Far-Field Approximation Learning Method for Millimeter-Wave Short-Range Imaging

Abstract

Based on the far-field approximations, a deep learning-based method is proposed for millimeter-wave short-range imaging. By using convolutional neural networks, the distortions caused by the far-field approximations and limited-aperture measurements could be corrected. Dissimilar to traditional algorithms, the proposed method has no restrictions on the placements of the antenna arrays and single-frequency illuminations are sufficient for the generations of 3D high-resolution reflectivity maps. In addition, it is fast to generate the input of neural network since the algorithm is based on inverse Fourier transform, which is ideal for generating training dataset. The performance of the proposed method is verified using both synthetic and experiment data. It is also demonstrated that enlarging the k-space coverage, which can be accomplished by increasing the dimensions of the antenna arrays, is able to improve the resolution of the proposed method.