volume | PIER Journals

Abstract

Chipless Radio Frequency Identification (CRFID) systems have emerged as a cost-effective and scalable solution for various identification and tracking applications. However, multi-tag classification remains a significant challenge due to overlapping signal characteristics and the absence of on-chip processing, which hinders accurate tag differentiation, increases interference, reduces classification accuracy, and necessitates advanced signal processing techniques for reliable identification. This study presents a novel machine learning-based approach utilizing a Classifier Chain-AdaBoost (CC-AdaBoost) model to improve multi-tag classification accuracy. Unlike conventional methods that rely on calibration or background subtraction, the proposed approach directly processes raw time-domain signals, enabling efficient and accurate classification of multiple tags simultaneously. The model is evaluated on simulated CRFID data, achieving an overall accuracy of 85%. Performance metrics such as accuracy, Hamming loss, Jaccard score, and F1-score are analysed to assess both overall classification performance and label-wise evaluation. Results indicate that CC-AdaBoost effectively differentiates tag classes, particularly excelling in high-confidence classifications while maintaining a balance between precision and recall. This study demonstrates the feasibility of CC-AdaBoost for real-world CRFID applications and suggests potential improvements for optimizing multi-tag recognition in complex environments.

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Mr. Athul Thomas

Dr. Midhun Muraleedharan Sylaja

Dr. James Kurian