Pinpoint Achilles’ Heel in RFID Localization: Phase Calibration of RFID Antenna based on Linear Localization Model

Abstract

In the context of Industrial Internet of Things (IIoT), RFID technologies have been widely applied to locate or track tagged objects for achieving item-level intelligence. However, prior localization work encounters two main issues. First, the phase measurement usually contains physical deviation. Existing localization work generally takes the physical center of an RFID antenna as its phase center, which is a key factor in improving localization accuracy but actually different from the physical center in practice. Second, the non-linear localization model is likely to be too complex to run on edge nodes with limited computing resources. In this paper, we present a LInear localizatiON solution, called LION, to perform the phase calibration for antennas with no need for the complex computation nor strong limitations. Specifically, we provide a novel lightweight model to pinpoint the actual antenna position quickly and accurately. Compared to previous localization methods, we reduce the intersection of circles or hyperbolas into radical lines, which greatly reduces the computation cost while guaranteeing the high accuracy. Further, to adapt to the complex environment with various ambient noise and multi-path effect, we leverage the weighted least square method to determine the optimal position. Moreover, we propose an adaptive parameter selection scheme to automatically choose optimal parameters for localization. In this way, LION is able to perform the accurate localization robustly. We implement LION using commercial RFID devices, and evaluate its performance extensively. Experimental results show the necessity of phase calibration as well as the high time efficiency of LION, e.g., the average accuracy improves by 6× and 2.1× for 2D and 3D localization, and the average time consuming is 0.02s and 1.8s for 2D and 3D cases.