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UHF RFID Multiparameters Analog Sensor System Based on Time-Series Classification Algorithm
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DOI：10.1109/JIOT.2026.3671806.png)
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Abstract
En 中文
Ultrahigh frequency radio frequency identification (UHF RFID) is widely used for item-level traceability. Existing approaches often exploit analog features of backscattered signals to sense a single environmental factor, typically assuming static measurement setups. In this article, we categorize antenna-sensitive factors into two types: time-independent and time-dependent, and propose a classification algorithm to decouple them from time-series analog features. First, we apply dynamic time warping (DTW) barycenter averaging (DBA) to cluster features under different power levels for each time-independent factor, constructing offline models. Second, we combine DTW similarity between online measurements and offline References with model-based state estimation to identify the time-independent factor. Then, the time-dependent factor is estimated using a sliding-window cumulative probability method. We validate our approach through an infusion monitoring system using a flexible passive UHF RFID tag attached externally to the infusion tube. Here, liquid level represents the time-dependent factor, while liquid material is the time-independent factor. Experimental results demonstrate that our method accurately identifies both liquid level and material under varying measurement setups.
Keywords:
Infusion monitoring
multiparameters
time-series classification
ultrahigh frequency radio frequency identification (UHF RFID)
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8.9
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1.4W
Citations:
7.8W