Abstract
In this paper, an indoor real-time location tracking system is proposed using ultra-high frequency passive radio frequency identification technology. A dynamic k nearest neighbor driven hidden Markov model is developed to track subsequent positions of moving objects by combining static localization with dynamic tracking. Statistically validated regression models are developed to relate the tags’ received signal strength indicator and distance, which are iteratively updated over time to accommodate environmental changes. These models are utilized to generate a fingerprint database, which is used by the dynamic k nearest neighbor to estimate the set of potential locations for targeted tagged objects. The probabilistic likelihoods of tags’ potential locations based on the current location and the received signal strength indicator(s) are used to construct the hidden Markov model’s transition and emission matrixes, respectively. Finally, the performance of the proposed framework is evaluated by a set of experiments given different factors, such as movement pattern and speed. Results reveal that our proposed framework can track objects with an average tracking error as low as 0.36m.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
