{"title":"Improving accuracy of indoor localization system using ensemble learning","authors":"H. Tran, T. Nguyen, T. V. Huynh, Nhiem Quoc Tran","doi":"10.1080/21642583.2022.2092782","DOIUrl":null,"url":null,"abstract":"Recent innovations in Light-emitting diode (LED) technology and Internet of Things applications have promoted the development of visible light communication and localization applications. LED-based indoor positioning application has been a potential topic attracting the attention of many researchers because this positioning technique provides high accuracy, low cost, simple operation, and medium complexity. This paper focuses on analyzing the positioning quality with different LED layout structures. Furthermore, we consider the influence of noise in these models through the ensemble learning algorithm. We also combine the ensemble learning method with the trilateration algorithm in the proposed solution. The numerical simulation results show that the proposed solution respectively achieved a positioning accuracy of 0.023, 0.011, and 0.009 m when we considered the negative effect of all noises in 3 distinct layouts: 3 LEDs, 4LEDs, and 5 LEDs.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2022.2092782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 2
Abstract
Recent innovations in Light-emitting diode (LED) technology and Internet of Things applications have promoted the development of visible light communication and localization applications. LED-based indoor positioning application has been a potential topic attracting the attention of many researchers because this positioning technique provides high accuracy, low cost, simple operation, and medium complexity. This paper focuses on analyzing the positioning quality with different LED layout structures. Furthermore, we consider the influence of noise in these models through the ensemble learning algorithm. We also combine the ensemble learning method with the trilateration algorithm in the proposed solution. The numerical simulation results show that the proposed solution respectively achieved a positioning accuracy of 0.023, 0.011, and 0.009 m when we considered the negative effect of all noises in 3 distinct layouts: 3 LEDs, 4LEDs, and 5 LEDs.
期刊介绍:
Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory