Trio: Utilizing Tag Interference For Refined Localization Of Passive RFID

Han Ding Xi'an Jiaotong University, P.R. China
Jinsong Han Xi'an Jiaotong University, P.R. China
Chen Qian University of California at Santa Cruz, USA
Fu Xiao Nanjing University of Posts and Telecommunications, P.R. China
Ge Wang Xi‘an Jiaotong University, P.R. China
Nan Yang Xi'an Jiaotong University, P.R. China
Wei Xi Xi'an Jiaotong University, P.R. China
Jian Xiao Chang'an University, P.R. China


We study a new problem, refined localization, in this paper. Refined localization calculates the location of an object in high precision, given that the object is in a relatively small region such as the surface of a table. Refined localization is useful in many cyber-physical systems such as industrial autonomous robots. Existing vision-based approaches suffer from several disadvantages, including good lighting conditions, line of sight, pre-learning process, and high computation overhead. Also vision-based approaches cannot differentiate objects with similar colors and shapes. This paper presents a new refined localiza-tion system, called Trio, which uses passive Radio Frequency Identification (RFID) tags for low cost and easy deployment. Trio provides a new angle to utilize RF interference for tag localization by modeling the equivalent circuits of coupled tags. We implement our prototype using commercial off-the-shelf RFID reader and tags. Extensive experiment results demonstrate that Trio effectively achieves high accuracy of refined localization, i.e., < 1 cm errors for several types of main stream tags.

You may want to know: