Deep Learning-Based Material Characterization Using FMCW Radar With Open-Set Recognition Technique

Abstract

This article proposes a low-cost and practical alternative to vector network analyzers (VNAs) for characterizing dielectric materials using a calibrated frequency-modulated continuous wave (FMCW) radar measurement setup and a machine learning (ML) model. The calibrated FMCW radar measurement setup has the ability to accurately measure the S-parameters of dielectric materials. In addition, an ML model is developed to extract material parameters such as thickness, dielectric constant, and loss tangent with high accuracy. K-means clustering was additionally applied to significantly reduce the complexity of the neural network (NN). Additionally, a state-of-the-art open-set recognition (OSR) technique was adopted to simultaneously classify known classes and reject unknown classes. The developed model uses a modified version of the class anchor clustering (CAC) distance-based loss, which outperforms the conventional cross-entropy loss. The proposed model was evaluated on several dielectric materials and compared to reference measurements using a VNA and curve fitting. The results indicate that the proposed model is accurate and robust, and that the calibrated radar sensor provides a practical and cost-effective alternative to VNAs in characterizing dielectric materials, as long as the material parameters are within the defined limits.