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

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.