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Terahertz (THz) imaging has an outstanding advantage of high resolution due to the high frequency and has promising potential in VideoSAR. However, limited to the THz source power and the air absorption, the THz image usually has a low SNR and is susceptible to noise in remote sensing and imaging. In order to improve the quality of THz images, a THz image enhancement method is proposed based on the noise2noise idea. The THz images are reconstructed with the AFBP algorithm. They are organized as noisy image pairs and filtered with a mask to remove the influence of moving targets. Then, the Noise2Noise network is constructed based on the CNN network and takes the noisy image pair as input and reference. In the training stage, 1000 noisy image pairs are used as the training set and 100 noisy images are used as the test set to verify the performance of the proposed method. The experimental results based on real VideoSAR data demonstrate that the proposed method is capable of suppressing noise and enhancing the THz image.

This paper presents a high-gain slotted ridge waveguide array antenna (SRWAA) with inductive diaphragms, which can realize a wide-sector beam with a low sidelobe simultaneously. The proposed antenna can cover a wide detection range and avoid interference from other directions. The expected excitation distribution for the antenna array is extracted through a beamforming method. To reduce the influence of the dispersion phenomenon on signal quality, inductive diaphragms are inserted into the sidewall of the ridge waveguide, which is fully analyzed from the point of the equivalent circuit. A cut-off-mode power divider is utilized, which can control the power ratio flexibly. A SRWAA working at 24.125 GHz, including a six-way feeding network, and a 6×24 slot array with the size of 330 mm × 66.8 mm is designed and fabricated. The measured sidelobe level (SLL) and half-power beamwidth (HPBW) in the elevation plane are -19.6 dB and 54.41°, with the counterparts in the azimuth plane -29.8 dB and 3.15°, respectively. The measured peak gain is 22.3 dBi at 24.125 GHz. The measured results are in good agreement with the simulated ones. This work has the potential to be applied in air detection, anti-unmanned aerial vehicles (UAVs), meteorological radar, and imaging radar.

Video synthetic aperture radar (SAR) has been found very useful in ground moving target indication (GMTI) and tracking. The dynamic shadows in video SAR imagery sequences indicate the real positions of moving targets, which can be utilized in target detection and tracking. Unfortunately, the shadow-based method often fails when the shadows are not sufficiently developed. On the other hand, the traditional energy-based GMTI methods exhibit performance degradation when SAR images of a moving target are distorted or smeared. Neither of these two methods can stand alone to provide robust detection and tracking of moving targets. This article presents a joint processing framework for video SAR GMTI and tracking by combining the target shadow and echo energy information, which effectively lowers the false alarm and miss-detection rate. This approach is very suitable for real-time surveillance of ground moving targets in a single-channel video SAR system. The proposed approach has been verified by using the simulated and the real video SAR data.

Sea-Surface small target detection is challenging for maritime radar. Unfortunately, conventional detection methods are often limited to complex marine environment and low signal-to-clutter ratio (SCR). This article presents a multi-frame detection approach for sea-surface small target by using deep convolutional neural network. The moving targets can be reconstructed and detected from the sequential Range-Doppler (RD) spectra. A two-step detection framework is proposed, where the intra-frame and the inter-frame detection is achieved by using the differences of features and inter-frame correlations between moving target and sea clutter, respectively. The proposed approach has been verified on both the simulated and real sea-surface small targets, which shows better detection performance than the conventional multi-frame detection algorithms. Additionally, this approach exhibits acceptable generalization ability.

An imaging radar based on an electronically displaced-phase-center antenna (eDPCA) has been reported recently, which provides great application potentials in many areas. To overcome the degradation of the SAR image quality caused by the inherent pattern fluctuations of the eDPCA, we propose a modified virtual array method to correct the irregularities in the eDPCA-based system. The effectiveness of the proposed approach is validated through experimental examples on a simulated eDPCA imaging system at 157 GHz.

THz time-domain spectroscopy system (THz-TDS) can provide spectral information about materials, but the data acquisition takes a long time in THz imaging applications. The compressed sensing algorithm is a computational imaging technique. In this paper, we present a THz compressed sensing imaging method based on an improved linearized-ADMM algorithm. Linearized ADMM methods linearize access to the optimal solution through their gradient evaluations and can effectively solve the sparse recovery problem. The proposed algorithm needs much fewer THz spectra and reduces acquisition time. First, the Gaussian filter in the image domain and the Nesterov acceleration method are adopted in the iterative process to facilitate the convergence speed. Secondly, the MAD (Median absolute deviation) is chosen to calculate the threshold in the soft-shrink step. MAD is a robust measure of sample differences in univariate data sets. It is more flexible than the standard deviation in handling outliers in data sets, and can greatly reduce the impact of outliers on data sets. Thirdly, the L1-reweighted algorithm and averaging sparsity technique are used to enhance the performance and stability of the proposed approach. Finally, the THz beam pattern is taken into consideration to improve image resolution. The experimental results demonstrate that the proposed method has a good performance in THz imaging.

Accurate global terrestrial evapotranspiration (ET) estimation is essential to better understand Earth’s energy and water cycles. Although several global ET products exist, recent studies indicate that ET estimates exhibit high uncertainty. With the increasing trend of extreme climate hazards (e.g., droughts and heat waves), accurate ET estimation under extreme conditions remains challenging. To overcome these challenges, we used 3-hour and 0.25° Global Land Data Assimilation System (GLDAS) datasets (net radiation, land surface temperature (LST), and air temperature) and three-temperature (3T) model, without resistance and parameter calibration, in global terrestrial ET product development. The results demonstrated that the 3T model-based ET product agreed well with both global eddy covariance (EC) tower (root mean square error (RMSE) = 24.9 mm month-1) and basin-scale water balance observations (RMSE =116.0 mm yr-1). The 3T model-based global terrestrial ET product was comparable to other common ET products, i.e., MOD16, P-LSH, PML, GLEAM, GLDAS, and Fluxcom, retrieved from various models, but the 3T model performed better under extreme weather conditions in croplands than did the GLDAS, attaining 9.0–20 % RMSE reduction. The proposed ET product could provide periodic and large-scale information to support water cycle-related studies. The dataset is freely available at the Science Data Bank (http://doi.org/10.57760/sciencedb.o00014.00001, Xiong et al., 2022).

Existing approaches for SAR image registration focus on the global transformation correction between SAR images. However, there are often local deformations between images. Due to the time-changing viewpoint of video SAR, the images suffer a lot from local deformations, which can result in false alarms in moving target detection. This article presents an unsupervised image registration approach for the use of video SAR moving target detection, which has good registration performance and acceptable processing efficiency. The designed unsupervised learning-based framework is a cascade of two convolutional neural networks. The first network directly predicts the parameters of the rigid transformation between the reference and unregistered images, and recovers the global transformation between them. Then, the second network uses the reference image and the registered image from the first network as input and then predicts a displacement field. After that, we put a limitation on the predicted displacement field to prevent moving target shadows from being aligned. Finally, the displacement field with limitation is used to compensate local deformations between the two images. Processing results of real video SAR images have shown good performance of the proposed approach with convincing generation ability.

Moving target shadows continuously appear in the sequential images produced by video synthetic aperture radar (SAR), which conducts research on shadow-based detection and tracking of moving targets. This article addresses a typical shadow-based detection and tracking problem of dim high-maneuvering targets in the complex background. Building upon the dynamic programming-based track-before-detect (DP-TBD) algorithm, this article proposes a joint-processing-strategy-based DP-TBD (JP-DP-TBD) algorithm to detect and track multiple high-maneuvering targets in video SAR. Benefiting from both of the location and radial velocity information of the targets in the video SAR image and range-Doppler (RD) spectrum, the JP-DP-TBD algorithm can screen the state candidate region and retain more states that conform to the motion law of the target. Meanwhile, we adopt a dual-frame close-range matching mechanism to complete the automatic initialization of the candidate target state, whose validity has been verified on the real video SAR data. Experimental results on simulated data demonstrate that the proposed algorithm has better detection ability and fewer false alarms in comparison with other TBD algorithms.

By exploiting the sparsity of signal sources in the spatial domain, compressive sensing (CS) based direction of arrival (DOA) estimation has emerged as a promising approach especially in the case of a limited number of snapshots. However, due to the use of a large overcomplete dictionary obtained from a predefined grid, CS-based DOA estimation methods normally suffer from high computational complexity and the grid mismatch problem. Many methods, in particular sparse Bayesian learning (SBL) based off-grid methods, have been developed to address the grid mismatch problem at the cost of high complexity. In this work, we develop a new method for DOA estimation based on marginal likelihood maximization, where the array manifold matrix is learned incrementally, which is in contrast to the use of overcomplete dictionaries or grid matrices in existing CS or SBL based methods. We show that the problem of marginal likelihood maximization over multiple variables can be greatly simplified to maximization of a simple cost function over a sole variable (angle), which enables the learning of the manifold matrix and the development of an efficient solver. The grid mismatch problem is circumvented and the manifold matrix during learning is kept in a small size (slightly larger than the number of sources), leading to low computational complexity. Simulation results demonstrate the merits of the proposed method in terms of performance and computational complexity, compared to state-of-the-art SBL-based methods.

Video synthetic aperture radar (SAR) has shown great potentials in detection and tracking of slow ground moving targets. The classical shadow-aided detection was applied in video SAR, and most recently, the deep learning approach has been developed for shadow-aided moving target detection. This article presents a joint moving target detection approach for video SAR using a dual faster region-based convolutional neural network (Faster R-CNN), which algorithmically combines the shadow detection in the SAR image and the Doppler energy detection in the range-Doppler (RD) spectrum domain, and this new approach can suppress false alarm sufficiently. Video SAR image and its corresponding low resolution RD spectrum are fed into the developed dual Faster R-CNN. A correct detection can be achieved if the shadow of a moving target and its Doppler energy are simultaneously detected by paired region proposals, which are obtained by sharing the region proposals of two independent region proposal networks (RPNs). Therefore, the performance of moving target detection can be significantly improved by using diverse features in different domains. This proposed approach has been verified by both the simulated and real video SAR data. Compared to other classical methods, our approach exhibits a great detection performance in terms of fewer false alarms and acceptable missing alarms.

Video synthetic aperture radar (SAR) has been found to be very valuable for detection and tracking of slow moving targets and for observing changes over short time periods. Shadows produced by target motion in sequential radar images can be used to detect targets themselves. This article presents a framework for shadow-aided moving target detection using deep neural networks (DNNs) in video SAR. The faster region-based convolutional neural network (Faster-RCNN) is first used to detect shadows in a single frame, and then all the detections in each frame are filtered by the improved density-based clustering algorithm to mitigate false alarms. The missing alarm in detection can be suppressed by using the designed bi-directional long-short-term memory (Bi-LSTM) network. The proposed approaches have been tested by both the simulated and the real data, and a comparison to classical processing is given. This article reveals that the DNN techniques are very suitable for video SAR moving target detection.

This article presents a novel radar imaging approach through an electronically displaced-phase-center antenna (eDPCA). In this approach, a reflectarray is used to directly scan a beam across a parabolic reflector, where the antenna phase center is displaced electronically. The proposed eDPCA-based imaging radar can provide a very high frame rate and does not depend on the motion between the target and the radar platform. The feasibility of the eDPCA-based imaging radar is investigated and validated through a designed example eDPCA at 157 GHz and the simulation experiments for a point target and a complex target, respectively. The effects of practical impairments such as errors in phase centers and position-dependent fluctuations in antenna patterns on the imaging quality are examined. Furthermore, by applying the precompensation method suggested in this article, the imaging procedure could be performed by using classic synthetic aperture radar (SAR) algorithms.

It is challenging to recognize individuals when they move in the radar field of view due to the superimposition of micro-Doppler signatures. This paper presents a multi-person recognition approach by separating micro-Doppler signatures of multiple persons into their individual components. The preliminary separation can be obtained by their range difference in a high resolution radar. A multi-task learning network is designed for both the fine separation of micro-Doppler signatures and the personnel recognition. A frequency modulated continuous waveform (FMCW) radar that operates at 77 GHz for automotive applications is used in experiments. The proposed deep-neural-network-based approach gives a convincing result in the test.

Urban evapotranspiration (ET) is an effective way to mitigate ecological challenges resulting from rapid urbanization. However, the characteristics of urban vegetation ET, especially how they respond to meteorological factors and soil water, remains unclear, which is crucial for urban ET regulation. Therefore, this study measured the actual ET rate of an urban lawn (ETlawn) using the Bowen ratio system and an urban tree (Ttree) by a sap flow system in the hot summer of a subtropical megacity, Shenzhen. The results showed that the daily ETlawn was more restricted by energy (Rs) and diffusion conditions (vapor pressure deficit, VPD), while the daily Ttree was more restricted by VPD and relative extractable water (REW) in the urban area. The daily Ttree decreased when the REW was lower than 0.18, while the daily ETlawn started to decrease when it was lower than 0.14. When REW was lower than 0.11, the Ttree stayed at a relatively low level. The impacts of VPD was more evident on the diurnal Ttree than on the diurnal ETlawn. Wind speed had a scarce impact as it was relatively low in urban areas. This study clarifies the different responses of ETlawn and Ttree to meteorological factors and soil water based on actual ET. The results are of great significance for the knowledge of urban forestry and urban hydrology.

A polarization-insensitive diffusion metasurface using a period-changed unit cell is presented for reducing the radar cross-section (RCS) of metallic objects in ultrawideband. Two metallic Minkowski loops are proposed as coding elements, different from traditional designs. The “0” element is constructed by period-changed unit cells to achieve a 180 ± 30° phase difference with the same reflection amplitude of nearly −0.9 dB in an ultrawideband from 7.1 to 29.2 GHz. Multilayer geometry with a thickness of 4.5 mm (about 0.105λ0 at the lowest operating frequency) and rotational symmetry loops are used to realize the ultrawideband characteristic and polarization-insensitive behavior. For verification, a polarization-insensitive diffusion metasurface is designed, fabricated, and measured. The simulated and measured results of the diffusion metasurface are in good consistency and the results both show that the metasurface enables a 10 dB backscattering reduction over an amazing ultrawideband ranging from 7.1 to 29.2 GHz (BW of 122%).