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.

In recent years, various fast imaging algorithms have been developed for near-field millimeter-wave (MMW) sparse multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) system, and most of them are implemented through fast Fourier transform (FFT). However, these algorithms have strict requirements on the uniformity and sparsity of MIMO arrays, and hardly guarantee high-quality image reconstruction under a large dynamic range. This article proposes an efficient MMW image reconstruction algorithm based on alternating direction method of multipliers (ADMM) framework. First, the forward sensing operator is constructed according to the existing FFT-based imaging model. Then we substitute it into the traditional ADMM framework, and at the same time update the iterative solving process, which avoids large-scale matrix inversion and significantly reduces the computational burden. Moreover, in view of the sensitivity of the effective regularization parameter to different imaging scenes and array parameters, a fast and effective regularization parameter selection method is developed in this article by employing image correlation (IC) as the evaluation indicator, which contributes to realize the high-precision and fast reconstruction of the target image to the maximum extent. Finally, a large amount of simulation and experimental results demonstrate the superiority of the proposed algorithm in computational efficiency and imaging quality.

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.

Short-range millimeter-wave (MMW) multiple-input multiple-output synthetic aperture radar (MIMO-SAR) imaging technology has great development opportunities in the areas of security monitoring, medical diagnosis, and nondestructive evaluation. However, the existing MIMO-SAR fast imaging algorithms typically employ fast Fourier transform (FFT) to invert the target image, which has a serious influence on imaging quality under the highly displayed dynamic range. To address this deficiency, this article presents an adaptive alternating direction method of multipliers (ADMM)-based method on the basis of the state-of-the-art MIMO-SAR fast image-solving model. Different from the traditional ADMM, the proposed algorithm avoids the multiplication and inversion of a large-scale sensing matrix, and the computation is significantly reduced. In addition, adaptive weighting is introduced to adjust the intensity of the ${\ell _{1}}$ regularization item, with the objective of mitigating the loss in imaging accuracy caused by the state-of-the-art fast algorithm. On this foundation, considering the sensitivity of the optimized regularization coefficient to different targets and MIMO array configurations, an efficient optimized regularization coefficient searching method is developed, which greatly enhances the accomplishment of high-quality target image reconstruction in different scenarios as quickly as possible. Numerical simulations and experimental results validate the effectiveness of the proposed algorithm compared with those of the existing FFT-based algorithms.

Video synthetic aperture radar (ViSAR) operating at low-frequencies generally suffers from low frame-rate and high latency due to its wide synthetic aperture and long dwell time. To conquer the problems mentioned above, this article proposes a ViSAR imaging processing framework based on the terahertz (THz) regime. The proposed method only needs a data repetition ratio of 0% rather than 80% for the microwave to reach the 0.2m imaging resolution and 5Hz frame rate with low latency thanks to the designed high pulse repetition frequency (PRF) system and short wavelength of THz wave. Thus, robust estimations of baseband Doppler centroid and Doppler rate are proposed based on the multi-core adaptive weighting and imaging knowledge prior, which handles the Doppler sensitivity of the THz regime effectively. Finally, airborne experiments are carried out to validate the superiority of dynamic situational awareness and the potential of moving targets indication in the THz-ViSAR.

In this letter, an efficient millimeter-wave (MMW) frequency-domain imaging algorithm is presented for 3-D object reconstruction of the near-field multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) with nonuniform transmitting array. For each single-input–multiple-output (SIMO) case of the MIMO-SAR setup, the precise spectral echo expression is derived by spherical-wave decomposition and fast Fourier transform (FFT) operations. To obtain the linear phase in range direction, the multiorder approximation theory is employed to expand the coupling phases, and thus the target subimage can be reconstructed by utilizing phase compensation, central wavenumber approximation, and inverse FFT. Finally, the fully focused 3-D image for MIMO setup will be realized through accumulating all the SIMO subimages. Compared with the existing representative algorithms, our method avoids complicated interpolation and achieves high-quality fast imaging with only matrix multiplication and a small amount of iteration operations. The experimental results validate the effectiveness of the proposed algorithm.