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The demand for millimeter-wave (mm-wave) technology is growing rapidly for commercial as well as military applications due to its inherent advantages such as high speed, bandwidth, and resolution. These applications include broadband wireless (together with 5G and Beyond), multimedia, the Internet of Things (IoT), space and defense, automotive radar, imaging sensors, and biomedical devices –. For these applications, integration and packaging of mm-wave modules in a compact size yet at a low cost while maintaining high reliability and repeatability is the key challenge to industries –. The multilayer/3D multichip module (MCM) or system-in/on-package (SiP/SoP) with antenna-in-package technologies, with high-quality (high-Q) off-chip passive components and interconnects, are widely regarded as excellent means for meeting these advanced requirements –. [ABSTRACT FROM AUTHOR]

In characteristic mode analysis (CMA), solving the characteristic modes (CMs) from the generalized eigenvalue problem takes the main part of the workload. In this letter, the algorithm based on the Sherman–Morrison–Woodbury (SMW) formula is embedded into the implicitly restarted Arnoldi method to improve the efficiency of solving the CMs. In this method, the impedance matrix is compressed into a product of block-diagonal matrices by using the SMW formula with adaptive cross approximation algorithm. Benefiting from the efficient compression, calculating the inverse matrix of the impedance matrix and the matrix vector products related to the solution of the generalized eigenvalue problem is accelerated. Consequently, the computational time and memory requirement in CMA are both reduced. Some numerical examples are demonstrated to show the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this letter, a novel microwave coincidence imaging (MCI) method based on the attributed scattering model (ASM) is proposed. Unlike the classical MCI model which assumes the target as a set of discrete scatterers, the ASM-based MCI equation contains three kinds of reference matrices corresponding to the point-scatterers (PSs), the line-segment-scatterers (LSSs) and the rectangular-plate-scatterers (RPSs), respectively. Hence the ASM-based MCI could resolve richer information about the object geometries. By solving the imaging equation via the alternating direction method of multipliers (ADMM) algorithm, the scattering coefficients will be obtained and the target can be reconstructed according to the presetting parameter sets. Meantime, the ASM-based MCI also earns the superresolution ability like the classical MCI, which is brought in by the temporal-spatial orthogonal radiation field. Simulations and experiment are carried out to demonstrate the performance and superresolution ability of proposed method. The ASM-based MCI makes contributions to the progress of radar forward-looking imaging theory and technology. [ABSTRACT FROM AUTHOR]

Wideband microwave signals with high time-frequency linearity for high-resolution radar applications can be optically generated using high-speed electronic waveform generators. Frequency-shifting modulation in an optical cavity provides an attractive approach to generate broadband microwave signals with reduced complexity requiring only MHz-level electronics. However, the in-loop signal instability and inter-pulse interference usually cause amplitude fluctuations, leading to limited signal-to-noise ratio and signal bandwidth. Here, we overcome these challenges and demonstrate, for the first time, the photonic generation of 30-GHz-wide stepped-frequency (SF) signals with 100 MHz frequency steps defined by an MHz-level electrical oscillator. We achieved this performance by mitigating the in-loop polarization scrambling and inter-pulse interference using a polarization-maintaining cavity and a high-extinction optical switch. This allows stable consecutive acousto-optic frequency-shifting modulation that significantly improves the signal-to-noise ratio. While achieving a bandwidth surpassing the state-of-the-art demonstrations based on wideband electronics, our approach alleviates the necessity for high-speed signal generators or wideband tunable lasers. To exemplify the utility, we systematically evaluate the signal quality and show its applications in radar imaging compared to those using electrical waveform generators. [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]

The rapid development of artificial electromagnetic (EM) materials provides unprecedented freedom to manipulate EM waves. In recent years, reflective spectrum conversion technology based on EM materials has been widely studied due to its multiharmonic generation capability. The possibility of its application in wireless communication is explored. However, the research on spectrum conversion is mainly based on periodic modulation waveforms. The modulation effect of nonperiodic for EM materials has not been studied so far. In this article, a nonperiodic time-varying active frequency selective surface (AFSS) is proposed to manipulate the spectral distribution of harmonics. By dynamically controlling the amplitude of the reflected echo, a continuous Doppler spectrum is generated. On this basis, the imaging characteristics of nonperiodic time-varying AFSS are analyzed in detail, and the defocusing phenomenon of the target image is discovered. Finally, the radar imaging experiments for nonperiodic time-varying AFSS are conducted to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The research into curvilinear flight synthetic aperture radar (CF-SAR) is the inevitable result of the comprehensive practicality of SAR. The flight path of the SAR platform in real applications, which is highly nonlinear or curvy due to three-dimensional velocity and acceleration, cannot be described by the traditional uniform linear motion model. New mathematical models, signal characteristics, imaging algorithms, and system design criteria must be proposed and investigated for CF-SAR. This paper provides a comprehensive overview of CF-SAR. Firstly, the basic concept, unified model, and general signal characteristics of CF-SAR are defined, derived, and analyzed, respectively. Additionally, the advantages and drawbacks of current methodologies are reviewed. Discussions on the CF-SAR's applications are presented from the perspective of typical platforms, new configurations, and advanced technologies, which are suitable means to fulfill the increasing user requirements. Finally, the challenges faced by CF-SAR are summarized, and some future trends for the study of CF-SAR are explored. Hopefully, this paper will serve as a reference for SAR researchers/engineers and stimulate the future development and actual application of CF-SAR. [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]

Computational microwave imaging (CMI) based on the frequency diversity metasurface apertures (FDMAs) is an emerging technology and has attracted wide attention. FDMA-based CMI (FDMA-CMI) can be considered as microwave compressive sensing imaging with the frequency diversity pattern of the FDMA being the sensing matrix and solved by sparse signal reconstruction algorithms. However, the imaging quality is affected by the sensing matrix error and off-grid error seriously. In this article, we propose a novel algorithm for FDMA-CMI, referred to as off-grid sparse Bayesian learning (SBL) method based on sinc interpolation (OGSISBL), by taking both the off-grid error and sensing matrix error into account. First, we establish the measurement model with both the off-grid error and sensing matrix error. Specifically, the off-grid error is represented as a set of parameters to be estimated in the measurement model, and the sensing matrix error is represented as the amplitude–phase drift of the transceiver channels of the imaging system due to the principle of the FDMA. Then, under the framework of the SBL, a robust imaging algorithm OGSISBL is developed via the variational Bayesian expectation maximization (VBEM), which can not only recover the amplitude and position of the return of the scattered, but also simultaneously calibrate the amplitude–phase drift of the transceiver channels and the off-grid error. The performance of the proposed algorithm is evaluated by both the simulation data and the measured data collected by the self-designed experimental FDMA-CMI system, and the results validate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the single channel algorithm (SCA) is first applied to Soil Moisture and Ocean Salinity (SMOS) brightness temperatures at vertical polarization (${\mathrm{ TB}}_{V}$) to estimate SM at coarse resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5°, and 52.5°)-adaptive calibration of single scattering albedo ($\omega$) and soil roughness ($h_{s}$) parameters. These effective parameters are used together with fine-scale multiangular Sentinel-1 backscatter in a single-pass active–passive downscaling approach to estimate ${\mathrm{ TB}}_{V}$ at fine scale (1 km) for each SMOS incidence angle. These ${\mathrm{ TB}}_{V}\mathrm {s}$ are finally inverted to obtain the corresponding high-resolution SM maps. Results over the Iberian Peninsula for year 2018 show an increasing trend of $\omega $ and a decreasing trend of $h_{s}$ with SMOS incidence angle, with almost no variability of $\omega $ across land cover types. The active–passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fine ${\mathrm{ TB}}_{V}$ maps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolution SM maps have maximum differences in mean and standard deviation of 0.016 and 0.015 m3/m3, respectively, and compare well with in situ measurements. Our results indicate that model-based microwave approaches to estimate SM can be adequately adapted to account for the incidence angle diversity of planned missions, such as Copernicus Microwave Imaging Radiometer (CIMR), Radar Observing System for Europe in L-band (ROSE-L), and Sentinel-1 next generation. [ABSTRACT FROM AUTHOR]

Terahertz (THz) regime has shown superior performance in terms of reflecting the details of target structures. However, the extended structures (ESs) may be discretized into endpoints in synthetic aperture radar (SAR) images if the observation aperture deviates from the specular orientation of ESs, which deteriorates subsequent image interpretation and intelligent imaging. Taking the tank barrels as the object, this article proposes a novel solution to detect and reconstruct the critical ES by effectively exploiting the component prior and imaging characteristics (CPIC). The core of the proposed method lies in converting the phase matching of the multiview methods into object detection based on CPIC and deep neural network. First, the CPIC of tank barrels is analytically determined and regarded as the theoretical basis of datasets annotation. Thus, the modified multiscale object detection network is constructed to enhance the detection performance and estimate the orientation of barrels. Finally, comprehensive simulation, anechoic chamber, and field experiments are carried out to validate the effectiveness of the proposed methods. The results show that the proposed method can outperform the existing object detection networks in terms of the detection accuracy of multiscale objects and outperform the existing multiview methods in terms of time need with comparable accuracy. [ABSTRACT FROM AUTHOR]

The microwave photonic (MWP) radar has the capability of generating ultrawideband (UWB) signals. It is a challenge to realize accurate extraction of attributed scattering centers (ASCs) from MWP signals. This manuscript presents a scattering parameter estimation method in the image domain for UWB MWP signals. The polar-to-rectangular resampling is required for UWB MWP signals. Therefore, a range-azimuth decoupled representation based on the ASC model is formed. The model parameter estimation is converted into an optimization problem, where the statistics of the target signal and the features of interest are modeled to provide prior information. The distribution spread maximization (DSM) and peak magnitude maximization (PMM) principles in the optimization embody this prior information. The particle swarm optimization (PSO) is utilized to search for the parameters of each ASC in the image domain. Moreover, the orthogonal matching pursuit (OMP) algorithm is introduced to avoid repeated computation. Experimental results conducted on the simulated data, XPATCH data, and real data confirm the effectiveness of the proposed method. The proposed method takes into account the specific features of UWB MWP signals, which are neglected in the existing studies. Therefore, the proposed method performs better in extracting ASC parameters from UWB MWP signals in terms of accuracy and more complete sets. [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, a novel low-cost frequency scanning-based synthetic aperture radar (F-SAR) system is proposed for high-resolution imaging. F-SAR scheme is demonstrated for personal screening for the first time. A hybrid scanning model is presented to illuminate the human body, namely, frequency control beam steering in cross-track and mechanical scanning in along-track. A frequency scanning-based tomographic SAR focusing algorithm is proposed. Accordingly, a power spectrum estimation algorithm in cross-track and synthetical aperture processing in along-track are compounded in the signal processing framework. Unlike a variety of multiple input multiple output (MIMO) array imaging which illuminates the object by switching different channels sequentially over separate time intervals, the F-SAR concept sweeps across all swaths over each chirp by employing a frequency scanned beam. Herein imaging time and hardware cost can be significantly cut down by dimensionality reduction from the MIMO system to a single input single output (SISO) system. Furthermore, to fulfill the particular scheme of the F-SAR system, a novel combo antenna module, leaky-wave slotted waveguide combined with a cylindrical lens, is proposed and achieved at 78–92-GHz band, by which a steering fan-beam focused at the object under test (OUT) plane is radiated. The radiation beam is focalized by the cylindrical lens accompanied by frequency sweeping, while is diffused in along-track. The frequency-controlled beam-steering concept and the fan-beam focusing mechanism are verified by the chamber measurements. The measured scanning range is 23°. Beamwidth of 11–16 mm is obtained at the focal plane. Beam steering in cross-track together with mechanical scanning in along-track can attain a field of view (FoV) of 0.54 2 $\text{m}^{2}$. Through-clothes imaging of person-borne concealed objects and the real-time capability are demonstrated by experiments. Resolutions in both along-track and cross-track can reach 5 mm. [ABSTRACT FROM AUTHOR]

The design and development of a hybrid Gaussian pulse radar transceiver, including transmitter and receiver, is presented. The transmitter is realized using a transistor-based square pulse generator, a tunable step recovery diode (SRD) Gaussian pulse-shaping network, and a differentiator. The SRD circuit design is modified using a high-pass filter to have a low ringing and narrow pulsewidth. The output pulse with a broadband amplifier has a power of 500 mW, at a pulse repetition frequency (PRF) of 10 MHz, and tunable pulsewidth ranges from 56 to 300 ps. A 90° coupler is configured to convert the Gaussian pulse into a monopulse. Then, a transmitter antenna is used to radiate the pulse, and a separate receiving antenna is used to receive the reflected pulse. The receiver is comprised of a local Gaussian pulse generator, a radio frequency mixer (RF-mixer) accompanied by a low-pass filter (LPF), a low-frequency amplifier (LFA), and an analog-to-digital converter (ADC). The RF-mixer is used for expanding the receiving pulse from the receiving antenna by a factor of 100000. The LPF and LFA are used to detect the envelope of the output of the RF-mixer and amplify it. The ADC is interposed between the LFA and a processor to digitize the expanded pulse for data processing. The validity and reliability of the digitized expanded pulse compared to those received using a sampling oscilloscope are 93% in agreement, which is remarkable. A version of the system is also developed for a single-antenna operation by utilizing an ultrawideband (UWB) coupler as a semicirculator. Both the systems are compared, and the benefits of each are illustrated. The flexibility and performance of the designs are analyzed. The proposed system demonstrates subcentimeter imaging capabilities. [ABSTRACT FROM AUTHOR]

Clutter suppression plays an important role in a synthetic aperture radar (SAR) system. The conventional SAR imaging methods are useful for distinguishing the echo signal and noise, but cannot separate the target signal from background clutter. Inspired by the signal separation ability of morphological component analysis (MCA), a novel SAR imaging method based on MCA is proposed to suppress the strong background clutter. In the new model, the SAR echo is considered as a linear superposition of target signal, clutter signal, and noise. According to different characteristics of morphological components, clutter dictionary and target dictionary are constructed to sparsely represent the clutter component and target component, respectively. Then, the MCA method based on the sparse representation and morphological diversity of signals is employed to decompose the SAR echo into the target signal, clutter signal, and noise. Finally, the separated target signal is processed to obtain the ultimate SAR image. Experimental results from simulated and real SAR data are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Range-Doppler detection with pulse sequences exists widely in Radar, sonar, ultrasound imaging, and many other engineering applications. However, the classic methods require a high sampling rate for wideband pulses, and the Doppler estimation with slow time sub-Nyquist sampling may suffer from the phase wrapping. In this brief, a sequential-based range-Doppler estimation method with fast and slow time sub-Nyquist sampling is proposed. Utilizing the finite rate of innovation (FRI) theory, the range parameters can be estimated from several Fourier coefficients. The fast time sampling rate is determined by the number of the unknown parameters, instead of the signal bandwidth. Then, the aliasing-free Doppler parameters are estimated by the subspace method and the Chinese remainder theorem (CRT). The sequential-based method enables to estimate each Doppler parameter separately, and the Doppler estimation is transformed into a robust CRT problem, which enhances the robustness of the Doppler estimation. Simulation and hardware results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, deep neural networks are increasingly popular in the field of radar high-resolution range profiles (HRRPs) target recognition. Unfortunately, recent researches have revealed that a deep-learning classifier can be easily fooled by adding small perturbations to the input, named adversarial attack. This provides us an inspiration for radar deceptive jamming signal generation in electronic countermeasures (ECMs). However, the perturbations generated by these adversarial attacks are usually of complex envelopes and quite low power, making it challenging for jammers to generate such actual jamming signals. To solve that issue, we propose a practical deceptive jamming generation method that learns the vulnerable range cells in an HRRP sample and injects several jamming pulses with specific amplitudes into these range cells. Such jamming signals are easy to generate and can deceive the radar automatic target recognition (RATR) model to output the wrong target category prediction with high confidence. To avoid the requirement of the recognition network structure information, we leverage the differential evolution optimization algorithm (non-gradientbased). Further, to provide the potential of real-time jamming signal generation during the test, an encoder is constructed not only to learn the separable features but also to find the vulnerable range cells and the specific amplitudes of the jamming pulses. In the experiments, we apply the proposed attack algorithms to fool the one-dimensional convolutional neural network-based HRRPRATR models. The extensive experimental results on measured aircraft HRRP dataset prove that the proposed algorithms achieve a promising attack performance and serve as a practical and fast deceptive jamming generation method. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM) signal is a superior dual-functional waveform for the integration of radar sensing and communication in intelligent transportation. But the sensitivity to phase noise is a serious issue introducing interference and causing performance degradation during demodulation. In this paper, we explore the essential mechanism of the action and generation of phase noise through theoretical analysis, where the OFDM demodulation process and power spectrum density (PSD) of phase noise is discussed in the frequency domain, and draw the conclusion that high-speed phase jitter will cause unrecoverable deterioration of OFDM demodulation. Therefore, a photonics-aided radar and communication integrated system based on Optoelectronic oscillator (OEO) is proposed. The positive feedback oscillation with long energy storage time make the phase noise pattern of OEO just suitable to against the phase noise sensitivity of OFDM. A proof-of-concept experiment is demonstrated at 24 GHz with 2 GHz bandwidth to verify the radar sensing and communication function. A two-dimensional radar imaging with a range resolution of 0.075 m and velocity resolution of 4.4 km/h, a communication capacity of 6.4 Gbps is obtained. A quantitative performance comparison is also carried out. By using an ordinary microwave source and OEO separately, the demodulation constellation and error vector magnitude (EVM) under different subcarrier spacing is measured and compared. The result is corresponding to our analysis with the EVM decreasing from 12.5% to 4.7% under subcarrier spacing of 125 kHz. [ABSTRACT FROM AUTHOR]

A 139.5–157.7-GHz $D$ -band I/Q radar receiver with an on-chip antenna and a spillover resilient $N$ -path baseband filter is presented. Spillover and its manifestation based on the chirp duration are discussed, and a filter for spillover mitigation is implemented. The radar is characterized by 18-GHz radio frequency (RF) bandwidth, 13-mm range resolution, 55-dB conversion gain, 8-dB NF, and 26-dB narrowband spillover attenuation. The receiver is also capable of selectively mitigating close-by large reflectors, and the system power consumption is 67 mW. [ABSTRACT FROM AUTHOR]

Foreign object debris(FOD)generally refers to a certain type of foreign substances,which can influence the taking off and landing of airport flights.Currently,FOD monitoring in the vast majority of airports across the world is operated manually.In order to meet the urgent need for FOD monitoring measures,the imaging technology based on arc synthetic aperture(ArcSAR)is used to detect the airport FOD.The signal model simulation of ArcSAR is conducted by comparing the relative merits between real aperture scanning and ArcSAR,and the simulation data is compared with measured data for verification.The results show that,for the same object,the detection range of ArcSAR can be increased by 2~3 times,and Arc SAR can effectively inhibit“flickering”clutters such as rain and snow.

Abstract High‐resolution inverse synthetic aperture radar (ISAR) imaging can produce electromagnetic images of a target in the range and cross‐range domain. However, to make full use of an ISAR image in target detection and recognition, we need to rescale the image in a homogeneous range and cross‐range domain, and further, to estimate the effective rotational velocity (RV) of the target. A precise ISAR image cross‐range scaling method is proposed. A candidate RV is obtained based on the bisection method. Then, the effective rotation angle (RA) between two sequential rescaled images is characterised by the RA between the major axes of the images. These axes are extracted by principle component analysis (PCA). A precise estimation of the RV is obtained through iterative PCA and bisection. Simulated and real data experimental results validate the effectiveness of the proposed method.

Abstract Pulse repetition frequency (PRF) is an essential parameter in synthetic aperture radar (SAR). However, a small PRF value will cause spectrum ambiguity and a large value will increase the computational complexity. In highly squinted SAR imaging with maneuvering platform, the traditional PRF design methods used are airborne and space borne. SAR systems perform with a low precision because of the complex geometric configuration and system deviations. In order to adapt to the maneuvering platform SAR system, a modified design method of PRF is proposed in this paper. First, the azimuth spectrum of echoes is analyzed by establishing the SAR geometry base on the single point equivalent squint (SPES) model. Subsequently, the calculation of azimuth bandwidth is transformed into an optimization problem, and the lower limit of PRF is obtained by the Golden Section Method. Besides, the criteria of spectrum unambiguity and time sequence constraints in PRF design is also discussed in detail. Simulation experiments demonstrate the feasibility and accuracy of the proposed method.

The detection and quantification of fractures in rocks, as well as the detection of lithological changes, are of particular interest in scientific fields, such as construction materials, geotechnics, reservoirs and the diagnostics of dielectric composite materials and cultural heritage objects. Therefore, different methods and techniques have been developed and improved over the years to provide solutions, e.g., seismic, ground-penetrating radar and X-ray microtomography. However, there are always trade-offs, such as spatial resolution, investigated volume and rock penetration depth. At present, high-frequency radars (>60 GHz) are available on the market, which are compact in size and capable of imaging large areas in short periods of time. However, the few rock applications that have been carried out have not provided any information on whether these radars would be useful for detecting fractures and lithological changes in rocks. Therefore, in this work, we performed different experiments on construction and reservoir rocks using a frequency-modulated continuous wave radar working at 300 GHz to evaluate its viability in this type of application. The results showed that the radar quantified millimeter fractures at a 1 cm rock penetration depth with a sensitivity of 500 μm. Furthermore, lithological changes were identified, even when detecting interfaces generated by the artificial union of two samples from the same rock.

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]

A 360° video synthetic aperture radar (VideoSAR) appears as an interactive observation capability to understand the dynamic scenarios. VideoSAR system mounted on an unmanned aerial vehicle (UAV) delivers a highly flexible and cost-effective approach. In this letter, the UAV-borne repeat-pass circular stripmap (CSM) VideoSAR observation is formulated in the format of video area overflown to enhance the wide-area surveillance capability. A completely two-dimensional interpolation-free polar format algorithm is employed, especially in terms of repeat-pass CSM-VideoSAR configuration. We conduct the repeat-pass CSM-VideoSAR experiments on multirotor UAV-borne system in the interests of imaging demonstration and promoting the inspirations on the related open issues. [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]

Track-before-detect (TBD) algorithm has been used to track weak moving target shadows in video synthetic aperture radar (SAR), where strong maneuverability may deteriorate tracking performance. Fortunately, the Doppler characteristic of the target can additionally provide velocity guidance for tracking. This article presents ground moving target indication (GMTI) results of the multichannel video SAR raw data, and a tracking approach is proposed based on the Doppler supervision in an improved dynamic programming-based TBD (DP-TBD) framework that uses a dual-domain merit function. Both the sequential high-resolution SAR images and low-resolution range-Doppler (RD) spectra are used, and the clutter in the RD spectrum is suppressed by using an adaptive displaced phase center antenna (ADPCA) technique. Fine states can be searched in dual-domain through resampling from random expansion in state initialization and transition. The inverse shadow amplitude and Doppler energy of the same potential target are simultaneously integrated to determine whether the target exists. The proposed Dual-DP-TBD can deal with the tracking of a time-varying number of targets in successive measurements. Compared to other TBD algorithms, this approach has fewer missing alarms and false alarms, thanks to precise velocity estimation constrained from diverse features both in SAR image and RD spectrum. [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]

Three-dimensional (3-D) imaging plays a vital role in the recognition of spinning space debris. However, the image may be blurred due to the range migration caused by fast rotation of space debris. Moreover, the image quality, which depends on the estimation accuracy of Doppler frequency and chirp rate of scattering centers, is influenced by cross-terms and sidelobes. In this article, we propose a novel 3-D imaging method based on smoothed Lv distribution (SLVD). Firstly, the selection criterion for best imaging time based on the time–frequency moment is proposed to guarantee that the echo is approximated as a linear frequency modulation (LFM) signal. Then, we operate the Khatri–Rao product on the centroid frequency and chirp rate (CFCR) representation and the range-Doppler (RD) image to obtain the 3-D image. To decrease the influence of range migration, we process a short time window during the RD imaging procedure. For cross-term and sidelobe suppression, the SLVD is proposed to obtain the CFCR representation by expressing the Lv distribution (LVD) in a convolution form and introducing a centroid frequency window. Experimental results verify the effectiveness of the proposed imaging method and the good performance of the proposed SLVD for cross-term and sidelobe suppression. [ABSTRACT FROM AUTHOR]

Bistatic synthetic aperture radar (BiSAR) has received more and more attention because of its forward-looking imaging capability and configuration flexibility. For BiSAR moving target imaging (MTIm), its noncooperative motion leads to unknown range cell migration (RCM) and additional phase modulation. Consequently, MTIm in BiSAR faces two main challenges: 1) the unknown RCM correction and Doppler parameter estimation are tightly coupled and 2) the Doppler parameters of the extended moving target’s different scattering points are different, i.e., the Doppler parameters are spatially variant. To cope with these problems, an optimal polar format refocusing method for bistatic SAR MTIm is proposed. First, the main part of tight coupling and spatial variation effects caused by the BiSAR platforms is eliminated, while the moving target is 2-D defocused and shifted. Then, we analyze the characteristics of 2-D defocused and shifted of the moving target in BiSAR and give the analytical expressions. Basis this, a new bistatic polar format transformation is introduced, in which the degree of freedom of defocusing result is reduced from 2-D to only 1-D. After that, the parameter estimation and refocusing issues are transformed into a constrained optimization problem (COP), and differential evolution (DE) is applied to solve the COP and obtain the refocusing results. Finally, considering the spatial variation of the extended moving target, compensation processing is performed to relocate each scattering point. Numerical simulations verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We present imaging results with dual-band millimeter- and submillimeter-wave hologram and deep neural networks (NNs). The imaging method uses a single transceiver, which interrogates the region of interest (RoI) through a dispersive transmission-type hologram. The hologram was designed to cover two bands 50–75 and 220–330 GHz. Two separate single-transceiver imaging experiments were carried out with two test objects translated in the RoI at $101\times101$ locations. NNs were trained to images of the test objects with wideband reflection spectra from the RoI as the input. The deep NNs were based on deconvolutional (DC) layers that mapped the latent information of the test objects in the spectra to image pixel values. The two $\sim 10$ -cm test objects were imaged in 200 $\times200$ mm2 and $300\times300$ mm2 field-of-view at 600 mm from the hologram aperture (19°–28° angular field-of-view). The experimental resolution was estimated from point-spread functions extracted from the predicted images. The full width at half maximum resolution was 21 and 16.5 mm, for the 50–75 and 220–330 GHz bands, respectively. These are close to the theoretical limits of 25–19 mm, for the lower band, and 19–16 mm for the higher band as predicted with hologram aperture size and edge taper. Augmented reflections were constructed from corner-cube measurements to evaluate the ability to predict the images of vast collection of objects. The results with augmented data show performance comparable with the experimental ones with limited test object space. The latent representations for both the experimental and augmented data indicate sparsity—a demonstration of feasibility to generalize from reflection spectra to images. The performance of the developed imaging technique is in par with the current, multichannel state of art, and has the advantage of substantially reduced hardware complexity. [ABSTRACT FROM AUTHOR]

We study the problem of identifying the parameters of a linear system from its response to multiple unknown waveforms. We assume that the system response is a scaled superposition of time-delayed and frequency-shifted versions of the unknown waveforms. Such kind of problem is severely ill-posed and does not yield a unique solution without introducing further constraints. To fully characterize the system, we assume that the unknown waveforms lie in a common known low-dimensional subspace that satisfies certain properties. Then, we develop a blind two-dimensional (2D) super-resolution framework that applies to a large number of applications. In this framework, we show that under a minimum separation between the time-frequency shifts, all the unknowns that characterize the system can be recovered precisely and with high probability provided that a lower bound on the number of the observed samples is satisfied. The proposed framework is based on a 2D atomic norm minimization problem, which is shown to be reformulated and solved via semidefinite programming. Simulation results that confirm the theoretical findings of the paper are provided. [ABSTRACT FROM AUTHOR]

This brief presents an overview of the recent progress on silicon-based millimeter-wave SoCs for short-range radar in imaging and sensing applications. The process, operating frequency, and basic architectures for short-range millimeter-wave radar are firstly summarized. Then, topology and performance of excellent radar SoCs are reviewed from different applications: automotive radar, gesture recognition, vital signs monitoring, security and biomedical sensing, industrial detection and imaging. Future trends in millimeter-wave SoCs for short-range radar are also discussed. [ABSTRACT FROM AUTHOR]

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric users (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate this double spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. First, a beamformer-based NOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Next, the system is extended to the scenario of multiple pairs of R- and C-users, where a cluster-based NOMA-aided joint Rad-MU-Com framework is proposed. A joint beamformer design and power allocation optimization problem is formulated for the maximization of the sum of the unicast rate at each C-user, subject to the constraints on both the minimum multicast rate for each R&C pair and on accuracy of the radar beam pattern for detecting multiple R-users. The resultant joint optimization problem is efficiently solved by another penalty-based iterative algorithm developed. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed schemes over the benchmark schemes employing conventional transmission strategies. [ABSTRACT FROM AUTHOR]

In this paper, we take the first step towards quantifying the fundamental performance trade-offs between imaging and communication supported simultaneously using the same network resources. We analyze an uplink system configuration with a full-duplex base station (BS) illuminating an imaging scene while receiving data from a communication user. Our main contributions are two-fold. First, we propose a unified signal space analysis framework based on the degrees of freedom metric to characterize the trade-offs between the two operations in the high signal-to-noise ratio regime. Second, we propose a dual-function joint processing scheme, decode-and-image, that allows the BS to simultaneously form an image of the scene while decoding the uplink user’s data. Our analysis and proposed scheme highlight the benefits of exploiting the uplink signals for imaging, at the cost of increased cooperation between the BS and uplink user. Moreover, our proposed scheme outperforms traditional schemes that enable dual-function operation via spatial or temporal isolation of imaging and communication signals. [ABSTRACT FROM AUTHOR]

The millimeter-wave (mmWave) radar technology has attracted significant attention because it is susceptible to environmental lighting, wall shielding, and privacy concern. This article proposes a novel noninvasive human activity recognition system using a mmWave radar. The proposed framework first transforms mmWave signals into point clouds. Generally speaking, it consists of four major components: denosing, enhanced voxelization, data augmentation, and dual-view machine learning to lead to accurate and efficient human activity recognition. The proposed new methodology considers the spatial–temporal point clouds in physical environments through a modified voxelization approach, enriches the sparse data based on the symmetry property of radar rotations, and learns the activity using a dual-view convolutional neural network. To evaluate the performance of the proposed learning models, a dataset involving seven different activities has been established using a mmWave radar platform. The experimental results have demonstrated that the proposed system can achieve 97.61% and 98% accuracies during the tests of fall detection and activity classification, respectively. In comparison, the proposed scheme greatly outperforms four other conventional machine learning schemes in terms of the overall accuracy. [ABSTRACT FROM AUTHOR]

We present an approach to image deconvolution in active incoherent millimeter-wave (AIM) imaging. While traditional incoherent imaging systems capture thermal radiation from the scene, AIM imaging uses the transmission of noise signals to increase the signal-to-noise ratio (SNR) while maintaining the necessary space–time incoherence required for image reconstruction. Images formed by any imaging technique are corrupted by the impulse response, or point spread function (PSF), of the imaging system; however, the degradation of the image due to the PSF can be alleviated significantly via deconvolution if the PSF is characterized. Typically, estimation of the PSF is challenging due to spatial undersampling and the impact of noise. In this work, we experimentally demonstrate an approach to estimate the PSF of AIM imaging systems by averaging PSF estimates from multiple point targets using a shift-and-add method in a 38-GHz imaging system. We demonstrate image deconvolution using the CLEAN algorithm to reconstruct a scene with multiple targets. [ABSTRACT FROM AUTHOR]

Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security. In this paper, we focus on the relatively unexplored area of using low-cost 77 GHz mmWave radar for the carried objects detection problem. The proposed system is capable of real-time detecting three classes of objects - laptop, phone, and knife - under open carry and concealed cases where objects are hidden with clothes or bags. This capability is achieved by the initial signal processing for localization and generating range-azimuth-elevation image cubes, followed by a deep learning-based prediction network and a multi-shot post-processing module for detecting objects. Extensive experiments for validating the system performance on detecting open carry and concealed objects have been presented with a self-built radar-camera testbed and collected dataset. Additionally, the influence of different input formats, factors, and parameters on system performance is analyzed, providing an intuitive understanding of the system. This system would be the very first baseline for other future works aiming to detect carried objects using 77 GHz radar. [ABSTRACT FROM AUTHOR]

In an ever-increasingly complex electromagnetic environment, automatic radar signal recognition is becoming vital. Convolutional neural networks have been widely used for radar signal recognition, but deep learning-based algorithms only recognize trained classes. Recognizing novel radar signals with few-shot samples in an open environment is still a challenging research problem. In this letter, a few-shot learning algorithm based on the tensor imprint algorithm and convolutional classification layer is proposed for radar signal recognition, and the proposed convolutional classification layer can avoid spatial information loss caused by the global pooling layer and the fully connected layer. In addition, the lightweight re-parameterization multi-channel multi-branch convolutional neural network (RepMCMBNet) is proposed for feature extraction. The model is trained on a dataset containing 8 types of radar signals, and achieves high recognition accuracy in a test dataset containing 12 types of radar signals. The overall recognition accuracy of the proposed tensor imprint algorithm achieves 93.9% at −6 dB when the number of samples is 5. [ABSTRACT FROM AUTHOR]

Accurate Burden Surface Profile (BSP) detection is important for the operation of Blast Furnace (BF). The signal-to-noise ratio of radar signals changes greatly during both the charging period of BF and the long maintenance period of the radar device, which increases the difficulty of radar BSP detection. The traditional radar BSP detection method based on signal energy relies on manually selected detection thresholds according to the noise intensity. Hence, the accuracy of the traditional radar BSP detection method is not reliable in the long term. To address this problem, we propose a novel learning-based Key Points estimation (KP-BSP) method to detect the key points of radar reconstructed BSP image, and a new Key Points-based Connected Region Noise Reduction (KP-CRNR) algorithm to remove the noise-affected regions. The prediction deviation at detected key points (at the positions of the mechanical probes) is then used to correct the radar detection results, leading to the improvement of radar detection accuracy. The experimental data were collected from Wuhan Iron Steel Company No.7 BF. The results show that the proposed methods can achieve an average RMSE of 0.0156m, which is improved by more than 50% compared with previous methods. The long-term reliability of the proposed method is also demonstrated in this dataset. [ABSTRACT FROM AUTHOR]

Chipless RFID tag decoding has some inherent degrees of uncertainty because there is no handshake protocol between chipless tags and readers. This article initially compares the outcome of different pattern recognition methods to decode some frequency-based tags in the mm-wave spectrum. It will be shown that these pattern recognition methods suffer from almost 2%–5% false decoding rate. To overcome this misdecoding problem, two novel methods of making images of the chipless tags are presented. The first method is making 2-D images based on side-looking aperture radar concepts, and the second one is making virtual 2-D images from the 1-D backscattering signals. Then, a 2-D decoding algorithm is suggested based on a convolutional neural network to decode those tag images and compare the results. It is shown that this combined decoding method has very high accuracy, and it almost eliminates any ambiguity and false decoding problems. This is the first time a deep learning method is used with image construction methods to decode chipless RFID tags. [ABSTRACT FROM AUTHOR]

Nearfield (NF) 3-D imaging provides an effective solution of objects’ radar cross section (RCS) within a compact range. This article proposes a precise RCS extrapolation via NF 3-D imaging with adaptive parameter optimization Bayesian learning (APOBL), i.e., first, in the process of NF 3-D imaging, objects’ scattering centers may vary with the observation angle, while it is hard for the existing Bayesian learning via presetting parameters to reach an optimal estimation. For this issue, we present a parameter self-adaption solution, improving precision, and stability. Second, we also apply a block-based optimization idea in Bayesian-learning-based 3-D imaging, ensuring NF 3-D imaging quality. Third, in the process of RCS extrapolation, we apply a weighted Green’s function operator into the 3-D imaging-based NF-to-far-field (NF–FF) compensation, further ensuring high precision. The simulation and experiment results verify that the proposed method has an advantage in precision over the existing 3-D imaging-based RCS extrapolation methods. [ABSTRACT FROM AUTHOR]