DIMAR: A Contactless Material Identification Algorithm for Complex Permittivity of Dielectrics via Moving RFID System

Radio frequency identification (RFID)-based sensing technology has recently gained extensive attention in numerous applications. However, contactless sensing still faces significant challenges. The existing works are generally limited by backscattered signals of phase and the received signal strength (RSS) as an intuitive combination of feature parameters. In this article, DIMAR, an algorithm leveraging moving tag is presented to enable contactless identification of dielectric materials by determining the dielectric constant using a UHF RFID system. In this framework, by blocking the line-of-sight (LOS) link between the reader and tag, we present a theoretical analysis of the influence of material reflection on RSS from the perspective of electric fields. Then the feature model of a cost function is proposed with respect to the reflection coefficient and the complex permittivity, i.e., the relative permittivity and conductivity. Covariance matrix adaptation evolution strategy (CMA-ES) is introduced to solve the mathematical model, and density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for noise reduction to deal with environmental distractions. Furthermore, due to the nonconvexity of the model, we provide a geometric analysis for the convergence verification within the domain range, and further a numerical analysis to verify rationality and convergence of CMA-ES. Experiment and simulation results show that DIMAR performs superiorly in identification accuracy even in multipath environments. And since the measured materials have close proximity of the properties, DIMAR can achieve fine-grained identification resolution.

Automated summary

This article introduces DIMAR, an algorithm that uses a UHF RFID system to identify dielectric materials through a mobile tag. The algorithm analyzes the influence of material reflection on received signal strength from the perspective of electric fields by blocking the line-of-sight link between the reader and tag. The algorithm solves the mathematical model using covariance matrix adaptation evolution strategy and reduces noise using density-based spatial clustering algorithm. Experimental results show that DIMAR achieves superior identification accuracy even in multipath environments.