Ma P.","year":"2006","journal-title":"Control Engineering of China"},{"key":"e_1_2_9_2_2","first-page":"1233","article-title":"Basic methods and progress of information fusion (II)","volume":"29","author":"Pan Q.","year":"2012","journal-title":"Control Theory & Applications"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18010292"},{"key":"e_1_2_9_4_2","volume-title":"Multi-Source Information Fusion","author":"Chongzhao H.","year":"2010"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.1981.309178"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.1986.310699"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.1987.313355"},{"key":"e_1_2_9_8_2","article-title":"Decentralized multi-sensor management algorithm based on covariance control for target tracking","volume":"1","author":"Han H. 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