Abouzaid, SalahSalahAbouzaidJaeschke, TimoTimoJaeschkeKueppers, SimonSimonKueppersBarowski, JanJanBarowskiPohl, NilsNilsPohl2024-02-272024-02-272023https://publica.fraunhofer.de/handle/publica/46254610.1109/TMTT.2023.32760532-s2.0-85161020036This 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.enClass anchor clustering (CAC)frequency-modulated continuous wave (FMCW) radarK-means clusteringmaterial characterizationmaterial classificationopen-set recognition (OSR)vector network analyzer (VNA)journal article