Indoor Localization Using Augmented UHF RFID System for the Internet-of-Things

Abstract

Indoor localization with proximity information in ultra-high-frequency (UHF) radio-frequency-identification (RFID) is widely considered as a potential candidate of locating items in Internet-of-Things (IoT) paradigm. First, the proximity-based methods are less affected by multi-path distortion and dynamic changes of the indoor environment compared to the traditional range-based localization methods. The objective of this dissertation is to use tag-to-tag backscattering communication link in augmented UHF RFID system (AURIS) for proximity-based indoor localization solution. Tag-to-tag backscattering communication in AURIS has an obvious advantage over the conventional reader-to-tag link for proximity-based indoor localization by keeping both landmark and mobile tags simple and inexpensive. This work is the very first thesis evaluating proximity-based localization solution using tag-to-tag backscattering communication.Our research makes the contributions in terms of phase cancellation effect, the improved mathematical models and localization algorithm. First, we investigate the phase cancellation effect in the tag-to-tag backscattering communication, which has a significant effect on proximity-based localization. We then present a solution to counter such destructive effect by exploiting the spatial diversity of dual antennas. Second, a novel and realistic detection probability model of ST-to-tag detection is proposed. In AURIS, a large set of passive tags are placed at known locations as landmarks, and STs are attached mobile targets of interest. We identify two technical roadblocks of AURIS and existing localization algorithms as false synchronous detection assumption and state evolution model constraints. With the new and more realistic detection probability model we explore the use of particle filtering methodology for localizing ST, which overcomes the aforementioned roadblocks. Last, we propose a landmark-based sequential localization and mapping framework (SQLAM) for AURIS to locate STs and passive tags with unknown locations, which leverages a set of passive landmark tags to localize ST, and sequentially constructs a geographical map of passive tags with unknown locations while ST is moving in the environment. Mapping passive tags with unknown locations accurately leads to practical advantages. First, the localization capability of AURIS is not confined to the objects carrying STs. Second, the problem of failed landmark tags is addressed by including passive tags with resolved locations into landmark set. Each of the contributions is supported by extensive computer simulation to demonstrate the performance of enhancements.