Abstract
To solve the problems of high computational complexity and unstable image quality inherent in the compressive sensing (CS) method, we propose a complex-valued fully convolutional neural network (CVFCNN)-based method for near-field enhanced millimeter-wave (MMW) three-dimensional (3-D) imaging. A generalized form of the complex parametric rectified linear unit (CPReLU) activation function with independent and learnable parameters is presented to improve the performance of CVFCNN. The CVFCNN structure is designed, and the formulas of the complex-valued back-propagation algorithm are derived in detail, in response to the lack of a machine learning library for a complex-valued neural network (CVNN). Compared with a real-valued fully convolutional neural network (RVFCNN), the proposed CVFCNN offers better performance while needing fewer parameters. In addition, it outperforms the CVFCNN that was used in radar imaging with different activation functions. Numerical simulations and experiments are provided to verify the efficacy of the proposed network, in comparison with state-of-the-art networks and the CS method for enhanced MMW imaging.
Highlights
Received: 28 November 2021Millimeter-wave (MMW) imaging is widely applied in remote sensing [1,2], indoor target tracking [3], concealed weapons detection [4,5], and so on
We propose a complex-valued fully convolutional neural network (CVFCNN) with a generalized form of complex parametric rectified linear unit (CPReLU) activation function for MMW 3-D imaging
We proposed a complex-valued fully convolutional neural network (CVFCNN) with a generalized CPReLU activation function for undersampled near-field
Summary
Millimeter-wave (MMW) imaging is widely applied in remote sensing [1,2], indoor target tracking [3], concealed weapons detection [4,5], and so on. It offers high spatial resolutions of the target under test, even behind some kind of barrier. Deep learning methods have received much attention in many fields, such as radar monitoring [9,10], hand-gesture recognition [11,12], and radar imaging [13,14]
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