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Due to its high resolution and deep penetration depth, microwave ultrawideband radar is a promising tool for the non-destructive testing (NDT) of transportation infrastructure. Microwave radiation can also be used to reconstruct the dielectric properties of objects and therefore can be used to detect an air cavity or metallic rust in concrete. We used contrast source inversion (CSI) as one of the most promising inverse scattering schemes. To resolve the observation domain limitations of NDT, radar imaging method, also known as range points migration (RPM) method, was first incorporated into the inverse scattering algorithm based on CSI as a region of interest (ROI) estimator, which substantially improves the accuracy of complex permittivity reconstruction. In addition, the ROI optimizing scheme based on the CSI cost function is used to enhance ROI accuracy. The effectiveness of the proposed methods is validated via finite-difference time-domain (FDTD)-based numerical simulation, which assumes typical NDT model. [ABSTRACT FROM AUTHOR]

Fast detection of hazardous objects in millimeter-wave personnel screening can increase the operating efficiency of secure environments. In airports, accurate automatic detection and classification of body-worn objects can reduce the number of pat-downs while keeping the transportation environment safe and secure. Since many benign objects, such as paper and leather, are lossy materials, being able to characterize them and set them aside from the secondary checks will significantly reduce the screening process time. In this article, we introduce an effective image-processing-based method for characterizing lossy materials that might be concealed under clothing at checkpoints. The method has been combined with a modified version of the previously developed algorithm that can be used specifically to characterize lossless materials. The proposed algorithm can automatically distinguish lossless materials from lossy ones and calculate their thickness and permittivities. Starting from the radar reconstructed image showing a cross section of the body, we extract the nominal body contour using the Fourier series, separate body, and object responses, categorize the object as lossy or lossless based on the depression and protrusion of the body contour, and, finally, predict possible values for the object’s permittivity and thickness. Our resulting classification is good, implying fewer nuisance alarms at checkpoints. [ABSTRACT FROM AUTHOR]

Traditionally, inverse synthetic aperture radar (ISAR) imaging and target tracking are two separate and independent processes. ISAR imaging is usually achieved after data collecting and tracking for a long time, resulting in the problems of low imaging efficiency, information loss, and probing resource allocation difficulty. In this article, we proposed an integration method of tracking and imaging with wideband radar by constructing the relation among signal, data, and image domains, which can realize accurate tracking and high data-rate imaging at the same time. In this integrated imaging and tracking method, a complex-valued reference high-resolution range profile (HRRP) is generated and predicted by exploiting the relation among range-Doppler image, echo in time domain, and target's motion state. Then, a Bayes estimation model is built to extract range phase error between the predicted HRRP and the real-measured HRRP, and the Kalman filter can update the motion state. In addition, in order to increase the efficiency, the decentralized Kalman filter is also introduced to realize the subaperture parallel processing and data fusion in this article. Finally, the sequential motion compensation is carried out to realize accurate motion compensation and sequential imaging. The simulated and real-measured data confirm the proposed algorithm's robust imaging and tracking performance. Experimental results show that the proposed method has excellent efficiency and can simultaneously realize sequential imaging and accurate tracking. [ABSTRACT FROM AUTHOR]

With the increasing of on-orbit satellites, target dynamic monitoring becomes more and more important in space situation awareness applications. Currently, there are some exploratory methods monitoring attitude-stabilized targets using various high-resolution remote sensing technologies. However, it is still a big challenge to achieve dynamic estimation of spin satellite with single sensor. Inspired by the existing matching based works, this article presents a dynamic estimation method of spin satellite using single-station inverse synthetic aperture radar (ISAR) images. When the angle accumulation is set to be a constant in each ISAR imaging period, the projection change of target structures in the long-term observation sequence is described with the hidden Markov model to build the mathematic expression of target on-orbit state. In this condition, target sequential attitude can be solved with Viterbi decoding algorithm even the azimuth scaling of each image is difficulty due to target spin motion. Then, combined with the radar observation geometry, target sequential attitude parameters are substituted into a spin parameter optimization, which is solved by particle swarm optimization algorithm. In the end, target instantaneous attitude vectors and spin speed are used to express its dynamic during the observation period. Simulation experiments of a typical spin spacecraft, Tiangong-I, confirm the feasibility of the proposed algorithm, and its superiority is also investigated by comparison with the existing work. [ABSTRACT FROM AUTHOR]

The advantages of sub-terahertz technology (sub-THz, 200–700 GHz) have already been advanced for imaging and recognition of a space object's state from a space-based sensor using inverse synthetic aperture radar (ISAR). The technology benefits from wide absolute signal bandwidths, giving high range resolution and from enhanced sensitivity to surface texture. An experimental validation of such a system in controlled laboratory conditions is presented. Possible image formation methods are proposed and evaluated experimentally. Images of real parts of satellites have been produced at sub-THz frequencies and compared to lower frequency images. Other results include sub-THz bistatic ISAR, and sub-THz cross-polarized images which highlight the scattering from rough surfaces while attenuating the very bright scatterers seen from conventional monostatic images. [ABSTRACT FROM AUTHOR]

Range migration (or range focusing) techniques are widely used in optical, acoustic, and microwave real-time image reconstruction methods. They have been successfully applied to far-field 3-D imaging where they rely on plane-wave assumptions, which ignore the data amplitude variation over the acquisition aperture. The accuracy of the plane-wave assumption, however, quickly degrades in close-range imaging, where amplitude variations are significant and where the range to the target is on the order of the range sampling step. Here, we present a range-focusing method of improved accuracy, which is applicable to both far-zone and close-range monostatic radar. It refocuses the system point-spread function (PSF) to any range location, taking into account both magnitude and phase changes. The approach can be applied with any Fourier-based imaging algorithm utilizing the Lippmann–Schwinger equation as the underlying scattering model. Here, it is validated through examples based on simulated and measured data where the images are reconstructed with quantitative microwave holography (QMH). QMH employs measured PSFs to achieve quantitative imaging in real-time. The proposed range-migration method is applicable with measured PSFs, too, leading to reduced system-calibration effort and the ability to focus an image at any desired range. [ABSTRACT FROM AUTHOR]

We present an approach to automatically generate semantic labels for real recordings of automotive range-Doppler (RD) radar spectra. Such labels are required when training a neural network for object recognition from radar data. The automatic labeling approach rests on the simultaneous recording of camera and lidar data in addition to the radar spectrum. By warping radar spectra into the camera image, state-of-the-art object recognition algorithms can be applied to label relevant objects, such as cars, in the camera image. The warping operation is designed to be fully differentiable, which allows backpropagating the gradient computed on the camera image through the warping operation to the neural network operating on the radar data. As the warping operation relies on accurate scene flow estimation, we further propose a novel scene flow estimation algorithm which exploits information from camera, lidar and radar sensors. The proposed scene flow estimation approach is compared against a state-of-the-art scene flow algorithm, and it outperforms it by approximately 30% w.r.t. mean average error. The feasibility of the overall framework for automatic label generation for RD spectra is verified by evaluating the performance of neural networks trained with the proposed framework for Direction-of-Arrival estimation. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel processing algorithm for frequency-modulated continuous-wave (FMCW) ice sounding radar, which addresses the along-track focusing of internal reflecting horizons. It is a modified frequency scaling algorithm that can handle the two-layer medium imaging model as well as the Doppler impact of the motion within the sweep in a nadir-looking radar. The proposed algorithm’s comprehensive derivation and implementation processes are described. In the range-Doppler domain, a really effective formulation for radar signals is also presented, which considers refraction effects and electromagnetic wave propagation velocity changes at the interface of two different media. Point target simulations are carried out to demonstrate the algorithm’s performance. The proposed method is also tested on the real data acquired by the shallow-layer-detection ice sounding radar (SLDISR). The result shows the applicability of the proposed method for imaging the ice sheet. [ABSTRACT FROM AUTHOR]

Target classification is of great value when the number of transceivers in a sensing system is relatively small. This article proposes a high-accuracy and efficient classification method with machine learning techniques on frequency-modulated-continuous-wave (FMCW) radar. We first establish the novel mapping relationship from physical space to range-Doppler (PS-RD) image based on wave propagation theory, by which four physical features that effectively capture the kinematic and geometrical characteristics of targets, including speed, total reflectivity (ToRe), area, and incidence angle, are extracted from range-Doppler (R-D) image. Then, a multilayer perceptron (MLP) with a single hidden layer is employed to realize the classification. Since the above-mentioned four physical features, derived from wave physics, are chosen as the input of the neural network, our classifier does not work in a black-box way. The computational complexity of the whole classifier is the same as that of a 2-D fast Fourier transform (FFT), which guarantees a real-time operation. As an example, the proposed classifier is applied to the automotive radar system, where road targets are to be classified into five categories, including pedestrian, bike, sedan, truck/bus, and other static objects. Real-world data obtained from 77 GHz FMCW radars are provided for validation, where the proposed physics-assisted classifier turns out to outperform the state of the art in automotive radar application. The overall accuracy of the real data is about 99% even with complex multiple-target cases. [ABSTRACT FROM AUTHOR]

Deep learning with its rapid development and advancement has achieved unparalleled performance in many areas like computer vision as well as cognitive radio and signal recognition. However, the performance of most deep neural networks would suffer from degradation in the data mismatch scenario, e.g., the test dataset has a related but nonidentical distribution with the training dataset. Considering the noise corruption, a classifier's accuracy might drop sharply when it is tested on a dataset with much lower signal-to-noise ratio compared to its training dataset. To address this dilemma, in this work, we propose an efficient denoising and classification network (DNCNet) for radar signals. The DNCNet consists of denoising and classification subnetworks. First, a radar signal detection and synthetic mechanism is designed to generate pairwise clean data and noisy data for the DNCNet to train its denoising subnetwork. Then, a two-phase training procedure is proposed to train the denoising subnetwork in the first phase and strengthen the mapping between the denoising results and perceptual representation in the second. Experiments on synthetic and benchmark datasets validate the excellent performance of the proposed DNCNet against state-of-the-art methods in terms of both signal restoration quality and classification accuracy. [ABSTRACT FROM AUTHOR]

We derive a detector function for quantum two-mode squeezing (QTMS) radar and noise radar that is based on the use of a generalized likelihood ratio (GLR) test for distinguishing between the presence and absence of a target. In addition to an explicit expression for the GLR detector, we derive a detector function which approximates the GLR detector in the limit where the target is small, far away, or otherwise difficult to detect. When the number of integrated samples is large, we derive a theoretical expression for the receiver operating characteristic (ROC) curve of the radar when the GLR detector is used. When the number of samples is small, we use simulations to understand the ROC curve behavior of the detector. One interesting finding is that there exists a parameter regime in which a previously-studied detector outperforms the GLR detector, contrary to the intuition that LR-based tests are optimal or nearly so. This is because neither the Neyman–Pearson lemma, nor the Karlin–Rubin theorem which generalizes the lemma to composite hypotheses, hold in this particular problem. However, the GLR detector remains a good choice for target detection in certain regimes. [ABSTRACT FROM AUTHOR]

In an airborne maritime radar, inverse synthetic aperture radar (ISAR) is used to image and classify non-cooperative targets. In this article, we investigate a 3D-ISAR as an alternative representation that can potentially improve the final target classification by automatically extracting key features from the radar data, such as the length, width, and height of a target. The technique works by estimating the target scatterers’ positions using measurements from multiple ISAR images. This has previously been achieved using a single receive channel that exploits the temporal motion of the target or with a multichannel dual-baseline interferometric system that uses the phase differences in both the along- and across-track directions. In this article, we combine aspects of these two techniques using a single along-track baseline suitable for airborne maritime radar systems. [ABSTRACT FROM AUTHOR]

Accurate translational motion compensation is critical for inverse synthetic aperture radar (ISAR) imaging, especially for datasets with low signal-to-noise ratio (SNR) or sparse aperture. Among the existing translational motion compensation methods, the methods that based on the polynomial model have profound noise robustness but are easily affected by the variation of the reference distance. The methods that based on the range profile alignment have good versatility but are far from ideal under low SNR and sparse aperture environment. Therefore, we propose a novel and robust translational motion compensation method to overcome the shortcoming of the existing methods. Specifically, we estimate the translational phase error sequence by maximizing Laplacian average gray level and peak value. It can improve the robustness of translational motion compensation for noise and sparse aperture. Since such principle creates a large-scale multiobjective optimization problem, we design weighted optimization framework—second-order drift particle swarm optimization (WOF—SDPSO) to solve it accurately. WOF-SDPSO achieves full exploration of high-dimensional variable space by variable dimension reducing, random drift update, and second-order oscillation convergence. These ensure the global optimal solution easier to be found. Experimental results of the measured dataset prove that the proposed method is effective and accurate under the conditions of low SNR and sparse aperture. Moreover, the proposed method is noise robust for polynomial translational motion model with sparse aperture. [ABSTRACT FROM AUTHOR]

The spatial correlation of ice-sounding data can be used to trace internal isochronic layers and synchronize the age–depth relationship between different ice core sites, which is difficult using existing imaging methods. In this study, we propose a new algorithm to address the opportunity that applying spatial correlation to ice-sheet imaging offers. The algorithm is a spatial-correlation-based ice-sounding imaging method using fast back projection (FBP) that successfully improves the spatial correlation of imaging results with high efficiency. We give the specific steps to implement the algorithm and apply it to simulate both point targets and ice-sounding radar data to demonstrate its validity in imaging ice sheets. Furthermore, compared with two previous methods, the proposed algorithm improves the spatial correlation without degrading the ability of signal-to-noise ratio (SNR) improvement and processing efficiency. [ABSTRACT FROM AUTHOR]

Dynamic deformation measurement is an important approach to monitor structure stability. Due to its rapid imaging capabilities, ground-based multi-input–multi-output (GB-MIMO) radar has shown great application potential in bridge structure monitoring. This article proposes a dynamic deformation estimation method based on radar image series. First, an improved clutter suppression method is used to overcome the estimation error in complex working status. Second, a two-step pixel extraction method is adopted to ensure both the quantity and quality of sample pixels. Finally, frequency- and time-domain environmental stimulating methods are jointly considered for stable mode shape estimation. This article systematically measured the dynamic deformations of bridge structures, including two suspension bridges and a cable-stayed bridge. The time-series deformation, deflection, vibration frequency, and amplitude on the bridge structure are obtained by image-domain measurement. For the first time, the mode shapes of bridges are obtained through GB-MIMO radar. The correctness of structure parameter measurement is verified with the finite element model (FEM). Experimental results prove that with the proposed method, stable results could be acquired against weak and complex stimulation conditions. [ABSTRACT FROM AUTHOR]

This article presents detailed geometric analyses of ground clutter in bistatic passive airborne radars. Analytic closed-form solutions are derived for finding the intersection of iso-ranges and iso-velocities. This allows clutter bistatic coordinates to be easily converted to its Cartesian coordinates. Based on those solutions, a theoretical clutter map in the bistatic coordinates can be calculated assuming uniform clutter distribution on the Earth’s surface. This is done by first converting a resolution cell in the bistatic coordinates into a corresponding cell in the Cartesian coordinates. Next, the power distribution in the bistatic coordinates is calculated taking into account range dependency, incident angles, and the radiation patterns of the transmitter and the receiver. The aim of this analysis is to characterize clutter in terms of expected mean power map rather than in terms of statistical distribution. The obtained results provide insights into the expected characteristics of clutter, which can be helpful in designing signal processing algorithms for ground moving target indication (GMTI) and synthetic aperture radar (SAR). The theoretical clutter distribution on the range-velocity map is compared with real-life data acquired with a Digital Video Broadcasting–Terrestrial (DVB-T)-based passive radar and good agreement between theory and measurement is presented. [ABSTRACT FROM AUTHOR]

The holographic subsurface radar (HSR) has been a promising geophysical electromagnetic technique for detecting shallowly buried targets with high lateral resolution image. However, radar images are considerably interpreted by strong reflections from rough surface and inhomogeneity in media of interest. In this article, we focus on mitigating the clutter in HSR applications using a learning-based approach, which requires neither prior information regarding the penetrable medium characteristics nor analytic framework to describe the through-medium interference. The generative adversarial network (GAN) with attentive subspace projection is developed to remove the clutter and recover the target image. The subspaces containing target response are selected with the multihead attention preliminarily. Then, the generative network will further focus on the target regions, and the discriminative network will assess the generated results locally and globally. Experiments using real data were conducted to demonstrate the effectiveness of our approach. The visual and quantitative results show that the proposed approach achieves superior performance on removing clutter in HSR images compared with the state-of-the-art clutter mitigation approaches. [ABSTRACT FROM AUTHOR]

To realize high azimuth resolution for sea-surface targets, this article proposes a superresolution imaging method that relies on the Weibull distribution. The proposed method introduces the generalized Gaussian distribution and Weibull distribution to represent the statistical distribution function of the target prior information and sea clutter, respectively. The corresponding objective function was derived under the maximum a posteriori (MAP) criterion. To address the nonlinearity of the objective function, this article adopts the Newton–Raphson iterative method to resolve it. Simulations and experimental data assessment indicate that the proposed method has superior superresolution imaging performance compared with other traditional superresolution methods for sea-surface target imaging. [ABSTRACT FROM AUTHOR]

Typhoons, a kind of devastating natural disaster, have caused incalculable damages worldwide. The meteorological radar image is essential for weather forecasting, especially typhoons. The weather nowcasting (future 0–6 h) can be implemented via extrapolating radar images without using the primary weather forecasting method—the numerical weather prediction model. However, the existing related techniques based on statistics or artificial intelligence were not efficient enough. In this article, a novel radar image extrapolation algorithm named dynamic multiscale fusion-generative adversarial network (DMSF-GAN) was proposed. DMSF-GAN captures the future radar image distribution based on current radar images through modifying the GAN. In the generative module of GAN, an auto-encoder consisting of dynamic inception-3-D and feature connection blocks extracts significant features from current radar images. The feasibility of the proposed model was verified on a real radar image dataset, and the experimental results proved that the proposed algorithm could effectively capture the location and pattern of the future radar echo, especially for typhoon weather systems. Compared with the mainstream methods of radar image extrapolation such as optical-flow and recurrent neural network (RNN)-based models, DMSF-GAN has a more superior and robust performance, which is also suitable for running on low-configuration machines. [ABSTRACT FROM AUTHOR]

Microwave photonic synthetic aperture radar (MWP-SAR) offers a larger signal bandwidth than conventional SAR, and its theoretical resolution can be improved to centimeter level. To achieve same order of magnitude resolution in the azimuth direction, long synthetic aperture is always required, which results in extremely high requirements for the accuracy of imaging procedure. Compared with the conventional SAR imaging algorithms, two major problems should be considered: 1) the scattering characteristics of target might vary from the frequency of signal and the angle of incidence, which seriously affects the coherence of received echo and 2) the imaging of the MWP-SAR system is more sensitive to motion errors and therefore requires higher accuracy of motion compensation processing. To solve the above issues, a high-precision imaging method for MWP-SAR is proposed. First, based on the attribute scattering center (ASC) model, this article analyzes the target scattering characteristics with different frequencies and incident angles. Then, according to the influence of scattering phase on MWP-SAR imaging, an optimal subaperture division algorithm is proposed to guarantee the coherence of each subaperture data and better imaging results. Furthermore, to compensate for the effects of high-order motion errors, this article proposes a motion error estimation algorithm based on subimage registration, where the full-aperture high-order motion errors can be divided into multiple linear components, and the flight trajectory of platform can be accurately reconstructed. Finally, a full-aperture time-domain high-precision imaging method is presented, and the effectiveness of the proposed formation is verified by both simulation and actual airborne MWP-SAR data processing. [ABSTRACT FROM AUTHOR]

In this study, we propose a generalized algorithm for far-field radar cross section determination by using 3-D synthetic aperture imaging with arbitrary antenna-scanning surfaces. This method belongs to a class of techniques called image-based near-field-to-far-field transformation. The previous image-based approaches have been formulated based on a specific antenna-scanning trajectory or surface, such as a line, plane, circle, cylinder, and sphere; the majority of these approaches consider 2-D radar images to determine the azimuth radar cross section. We generalize the conventional image-based technique to accommodate an arbitrary antenna-scanning surface and consider a 3-D radar image for radar cross section prediction in both the azimuth and zenith directions. We validate the proposed algorithm by performing numerical simulations and anechoic chamber measurements. [ABSTRACT FROM AUTHOR]

So far, researchers have proposed various methods to improve the quality of medical ultrasound imaging. However, in portable medical ultrasound imaging systems, features, such as low cost and low power consumption for battery longevity, are very important. Hence, most of the proposed algorithms have not been proper substitutes for the delay and sum (DAS) algorithm in portable clinical applications due to their high computational complexity and cost. In this article, a new algorithm is presented concentrating on reducing the computational complexity based on a technique that separates the signal from the correlated interferences to overcome the negative characteristics, particularly for portable applications such as high price, high power consumption, and off-axis clutters in the azimuth direction. Also, the proposed algorithm yields a higher contrast compared to that of the DAS algorithm while achieving a similar computation complexity order of ${O}$ (${n}$) similar to the DAS algorithm. Furthermore, the performed simulations confirm that the proposed method is able to achieve a better resolution almost twice as that of the filtered delay multiply and sum (F-DMAS) algorithm with the same sidelobe level. [ABSTRACT FROM AUTHOR]

Forward modeling of wave scattering and radar imaging mechanisms is the key to information extraction from synthetic aperture radar (SAR) images. Like inverse graphics in the optical domain, an inherently-integrated forward-inverse approach would be promising for SAR advanced information retrieval and target reconstruction. This paper presents such an attempt at inverse graphics for SAR imagery. A differentiable SAR renderer (DSR) is developed, which reformulates the mapping and projection algorithm of the SAR imaging mechanism in the differentiable form of probability maps. First-order gradients of the proposed DSR are then analytically derived, which can be back-propagated from rendered image/silhouette to the target geometry and scattering attributes. A 3D inverse target reconstruction algorithm from SAR images is devised. Several simulation and reconstruction experiments are conducted, including targets with and without background, using synthesized data or real measured inverse SAR (ISAR) data by ground radar. Results demonstrate the efficacy of the proposed DSR and its inverse approach. [ABSTRACT FROM AUTHOR]

In this paper, a feature extraction technique based on the electromagnetic (EM) representation of radar signals is presented. In particular, we focus on ground-penetrating radar (GPR) imaging, where we model the backscatter from varying 2-D geometric shapes with arbitrary local coordinate rotations. Due to the electrically small nature of buried targets and the bending of the radar signal at the air–soil interface, we focus on exact methods to model the surface current density induced on scattering surfaces. Overcomplete basis sets are derived from the EM descriptions to represent the scene sparsely. From this proposed modeling framework, we devise a novel methodology to exploit the prediction of scattering behavior to extract features for classification from radar scenes when multiple buried scattering surfaces are present. We see that our method can identify and reconstruct buried scattering geometries in the presence of false targets that are brought about by the nonlinear nature of the exact EM modeling methods. A noniterative algorithm based on the conjugate of Green’s function is developed to solve for the surface current in an unknown domain using multifrequency, multiaperture data. Our modeling and feature extraction algorithms are numerically validated for different target shapes buried in lossy soil profiles. [ABSTRACT FROM AUTHOR]

In this article, an active multireceiver low-THz imaging radar is used to generate 3-D images of automotive environments by combining time (difference) of arrival (TDoA) information with backprojection techniques to give height profiles of the objects in the scene. The large bandwidth of this system facilitates noncoherent height reconstruction using time of arrival alone. In doing so, the constraints on receiver coherency are relaxed compared to many height-finding techniques. 3-D reconstructions of common roadside targets and scenes are generated through a modified form of backprojection, adapted to use the time of arrival information of the signals from multiple receivers with different sensitivities, which are arranged in a quasi-multistatic configuration. Here, it is experimentally shown that the use of three vertically-aligned receivers is sufficient to provide a fine resolution in range, cross-range, and height. The system is used to generate 3-D reconstructions of a number of different scenarios, including pedestrians moving through a scene, and complex real-world road scenes. [ABSTRACT FROM AUTHOR]

The concept of sub-terahertz (sub-THz) imaging and recognition of space objects by a satellite-based inverse synthetic aperture radar (ISAR) is proposed and analyzed in this article. Operation at sub-THz frequencies in the band 200–400 GHz benefits from wide available bandwidth, high sensitivity to surface texture, and very small target features, and requires an antenna of small physical size. A feasibility study is presented based on use case scenarios of the relative motion of monitoring satellite and observed objects. Expected resolutions, power budget, and oscillator stability requirements in this frequency band are discussed. Image formation methods are proposed and evaluated with simulations. A simple electromagnetic modeling capability is proposed to create a comprehensive dataset of sub-THz ISAR images, with the orientation and aspect angles seen by the sensors, which can be used to train semantic classifiers. [ABSTRACT FROM AUTHOR]

This article presents an image-based radar detector, named neighborhood difference order statistics (NDOS) detector. Different from the classic likelihood ratio detector, the proposed detector treats the echo spectrum as an image and determines the existence of a target by comparing the difference between the test cell and its adjacent cells with a threshold. The closed-form expressions of probabilities of detection and false alarm are derived under Gaussian noise background and Swerling I target model. It is proved that the detection performance of the proposed detector approaches the optimal likelihood ratio detector when the homogenous noise power is known. When the noise power is unknown, we modify the detector into cell-averaging (CA) NDOS detector by estimating the noise power. Analytical derivations show that the CA-NDOS detector holds the constant false alarm rate (CFAR) property. Moreover, CA-NDOS detector possesses a better detection performance compared with two typical CFAR algorithms under the condition of typical reference window size. [ABSTRACT FROM AUTHOR]

This article proposes a new synthetic aperture radar (SAR), named as panoramic SAR, based on a combination of linear and rotational SARs, by which a large 360° panoramic view of the observed scene can be reconstructed. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The combined movement constitutes a 2-D synthetic aperture, and thus higher imaging resolutions can be obtained. The corresponding resolution analysis and the sampling criteria are discussed accordingly. Then, a novel dynamic piecewise compensation (DPC) algorithm, a recursive imaging process, is proposed to reduce the processing complexity significantly. The imaging implementation and the complexity are also studied respectively. Finally, a prototype of panoramic SAR is built based on an frequency-modulated continuous wave (FMCW) radar and a moving platform, and the simulation and experimental results are provided to validate the proposed panoramic SAR principle and the DPC algorithm. [ABSTRACT FROM AUTHOR]

As a classical sparse reconstruction algorithm, iterative soft thresholding (IST) is often used in SAR sparse imaging applications. However, for moving targets, the radar echo contains an unknown phase error, so it is difficult to directly apply the IST algorithm to reconstruct the SAR moving target image. In this article, a parametric IST (P-IST) algorithm is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In this algorithm, the echo phase error is modeled as a function of the phase compensation factor, and in the iteration process, a phase compensation factor optimization selection step based on residual energy minimization is added. Compared with the existing algorithms, the proposed P-IST algorithm can obtain the global optimal solution and has better efficiency and robustness. Both simulated data and measured data are used to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods are showing superior potential in imaging performance over classical matched filtering-based methods. However, the CS-based methods require much more computational cost to solve the iterative optimization composed of large-scale matrix operators. To hold the improvement of imaging performance and reduce the computational cost, in this article, we propose a novel SAR imaging method by a deep unfolded network (DUN) of an iterative shrinkage threshold algorithm (ISTA) with the approximated observation of a range-Doppler algorithm (RDA) operator. The proposed method takes the radar echoes as the input to learn the imaging procedure. First, the approximated observation is utilized in an SAR imaging model to reduce the size of the DUN. Moreover, we use ISTA as an example to introduce how to establish DUN with approximated observation, in which the detailed structure to handle the complex-valued radar echoes is also designed. Finally, the autoencoder is utilized to calculate the difference of the echoes rather than the imaging results, so that we can train the proposed network by unsupervised learning. The experiments of point targets, surface targets, and real scenes show that the proposed imaging method is superior in terms of imaging performance and computing efficiency. [ABSTRACT FROM AUTHOR]

The frequency scaling algorithm (FSA) is a popular imaging algorithm for dechirped SAR data. To obtain a large azimuth detection area, the miniature millimeter-wave (mmW) linear-frequency-modulated continuous-wave (LFMCW) surveillance radar requires a wider azimuth beamwidth, which leads to additional range frequency aliasing in FSA. Because of the adoption of the dechirp-on-receive technique, the sampling frequency is much smaller than the range bandwidth during near-field imaging, which further aggravates the aliasing effects. The target cannot be well focused, and it makes the weak targets submerged in the background. To acquire high-quality SAR images, an improved FSA using the local stretch operation is proposed. The aliasing bandwidth properties introduced by the FS operation and the desired objective range cell migration (RCM) factor are used in this proposed local-stretch FSA (LSFSA). The initial RCM factor is adjusted by the stretch operation to eliminate the frequency aliasing to a certain level without increasing the computing load. The LSFSA is suitable for solving the problem of range frequency aliasing in near-field side-looking SAR and high squint SAR with wide azimuth beamwidth. The proposed method is validated using reasonable simulations and convincing experiments. [ABSTRACT FROM AUTHOR]

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring, and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this article, an overall processing framework for target detection, parameter estimation, and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for pointlike targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space–time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research. [ABSTRACT FROM AUTHOR]

Earth-based repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) is a powerful tool to obtain the high-precision lunar terrain and deformation information, due to the short revisit period as well as the all-weather, long-time, and large-coverage characteristics of radar lunar observation. In this article, the baseline formation mechanism is investigated. Theoretical analyses show that the interferometric baselines mainly come from the lunar libration; however, the lunar libration may also result in a significant change of baselines and further lead to serious geometric decorrelation. To address this problem and ensure the Earth-based repeat-pass InSAR of the Moon, the precise relative motion model between the radar and the lunar target, considering lunar libration, is established, and the subradar point (SRP) wagging phenomenon is analyzed. Then, to evaluate the effects of SRP wagging on InSAR coherence, a geometric decorrelation model based on azimuth angle and incident angle is proposed. Based on that, the spatial–temporal baseline for Earth-based repeat-pass InSAR of the Moon is analyzed based on numerical simulation. Results show that the temporal baseline has obvious periodicity with a cycle of about 27 days. When the temporal baseline is an integral multiple of 27, the spatial baseline usually has the minimum value and the geometric decorrelation effect is relatively slight. Moreover, using the geometric decorrelation criteria, we examine the effects of radar frequency, position, and target location on the quantity of viable interferometric pairs, which has guiding significance for the selection of radar system parameters, radar position, and lunar target area. [ABSTRACT FROM AUTHOR]

Many high-resolution maritime radars for sea-surface small target detection transmit pulses with large time-bandwidth-products to increase transmitter power and to use the pulse compression technique for high range resolution. On the sea surface, besides small targets of interest, there exist many uninteresting large objects such as big ships, reefs, and offshore platforms for oil production. Large objects have too strong radar returns and their range sidelobes often mask wide range intervals. In these range intervals, masked small targets are difficult to be detected directly. In this article, a complete scheme is proposed to find small targets masked by range sidelobes of large objects in high-resolution maritime radars. In this scheme, high-precision reconstruction of radar returns of large objects is crucial because tiny reconstruction errors are enough to mask weak returns of small targets of interest. An algorithm using the linear programming and oversampled high-resolution range profile model of a large object is given to obtain high-precision reconstruction of the strong radar returns of a large object. From residual radar returns removed the reconstructed strong returns, a near-optimal coherent detector under sea clutter modeled by the spherically invariant random vector with inverse Gamma texture plus colored Gaussian noise is constructed to find masked small targets. Simulated experiments and real measured radar data are used to verify the effectiveness of the proposed scheme and method and reveal their limitations. The results show that the proposed scheme and method are all effective unless small targets fall into the range mainlobe regions of large objects. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) mitigation is critical to the proper operation of ultrawideband (UWB) radar systems because RFI can severely degrade the radar imaging capability and target detection performance. In this article, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the continuous time binary value (CTBV) technology. This sampling strategy compares the signal to a known threshold that varies with slow-time and can be used to achieve a high sampling rate and quantization resolution with simple and affordable hardware. This article establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We use an extended majorization-minimization-based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method for the extended 1bMMRELAX algorithm to improve its computational efficiency. Moreover, a sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases. [ABSTRACT FROM AUTHOR]

We propose a technique for landmine detection using forward-looking ground-penetrating radar. The detector is applied to radar images obtained from multiple viewpoints of the region of interest and is based on a robust version of the likelihood ratio test (LRT). We incorporate the statistical dependence between multiview images into the test via copula-based model. The test is designed to maximize the worst-case performance over all feasible pairs of target and clutter distributions, thereby eliminating the need for strong assumptions about the image statistics. We evaluate the detection performance of the proposed technique for different copula functions representing the dependence structure. Using electromagnetic modeled data of shallow-buried targets under varying ground surface roughness profiles, we demonstrate the superiority of the robust copula-based detector over existing parametric and robust LRT approaches designed under the assumption of statistical independence of multiview images. [ABSTRACT FROM AUTHOR]

Recently, radar-based human activity recognition (HAR) has attracted the attention of researchers as it has been proven that a deep learning (DL) model can be automatically determined by learning a radar dataset. However, unlike general optical image data, the process of collecting and labeling radar data for training the DL model requires considerable manpower and costs. Therefore, an approach that can learn the maximum number of features from a limited radar dataset is essential. Moreover, even if the DL models are trained using a dataset obtained from multiple geometries, performance can be degraded for geometries that are unknown to the trained model. Therefore, we propose a novel radar-based HAR combining a range–time–Doppler (RTD) map and a range-distributed-convolutional neural network (RD-CNN). Unlike the time–Doppler (TD) map, which is mainly used for radar-based HAR, the proposed RTD map provides several human activity-related features by extending the TD map to three dimensions according to the range. The proposed RD-CNN is a new DL model that performs HAR by using the RD layer to extract Doppler features, excluding the range information of RTD map. To verify the performance of the proposed model, experiments were conducted by using the University of Glasgow “radar signatures of human activities,” which is an open dataset for radar-based HAR research. The comparison results of the proposed model and CNNs with the same number of parameters demonstrated a higher recognition accuracy and a lower recognition error even for unknown geometries in the training dataset. [ABSTRACT FROM AUTHOR]

Although the through-the-wall radar imaging (TWRI) system working in the appropriate frequency band can penetrate the nonmetallic obstacles and sense the targets behind, its low imaging spatial resolution hinders the acquisition of more detailed information, such as human pose and shape. This article mainly discusses a deep learning-based human pose and shape recovery method from TWRI images. Inspired by cross-modal learning, the method follows a teacher–student learning pipeline that avoids the heavy cost of manual labeling. Specifically, a camera is attached to the self-develop radar system to simultaneously capture paired red-green-blue (RGB) images and TWRI images in a scenario without wall occlusion. A pose estimation framework (Hourglass) and a semantic segmentation framework (UNet) serve as the teacher network to convert the RGB images into the pose keypoints and the shape masks. By taking inspiration from the topological architecture of these frameworks, a student network radar pose shape network (RPSNet) is designed to extract the information from the corresponding radar images and predict the keypoints and masks that are close to the results above. Instead of learning two single-task objectives independently, multitasking learning is introduced to adaptatively learn common features. When applied to wall-occlusive scenarios, only the radar images are collected and fed into the student network for pose and shape recovery. The advantages of this method over computer vision-based methods for human recovery are demonstrated in scenarios both without and with wall occlusion. [ABSTRACT FROM AUTHOR]

For the target with 3-D rotation, the 3-D synthetic aperture radar (SAR) imaging is very important for the feature extraction and classification. To solve this issue, a novel 3-D imaging algorithm through the airborne SAR system with a single antenna is proposed in this article, which has great advantages for the simplification of system structure compared with the traditional interferometric system. The proposed 3-D airborne SAR imaging algorithm can be implemented with three steps: 1) the azimuth signal is modeled as multicomponent linear frequency modulation (LFM) signal due to the relative movement between the target and radar. 2) The scatterer height position can be obtained by estimating the frequency modulation rate (FMR) for the LFM signal. 3) The 3-D airborne SAR image is obtained via the range-Doppler (RD) algorithm. Furthermore, the reconstruction performance under different rotation patterns, including roll, pitch, and yaw, is analyzed. The availability of the presented novel technique is demonstrated by the results of simulated and real experimental data. [ABSTRACT FROM AUTHOR]

Multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) has extensive application prospects, mainly including the acquisition of multidimensional scattering information, high-resolution and wide-width (HRWS) imaging, and moving target indication (MTI). Its echo separation is the most technical challenge, and so far, the confirmation for orthogonal waveform encoding MIMO-SAR by airborne experiments has not been reported in any literature. Here, an echo separation experiment based on the segmented phase code (SPC) waveforms and an airborne digital beamforming SAR (DBF-SAR) system is demonstrated for the first time. In the experiment, the SPC waveforms are cyclically transmitted within the adjacent pulse repetition intervals (PRIs) to simulate multiple transmitters, and the scattered echoes are received by the 16-channel antennas in elevation at the same time. In the postprocessing, the echo signals of continuous PRIs are added to obtain the mixed echoes, and a detailed echo separation method is adopted. In the method, the mixed echo signals from close arrival angles and far arrival angles are separated by the time shift and weighting, and by the bandpass filtering and DBF technique, respectively. Through the presented method, the mixed echoes of dual-transmit and 16-receive (2T16R) SAR imaging mode are separated and imaged successfully. The experimental results not only validate the echo separation scheme but also indicate that it is very promising in future MIMO-SAR missions. [ABSTRACT FROM AUTHOR]

Holographic subsurface radar (HSR) is a promising geophysical electromagnetic technique to detect shallowly buried objects due to its high lateral resolution. However, the subsurface inspections and visualization of buried targets are prone to be impaired by strong clutter from the nonplanar surface reflections. In this article, a clutter mitigation method using dual-frequency cancellation and sparse feature enhancement is proposed for HSR data to distinguish the targets from background. The radar signals are received at two distinct frequencies under certain conditions to calculate the strong surface reflections. After cancellation of the estimated surface, a modified $\ell _{1}$ regularization is utilized to further mitigate the residual clutter and highlight the target signature. The effectiveness of the proposed method is evaluated on both numerical simulation and radar signals collected from real HSR systems. The visual and quantitative results demonstrate that the proposed method successfully removes the nonplanar surface clutter with the targets preserved. [ABSTRACT FROM AUTHOR]

It is challenging to estimate satellite targets’ absolute attitude and size with limited observational data. This article proposes an innovative way to jointly estimate satellite targets’ absolute attitude and size in the 3-D stable coordinates based on inverse synthetic aperture radar (ISAR) image interpretation with only one image. By taking advantage of the rectangular solar panels commonly equipped on satellites, this article extracts solar panel’s principal components, line features, and phase features of single ISAR imagery with principal component analysis (PCA), radon transform (RT), and minimum-entropy (ME)-based autofocus method, respectively. The projection relationship between these features and the absolute attitude and size of the satellite are established separately. Through multi-features fusion, a joint parameter estimation optimization function is established. This optimization is solved iteratively by the quasi-Newton method. The attitude and size parameters can be estimated simultaneously and rapidly, realizing the satellite state estimation under limited observation data. The excellent performance of the proposed algorithm is verified through different experiments. [ABSTRACT FROM AUTHOR]

Drone-borne ground-penetrating radar (GPR) has proven to be efficient and promising for high-resolution topsoil moisture mapping at the field scale. However, in practice, the radar incident angle may change as a function of the terrain slope and unstable flying conditions. In this respect, we analyzed the effect of radar incident angle on full-wave inversion for soil permittivity characterization. Specifically, using the frequency-domain radar equation, antenna characteristic functions with different incident angles were determined for a horn antenna as an example. Then, numerical analyses and field measurements were performed to quantify the errors on the retrieved permittivity resulting from incident angle drifts. In agreement with the radar equation concept, we observed that the angle- and frequency-dependent global transmission functions of the antenna correspond well to its radiation pattern. Numerical and field results show that errors in the permittivity estimation can be very significant as a function of the antenna radiation pattern and the incident angle. Nevertheless, if the incident angle is known and the characteristic antenna functions are determined as a function of the incident angle, full-wave modeling and inversion remain accurate, provided that the signal-to-noise ratio remains sufficient. [ABSTRACT FROM AUTHOR]

For weather radar, although a phased array system is useful for rapid scanning, its development cost is several times higher than that of radar with a dish-type antenna, and it requires relatively more antenna elements, analog-to-digital converters, and phase shifters. To reduce antenna elements with the devices of phased array systems, array geometry in a random or triangular pattern is often used. However, relatively fewer antenna elements result in observation accuracy degradation due to the increased sidelobe level or the wider beamwidth. We employ compressive sensing (CS) processing [i.e., L1 minimization (L1) and basis pursuit denoising (BPDN)] to reduce the number of antenna elements. We conducted numerical simulations for point and distributed like targets and discussed the observation accuracy using L1 and BPDN. Compared to the estimations made using a full array antenna, where BPDN can be estimated with extremely high accuracy given a 25% reduction in antenna elements, we also applied CS to real measurement data obtained using the PAWR. The BPDN was also very effective for measurement data. The novelty of the study is highlighted in its discussion of the feasibility of applying CS in observation data with the PAWR. The study findings provide a reference for development cost reduction and the mass production of PAWR. [ABSTRACT FROM AUTHOR]

A high-resolution vector-sum phase shifter fabricated using a 28 nm CMOS was developed for a 79 GHz imaging radar. The phase shifter incorporates a process and temperature (P/T) stable in-phase (I)/quadrature-phase (Q) signal generator with a robust matching network and highly linear variable gain amplifiers (VGAs) with a crossing metal-line layout. These techniques contribute to 0.5° phase-setting resolution and a 0.9° root-mean-square (rms) phase error with no calibration process. The developed phase shifters were applied to a 79 GHz imaging radar prototype that consists of eight-channel transmitters and eight-channel receivers. The measurement results show that our prototype can recognize human posture, recognize multiple people, and monitor vital signs. [ABSTRACT FROM AUTHOR]

General theory of holographic inversion with linear frequency modulation (GHI-LFM) has been proposed recently, providing the framework for further advances in the holographic methods for image reconstruction with millimeter-wave linear frequency modulation (LFM) radar. Here, accelerated holographic imaging (AHI)-LFM, an AHI method for LFM radar that exploits range stacking, is proposed. The method is developed and validated for the purpose of whole-body security screening. The image quality and the computational efficiency are compared with the original GHI-LFM algorithm. It is demonstrated that the AHI-LFM algorithm improves the reconstruction speed by at least an order of magnitude while preserving the same image quality for the image sizes arising in practical whole-body security screening. [ABSTRACT FROM AUTHOR]

High-performance millimeter-wave (mm-wave) multiple-in-multiple-out (MIMO) radars have been using efficient multiplexing with increasingly complicated radar waveforms, where conventional hardware architectures suffer from inevitably high design complexity and cost. This article presents a new software-defined mm-wave multistatic imaging system which provides great efficiency and flexibility to generate and process complicated radar waveforms. A general system block diagram and its signal model are proposed to use space-time-coded orthogonal frequency-division multiplexing (OFDM) for simultaneous MIMO transmission at the same time and frequency. The space-time coding (STC) is designed based on the Hadamard matrix which allows simple decoupling and decoding for imaging processing. A radar prototype is designed to validate the proposed system architecture, which operates at 83.5 GHz with a frequency bandwidth of 4.8 GHz. The mm-wave front-end consists of a metal-printed 8-by-8 waveguide array with a small element-separation of 9 mm to form 64 virtual channels for three-dimensional imaging. Experimental results show good imaging performance, giving great potential to design future cost-effective high-performance mm-wave imaging with efficient multiplexing. [ABSTRACT FROM AUTHOR]

Aiming at realizing on-chip real-time human activity recognition based on the miniaturized and integrated radar with limited computational resources, hyperdimensional computing (HDC) is a promising method due to its unique advantages including high energy efficiency, excellent robustness, lightweight model, and superfast learning process. This article reports on the first use of HDC in the radar system to recognize human activities. First, we design an image mask method to minimize the impact of interference in original radar micro-Doppler spectrograms and apply a fast envelope extraction method to fully reflect features of different human activities. Subsequently, we select the most applicable representation methods for HDC to encode and recognize these features. To better accelerate the convergence speed, enable subtle adjustments of the HDC model, and improve the recognition accuracy, we propose a new adaptive retraining method, Mask-Retraining. Finally, the accuracy of recognizing six human activities using HDC with Mask-Retraining reaches 92.93%. Experimental results and comparisons between HDC and ten recognition algorithms demonstrate the validity of our methods. In most cases, HDC outperforms other algorithms being compared both in recognition accuracy and computational cost. Especially, HDC becomes the prime and irreplaceable candidate when only small datasets or finite data points are available. [ABSTRACT FROM AUTHOR]

Automotive radars, along with optical sensors such as cameras or lidars, offer a reliable way of obtaining the 3-D information about the environment. Of particular interest in autonomous driving (AD) is the reliable detection of particularly vulnerable road users (VRUs). Modern radar sensors are able to detect, distinguish, and track targets with high resolution. Relying on that, a backscattering model of complex traffic targets can be generated from the reflected signals of their scattering points (SPs). These models can be employed in the radar channel simulations for verification methods of advanced driver assistance systems. Therefore, in this work, different persons as the most vital VRUs are measured with high radial and high angular resolution. The necessary signal processing steps are explained in detail for the determination of the relevant SPs. Thus, the corresponding radar cross section (RCS) values can be assigned to certain body regions. In addition to real persons, further measurements are compared with a dummy of the corresponding size. Based on the measurement results, not only accurate models of road users can be derived, but also the measurement results can be employed for calculating wave propagation in traffic scenarios. From the measured SPs, the classification of the persons by size and stature is derived. [ABSTRACT FROM AUTHOR]

Radar-based near-field 3-D imaging technology has recently attracted increasing attention in the field of security systems. This article presents a novel multiple-input–multiple-output (MIMO) synthetic aperture radar imaging algorithm for security checkpoints attached to a moving walkway. The proposed algorithm is characterized by transmitting-signal multiplexing on the Doppler frequency domain, which allows for a reduced data acquisition time compared with that of conventional time-division multiplexed MIMO systems and therefore achieves the image reconstruction of dangerous objects with smaller motion errors. This algorithm can be applied to 1-D antenna array systems, and image reconstruction is based on a back-projection algorithm with some modifications. The mathematical derivation of the algorithm and its advantages and disadvantages in comparison with those of a conventional system are detailed in this article. Details of numerical simulation of a point target as well as the experimental results of image reconstruction of a pinball and toy gun, using cascaded millimeter-wave sensors, are also presented for verifying the performance of the algorithm. [ABSTRACT FROM AUTHOR]