Identification of Materials Using a Microwave Sensor Array and Machine Learning

Abstract

Material identification has many applications in non-destructive testing, chemistry, infrastructure maintenance, etc. Here, for this purpose, we propose a technique based on the use of a microwave sensor array with the elements of the array resonating at various frequencies within a wide range and applying machine learning algorithms on the collected data. Compared to the widely use single resonating sensors, the proposed methodology allows for material characterization over a wide frequency range which, in turn, improves the accuracy of the material identification procedure. The performance of the proposed methodology is tested via the use of easily available materials such as woods, cardboards, and plastics.