Leveraging read rates of passive RFID tags for real-time indoor location tracking.

RFID (radio frequency identification) technology has been widely used for object tracking in many real-life applications, such as inventory monitoring and product flow tracking. These applications usually rely on passive RFID technologies rather than active ones, since passive RFID tags are more attractive than active ones in many aspects, such as lower tag cost and simpler maintenance. RFID technology is also important for indoor location tracking systems that require high degree of accuracy. However, most existing systems estimate object locations by using active RFID tags, which usually incur localization error of more than one meter. Although recent studies begin to investigate the application of passive tags for indoor location tracking, these methods are far from deployable and research of this application is still in its infancy. In this paper, we propose a new indoor location tracking system, named PassTrack, which relies on the read rates of passive RFID tags for location estimation. PassTrack is designed to tolerate noise arising from external environmental factors, by probabilistically modeling the relationship between tag read rate and tag-reader distance, and updating the model parameters based on the current readings of reference tags. Besides tolerance of noise, PassTrack is also outstanding in terms of localization accuracy and efficiency. Several new approaches for location inference are supported by PassTrack, and the best one incurs an average error of around 30 cm, and is able to carry out over 7500 location estimations per second on an ordinary machine. Furthermore, as a result of using passive RFID tags, PassTrack also enjoys the many other benefits of passive RFID tags mentioned before. We have conducted extensive experiments on both real and synthetic datasets, which demonstrate that our PassTrack system outperforms the previous localization approaches in localization accuracy, tracking efficiency and space applicability.