An Efficient UHF RFID Tag Estimation Technique Based on Belief Function Theory

Abstract

Radio frequency identification (RFID) technology has revolutionized various industries by enabling automatic identification and tracking of objects. However, in practical RFID systems, collisions occur when multiple tags are present within the coverage area of a single reader, resulting in communication interference and inaccurate tag count estimation. An accurate estimation of the number of tags in RFID systems is crucial for efficient tag identification and system performance. In this paper, we propose a new accurate ultrahigh frequency (UHF) RFID tag quantity estimation technique based on belief function theory, also known as Dempster–Shafer theory (DST). DST offers a robust framework for reasoning under uncertainty and has demonstrated its effectiveness in addressing intricate information–fusion challenges. The proposed technique, operating at the physical layer, uses belief functions and mass assignment principles to model the uncertainty associated with tag collisions, enabling accurate estimation. By merging evidence from multiple sources, such as time delays and signal power levels, the method improves the reliability of the estimation process. The simulation results demonstrate that the proposed technique achieves superior performance in terms of the estimation error and success rate, outperforming other estimation methods reported in the literature. Moreover, the experimental results corroborated the simulation results and demonstrated the capability and effectiveness of DST in addressing the challenges of collisions in RFID technology.