[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119771-en":3,"doc-seo-119771-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119771,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Optimization of Visible Light Positioning in Industrial Applications using Machine Learning - Accepted Version","This paper investigates the error performance of visible light positioning (VLP) systems for 3D indoor drone localization using machine learning algorithms. Received signal strength is leveraged to track drone position under different smoky channel conditions that emulate industrial environments. Machine learning based artificial neural networks are trained on diverse datasets to model correlations between RSS measurements and position errors, capturing nonlinear relationships and extracting relevant features. Results show improved real-time localization accuracy with robustness against atmospheric attenuation and significant reductions in average error and strong RMSE and R-squared metrics.","Please cite the Published Version  \nAlkandari, Youniss, Ijaz, Muhammad, Ekpo, Sunday , Adebisi, Bamidele, Soto, Ismael, Zamorano-Illanes, Raul and Azurdia, Cesar (2023) Optimization of Visible Light Positioning in Industrial Applications using Machine Learning. In: Fourth South American Conference on Visible Light Communications (SACVLC 2023), 08 November 2023-10 November 2023, Santiago, Chile.  \nPublisher: IEEE  \nVersion: Accepted Version  \nDownloaded from: [https://e-space.mmu.ac.uk/632556/](https://e-space.mmu.ac.uk/632556/)  \nUsage rights:  In Copyright  \nAdditional Information:  2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  \nEnquiries:  \nIf you have questions about this document, contact [openresearch@mmu.ac.uk. Please](openresearch@mmu.ac.uk. Please) include the URL of the record in e-space. If you believe that your, or a third party's rights have been compromised through this document please see our Take Down policy (available from [https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines](https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines))  \nOptimization of Visible Light Positioning in Industrial Applications using Machine Learning  \nYouniss Alkandari, Muhammad Ijaz, Ismael Soto, Raul Zamorano-Illanes Cesar Azurdia  \nSunday Ekpo, Bamidele Adebisi Department of Electrical Engineering Department of Electrical Engineering  \nSchool of Engineering Santiago de Chile University Universidad de Chile  \nManchester Metropolitan University Santiago, Chile Santiago, Chile  \nManchester, United Kingdom {ismael.soto, raul.zamorano}, @[usach.cl](usach.cl) [cesarazurdia@uchile.cl](cesarazurdia@uchile.cl)[ ](cesarazurdia@uchile.cl)Youniss.Alkandari, m.ijaz, @[stu.mmu.ac.uk](stu.mmu.ac.uk)  \nAbstract—This paper investigates the error performance of visible Light Positioning (VLP) systems for 3D indoor drone localization using machine learning algorithms. Received Signal Strength is used to track the position of the drone and different smoky channel conditions to emulate an industrial environment. VLP systems utilize visible light communication (VLC) and indoor positioning, providing a low-cost and interference-free solution for precise drone localization. Machine learning (ML) based artificial neural network (ANN) is used to trained on diverse datasets and correlations between received signal strength (RSS) measurements and position errors. The results demonstrate that ML enables accurate real-time drone position estimation, compensating for atmospheric attenuation. The trained models achieve significantly improved localization accuracy and capturing non-linear relationships between input features and drone location. Furthermore, machine learning algorithms extract relevant features, reducing the impact of noise and atmospheric attenuations. ML process enhances the VLP system’s robustness, resulting in remarkable localization accuracy improvements compared to the attenuated path with average error values from 21.9 cm to 5.9 cm. The trained ML models achieve RMSE values of 0.044772 and 0.067523, respectively, with high R-squared values of 0.999. Furthermore the error histogram analysis confirms accurate drone location estimation, even in the presence of atmospheric attenuations.  \nIndex Terms—Indoor Localization, Industrial Environment, Visible Light Positioning, Atmospheric Attenuation, Machine Learning.  \nI. INTRODUCTION  \nUnmanned Aerial Vehicles (UAVs), also known as drones, have emerged as versatile devices for use in indoor industrial applications, providing automated autonomous operations in industrial applications and cost-effective solutions for managing supplies ","cbCail940X7uda6w","https://ap.wps.com/l/cbCail940X7uda6w","pdf",6767390,1,7,"English","en",105,"# Abstract\n# Index Terms\n# I. Introduction\n## UAVs and industrial use cases\n## VLP with VLC and indoor positioning\n## Challenges from atmospheric attenuation\n# II. (Introductory material continues)","[{\"question\":\"What localization signal is used to track the drone in this work?\",\"answer\":\"The paper uses received signal strength (RSS) to estimate the drone’s position in a visible light positioning (VLP) framework.\"},{\"question\":\"How does the study emulate industrial atmospheric conditions?\",\"answer\":\"It introduces different smoky channel conditions to represent industrial environments where smoke, fog, dust, and particles attenuate the RSS received by the drone.\"},{\"question\":\"What machine learning approach is used and what impact does it have?\",\"answer\":\"The study employs machine learning based artificial neural networks trained on datasets linking RSS measurements to position errors. The trained models improve localization accuracy, compensate for atmospheric attenuation, and reduce average error and achieve strong RMSE and R-squared values.\"}]","Optimization of Visible Light Positioning in Industrial Applications using Machine Learning - Accepted Version | PDF",1785726236,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimization-of-visible-light-positioning-in-industrial-applications-using-machine-learning-accepted-version","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimization-of-visible-light-positioning-in-industrial-applications-using-machine-learning-accepted-version/119771/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What localization signal is used to track the drone in this work?","Question",{"text":75,"@type":76},"The paper uses received signal strength (RSS) to estimate the drone’s position in a visible light positioning (VLP) framework.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study emulate industrial atmospheric conditions?",{"text":80,"@type":76},"It introduces different smoky channel conditions to represent industrial environments where smoke, fog, dust, and particles attenuate the RSS received by the drone.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning approach is used and what impact does it have?",{"text":84,"@type":76},"The study employs machine learning based artificial neural networks trained on datasets linking RSS measurements to position errors. 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