A fingerprint-based localization algorithm based on LSTM and data expansion method for sparse samples

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dc.contributor.authorJia, Bing
dc.contributor.authorQiao, Wenling
dc.contributor.authorZong, Zhaopeng
dc.contributor.authorLiu, Shuai
dc.contributor.authorHijji, Mohammad
dc.contributor.authorDel Ser, Javier
dc.contributor.authorMuhammad, Khan
dc.contributor.institutionIA
dc.date.issued2022-12
dc.descriptionPublisher Copyright: © 2022 Elsevier B.V.
dc.description.abstractThe accuracy of WiFi fingerprint-based localization is related to the number of reference points, generally, to obtain better positioning accuracy, enough samples must be collected, which will inevitably lead to a huge sampling workload. Thus, it will be of great significance to design an algorithm using sparse samples to achieve positioning accuracy like that of dense samples. This paper proposes a WiFi fingerprint-based localization algorithm using Long Short-Term Memory Network (LSTM) with explainable feature and a sparse sample expansion algorithm (PGSE) based on Principal component analysis and Gaussian process regression for sparse samples. Specifically, in the case of limited number of collected reference points, principal component analysis is used to select the access point, and Gaussian process regression is used to model the reference point coordinates and the corresponding received signal strength values in the training sample set, to expand the signal data and construct a new fingerprint database. The effectiveness of the PGSE algorithm is verified by using the public dataset ’UJIIndoorLoc’. At the same time, the applicability of PGSE expansion algorithm to data with temporal information is verified in the fingerprint-based localization method. In addition, this paper also proposes a WiFi-RSSI indoor localization method based on Long Short-Term Memory Network. Lots of experiments are conducted in the actual scenes and the results are compared with several existing methods. The results indicate that the proposed method improves the precision of indoor localization on an average level compared to state-of-the-art methods.en
dc.description.sponsorshipThis work was supported by the Government of China . Thanks to the National Natural Science Foundation of China (No. 42161070 , 41761086 and 41871363 ), the Natural Science Foundation of the Inner Mongolia Autonomous Region (No. 2019MS06030 ), the Natural Science Foundation of the Inner Mongolia Autonomous Region (No. 2017JQ09 ) and the Grassland Elite Project of the Inner Mongolia Autonomous Region (No. CYYC5016 ). Javier Del Ser also acknowledges funding support from the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1456-22 ).
dc.description.statusPeer reviewed
dc.format.extent14
dc.identifier.citationJia , B , Qiao , W , Zong , Z , Liu , S , Hijji , M , Del Ser , J & Muhammad , K 2022 , ' A fingerprint-based localization algorithm based on LSTM and data expansion method for sparse samples ' , Future Generation Computer Systems , vol. 137 , pp. 380-393 . https://doi.org/10.1016/j.future.2022.07.021
dc.identifier.doi10.1016/j.future.2022.07.021
dc.identifier.issn0167-739X
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85136637407&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofFuture Generation Computer Systems
dc.relation.projectIDDepartment of Education of the Basque Government, IT1456-22
dc.relation.projectIDGovernment of China
dc.relation.projectIDGrassland Elite Project of the Inner Mongolia Autonomous Region, CYYC5016
dc.relation.projectIDNational Natural Science Foundation of China, NSFC, 42161070-41761086-41871363
dc.relation.projectIDNatural Science Foundation of Inner Mongolia, 2019MS06030-2017JQ09
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsGaussian process regression
dc.subject.keywordsIndoor location
dc.subject.keywordsIoT
dc.subject.keywordsLong Short-Term Memory
dc.subject.keywordsSparse samples
dc.subject.keywordsWiFi fingerprint-based localization
dc.subject.keywordsXAI
dc.subject.keywordsSoftware
dc.subject.keywordsHardware and Architecture
dc.subject.keywordsComputer Networks and Communications
dc.titleA fingerprint-based localization algorithm based on LSTM and data expansion method for sparse samplesen
dc.typejournal article
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