GLAC: High-Precision Tracking of Mobile Objects With COTS RFID Systems

This paper presents GLAC, the first 3D localization system that enables millimeter-level object manipulation for robotics using only COTS RFID devices. The key insight of GLAC is that mobility reduces ambiguity (One-to-many mapping relationship between phase and distance) and thus improves accuracy. Unlike state-of-the-art systems that require extra time or hardware to boost performance, it draws the power of modeling mobility in a delicate way. In particular, we build a novel framework for real-time tracking using the Hidden Markov Model (HMM). In our framework, multiple Kalman filters are designed to take a single phase observation for updating mobility states, and a fast inference algorithm is proposed to efficiently process an exponentially large number of candidate trajectories. We prototype GLAC with only UHF tags and a commercial reader of four antennas. Comprehensive experiments show that the median position accuracies of x/y/z dimensions are within 1 cm for both LoS and NLoS cases. The median position accuracy for slow-moving targets is 0.41 cm, which is <inline-formula> <tex-math notation="LaTeX">$2.2\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$17.3\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$14.9\times $ </tex-math></inline-formula> better than TurboTrack, Tagoram, and RF-IDraw, respectively. Also, its median velocity accuracy is at least <inline-formula> <tex-math notation="LaTeX">$20\times $ </tex-math></inline-formula> better than all three competitors for fast-moving targets. Besides accuracy, it achieves more than <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> localization time gains over state-of-the-art systems.