Convolutional neural networks (CNN) have successfully been employed to tackle several remote sensing tasks such as image classification and show better performance than previous techniques. For the radar imaging community, a natural question is: Can CNN be introduced to radar imaging and enhance its performance? This letter gives an affirmative answer to this question. We first propose a processing framework by which a complex-valued CNN (CV-CNN) is used to enhance radar imaging. Then we introduce two modifications to the CV-CNN to adapt it to radar imaging tasks. Subsequently, the method to generate training data is shown and some implementation details are presented. Finally, simulations and experiments are carried out, and both results show the superiority of the proposed method on imaging quality and computational efficiency.

Recently, millimeter-wave 3D holography techniques employing a scanning 1D multiple input multiple output (MIMO) array have shown several superiorities for short-range applications than traditional single input single output ones. However, current imaging algorithms for this emerging regime are not satisfied, either too slow as a back projection manner is used or of poor quality since several steps of approximations are introduced. In this paper, two fast fully focused imaging algorithms are developed towards fixing these drawbacks. Both algorithms are based on the assumption that the receivers are evenly distributed. The first algorithm further hypothesizes that the transmitters are also evenly located, while the second algorithm needs looser restrictions that the transmitters can be arbitrarily positioned. The frequency domain expressions of the modified Kirchhoff method are also derived and used to promote the precision of the proposed algorithms. In addition, several implementation issues, including resolution, sampling criteria, and computational complexity are discussed. Finally, both simulation and experimental results validate the effectiveness of the proposed methods on reconstruction quality and computational efficiency.

A novel interferometry technique is proposed and introduced into active millimeter-wave (MMW) holography. Different from optical holographic interferometry, which relies on a reference light source or the interferometric synthetic aperture radar where data from apertures of different elevations are interfered, data are interfered between different frequencies in the proposed method. Higher ranging accuracy is achieved, since the residual phase information which is always neglected is utilized by the interferometry manner. The proposed method also avoids interpolations in the traditional MMW holographic imaging algorithms, by which the computational complexity can be reduced. First, theoretical derivation of the 3-D reconstruction method based on frequency interferometry is presented, and detailed algorithms considering noise resistance and phase unwrapping are designed. Then, numerical simulations are conducted to validate our methods. Different aspects of the proposed algorithms' performance are analyzed and compared with previous methods. Finally, laboratory experiments are carried out. Both the simulation and experimental results validate the superiority of our method on reconstruction accuracy and computational efficiency.

Based on a novel prototype terahertz screening system, its ability of fast three-dimensional (3-D) image reconstruction is demonstrated. We first give a brief introduction of the system. Then detailed analyses on signal model, system calibration, the influences of approximations, and the imaging performances are conducted. At last, proof-of-concept experiments are performed and preliminary 2-D and 3-D imaging results are presented. We show that the imaging problem can be resorted to a problem of spectral estimation finally. At this stage on this prototype system, a 180 cm$\,\times\,$90 cm region at 4-m range costs less than 0.1 s for 2-D imaging, 1 s for 3-D imaging and an extra 2 s for mechanical scanning. Faster speeds are on the horizon as hardware and software updates are ongoing. These results show its potential of real-time 3-D imaging for standoff concealed threats detection.

A novel cylindrical millimeter-wave holography regime using a multi-input-multi-output (MIMO) array is first introduced. More importantly, an accurate and fast 3-D imaging algorithm is developed for this regime. Although many efficient imaging algorithms for synthetic aperture radar (SAR) or circular SAR have been developed, they can hardly be used for this new regime due to the compound effects of a spherical wavefront, MIMO geometry, and cylindrical observation aperture. In this paper, we develop an efficient algorithm mainly based on the ideas of a spherical wave decomposition, circular convolution, and nonuniform fast Fourier transform. The proposed algorithm achieves similar imaging quality with the golden-standard back-projection algorithm (BPA) while costs much less time. Compared with the BPA, the proposed algorithm belongs to a kind of the frequency-domain method built upon range migration techniques. Detailed derivations are first presented. Then, several important issues including resolutions, sampling criteria, and computational complexities are analyzed. Finally, a series of simulations and experiments are carried out, and all the results verify the effectiveness of the proposed algorithm on both accuracy and efficiency.

For near-field synthetic aperture radar (SAR) imaging, frequency domain imaging algorithms typically require the array elements to be uniformly arranged to facilitate the application of fast Fourier transform (FFT) operations; this greatly restricts the array types that can be used for practical applications. For arrays with non-uniformly positioned antennas, no general and efficient imaging algorithm is available yet. To address this issue, an effective algorithm for efficient three-dimensional (3D) SAR imaging with arbitrary linear multi-input-multi-output (MIMO) array topologies is proposed. Specifically, non-uniform FFT (NUFFT) technology is utilized to tackle the non-uniform sampling issues in both the spatial and the wavenumber domain. Despite the intuitive idea, to achieve satisfied imaging, some issues are non-trivial and should be carefully addressed. Both simulations and experiments show the superiority of the proposed algorithm on computational complexity while maintain the same high imaging precision compared with the back-projection algorithm (BPA). Moreover, we suggest that the concept of NUFFT-based imaging can also be generalized to other imaging regimes with arbitrary array configurations.