RCS Recognition Method of Corner Reflectors based on 1D-CNN

As a device for simulating electromagnetic scattering characteristics, corner reflectors can simulate the electromagnetic scattering characteristics of complex ground and sea targets through the combination of a variety of shapes and sizes of corner reflector structures. This paper proposes a one-dimensional convolutional neural network (1D-CNN) model, which can quickly and accurately identify and classify corner reflectors based on the corner reflector RCS. The experimental results show that the 1D-CNN built in this paper can effectively classify the corner reflectors and classify the size between classes on the RCS data sets. The experimental results in this paper provide a theoretical and experimental basis for the subsequent classification and recognition of complex objects composed of multiple corner reflectors based on deep neural networks.