[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119620-en":3,"doc-seo-119620-105":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},119620,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Optimizing Indoor Localization Using RSSI and IQ Data with Machine Learning - Thesis","Indoor localization systems require accurate positioning in complex indoor radio environments, especially when GPS is unavailable. This thesis optimizes indoor localization by combining RSSI and IQ data with machine learning, focusing on how Bluetooth Low Energy can support Angle of Approach and more reliable raw-data interpretation. The work reviews localization technologies, details BLE AoA and IQ-based calculations, defines a phased methodology with implementation and setup, evaluates results through testing, and analyzes performance while identifying limitations, challenges, and proposed solutions to improve deployment.","Roger Williams University  \nDOCS@RWU  \n\n| Computer Science | Engineering, Computing, and Construction Theses |\n| --- | --- |\n\n2025  \nOptimizing Indoor Localization Using RSSI and IQ Data with Machine Learning  \nGokdeniz Tingur  \nFollow this and additional works at: [https://docs.rwu.edu/computerscience_theses](https://docs.rwu.edu/computerscience_theses)  \n Part of the Computer Engineering Commons  \nOptimizing Indoor Localization Using RSSI and IQ Data with Machine Learning  \nGokdeniz Tingur  \nBachelor of Science  \nComputer Science  \nSchool of Engineering, Computing and Construction  \nManagement  \nRoger Williams University  \nApril 2025  \nOptimizing Indoor Localization Using RSSI and IQ Data with Machine Learning  \nDate of Signature  \n| Gokdeniz Tingur\u003Cbr>Author | 5/14/2025 |\n| --- | --- |\n| Dr. Issa Ramaji\u003Cbr>Advisor | 5/14/2025 |\n\nDr. Robert Griffin  \nDean of the SECCM  \nTable of Contents  \nABSTRACT` ............................................................................................................................................... v  \nI. INTRODUCTION ................................................................................................................................... 1  \nII. OVERVIEW OF LOCALIZATION TECHNOLOGIES .................................................................. 2  \nA. Radio Frequency Identification Tags (RFID Tags) ...................................................................... 2  \nB. GPS .................................................................................................................................................. 3  \nC. ZigBee.............................................................................................................................................. 3  \nD. Bluetooth Low Energy (BLE): RSSI/Fingerprinting .................................................................... 4  \nIII. CURRENT STATE OF BLE ANGLE OF APPROACH ................................................................. 6  \nA. How Bluetooth Low Energy AoA Works........................................................................................ 6  \nB. Data Interpretation from Raw Data ............................................................................................... 7  \n1. IQ Data ....................................................................................................................................... 7  \n2. Phase difference and calculations ............................................................................................ 8  \n3. Application of IQ data: Angle of Approach ............................................................................ 8  \nIV. APPLICATION OF MACHINE LEARNING FOR INDOOR LOCALIZATION ..................... 12  \nV. METHODOLOGY............................................................................................................................... 16  \nA. Implementation and Setup ............................................................................................................ 16  \nB. Phase I........................................................................................................................................... 19  \nC. Phase II ......................................................................................................................................... 20  \nD. Phase III........................................................................................................................................ 20  \nC. Testing of Phase III ...................................................................................................................... 23  \nD. Phase IV ........................................................................................................................................ 23  \nE. Phase V.......................................................................................................................................... 23  \nF. Phase VI ........","cbCaib3tiL1HrCPM","https://ap.wps.com/l/cbCaib3tiL1HrCPM","pdf",2476365,1,46,"English","en",105,"# ABSTRACT\n# I. INTRODUCTION\n# II. OVERVIEW OF LOCALIZATION TECHNOLOGIES\n## A. Radio Frequency Identification Tags (RFID Tags)\n## D. Bluetooth Low Energy (BLE): RSSI/Fingerprinting\n# III. CURRENT STATE OF BLE ANGLE OF APPROACH\n# IV. APPLICATION OF MACHINE LEARNING FOR INDOOR LOCALIZATION\n# V. METHODOLOGY\n## A. Implementation and Setup\n# VI. RESULTS\n# VII. DISCUSSION\n# VIII. LIMITATIONS, CHALLENGES AND PROPOSED SOLUTIONS\n# References","[{\"question\":\"What indoor localization approach does the thesis propose using RSSI and IQ data?\",\"answer\":\"The thesis combines RSSI and IQ data with machine learning to improve indoor localization accuracy. It emphasizes interpreting raw BLE data and using it for Angle of Approach related estimation.\"},{\"question\":\"How does the thesis explain Bluetooth Low Energy angle of approach (AoA)?\",\"answer\":\"It describes how Bluetooth Low Energy AoA works and how raw data can be interpreted. The document covers IQ data and phase difference calculations that support Angle of Approach.\"},{\"question\":\"What does the methodology include and how are results evaluated?\",\"answer\":\"The methodology is organized into multiple phases, including implementation and setup, and phased testing (including Phase III testing). Results are then presented and discussed, followed by analysis of limitations and proposed solutions.\"}]","Optimizing Indoor Localization Using RSSI and IQ Data with Machine Learning - Thesis | PDF",1785725344,116,{"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},"optimizing-indoor-localization-using-rssi-and-iq-data-with-machine-learning-thesis","",{"@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/optimizing-indoor-localization-using-rssi-and-iq-data-with-machine-learning-thesis/119620/",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 indoor localization approach does the thesis propose using RSSI and IQ data?","Question",{"text":75,"@type":76},"The thesis combines RSSI and IQ data with machine learning to improve indoor localization accuracy. It emphasizes interpreting raw BLE data and using it for Angle of Approach related estimation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis explain Bluetooth Low Energy angle of approach (AoA)?",{"text":80,"@type":76},"It describes how Bluetooth Low Energy AoA works and how raw data can be interpreted. The document covers IQ data and phase difference calculations that support Angle of Approach.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the methodology include and how are results evaluated?",{"text":84,"@type":76},"The methodology is organized into multiple phases, including implementation and setup, and phased testing (including Phase III testing). 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