Timing: Tag Interference Modeling for RFID Localization in Dense Deployment

Due to low cost and low complexity, passive ultrahigh-frequency radio-frequency identification (UHF RFID) is gaining popularity in indoor localization. According to current RFID channel models, multipath propagation and noise are two significant factors that affect the accuracy of measured RF signals. To minimize the impacts of measurement errors on localization, scene analysis algorithms are proposed and got lots of attention in recent years. These algorithms make use of reference tags as similarity markers in neighboring locations to improve localization accuracy. However, densely deployed tags induce a new challenge, antenna interference, leading to the poor signal similarity between nearby tags. Nonetheless, there are no refined channel models for explaining the effect of spacing distances between tags on RF signal fluctuations, thus the choice of spacing distances becomes an outstanding issue for scene analysis localization algorithms. In this article, we propose Timing, the Tag Interference ModelING, for analyzing the relationship between signal fluctuations and spacing distances. Unlike inductive coupling in the near field, as the tags are all located at the far field of interacted antenna, we introduce microwave transmission line theory for channel model analysis. The experimental results verify that Timing is well suited to authentic situations. Additionally, we examine the performance of the ${k}$ -nearest neighbor ( ${k}$ -NN) algorithm under various spacing distances. The findings demonstrate that the spacing distance identified by Timing as a stable point achieves better accuracy with scene analysis algorithms. Therefore, Timing could be used as a critical foundation for the choice of spacing distances in future tag deployment applications.

Automated summary

Passive ultrahigh-frequency radio-frequency identification (UHF RFID) is gaining popularity in indoor localization due to its low cost and low complexity. However, densely deployed tags induce antenna interference, leading to poor signal similarity between nearby tags. This article proposes a tag interference model, called Timing, to analyze the relationship between signal fluctuations and spacing distances. Experimental results demonstrate its suitability in authentic situations and show that the spacing distance identified by Timing achieves better accuracy in scene analysis localization algorithms.