In the area of near-field millimeter-wave imaging, the generalized sparse array synthesis (SAS) method is in great demand. The traditional methods usually employ the greedy algorithms, which may have the convergence problem. This paper proposes a convex optimization model for the multiple-input multiple-output (MIMO) array design based on the compressive sensing (CS) approach. We generate a block shaped reference pattern, to be used as an optimizing target. The pattern occupies the entire imaging area of interest in order to involve the effect of each pixel into the optimization model. In MIMO scenarios, we can fix the transmit subarray and synthesize the receive subarray, and vice versa, or doing the synthesis sequentially. The problems associated with focusing, sidelobes suppression, and grating lobes suppression of the synthesized array are examined in detail. The simulation results reveal that the proposed method can efficiently synthesize a sparse array within a significantly shorter time in comparison to state-of-the-art techniques. As a result, it can be applied to generate arrays with large apertures. Additionally, both numerical and experimental results demonstrate that the synthesized sparse array offers superior image quality when compared to both state-of-the-art and commonly used sparse arrays with an equivalent number of antenna elements.

Millimeter-Wave (MMW) imaging is a promising technique for contactless security inspection. However, the high cost of requisite large-scale antenna arrays hinders its widespread application in high-throughput scenarios. Here, we report a large-scale single-shot MMW imaging framework, achieving low-cost high-fidelity security inspection. We first analyzed the statistical ranking of each array element through 1934 full-sampled MMW echoes. The highest-ranked elements are preferentially selected based on the ranking, building the experimentally optimal sparse sampling strategy that reduces antenna array cost by one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, realizing robust and accurate MMW image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at such a low sampling ratio. With the strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging.

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.

We propose an efficient wavenumber domain approach for polyline array-based millimeter-wave imaging. The polyline multiple-input–multiple-output (MIMO) array provides more uniform illumination of the target than the planar MIMO array while enjoying the planar array’s easy manufacturing. However, no existing efficient imaging algorithms deal with such an array topology. This article presents an approach that first implements a polyline-to-arc transformation on the polyline array, accompanied by a phase calibration method to maintain the transformation precision. The wavenumber domain technique is then employed to perform image reconstruction. This processing enables the transformation of the polyline array scheme into a monostatic cylindrical aperture, for which efficient imaging algorithms exist. The imaging performance and computational efficiency of the proposed approach are demonstrated with both simulations and experiments.

This article proposes a polyline-shaped array-based system scheme, associated with mechanical scanning along the array’s perpendicular direction, i.e., the vertical direction, for near-field millimeter-wave (MMW) imaging. Each section of the polyline is a chord of a circle with equal length. The polyline array, which can be realized as a monostatic array or a multistatic one, is capable of providing more observation angles than the linear or planar arrays. Furthermore, we present the related 3-D imaging algorithms based on hybrid processing in the space domain and the spatial frequency domain. The nonuniform fast Fourier transform (NUFFT) is utilized to improve the computational efficiency. The proposed algorithm runs even faster than the wavenumber domain algorithms for linear or circular-arc multiple-input–multiple-output (MIMO) arrays. This is due to the fact that the latter needs higher dimensional processing that requires more memory usage and time. Numerical simulations and experimental results are provided to demonstrate the efficacy of the proposed technique in comparison with the most commonly used arrays and algorithms.

A convex optimization-based method of sparse array synthesis (SAS) for wideband near-field millimeter-wave imaging is proposed by extending our previous work. We construct a monostatic sparse array synthesis optimization model from the electromagnetic propagation formula. The reweighted l ₁ norm decoding algorithm is utilized to enhance the sparsity. A modified iterative element weighting merging method is also proposed to put constraints on the minimum element spacing to synthesize a practicable sparse layout. Through the proposed SAS method, the customized sparse monostatic array for near-field imaging can be generated with different schemes, such as 1-D linear arrays, 2-D planar arrays, etc. The imaging performance of the synthesized planar sparse array, is studied by examining the properties of focusing, sidelobe-suppression, and grating lobe suppression both theoretically and by simulation. It is shown that the optimized array is superior to the arrays with equally spaced antennas or randomly spaced antennas, using approximately the same number of antenna elements. Experimental results further indicate the advantages of the optimized wideband sparse array through the three-dimensional imaging reconstruction.