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The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

Linked to the increasing availability of datasets for radar-based human activity recognition (HAR), in this Student Highlights contribution, we report on a classification project that a group of 23 graduate students performed at TU Delft. The students were asked to work in groups of 2-3 members and to use the publicly available University of Glasgow dataset to develop the best classification pipeline as possible. This involved development and justification of both choices for the preprocessing techniques on the radar data (e.g., time-frequency distributions and cleaning of the signatures), and for the classification algorithms (e.g., the type of the algorithm, the hyperparameters' selection, the training-validation-testing split). While this student activity was performed at a small scale and with educational rather than research aims, we are happy to report it to the AESS readership, as we believe that such initiatives with open datasets sharing and classification algorithm benchmarking are beneficial for the wider radar research community. Furthermore, a list of publicly available datasets for radar-based HAR that can be used for similar initiatives is also reported in this article.

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.

Marine X-band radar has been suggested to be capable of monitoring significant wave heights in both offshore and open sea areas. In contrast to studies on offshore radar, significant wave height estimations from coastal radar images, which exhibit complicated radar backscattering features, have received little attention. This study proposes a method for retrieving the significant wave height from coastal areas that are often influenced by nononshore winds. The square root of the signal-to-noise ratio in radar images has been widely applied to estimate the significant wave height. However, nononshore wind cases show a poor correlation between the square root of the signal-to-noise ratio and the in situ significant wave height. In addition, the spectral shapes from radar images in nononshore wind cases are very different from those in onshore wind cases. To improve the significant wave height estimations from coastal radar images, we implement an artificial neural network algorithm. After training and testing the algorithm, we confirm that the estimated significant wave heights are more reliable for both onshore and nononshore wind cases if the square root of the signal-to-noise ratio, power from nearshore radar subimages, and in situ wind components are included in the input layer of the neural network.

High circular polarization ratio (CPR) characteristics were found in permanently shaded regions (PSRs) near the lunar poles. High CPR was regarded as a water ice index. The compact-polarimetric (CP) miniature radio frequency (Mini-RF) radar transmits left-circularly polarized signals and receives horizontally polarized (SHL ) and vertically-polarized (SVL ) echoes from the lunar surface. Statistics of the CPR data show its relations with the relative phase (δ ) between SHL and SVL and the degree of polarization (m) but few interpretations were provided. The average CPR data reach the maximum and minimum at δ =± 90°, respectively. As m becomes very small, the CPR approaches 1. It has been found that CPR is also affected by surface roughness and incidence angle of radar waves. The CPR is now expressed in CP mode to explain the Mini-RF observation. Full-polarimetric radar echoes and CP parameters of the lunar surface are numerically simulated using the bidirectional analytic ray-tracing method. Single-bounce and multiple-bounce scattering components are included in the simulation. Radar images of the lunar crater are simulated with the digital elevation model (DEM) data. The H-α decomposition derived from the full-polarimetric simulation is presented to analyze δ and m. Simulated radar images with different surface roughness are analyzed statistically to study the functional dependences of δ , m, and CPR on incidence angle and roughness. Relationships among δ , m, and CPR are used to analyze the effects of incidence angle, roughness, TiO₂ , and rock abundance on the scattering components. The CPR, m, and δ of PSR craters of different ages are compared with those of nonpolar craters. The results indicate that the CPR, m, and δ are unlikely to be unambiguous evidence of water ice.

The higher bandwidth inverse synthetic aperture radar (ISAR) can obtain the higher resolution radar images, which can provide more target information and help improve radar target detection and recognition. It is essential to study how to achieve a precise high-resolution (HR) ISAR image utilizing limited measurement echoes. The existing neural-network-based ISAR imaging methods extract features only from limited measurement echoes, and the common features in HR ISAR images are not utilized sufficiently, which limits the imaging performance improvement. Moreover, in their loss functions, there are no explicit constraints on the correct recovery of strong scattering points, which are important in reflecting the target characteristics. In this letter, we propose a conditional probabilistic generative model to achieve the HR ISAR imaging. By optimizing the well-designed Kullback-Leibler (KL) divergence between conditional prior and approximate posterior probability distribution in the loss function, the common features contained in training HR radar images can be learned, and a suitable prior probability distribution for the latent variable can be obtained. To accurately recover the positions and relative amplitudes of strong scattering points, we blend a similarity measurement that is sensitive to the large values' locations in a vector with the adversarial loss. Both visual and numerical results of extensive experiments prove that the proposed model can obtain enhanced effectiveness and efficiency compared with some counterparts.

The sparse-driven radar imaging can obtain the high-resolution images about target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In this paper, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. Firstly, the radar sparse imaging problem is analyzed and the calculation problem to be solved by quantum algorithms is determined. Then, the corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity, and the quantum-enhanced reconstruction algorithm for sparse imaging is proposed. Finally, the computational complexity of the proposed method is analyzed, and the simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method., Comment: 5 pages, 3 figures

Oils floating on the sea surface can be observed as ``dark'' patches on radar images since the backscattered signals from the contaminated area are reduced in two dominant ways. First, oil slicks could damp short gravity and capillary waves on the sea surface responsible for backscattering energy. Second, the oil-covered sea surface permittivity decreases significantly if the oil film is sufficiently thick or mixed with seawater, i.e., oil emulsion. In this article, the geometry of the oil-covered sea surface is accounted for by the damping of sea waves, which is described by the model of local balance (MLB) combined with the sea wave spectrum. The radar backscattering is predicted by the advanced integral equation method (AIEM) model. The reflection coefficients are calculated based on a layered-medium model to analyze the impact of oil thickness and emulsions on the radar scattering. Numerical simulations demonstrate that: 1) the sensitivity to oil thickness and water content of the oil spills increases when the radar frequency increases; 2) the backscattering signals exhibit a nonlinear behavior with respect to oil thickness; and 3) high wind speed can generally narrow the difference between the radar backscattering from the clean and oil-covered sea surface, while the incidence angle has little effect. Numerical simulations are then compared with the multifrequency synthetic aperture radar observations acquired during the Gulf of Mexico Deepwater Horizon (DWH) oil spill accident and the 2011 Norwegian Clean Seas Association for Operating Companies (NOFO) oil-on-water exercise. Comparison results show that it is possible to estimate the oil thickness at reasonably good accuracy.

Due to the requirements of portability and omni-directivity, the planar bow-tie antenna is widely used in impulse through-wall radar (ITWR). When the planar bow-tie antenna is used to radiate the ultrawide bandwidth (UWB) signal, the ringing phenomenon of the transmitted signal would be serious, which can damage the quality of radar imaging. The previous solutions for this problem are the usage of various hardware loadings; however, those loadings could cause signal energy loss and reduce the signal gain. Alternatively, this letter studies a deconvolution-technique-based signal processing approach to suppress the signal ringing. Because the proposed approach does not require any hardware loadings on the antenna, it can help improve the signal-to-noise ratio (SNR) and significantly reduce the energy loss of signal. The effectivity of this signal processing approach is verified by the radar detecting experiments.

Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.

Nonline-of-sight (NLOS) radar imaging is a novel technique that can inverse the scattering characteristics of targets in the NLOS area, which has been one of the hot pots of radar imaging field. However, the existing NLOS radar mainly focuses on 1-D or 2-D imaging, which inevitably suffers from the geometric loss of real 3-D scenes, and its applications are restricted in the urban environment. In this article, we propose an NLOS radar 3-D imaging model and method for looking around corner (LAC) situation by multi-input-multioutput (MIMO) millimeter-wave (mmW) array antennas. In this scheme, first, the model of NLOS radar 3-D imaging with mmW MIMO antennas is established and the multipath scattering of targets with this model is analyzed. Then, the theoretical resolution of LAC 3-D imaging is derived and discussed. Second, exploiting the three bounces of LAC and extraction of linear structure, an effective imaging algorithm with mirror projection theory and Radon transform, dubbed as mirror symmetry backprojection (MSBP), is proposed for 3-D image focusing. Moreover, to suppress the uncertainties of phase caused by both LAC and system error, the minimum entropy principle is introduced to MSBP. Finally, an NLOS 3-D imaging system with 77-GHz mmW MIMO radio frequency module and 2-D rails is developed. Different types of targets, such as metal balls and ornaments, are tested in LAC. The results demonstrate that our NLOS technique can not only provide a high-quality 3-D focusing of the hidden targets but also extract positions of targets without prior knowledge of the NLOS area.

In this letter, a forward-looking electromagnetic wave imaging method using radial scanning multichannel radar is proposed. Compared to the current forward-looking imaging systems, the method requires less hardware complexity due to the reduced number of channels, which is especially advantageous for millimeter-wave and higher frequency systems aiming for a high-resolution. By digitally processing the radially scanned radar data, a 3-D radar image is obtained. An image formation algorithm that exploits the computational efficiency of the fast Fourier transform is proposed. The effects of the radar parameters are discussed by analyzing the impulse response function. Moreover, the rotational sampling requirement, which relates to the field of view, is derived. A numerical simulation was performed to demonstrate the 3-D imaging capabilities of the proposed method. The results show that the proposed method enables radar imaging using a substantially fewer number of channels.

Low imaging spatial resolution hinders through-the-wall radar imaging (TWRI) from reconstructing complete human postures. This letter mainly discusses a convolutional neural network (CNN)-based human posture reconstruction method for TWRI. The training process follows a supervision-prediction learning pipeline inspired by the cross-modal learning technique. Specifically, optical images and TWRI signals are collected simultaneously using a self-develop radar containing an optical camera. Then, the optical images are processed with a computer-vision-based supervision network to generate ground-truth human skeletons. Next, the same type of skeleton is predicted from corresponding TWRI signals using a prediction network. After training, the model shows complete predictions in wall-occlusive scenarios solely using TWRI signals. Experiments show comparable quantitative results with the state-of-the-art vision-based methods in nonwall-occlusive scenarios and accurate qualitative results with wall occlusion.

To improve the life-detection radar resolution under certain hardware conditions, in this letter, a deep mutual learning generative adversarial network model (Deep Mutual GAN) is proposed. In the proposed model, the generator can improve the angular resolution of the input low-resolution radar image by five times, which is enough to meet our requirements for the resolution of life detection. We innovatively use two generators in GAN with the same network structure and make the two generators learn from each other. In this way, the learning process of a generator is not only achieved by its confrontation with the discriminator but also guided by another generator. As a result, the knowledge of the generator is no longer only obtained through its own learning; each generator learns knowledge from another generator while learning knowledge by itself. The proposed model can effectively make the convergence of GAN more stable and improves the super resolution effect. We also introduce the details of the network structure of generator and discriminator, in which residual learning and a symmetrical network structure are applied. The experimental results show that the proposed method can achieve state-of-the-art imaging effect, which is meaningful for subsequent target detection and recognition.

Radar imaging with 1-bit data is attractive thanks to its low storage and transmission burden. Existing 1-bit radar imaging methods cannot satisfactorily suppress the artifacts in the imaging result induced by 1-bit quantization error and noise. In this article, we propose a new 1-bit compressive sensing (CS) based algorithm, i.e., the adversarial-sample-based binary iterative hard thresholding (AS-BIHT) algorithm, to improve the 1-bit radar imaging performance. First, we formulate a parametric model for 1-bit radar imaging with a new adjustable quantization level parameter. The parametric 1-bit radar imaging model updates the imaging scene and the quantization level parameter in an iterative fashion based on adversarial samples. Then, we design a mechanism to generate adversarial samples by attacking the 1-bit radar imaging model to resist the quantization consistency condition, such that forcing quantization consistent reconstruction on adversarial samples mitigates the quantization error and noise. The quantization level parameter is then tuned based on the adversarial samples. In this way, the ability of the model to adapt to echo data contaminated by noise and quantization error is enhanced, and the artifacts are well suppressed. Simulation and experimental results on real radar data demonstrate the effectiveness of the proposed AS-BIHT algorithm in 1-bit radar imaging.

Polysomnography (PSG) is the gold-standard for sleep apnea and hypopnea syndrome (SAHS) diagnosis. Because the PSG system is not suitable for long-term continuous use owing to the high cost and discomfort caused by attached multi-channel sensors, alternative methods using a non-contact sensor have been investigated. However, the existing methods have limitations in that the radar-person distance is fixed, and the detected apnea hypopnea (AH) event cannot be provided in real-time. In this paper, therefore, we propose a novel approach for real-time AH event detection with impulse-radio ultra-wideband (IR-UWB) radar using a deep learning model. 36 PSG recordings and simultaneously measured IR-UWB radar data were used in the experiments. After the clutter was removed, IR-UWB radar images were segmented by sliding a 20-s window at 1-s shift, and categorized into two classes: AH and N. A hybrid model combining the convolutional neural networks and long short-term memory networks was trained with the data, which consisted of class-balanced segments. Time sequenced classified outputs were then fed to an event detector to identify valid AH events. Therefore, the proposed method showed a Cohen’s kappa coefficient of 0.728, sensitivity of 0.781, specificity of 0.956, and an accuracy of 0.930. According to the apnea-hypopnea index (AHI) estimation analysis, the Pearson’s correlation coefficient between the estimated AHI and reference AHI was 0.97. In addition, the average accuracy and kappa of SAHS diagnosis was 0.98 and 0.96, respectively, for AHI cutoffs of ≥ 5, 15, and 30 events/h. The proposed method achieved the state-of-the-art performance for classifying SAHS severity without any hand-engineered feature regardless of the user’s location. Our approach can be utilized for a cost-effective and reliable SAHS monitoring system in a home environment.

This letter deals with an imaging strategy able to manage effectively data collected on multiple lines by means of a Mini-Unmanned Aerial Vehicle (M-UAV) radar system operating in Sounder mode. The strategy, named multilines imaging approach (MIA), allows an effective 3-D pseudo-representation of the investigated volume. At the first step, MIA faces the problem of reconstructing 2-D domains (slices) by exploiting data collected on one or more lines. Then, MIA interpolates the 2-D reconstructions to provide the 3-D representation. The imaging of each slice is formulated as a linear inverse scattering problem, which is solved by means of the truncated singular value decomposition (TSVD) regularization scheme. The MIA effectiveness is assessed by processing real data collected at Archeological Park of Paestum and Velia, Paestum, Italy.

Three upcoming Martian missions will deploy a ground-penetrating radar (GPR) to reveal the fine-resolution subsurface structure and dielectric properties of materials beneath the surface. Numerical forward simulations of radar echo using a model of the near-surface structure at the landing site can provide a valuable reference for processing and interpretation of future radar data collected on Mars. In this study, based on the geological information of the Jezero crater, a detailed stratigraphic model of the near-surface structure is derived, which includes several key features, for example, the randomness of the medium, terrain, and cracks. To identify correctly the reflections of subsurface interfaces and fractures from the radar image, a v(z) f-k migration is carried out, the performance of which is evaluated using the GPR data obtained near Antarctic Zhongshan Station since the electrical properties of Antarctic glaciers and Martian materials are to some extent comparable. The results in this work show that compared with common migration algorithm, the v(z) f-k method not only improves the clarity of radar image but also provides the permittivity profiles to infer the composition of the substrate, leading to a better understanding of Martian near-surface geology.

A microwave photonic multiple-input and multiple-output (MIMO) radar is proposed and demonstrated to implement high-resolution imaging. In the proposed system, multiple orthogonal linearly frequency modulated (LFM) signals are generated by heterodyning between two optical frequency combs, which enables a MIMO transmitting array with a simple and reconfigurable structure. The receiving array uses photonic frequency mixing to implement multiple channel separation and de-chirp processing simultaneously. This microwave photonic MIMO radar can have a large operation bandwidth and a large equivalent aperture, which helps to achieve high-resolution imaging in both range and azimuth directions. In the experiment, a microwave photonic 48 MIMO radar is established with a 2-GHz bandwidth in each channel. Based on this MIMO radar, high-resolution back-projection (BP) imaging with a theoretical range resolution of 7.5 cm and azimuth resolution of 1.85 is demonstrated. The experimental results can verify the feasibility of the proposed MIMO radar, which is a good solution to high-resolution radar imaging by combining microwave photonic and MIMO technologies.

Millimeter-wave (MMW) radar sensors are considered key components of autonomous vehicles. Because of the performance degeneration of cameras and lidars under inclement weather conditions, robust autonomy must rely on radar sensors to perform target detection and classification. Unlike basic target classifier methods in literature that make use of target velocity, the proposed approach is far more comprehensive and can be applied to targets with zero-Doppler. Depending on the radar type and target range, this paper presents four target classification models based on four different types of radar data: statistical RCS, distributed (time-domain) RCS, range-azimuth angle radar images and 3D radar images. The classification models are implemented by machine learning approaches artificial neural network (ANN) and convolutional neural network (CNN) with a comprehensive simulated dataset. Good classification accuracies are demonstrated, and the proposed model is validated with measured data. Different radar target classification approaches are compared, which clearly reveals the trade-off between classification performance and system complexity. The proposed radar target classification methods can be applied effectively to both static and dynamic targets, at near or far ranges, using traditional or imaging radars, resulting in improved safety for autonomous vehicles in a wide variety of complex environments.

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system., Comment: 8 pages, 11 figures, 5 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Radio frequency interference (RFI) can significantly pollute synthetic aperture radar (SAR) data and images, which is also harmful to SAR interferometry (InSAR) for retrieving elevational information. To address this issue, in recent years, a class of advanced RFI suppression methods has been proposed based on narrowband properties of RFI and sparsity assumptions of radar echoes or target reflectivity. However, for SAR echoes and the associated scene reflectivity, these assumptions are usually not feasible when the imaged scene is spatially extended. In view of these problems, this study proposes an InSAR-based RFI suppression method for the case of extended scenes. For this task, we combine the RFI-polluted SAR data with RFI-free interferometric data to form an interferometric SAR data pair. We show that such an InSAR data pair embeds an interferogram having the image amplitude multiplying by a complex exponential interferometric phase. We treat the interferogram as a kind of natural image and use discrete Fourier cosine transform (DCT) for its sparse representation. Then combining the DCT-domain sparsity with low-rank modeling of RFI, we retrieve the interferogram and reconstruct the SAR image via joint low-rank and sparse optimization. Numerical simulations show that the proposed method can effectively recover SAR images and interferometric phases from RFI-polluted SAR data.

Simultaneous multitarget vital signs measurement has become a hot issue for noncontact vital signs perception. However, there is still challenge in the multitarget heartbeat measurement due to the weakness of heartbeat signal and interference from complex environment. In this article, a new multiple-input–multiple-output (MIMO) continuous-wave (CW) radar system equipped with 2-D digital beamforming (DBF) is presented to measure the respiration and heartbeat of multiple human subjects at unknown positions simultaneously. Through 2-D beam scanning of the whole scene, a 2-D radar image is generated. From the image, the chest motion of multiple targets is accurately located. Then, the vital signs of targets are obtained through forming individual beams focusing on the chests of targets. Moreover, the low intermediate frequency (low-IF) architecture is adopted to minimize the impact of flicker noise in low-frequency amplifier stages. The experimental results demonstrate that the proposed MIMO 2-D imaging radar system can locate chest areas of multiple targets, suppress the clutters, and make vital signs measurement, heartbeat measurement in particular, more robust compared with single-input–multiple-output (SIMO) radar system in complex environment.

Radar imaging using the synthetic aperture radar (SAR) principle is a common method to obtain information about e.g., the surface of a target. However, most image formation algorithms for such systems assume (quasi-)static measurements. This may lead to errors in the processed images if the sensor is moving during the measurement process. This is especially the case for frequency-modulated continuous-wave (FMCW)-based sensors since the signal duration is longer than in a pulsed system and the achievable bandwidth is much larger and introduces additional challenges. Motion compensation in the context of radar imaging is usually related to the correction of deviations from an ideal trajectory. In contrast, this article presents a method to take the sensor movement during a single FMCW ramp into account and therefore addresses the effects caused by a continuous path during the transmit/receive process. Hence, faster movement can be achieved during the scanning of the synthetic aperture without being bound by stop-and-go approximations. In addition, it will be shown that the algorithm is suitable to reduce systematic errors due to aliasing caused by spatial sampling below the Nyquist rate. For this purpose, this article presents simulations and measurement results, obtained by an ultrawideband $D$ -band FMCW radar operating between 122 and 170 GHz.

The problem of strong sea clutter, e.g., sea spikes, may bring in low signal-to-clutter ratio (SCR) and cause great interference to radar marine target detection. However, the sea clutter suppression ability of current algorithms is limited with poor generalization under complex marine environment. In this letter, a novel sea clutter suppression generative adversarial network (SCS-GAN) is designed and employed for marine radar plan-position indicator (PPI) images detection. The SCS-GAN is based on residual networks and attention module, which includes residual attention generator (RAG) and sea clutter discriminator (SCD). In order to expand the data sets and improve generalization ability, clutter-free data set A, simulated sea clutter data set B (containing five types of sea clutter distributions), and actual sea clutter data set C are constructed by means of simulation and acquisition of real radar returns. At last, the parameter, i.e., clutter suppression ratio (CSR) is designed for evaluating the sea clutter suppression performances of the proposed method and other denoising and clutter suppression methods including CBM3D, denoising convolutional neural network (DnCNN), FFDNet, and Pix2pix. After testing with actual data, it is proved that the SCS-GAN has faster clutter removal speed, stronger generalization ability, and at the same time marine targets in images are remained completely.

In the actual scene of interferometric inverse synthetic aperture radar (InISAR) imaging, the noncooperative targets may make a nonuniform 3-D rotational motion (3-D-RM), which contributes not only to the time-variant Doppler modulation but also to the spatial-variant wave path difference (SVWPD). This, in turn, seriously degrades the 3-D geometry reconstruction accuracy of the targets. Furthermore, it is an enormous challenge to realize InISAR imaging from sparse frequency band and sparse aperture (SFB-SA) signals. This article seeks to address the problems of fine image registration and 2-D joint sparse reconstruction (2-D-JSR) for InISAR imaging with SFB-SA signals. With regard to the maneuvering targets with 3-D-RM, a novel SVWPD signal model is established. Moreover, a new algorithm, named joint wave path difference compensation (JWPDC) algorithm, is developed to perform fine image registration. It can not only combine multiple channels to achieve image registration but also jointly compensate for the non-SVWPD (NSVWPD) and SVWPD. A joint multichannel 2-D-JSR (JMC-2-D-JSR) ISAR imaging algorithm is also proposed according to the SFB-SA signal model to produce high-resolution ISAR images. Underpinned by the Bayesian compressive sensing (BCS) theory, the JMC-2-D-JSR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Through iterative processing of JMC-2-D-JSR and JWPDC, the high-quality 3-D InISAR images of maneuvering targets with 3-D-RM can be obtained. Extensive experimental results based on both simulated and real data corroborate the effectiveness of the proposed algorithm that outperforms other available InISAR imaging frameworks in 2-D imaging, 3-D imaging, and motion compensation.

We report on the realization of a multichannel imaging radar that achieves uniform 2-D cross-range resolution by means of a linear array of a special form of leaky-wave antennas. The presented aperture concept enables a tradeoff between the available range resolution and a reduction in the number of channels required for a given angular resolution. The antenna front end is integrated within a multichannel radar based on stepped-carrier orthogonal frequency-division modulation, and the advantages and challenges specific to this combination are analyzed with respect to signal processing and a newly developed calibration routine. The system concept is fully implemented and verified in the form of a mobile demonstrator capable of soft real-time 3-D processing. By combining radio frequency (RF) components operating in the $W$ -band (85–105 GHz) with the presented aperture, a 3-D resolution of less than ${1.5}^\circ \times {1.5}^\circ \times $ 15 cm is demonstrated using only eight transmitters and eight receivers.

As the electromagnetic environment in battlefields is more and more complex, automatic modulation recognition for radar signals is becoming vital and challenging. Traditional methods are more likely to cause lower recognition accuracy with higher computational complexity in low signal-to-noise ratio (SNR). Feature redundancy especially for handcrafted features is one of the shortcomings of deep-learning-based methods. In this article, a novel end-to-end sequence-based network that consists of a shallow convolutional neural network, a bidirectional long short-term memory (Bi-LSTM) network strengthening with a self-attention mechanism, and a dense neural network is constructed to recognize eight kinds of intrapulse modulations of radar signals. The autocorrelation functions of received radar signals are first calculated as autocorrelation features. Then, these features are employed as inputs of the proposed network which owns significant sequence processing advantages and adaptive selection ability of features. Finally, the proposed network outputs prediction modulations directly. The simulation results verify the robustness and effectiveness of autocorrelation features. And the proposed network achieves about 61.25% accuracy at −20 dB and more than 95% accuracy at −10 dB. Compared with four state-of-the-art networks, the proposed network has better recognition performance especially at low SNRs with much lower computational complexity. Results on measured signals also demonstrate that the proposed network outperforms these four networks.

We propose a drone classification method for polarimetric radar, based on convolutional neural network (CNN) and image processing methods. The proposed method improves drone classification accuracy when the micro-Doppler signature is very weak by the aspect angle. To utilize received polarimetric signal, we propose a novel image structure for three-channel image classification CNN. To reduce the size of data from four different polarization while securing high classification accuracy, an image processing method and structure are introduced. The data set is prepared for a three type of drone, with a polarimetric Ku-band frequency modulated continuous wave (FMCW) radar system. Proposed method is tested and verified in an anechoic chamber environment for fast evaluation. A famous CNN structure, GoogLeNet, is used to evaluate the effect of the proposed radar preprocessing. The result showed that the proposed method improved the accuracy from 89.9% to 99.8%, compared with single polarized micro-Doppler image. We compared the result from the proposed method with conventional polarimetric radar image structure and achieved similar accuracy while having half of full polarimetric data.

The synthetic aperture radar (SAR) image of ground moving target is easily defocused given the unknown relative motions between radar and target. In addition, the limitation of pulse repetition frequency for SAR easily induces the azimuth ambiguity (including Doppler center blur and spectrum ambiguity) of target signals, which causes the difficulty of moving target imaging. In this paper, a new efficient method for imaging and motion parameter estimation of ground moving targets with azimuth ambiguity is presented. Firstly, the time reversal process-2D scaled Fourier transform (TRP-2DSCFT) is developed to eliminate the effects of cross-track velocity and estimate the along-track velocity of target, simultaneously. Secondly, an operation based on discrete polynomial-phase transform and phase compensation function is proposed to estimate the cross-track velocity of target. Finally, a matched filter function based on estimated parameters is constructed to focus the moving targets. The well-focused result can be obtained by the presented method without the residual compensation errors. Moreover, the proposed method is computationally efficient given that the exhaustive searching steps are avoided. Additionally, the azimuth ambiguity can be effectively removed without the ambiguity number searching and pre-processing based on the prior information. The cross-terms for multiple target processing are analyzed. The effectiveness of the method is verified by both spaceborne and airborne real data results.

Linear frequency-modulated (LFM) continuous-wave (CW) radar is usually the first choice in synthetic aperture radar (SAR) imaging missions due to its relatively low cost and hardware simplicity. However, the use of continuous-wave unnecessarily introduces the problem of intrapulse motion. Furthermore, a large amount of data generated by the CW imaging process may overburden the onboard communication system with its high streaming data rate. On the other hand, a large amount of data may, indeed, not be necessary for high-resolution SAR imaging. In this article, we took full consideration of the intrapulse motion in LFMCW radar systems and modeled the phase preserving extended frequency scaling algorithm (EFSA) reconstruction process with far fewer data samples as compressive sensing problem and used subgradient descent algorithm with optimal step size to realize compressive sensing reconstruction. Our compressive sensing problem is different from the traditional direct inversion-based problem in that our reconstructed results contain both mainlobe and sidelobes. Comparisons were made with mainstream iterative soft thresholding-based reconstruction algorithm to demonstrate its capability to reconstruct large imaging scenes with high resolution. Both simulations and experiments with measured data have verified our proposed algorithm.

Forward-looking multichannel synthetic aperture radar (FLMC-SAR) is an important tool for modern remote sensing applications, which has the capability to reconstruct the high-resolution image of the front area. However, due to the azimuth-variant characteristics of the motion errors over a long aperture, FLMC-SAR data processing is usually a challenging task, especially when involving the motion compensation (MOCO) coupled with Doppler ambiguity resolving. To accomplish an accurate MOCO for FLMC-SAR, a novel parametric azimuth-variant MOCO approach is proposed in this article. Aiming at the coupling problem of MOCO and Doppler ambiguity resolving over the full aperture, we can decouple them through the subaperture division. As a full synthetic aperture is decomposed into several subapertures, the high-order motion errors of the full aperture can be decomposed into the first-order motion errors of the subaperture. On this basis, the mismatch of the space–time spectrum caused by the motion errors can be solved by spectral estimation, yielding Doppler ambiguity resolving for each subaperture. Meanwhile, the azimuth-variant characteristic of motion errors in FLMC-SAR system is characterized by a parametric angle-dependent quadratic phase error (QPE) model. The motion parameters are estimated by a joint multichannel angle estimation-based signal quadratic decomposition method. Immediately, the MOCO for ambiguous targets with different motion errors can be processed separately to improve the imaging performance. Experimental results based on both simulated and real data demonstrate that the proposed method is suitable for FLMC-SAR system.

It is always an important problem to recover sparse signals from observations corrupted by Gaussian noise and has been extensively investigated. In high-resolution maritime surveillance radars working at scan mode, ship classification, and recognition need to recover high-resolution range profiles (HRRPs) of ships from radar returns of several pulses with severe range sidelobe effect. Multipulse synthetic data by the aid of ship Doppler information are complex sparse signals corrupted by non-Gaussian correlated interference. In this article, a sparse recovery via iterative minimization (SRIM) method is proposed to estimate complex HRRPs of ships from multipulse synthetic data. The SRIM method adapts the non-Gaussianity nature of the interference in the multipulse synthetic data and ship complex HRRPs are modeled by the random sequences of the biparametric generalized Gaussian distributions (GGDs) (0 p ≤ 1). In the SRIM method, the parameters of the GGD model are iteratively searched by the minimal criterion of the Kolmogorov–Smirnov distance of the residue and the interference model. The SRIM method is compared with the recent linear-programming-based method and the classic sparse learning via iterative minimization (SLIM) method by using simulated and measured radar data and the results show that the SRIM method obtains the better performance.

Microwave photonic (MWP) radar has the advantages of generating and processing wide bandwidth microwave signals, reconfigurability, high immunity to electromagnetic interference compared to microwave electronic radar. It has the potential to be used in applications such as intelligent autonomous and cyber-physical systems. Recent advances in microwave photonic technology led to the generation, fast processing, and control of broadband signals. Because of the advancements in photonic technologies, next-generation microwave photonic radar is becoming more prominent. This article reviews the most recent advancements and future directions in MWP radars. This review article overviews the different components of microwave photonic radar, different design challenges, and issues pertaining to it. We present a comparative study of different MWP radars on different applications. It also discusses possible future research directions of MWP radar.

Ghost targets caused by inter-reflections are by design unavoidable in radar measurements, and it is challenging to distinguish these artifact detections from real ones. In this letter, we propose a novel approach to detect radar ghost targets by using LiDAR data as a reference. For this, we adopt a multimodal transformer network to learn interactions between points. We employ self-attention to exchange information between radar points, and local crossmodal attention to infuse information from surrounding LiDAR points. The key idea is that a ghost target should have higher semantic affinity with the reflected real target than the other ones. Extensive experiments on nuScenes [1] show that our method outperforms the baseline method on radar ghost target detection by a large margin.

Automatic detection of a falling person based on noncontact sensing is a challenging problem with applications in smart homes for elderly care. In this article, we propose a radar-based fall detection technique based on time-frequency analysis and convolutional neural networks. The time-frequency analysis is performed by applying the short-time Fourier transform to each radar return signal. The resulting spectrograms are converted into binary images, which are fed into the convolutional neural network. The network is trained using labeled examples of fall and nonfall activities. Our method employs high-level feature learning, which distinguishes it from previously studied methods that use heuristic feature extraction. The performance of the proposed method is evaluated by conducting several experiments on a set of radar return signals. We show that our method distinguishes falls from nonfalls with 98.37% precision and 97.82% specificity, while maintaining a low false-alarm rate, which is superior to existing methods. We also show that our proposed method is robust in that it successfully distinguishes falls from nonfalls when trained on subjects in one room, but tested on different subjects in a different room. In the proposed convolutional neural network, the hierarchical features extracted from the radar return signals are the key to understand the fundamental composition of human activities and determine whether or not a fall has occurred during human daily activities. Our method may be extended to other radar-based applications such as apnea detection and gesture detection.

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method.

Millimeter-wave and microwave short-range radar are among the most promising environmental sensing methods, with applications to situations that are otherwise optically invisible. This paper presents a Doppler velocity-enhanced range migration algorithm (RMA) that achieves both high spatial resolution and low-complexity three-dimensional (3-D) radar imaging. In this method, we address the problem of human body recognition with motion using the assumption that the motion of different human parts generates distinct micro-Doppler variations in the radar data. These variations could not only separate the RMA images with different Doppler velocities in a coherent integration-based radar imaging process, but enhances a noise reduction effect by decomposing in the Doppler velocity space. To achieve a low complexity in 3-D imaging, we incorporate the RMA method and Doppler velocity-based data decomposition. The results of numerical and experimental tests on a realistic human phantom with a walking motion demonstrate that our method provides more informative and highly separated 3-D images, even in much low SNR situation.

Limited by the total length, the total number of the antenna units as well as their topology, the radar images always suffered from the sidelobe/grating lobe which severely impacts the quality of the radar images. In this article, a convolutional neural network (CNN)-based radar image-enhancing method is proposed. Using the original radar images as the input samples and using their corresponding ideal radar images with no sidelobe/grating lobe as the label to train the CNN. A well-trained CNN can suppress the sidelobe/grating lobe in the radar images. The structure of the specific CNN, the generation methods of the samples and the labels, the training procedure of the CNN, as well as some other detailed implementation strategies are specifically illustrated in this article. The proposed method is utilized to suppress the sidelobe/grating lobe in both the simulated and real recorded radar images. Compared to other existing methods, the proposed method is with better sidelobe/grating lobe suppressing performance and better robustness.

Reasonable and effective resource allocation strategy can fully utilize the system potential of the radar sensor network especially under the limited resources. In this paper, a joint time and aperture resource allocation optimization problem of multi-target inverse synthetic aperture radar (ISAR) imaging in phased-array radar sensor network with game theory is investigated. The purpose of the resource allocation optimization problem is to complete the whole imaging tasks with high resolution within limited time and aperture resources. Obviously, the competition for radar resources exists among the targets under resource constraints. After analyzing the relationship between image resolution and time and aperture resources, the resource allocation optimization problem which is also a distributed decision problem is established and turns out to be an exact potential game. Thereafter the optimal resource allocation strategy is obtained by finding a Nash equilibrium (NE) strategy with an improved best response dynamic (IBSD) algorithm. Numerical experiments show the effectiveness of the proposed algorithm which can obtain a satisfied resource strategy profile.

The development of an automotive surface recognition system is an important and yet unsolved task. In the current study we are considering a novel approach to surface classification based on the analysis of the real road surface images obtained using the 79 GHz imaging radar and demonstrate the advantage of millimeter wave radar for surface discrimination for automotive sensing. The proposed experimental technique in combination with a convolutional neural network provides high surface classification accuracy.

Breast cancer represents one of the main reasons of death among women. As an alternative to the gold standard techniques for breast cancer diagnosis, microwave imaging has been proposed from research community and many microwave systems have been designed mainly to work at low microwave frequencies. Based both on the results of recent dielectric characterization campaigns on human breast ex-vivo tissues up to 50 GHz and on the promising feasibility studies of mm-wave imaging systems, in this article, we propose and test inverse scattering techniques as effective tool to process mm-wave data to image breast cancer. Differently from radar techniques so far adopted in conjunction with mm-wave imaging system, inverse scattering techniques turn out to be more versatile and robust with respect to the reduction of the amount of data and eventually also able to characterize the anomaly in terms of electromagnetic properties. In particular, in the above, two image reconstruction techniques, the Linear Sampling Method and the Born Approximation, are proposed and compared against both simulated and experimental data.

The Chinese Gaofen-3 satellite currently provides us with an open way to access fully polarimetric data in the C-band frequency. The noise equivalent sigma zero (NESZ) is a crucial factor when calibrating the additive noise in radar imagery. For most radar sensors, NESZ coefficients are stored in header files, but these are not provided for the Gaofen-3 products. The minimum eigenvalue estimator (MEE) and maximum likelihood estimator (MLE) are the two most common techniques used to derive the NESZ from polarimetric imagery. Nevertheless, the bias has been found to be higher than 5 dB compared with the noise measurement circuit (NMC) of the hardware. In this article, we propose a minimum noise envelope estimator (MNEE) for the robust estimation of the Gaofen-3 NESZ. In this article, we carried out an in-depth investigation to analyze the error sources of the MEE and MLE techniques. Based on our analysis, the MNEE framework requires the use of the ocean surface as a reference, and MNEE is combined with the minimum operation to suppress overestimation. In the experimental section, we describe how we validated the proposed algorithm with Radarsat-2 images, and the MNEE is treated as a tool to estimate the NESZ of Gaofen-3 polarimetric products. We found that the Gaofen-3 NESZ is generally less than −20 dB, which satisfies the design specification. The range-dependent NESZ coefficients are provided here to allow convenient noise correction for Gaofen-3 data users.

This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced.

In this article, to get a high-resolution radar image with distributed frequency modulated continuous waveform multiple-input–multiple-output (FMCW MIMO) radar, Bayesian matching pursuit (BMP)-based imaging methods are proposed, in which the received signals at the distributed FMCW MIMO radars are reformulated in terms of the (azimuth, range) patches in the image region of interest and the maximum a posterior (MAP) estimator that can estimate the azimuth angles and ranges of multiple targets is then derived. For a single FMCW MIMO radar, we first propose the BMP-based imaging, in which the support vector (indicating the presence of targets in the radar image patches) and the associated coefficients (the reflection coefficients of the associated targets) are iteratively updated. Because MAP estimation needs the combinatorial search over all possible candidates to find the nonzero elements in the support vector, an approximated MAP estimation method is newly proposed. In addition, by extending the BMP-based imaging for a single FMCW MIMO radar, we develop a new distributed BMP-based imaging method when multiple FMCW MIMO radars are spatially distributed. Through the simulation, we confirm that the proposed BMP-based radar imaging methods outperform the conventional backprojection method or the orthogonal matching pursuit-based method with slightly increased computational complexity.

In this article, a bidirectional thermal wave radar thermography (BTWRT) is used for the inspection of hybrid carbon/glass fiber reinforced polymer (C/GFRP) laminates with subsurface defects, and the reliability of BTWRT is deeply investigated based on the probability of detection (POD) analysis. Three classes of C/GFRP laminates are prepared for experimental study, and the hybrid volume ratio of glass fiber and carbon fiber is 1:4, 1:1, and 4:1, respectively. Each class of C/GFRP laminate includes a total of 72 artificial flat-bottom holes with different diameter-to-depth ratios. An 808-nm laser is used as external excitation and modulated by a bidirectional chirp signal. Thermal wave radar feature images are obtained by time-slice imaging algorithms [dynamic component extraction, Hilbert transform (HT), and analytical chirp correlation (ACC)] and conventional algorithms (cross-correlation, chirp lock-in, HT mean, and fast Fourier transform). The imaging quality and reliability of different algorithms are compared and evaluated through the signal-to-noise ratio (SNR) and POD assessment using a hit/miss method, respectively. The SNR comparison and POD assessment results indicate that the ACC time-slice algorithm is more suitable for C/GFRP delamination defect inspection using BTWRT in view of the optimal detectability and reliability. This work provides a valuable reference for BTWRT inspection of hybrid C/GFRP subsurface defects, and the ACC time-slice imaging algorithm has the potential to develop into a highly reliable vision inspection method for the industrial applications out of laboratory.

Terahertz (THz) radar imaging has widely adopted for security screening of standoff human body in recent years, owing to its capacities of high resolution, harmless, contactless, and penetrate clothing. However, THz radar security screening for walking human is still a challenge due to the non-rigid motion of walking. To figure it out, a THz radar security screening method for walking human torso is proposed with multi-angle synthetic aperture. In this method, the echo data of walking human torso component is assumed as a rigid motion model within a short time. Particularly, the torso component has the maximum ratio of surface area and echo energy, which means that its motion phase error can be detected after fractional Fourier transform processing. Besides, after the correction and imaging processing, the imaging results under different angles are fused by coherent integration with registration processing to improve the integrity of scattering information, which is incomplete within a short aperture due to self-occlusion and the changes in incident angles. The simulation experiments are conducted to verify the imaging quality and feasibility of the proposed method. Moreover, the real small aperture echo data of moving torso collected by our THz radar (that operates at 330GHz with the bandwidth of 30GHz and the output peak power of 5mW) is processed via the proposed method to indicate the significance of multi-angle imaging.

In this article, we present a novel method that incorporates the range points migration (RPM) method, $k$ -space decomposition-based accurate, and noise-robust range extraction filter for microwave or millimeter-wave (MMW) short-range radar using a considerably lower fractional bandwidth signal. The advantage for higher angular resolution in higher frequency systems, such as MMW radar, has been implemented to the incoherent-based RPM method, using the simple 1-D or 2-D Fourier transform-based processing to maintain the imaging accuracy in RPM processing for both the range and the angular directions. As an additional advantage of our method, it also offers data clustering in $k$ -space, which can enhance the imaging accuracy of the RPM method. The numerical and experimental tests demonstrated that the proposed method offers numerous advantages over the Capon-based super-resolution algorithm or coherent-based imaging approaches.

A conducting deep cavity is an essentially geometric structure, e.g., the inlet duct and the tail nozzle, of air vehicles and is a dominant scattering source of radar echoes. The scattering centers (SCs) induced by multiple reflections inside deep cavities are different from general SCs. Few references have studied the SC model for deep cavities. Therefore, a concise SC model is presented in this letter. In this model, an analytical expression for the projection distances of SCs along the line-of-sight (LOS) in deep cavities is derived by using the ray- tracing method. Rectangular cavities, cylindrical cavities, and a cavity composed of two cylindrical structures are investigated in this letter. The presented SC models are validated by a comparison between the radar images simulated by the model and those computed by a full-wave numerical method.