Utilizing Resonant Scattering Signal Characteristics of Magnetic Spheres via Deep Learning for Improved Target Classification

Abstract

Object classification using LAte-time Resonant Scattering Electromagnetic Signals (LARSESs) is a significant problem found in different areas of application. Due to their special properties spherical objects play an important role in this field both as a challenging target and analytical LARSES source. Although many studies focus on their detailed analysis the challenges associated with target classification by resonant LARSESs from multi-layer spheres have not been investigated in detail. Moreover existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and coatings. However especially for metamaterials magneto-dielectric inclusions require consideration of magnetic properties as well as dielectric ones. In this respect this study shows that the utilization LARSESs of magnetic spheres provides diverse information and features which result with superior object classification performance. For this purpose first time domain LARSESs are generated numerically for single and multi-layer radially symmetrical dielectric and magnetic spheres. Then by using emerging deep learning tools particularly Convolutional Neural Network (CNNs) which are trained with spheres having different material properties a high multi-layer object classification performance is achieved. Moreover by extending the proposed strategy to measured data via modern data augmentation and transfer learning techniques an improved classification performance is also obtained for more complex targets.