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.

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.

At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking.

The nonlinear viscoelastic behavior of composite solid propellants is studied by using a three-dimensional thermoviscoelastic constitutive model. In this model, the effects of the time-dependent Poisson ratio, aging, damage, and thermoviscoelasticity are considered. Viscoelastic constitutive equations are numerically discretized into an incremental form by using integration algorithms. An Abaqus-based user material subroutine is presented, and a structural analysis module for solid rocket motor grain is built. Based on the analysis module, the structural responses of solid rocket motor grain are analyzed when subjected to typical loadings during its service life. Obtained results indicate that, differing from the classic linear viscoelastic model, the present model gives satisfactory numerical results closely consistent to the actual performance variation of the propellant. The designed structural analysis module could be applied to the structural analysis for practical solid rocket motors.

Airborne terahertz video synthetic aperture radar (THz-ViSAR) exhibits excellent superiorities in the dynamic situation awareness and moving targets detection. However, limited by the recording precision of the inertial navigation system (INS) or global positioning system (GPS), data-driven motion compensation (MOCO) methods are challenging to eliminate motion errors for THz band. Particularly, platform vibrations that are previously negligible in the microwave band, become non-negligible, drastically degrading the focusing performance. To conquer aforementioned problems, this paper proposes an echo-driven robust MOCO framework suited for THz-ViSAR. First, the airborne motion error model (MED) is re-derived and corresponding effects are analyzed in detail. Based on the derived MED, two parts are required to estimate from the echo: radial errors and vibration errors. Thus, a forward estimation of candidate signal of interests (SoIs) is proposed based on local peaks of the average range profile and the prominence. For each candidate, radial and vibration errors are estimated from the unwrapped phase of the SoI via the polynomial and multi-component sinusoidal fitting in turn. Finally, MOCO functions are constructed and evaluated based on the image entropy and image contrast, which determines the optimal SoI. Airborne field experiments are carried out to validate the effectiveness and practicability of the proposed method.