Indoor localization using augmented ultra-high-frequency radio frequency identification system for Internet of Things

Abstract

This article addresses the problem of indoor localization with an augmented ultra-high-frequency radio frequency identification system. In the infrastructure of augmented ultra-high-frequency radio frequency identification system, a tag-like component called sense-a-tag applies envelope detection to capture the backscatter signals of other ultra-high-frequency tags in its proximity during their tag-to-reader communication. Such unique capability of sense-a-tag enables tag-to-tag backscatter communication. Tag-to-tag backscattering communication in augmented ultra-high-frequency radio frequency identification system 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. Through the investigation of tag-to-tag backscattering communication link, a new and more realistic probability model of detecting a tag by sense-a-tag is proposed. In augmented ultra-high-frequency radio frequency identification system, a large set of passive tags are placed at known locations as landmarks, and sense-a-tags are attached mobile targets of interest. We identify two technical roadblocks of augmented ultra-high-frequency radio frequency identification system 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 sense-a-tag, which overcomes the aforementioned roadblocks. The performance of localization algorithm is demonstrated by extensive computer simulations.

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

“…The development of new radio access standards prompted the exploration of new techniques to improve the location accuracy, which is based on the signals available from the wireless devices that comprise these standards [2][3][4][5][6]. In this communication, the problem of the indoor location, which is based on the signals available in the wireless devices that comprise Wi-Fi and Wi-Max networks within the broadband wireless systems, is presented [7][8][9][10]. The location process employs a fingerprinting technique that operates with the relationship between the levels of power and/or relative delays between signals due to the multipath by reflections effect [11].…”

Monte Carlo-Based Indoor RFID Positioning with Dual-Antenna Joint Rectification

“…The Monte Carlo algorithm provides a unique positioning scheme for RFID indoor application. The Monte Carlo algorithm has found applications in wireless positioning [31,32], and improved Monte Carlo algorithm in RFID-based localization [33,34]. The main idea of the Monte-Carlo based algorithm is to use substantial sample points from the state space to approximate the location distribution estimation.…”