Abstract
Indoor localization systems based on radio frequency identification (RFID) technologies are widely adopted in the Internet of Things (IoT). Many of these applications prefer to obtain the relative position of the target object rather than the absolute position. Existing relative positioning methods are subject to strict trajectory constraints and cannot work under nonstraight trajectories. RFID mobile localization which has high accuracy can also be used for relative positioning, but they require the acquisition of precise moving trajectory coordinates. To overcome these challenges, we propose a trajectory-robust relative localization algorithm (TRL), based on phase profiles’ correlation in this article. The intuition of TRL is that the phase of the backscattered signal has a linear mapping relationship with the distance between the reader and the antenna, so the closer the tag is to each other, the higher the correlation of their phase curves is. We use the dynamic time warping (DTW) algorithm to calculate the correlation between the phase profiles of different tags and achieve relative localization of all the tags by sequentially determining their position intervals in the tag sequence. Compared with the state-of-the-art solutions, TRL gives greater flexibility to antenna movement and the demand for trajectory coordinates, and is it fully robust to trajectory error. A series of experimental results show that TRL has significant advantages over the current state-of-the-art schemes in terms of both accuracy and computation time.
Published Version
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