{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/118675"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/118675","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Rapid RFID location and orientation recovery","abstract":"This thesis aims to enable virtual and augmented reality (VR/AR) systems to track objects accurately through occlusions via RFID localization. Currently, three major obstacles prevent the use of RFID localization in VR/AR systems: (1) there exists a trade-off between measurement speed and ability to deal with multipath, so systems which can produce accurate results either require a highly constrained environment or several seconds to localize; (2) past RFID localization techniques lack robustness to changes in tag orientation; and (3) current RFID orientation extraction methodologies are largely inaccurate. To overcome these challenges, this thesis presents RF-Reality, a new system that leverages a novel OFDM backscatter technique and differential channel estimation algorithm to perform accurate, rapid RFID position and orientation recovery.","abstract_html":"This thesis aims to enable virtual and augmented reality (VR/AR) systems to track objects accurately through occlusions via RFID localization. Currently, three major obstacles prevent the use of RFID localization in VR/AR systems: (1) there exists a trade-off between measurement speed and ability to deal with multipath, so systems which can produce accurate results either require a highly constrained environment or several seconds to localize; (2) past RFID localization techniques lack robustness to changes in tag orientation; and (3) current RFID orientation extraction methodologies are largely inaccurate. To overcome these challenges, this thesis presents RF-Reality, a new system that leverages a novel OFDM backscatter technique and differential channel estimation algorithm to perform accurate, rapid RFID position and orientation recovery.","abstract_has_math":false,"creators":["Selby, Nicholas Stearns"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Mechanical Engineering.","school":null,"contributors":[],"advisors":["Fadel Adib."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:21:56Z","subjects":["Mechanical Engineering."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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