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Fast settling phase locked loops (PLLs) play a pivotal role in many applications requiring rapid attainment of a stable frequency and phase. In modern communication standards, these PLLs are extensively utilized to guarantee precise compliance with dynamic resource allocation requirements. In processors, these PLLs manage dynamic voltage frequency scaling. Moreover, the fast-settling PLLs expedite the scanning of frequency spectra in sophisticated electronic radar set-ups, proving particularly advantageous for imaging and scanning radar applications. The rapid response exhibited by these PLLs is also harnessed in quantum technologies, catering to the urgent need for precise frequency adjustments to manipulate qubit states effectively. The strategies employed to attain fast-settling PLLs are primarily classified into five broad techniques in this article: enhanced phase frequency detection, hybrid multiple subsystems, VCO start-up, gear shift, and look-up table or finite state machine. This article explores the fundamental operational principles encompassing these techniques and presents optimal settling times for each method reported in the literature. Finally, the architectures utilizing these techniques will be evaluated based on their figure of merit (FoM), settling time, and tuning range. [ABSTRACT FROM AUTHOR]

Monostatic rapid single-view radar imaging technology is a technique that employs single incidence angle and single frequency point information to implement rapid monostatic radar imaging within a small angular field. Owing to its analytical expression, this technique can substitute the traditional frequency-angle- scanning imaging in a small angular range, facilitating the rapid generation of highly realistic radar imaging data slices for complex targets and environments. This technology has been significantly applied in scatter hotspot diagnostics and target recognition. In order to achieve the windowing effect equivalent to that of frequency-angle-scanning imaging, and to enhance the scattering feature of monostatic imaging while controlling sidelobes, this paper derives analytic windowed imaging formulas for monostatic radar. It then obtains analytical expressions for various typical monostatic windowing rapid radar imaging scenarios. This enables the monostatic rapid imaging technology to maintain high efficiency in its analytical expressions while achieving the windowing effect equivalent to traditional imaging. The validity and correctness of the analytical formula and software implementation have been confirmed through 1D, 2D, and 3D imaging verifications. This technology can provide a vast amount of training data for modern radars. [ABSTRACT FROM AUTHOR]

This article discusses the importance of radar systems and processing methods for space situational awareness (SSA). It highlights the vulnerability of the space environment due to the increasing number of satellites and space debris, as well as the growing threat of attacks. Radar sensors are particularly useful for SSA because they can detect small debris over long ranges and accurately track objects. The article presents various topics related to radar systems for SSA, including ground-based and space-based radar, multistatic radar networks, and cognitive radar and deep-learning methods. It concludes by providing an overview of the articles included in the special issue. [Extracted from the article]

Camouflage skills are important for understanding the relationship between predators and prey in ecological systems. Mimicry is a common strategy used by creatures to alter their appearance in order to blend in with their surroundings and avoid detection. This paper unveils a previously unknown camouflage strategy that creates false object information and positions of an object in the visual range of radar sensor vision. A dispersion‐modulated illusionary metasurface to realize focal spot coding in a 3‐dimensional space to fit the radar imaging process is proposed. As the radar image indicates, an aircraft image is created by a flat metasurface plane with good polarization adaptation ability. Design, simulation, and experimental results validate the study. This camouflage strategy indicates the complexity of the food chain and can be used by both predators and prey, providing accessible insights for maintaining biological diversity. The metasurface‐based system can be potentially utilized in future radar markers for marine traffic control systems. [ABSTRACT FROM AUTHOR]

In this study, we introduce a radar sensor that we have developed for the micro‐Doppler signature measurements together with three consecutive signal preprocessing routines that are to be used for primary detection and classification of human gait and movements. The developed C‐band radar sensor has been used in a series of human walking and running experiments that were conducted at Mersin University facilities. First, the raw data collected in time domain are transformed to two‐dimensional (2D) time‐frequency plane to investigate the Doppler characteristics of human movements. To detect and extract the main body signature of the human, a Kalman filter‐based torso signature detection methodology is introduced as the first preprocessing tool. Then, to be able to better detect the other body parts' signatures, the torso's response is thoroughly extracted from the 2D time‐Doppler image as the second preprocessing routine by applying a circular shift procedure along each time‐bin. As the last procedure, envelopes of the micro‐Doppler signatures on the 2D time‐Doppler image plane are determined by another routine based on human Doppler threshold method. To assess the success and the validity of the proposed pre‐signal processing tools, they are applied to the measured data collected via different experiments of human movements. After applying all these steps, the torso signature and envelopes of all other body parts' signatures are marked and determined with good fidelity. [ABSTRACT FROM AUTHOR]

In this paper, the application of a hybrid representation in wide‐angle synthetic aperture radar (WASAR) imaging is investigated, addressing the challenge of achieving sparse representation in the presence of complex electromagnetic scattering characteristics and highly anisotropic targets. A convolutional neural network is utilized to represent the two‐dimensional data within each azimuth aperture, while employing blind compressed sensing (BCS) to achieve sparse representation across different azimuth apertures. Convolutional neural network (CNN) excels in learning spatial hierarchies and local dependencies of two‐dimensional data but requires a large amount of training data. Isotropic targets within each azimuth aperture can be used for training conventional SAR data, while acquiring training samples for anisotropic targets poses challenges. To address this issue, BCS are integrated into WASAR imaging, eliminating the need for additional training data. By integrating these methods, a novel approach called hybrid‐WASAR is proposed, which incorporates two regularization terms into WASAR imaging and employs the alternating direction method of multipliers to iteratively solve the imaging model. Compared to traditional WASAR imaging techniques, hybrid‐WASAR improves the accuracy of reconstructing the backscattering coefficients of the targets, leading to a significant enhancement in overall imaging quality. [ABSTRACT FROM AUTHOR]

Frequency-diverse radar imaging is an emerging field that combines computational imaging with frequency-diverse techniques to interrogate the high-quality images of objects. Despite the success of deep reconstruction networks in improving scene image reconstruction from noisy or under-sampled frequency-diverse measurements, their reliance on large amounts of high-quality training data and the inherent uninterpretable features pose significant challenges in the design and optimization of imaging networks, particularly in the face of dynamic variations in radar operating frequency bands. Here, aiming at reducing the latency and processing burden involved in scene image reconstruction, we propose an adaptive sampling iterative soft-thresholding deep unfolding network (ASISTA-Net). Specifically, we embed an adaptively sampling module into the iterative soft-thresholding (ISTA) unfolding network, which contains multiple measurement matrices with different compressed sampling ratios. The outputs of the convolutional layers are then passed through a series of ISTA layers that perform a sparse coding step followed by a thresholding step. The proposed method requires no need for heavy matrix operations and massive amount of training scene targets and measurements datasets. Unlike recent work using matrix-inversion-based and data-driven deep reconstruction networks, our generic approach is directly adapted to multi-compressed sampling ratios and multi-scene target image reconstruction, and no restrictions on the types of imageable scenes are imposed. Multiple measurement matrices with different scene compressed sampling ratios are trained in parallel, which enables the frequency-diverse radar to select operation frequency bands flexibly. In general, the application of the proposed approach paves the way for the widespread deployment of computational microwave and millimeter wave frequency-diverse radar imagers to achieve real-time imaging. Extensive imaging simulations demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

A multistatic inverse synthetic aperture radar (ISAR) system can observe a target from multiple observation angles. Compared with the monostatic ISAR system, the multistatic ISAR system can obtain more spatial sampling data, which provides the ability for high-resolution ISAR imaging. In some cases, the locations of radars are close. There are overlaps among observing angles, which brings little cross-range resolution improvement. However, such scenes are less considered indicates the literature. In the scene with overlaps, the image obtained by each radar may be similar due to the similar observation angles. In this letter, a novel multistatic ISAR imaging model is proposed by applying the similarity prior as a constraint. And an efficient image reconstruction algorithm is derived based on the orthogonality of observation matrix. Compared with existing CS-based methods, the proposed method can be directly applied on multistatic ISAR echoes without rearranging, which is more convenient in practical applications. Experiment results of simulated and measured data show that the proposed method achieves better performance especially under low signal-to-noise ratio (SNR) conditions. [ABSTRACT FROM AUTHOR]

High-fidelity deception jamming can seriously mislead synthetic aperture radar (SAR) image interpretation and target detection, which is difficult to identify or eliminate through traditional anti-jamming methods. Based on the range–Doppler algorithm (RDA), an anti-jamming method for SAR using joint waveform modulation and azimuth mismatched filtering is proposed in this letter. The signal model of SAR echo after pulse compression with deception jamming is derived from the radar detection and jamming characteristics. A multiobjective cost function is introduced to suppress the jamming level and keep the real target information, parameterized with the waveform initial phase and the azimuth mismatched filter coefficients, which is optimized using the second-order Taylor expansion approximation and the alternating direction multiplier method (ADMM). The performance of this proposed method is evaluated through the convergence analysis and anti-jamming experiments on the point target and the distributed target scenarios. [ABSTRACT FROM AUTHOR]

The Doppler frequency of the echo signal exhibits time-varying and high-order characteristics due to the high-speed relative motion between the satellites in spaceborne inverse synthetic aperture radar (ISAR) imaging of space target, even during a short coherent processing interval (such as 1 s). Therefore, the selection of the optimal imaging time interval (OITI) is crucial to obtain a well-focused image. The existing methods do not consider the fact that the OITIs of scatterers on the target can also vary significantly. In this letter, an OITI selection method for spaceborne ISAR imaging of space targets based on the time–frequency analysis is proposed. First, the differences in optimal imaging instants (OIIs) of scatterers are analyzed. Then, based on the combination of the Margenau–Hill–spectrogram (MHS) and the S-method, the time–frequency curves can be obtained after extracting the time–frequency ridge and polynomial fitting. The OII of the entire target can be estimated after eliminating the differences in the OIIs by multiplying the constructed third-order phase terms with the signal. Finally, a more suitable OITI can be obtained because of the elimination of the differences in the OIIs. The effectiveness and advancement of the proposed method are verified by simulation and real data results. [ABSTRACT FROM AUTHOR]

Special attention has been devoted to joint waveforms based on radar signals to mitigate the tension of spectrum resources and simultaneously play a multifunctional role. Aiming at the shortcomings of conventional detection and jamming dual-functional systems, where wide beams are difficult to detect with high accuracy, while narrow beams are difficult to cover a wide range of jamming, this letter designs a multiple-input multiple-output (MIMO)-synthetic aperture radar (SAR) system with jamming capabilities. The characteristic of this signal is to use MIMO system multiple antennas to transmit frequency diversity wideband code division jamming signals to effectively generate wideband suppression jamming for conventional radars, including frequency-agile radars. At the same time, the wide beam characteristics of MIMO antennas can ensure the physical range of jamming. In addition, for the imaging part, digital beamforming (DBF) technology can be used to form the beam in a specific direction at the receiving end, resulting in high resolution. Simulation experiments confirm the system’s ability to achieve high-resolution imaging of specific target areas while maintaining robust jamming capabilities. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) using radar has been realized commonly using deep neural networks (DNNs). A change in radar operating frequency significantly changes the spectrogram characteristics compared to any other radar parameter. In this work, we consider three different approaches, viz., transfer learning, metric learning, and meta-learning (Reptile algorithm) for adapting the network developed for one radar operating frequency (source domain) to another operating frequency (target domain). Results and discussions on the performance of these algorithms on an openly available dataset are presented. [ABSTRACT FROM AUTHOR]

Vortex electromagnetic (EM) waves, which carry orbital angular momentum (OAM), can provide more information of the target in radar imaging and target detection. Pulsed radar is often used in current OAM radar design. However, compared with the pulsed radar, the frequency-modulated continuous-wave (FMCW) radar is small in size, light in weight, and low in power consumption, which can be applied into many practical scenarios. Therefore, this letter investigates into the OAM radar based on the FMCW system. It can be found that, apart from the inherent characteristics of small blind area and high measurement accuracy in FMCW system, the OAM-FMCW radar also owns an additional degree of freedom, which is independent of the frequency, finally leading to the result that the FMCW radar will not be affected by the range and velocity of the target when performing azimuth detection. Simulation results are given to validate the corresponding analysis of the proposed system. [ABSTRACT FROM AUTHOR]

Effective oil-spill monitoring is critical for timely response to minimize the impact on the environment. In response to the difficulty in extracting suspected oil films from marine radar images, a semantic segmentation method was proposed. In the preprocessing of training samples in similar scene, a slicing solution was used to compensate for the small sample of original data. The U-Net semantic segmentation network was used to classify oil film into two categories: real and suspected. Existing mainstream marine radar oil-spill identification methods were compared. Experimental results demonstrate that the proposed method achieves more reliability in oil-spill semantic segmentation. It can provide real-time information for oil-spill emergency response and disaster assessment. [ABSTRACT FROM AUTHOR]

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. Although the ambiguity can be resolved using multiple channels in azimuth, its azimuth resolution in the vicinity of flight path is usually poor due to the small angle variation. To solve the above problems, a hybrid real/synthetic aperture scheme for multichannel radar forward-looking imaging is proposed in this letter. In the scheme, the synthetic aperture imaging result with left/right ambiguity is obtained first using the information of platform motion. Then the real aperture superresolution imaging with regularized iterative adaptive approach (RIAA) is achieved using the instantaneous data of multiple channels. Finally, the two imaging results are fused to obtain the forward-looking superresolution image without left/right ambiguity. Simulation results are given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this letter, we designed a hemispherical refractive lens using ice for millimeter wave (MMW) imaging in the environment of −15 °C. Guided by geometrical optics theory, we calculated a suitable thickness of the lens to compensate for the phase of aperture field emitted by the antenna. Then, the 3 dB beamwidth was reduced from 17.6° to 2.1°. Meanwhile, the antenna gain is increased from 17 to 24 dB, which raises the maximum radar detection distance by 18%. Finally, we conducted experiments for MMW imaging to test the ice lens under different road conditions. Measuring results demonstrated that the image error rate has been significantly reduced and the peak signal-to-noise ratio (PSNR) has increased by more than 5 dB, which are consistent with simulated results. It should be noted that the ice lens can improve imaging quality and increase imaging resolution, making it widely applicable in the fields of Pole expedition and snow disaster rescue. [ABSTRACT FROM AUTHOR]

Along-track synthetic aperture radar interferometry (ATI) systems are used to detect a moving target and to estimate its radial velocity. A dual-channel (single-baseline) ATI system estimates the velocity ambiguously because there are many candidate velocities corresponding to the interferometric phase. To resolve the ambiguity of the estimates, a multichannel (multibaseline) ATI system is required. This letter proposes an efficient implementation technique of a multichannel ATI system by using a frequency-modulated continuous wave (FMCW) radar and an antenna arrangement method considering the inherent bi-static characteristic of FMCW radars. Also, a simple and robust radial velocity estimation algorithm suitable for the proposed system model is presented. The validity of the algorithm is confirmed with simulated ATI results. [ABSTRACT FROM AUTHOR]

Floods cause loss of lives and multibillion dollar damages every year. When these events strike, automated tools using remote sensing for quick mapping of the affected areas are critical for planning rescue activities and assessing impact. The most recent techniques to map floods are based on semantic segmentation and deep learning, which require large datasets and ground truth for training, which are difficult to get. To overcome this challenge, we propose an effective method for synthesizing patches of synthetic aperture radar (SAR) images containing open-land flooded areas. With bitemporal image acquisitions, we replace portions of land areas in the second acquisition with water pixels borrowed from permanent water bodies. Spatial patterns derived from elevation data help guide the process. With this approach, we build a large dataset with pre- and post-event Sentinel-1-based VH-polarized radar intensity images to train deep neural networks to map real-life floods. In a case study, we use an established U-Net architecture and show that a model version with less than 2% of the number of original parameters achieves almost identical flood detection accuracy. This allows for faster processing and is a clear advantage from an operational perspective. For comparison, we provide empirical segmentation results of four flood cases. F1 scores agree between 0.80 and 0.90 when compared with reference flood maps from the Copernicus service. This confirms that the approach is valuable for mapping areas affected by floods, e.g., during or immediately after catastrophic events, even in areas that were not included during model training. [ABSTRACT FROM AUTHOR]

This letter presents a novel method used to find the constant false alarm rate (CFAR) threshold after coherently compressing one or more dimensions of the radar image, generally the ones obtained from a phased array or multiple input multiple output (MIMO) diversity. The method provides a significantly large improvement in the numerical complexity of the processing chain at the cost of a slightly lower probability of detection for a fixed probability of false alarm. The method is particularly useful for real-time applications such as assisted and autonomous driving, where CFAR is applied to the range-Doppler map to generate a point cloud before angular estimation is done. The method may also be extended for Earth observation (EO) or surveillance applications where real-time processing is required. The proposed method is intended as a pre-processing step to the CFAR detection and is compatible with any CFAR algorithm that assumes Gaussian noise. The method implies finding a CFAR threshold calculated from a low-SNR non-coherent integrated radar signal and analytically mapping it to the high-SNR coherent integrated radar signal for improved detection. The proposed method is first presented theoretically and then evaluated in both simulated and experimental environments. [ABSTRACT FROM AUTHOR]

Sparse superresolution algorithms have been applied in scanning radar imaging to improve its azimuth resolution. However, the inverse matrix in each iteration is usually diagonal loading by the updating result, which leads to huge computational complexity for 2-D echo data. In this letter, a batch-processing superresolution framework is proposed to process the echo data in parallel. On the one hand, the optimization problem for sparse target recovery is modified as matrix form, which presents the batch-processing potential for 2-D echo data. On the other hand, the optimization problem is solved by the proposed alternating direction method of multipliers (ADMM)-based batch-processing framework, which can avoid high-dimensional matrix inversion along different range bins. Compared with traditional sparse superresolution methods, the proposed batch-processing framework is more suitable for 2-D echo data superresolution. [ABSTRACT FROM AUTHOR]

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In this study, a high-gain and dual-polarized Quad Ridged Horn Antenna (QRHA) is proposed and designed to be used in Obstacle Penetrating Imaging Radar (OPIR) applications. The quad ridged design is developed and then optimized by using the antenna design simulator software; CST Microwave Studio. The antenna parameters including the center frequency, frequency bandwidth and the antenna radiation pattern beam width are considered based on the requirements of OPIR applications. The final design is obtained for the center frequency of 2.785 GHz with a -10 dB bandwidth of at least 2.5 GHz. The final designed QRHA is able to provide a gain of 12.4 dB with a beam width of 36° at the center frequency of 2.785 GHz. [ABSTRACT FROM AUTHOR]

The range and azimuth information of a target can be obtained after coherent pulse accumulation of the traditional multiframe stepped‐frequency (SF) synthesis wideband echo and spectrum analysis, and high‐resolution two‐dimensional imaging of the target can be achieved. However, the accumulation of a certain number of pulses requires a long beam dwell time, which cannot meet real‐time imaging requirements for high‐speed radar moving platforms. To solve the above problems, a scanning imaging mode is proposed by combining forward‐looking imaging and scanning imaging, and a target echo signal model with the structure of scanning stepped‐frequency is constructed. The SF pulses are grouped and transmitted according to the scanning order, and the echo pulses are sorted and reorganised. After the timing compensation and range Doppler coupling compensation are completed, the target is located and projected. The proposed imaging mode can achieve high‐resolution scanning forward‐looking imaging and can basically attain an azimuth resolution of approximately 0.1° within the forward‐looking scanning range. This imaging mode has higher real‐time performance and a larger target imaging range than the traditional methods. Moreover, the simulation results showed good performance via the scanning imaging method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the application of a hybrid representation in wide‐angle synthetic aperture radar (WASAR) imaging is investigated, addressing the challenge of achieving sparse representation in the presence of complex electromagnetic scattering characteristics and highly anisotropic targets. A convolutional neural network is utilized to represent the two‐dimensional data within each azimuth aperture, while employing blind compressed sensing (BCS) to achieve sparse representation across different azimuth apertures. Convolutional neural network (CNN) excels in learning spatial hierarchies and local dependencies of two‐dimensional data but requires a large amount of training data. Isotropic targets within each azimuth aperture can be used for training conventional SAR data, while acquiring training samples for anisotropic targets poses challenges. To address this issue, BCS are integrated into WASAR imaging, eliminating the need for additional training data. By integrating these methods, a novel approach called hybrid‐WASAR is proposed, which incorporates two regularization terms into WASAR imaging and employs the alternating direction method of multipliers to iteratively solve the imaging model. Compared to traditional WASAR imaging techniques, hybrid‐WASAR improves the accuracy of reconstructing the backscattering coefficients of the targets, leading to a significant enhancement in overall imaging quality.

Synthetic Aperture Radar (SAR) renderings in 3D provide additional target information when compared to 2D by separating out features overlaid in height. However, the required 2D SAR aperture, when Nyquist sampled, necessitates large scanning times that would be impractical for most realistic collections. This research has developed a novel volumetric approach to sparse aperture 3D SAR imaging, which is applicable to bistatic SAR near‐field geometries, a generalization of far‐field cases. This approach is first demonstrated in simulation and then applied to a measured scene containing a model vehicle target, producing sub‐Nyquist sampled 3D SAR renderings. [ABSTRACT FROM AUTHOR]

Airborne forward-looking radar (AFLR) has been more and more impoatant due to its wide application in the military and civilian fields, such as automatic driving, sea surveillance, airport surveillance and guidance. Recently, sparse deconvolution technique has been paid much attention in AFLR. However, the azimuth resolution performance gradually decreases with the complexity of the imaging scene. In this paper, a data-driven airborne Bayesian forward-looking superresolution imaging algorithm based on generalized gaussian distribution (GGD- Bayesian) for complex imaging scene is proposed. The generalized gaussian distribution is utilized to describe the sparsity information of the imaging scene, which is quite essential to adaptively fit different imaging scenes. Moreover, the mathematical model for forward-looking imaging was established under the maximum a posteriori (MAP) criterion based on the Bayesian framework. To solve the above optimization problem, quasi-Newton algorithm is derived and used. The main contribution of the paper is the automatic selection for the sparsity parameter in the process of forward-looking imaging. The performance assessment with simulated data has demonstrated the effectiveness of our proposed GGD- Bayesian algorithm under complex scenarios.

Radar enables a convenient, contactless way for gait analysis in daily scenarios, such as in‐home health monitoring. However, the overlapping of human limb components could occur in both range and velocity dimensions of the radar echo, which makes it difficult to distinguish the limbs. In this study, a trajectory tracking and recombination method based on range‐Doppler‐time data is proposed for gait feature estimation under insufficient radar resolution. In the trajectory tracking step, the scattering amplitude is included for Kalman filtering, and the weighted joint nearest neighbour algorithm is proposed for data association. Then, the trajectories are recombined to construct the limb tracks based on the relative range‐Doppler angles. Finally, the limb tracks are used to estimate the spatial‐temporal and kinematic gait features. In our simulation, the trajectory tracking accuracies range from 98.2% to 99.6%. The gait feature estimation accuracies for simulation and measurement are no less than 91.9% and 93.8% respectively. [ABSTRACT FROM AUTHOR]

This letter proposes an improved synthetic aperture imaging algorithm to enhance the imaging performance in the Azimuth Missing Data (AMD) case. The proposed algorithm designs an effective phase compensation function based on the Range Cell Migration Correction (RCMC) echo and first reconstructs the complete RCMC echo from the AMD echo. The accuracy of echo reconstruction is improved, leading to a better imaging result. Numerical simulations and a real measured experiment verify the effectiveness of the proposed algorithm. Furthermore, since the range positions of targets can be located after RCMC processing, only non‐zero RCMC signals are reconstructed. Thus, the computational complexity superiority of the proposed algorithm is obvious, especially when the targets are sufficiently sparse in the range direction. [ABSTRACT FROM AUTHOR]

Since it is difficult to filter out the rainfall interference directly from the X-band marine radar image, it is necessary to detect whether the collected radar image contains rainfall interference to control the quality of the radar image. Aiming at the problem of rainfall recognition in X-band marine radar images, a new rainfall detection method is proposed by using principal component analysis (PCA) technology to reduce dimensions and extract features from radar images. Based on the calculated distance between the features to be tested and the known features, the $k$ -nearest neighbor (KNN) algorithm is utilized to determine the classification task of the radar image and recognize the rainfall radar image. The experimental result illuminates that the detection accuracy of the proposed method reaches 99.3% and is 2.0% higher than that of the support vector machine (SVM)-based method. Meanwhile, the proposed method shows good classification performance for the rain-contaminated images under different rainfall intensities. [ABSTRACT FROM AUTHOR]

With well-controlled orbits and a more accurate timing system of the new-generation synthetic aperture radar (SAR) satellites, the ability of high-precision geolocation with multiple SAR acquisitions has been verified based on radar reflectors. In this letter, SAR absolute and differential geolocation methods are introduced and implemented to retrieve the coordinates of two different types of radar reflectors: corner reflectors (CRs) and multidirectional dihedral reflectors (MDRs). The experimental results demonstrate that decimeter- even centimeter-level positioning accuracy can be obtained by SAR stereo geolocation based on multiaspect SAR acquisitions from RadarSAT-2 (RS-2), COSMO-SkyMed (CSK), Sentinel-1 (S1), and TerraSAR-X (TSX), respectively. [ABSTRACT FROM AUTHOR]

Through-wall radar (TWR) can image the target of interest and capture the human sensing information. However, the poor human interpretability of TWR images and the lack of effective supervision make the extraction of complete body contour intractable. This letter proposes dual UNet, an unsupervised human contour extraction method for TWR images. Specifically, the method adopts two UNets with the same structure. One serves as the encoder to convert the TWR images into the latent representation. Another serves as the decoder to reconstruct the latent representation into the original images. Reconstruction loss and smooth normalized cut loss are optimized together to offset the dependence on labels and supplement global segment constraints. After training and post-processing, the latent representation can be used as the result of contour extraction. Experimental results show that dual UNet stands out among unsupervised human contour extraction methods in both free space and wall-occlusive scenarios, opening the possibility of learning useful human sensing information from raw TWR images without manual annotations. [ABSTRACT FROM AUTHOR]

Near real-time ship monitoring is crucial for ensuring safety and security at sea. Established ship monitoring systems are the automatic identification system (AIS) and marine radars. However, not all ships are committed to carry an AIS transponder and the marine radars suffer from limited visibility. For these reasons, airborne radars can be used as an additional and supportive sensor for ship monitoring, especially on the open sea. State-of-the-art algorithms for ship detection in radar imagery are based on constant false alarm rate (CFAR). Such algorithms are pixel-based and therefore it can be challenging in practice to achieve near real-time detection. This letter presents two object-oriented ship detectors based on the faster region-based convolutional neural network (R-CNN). The first detector operates in time domain and the second detector operates in Doppler domain of airborne Range-Compressed (RC) radar data patches. The Faster R-CNN models are trained on thousands of real X-band airborne RC radar data patches containing several ship signals. The robustness of the proposed object-oriented ship detectors is tested on multiple scenarios, showing high recall performance of the models even in very dense multitarget scenarios in the complex inshore environment of the North Sea. [ABSTRACT FROM AUTHOR]

A multisensor data fusion algorithm is proposed in this study for life detection in cluttered environments. The method consists of four steps: data acquisition, data preprocessing, data fusion, and result sort. First, four kinds of data (RGB image, RGB video, infrared image, and radar wave) are acquired from three different sensors (RGB camera, infrared camera, and life detection radar). Then, four specified algorithms are used on each data for data preprocessing. Third, an ordered weighted average (OWA)-based data fusion is used to fuse the four single results. The result is a sort of four possible results ($\textit {Alive}$ , $\textit {Dead}$ , $\textit {Animal}$ , and $\textit {None}$). A series of cluttered environments are designed to verify the effectiveness of the proposed method. The results show that the data fusion method can effectively improve the success rate of life detection in cluttered environments. Specifically, the average accuracies of 97.8%, 96.7%, and 89.7% under different distances, light intensities, and shelter, respectively, are obtained. [ABSTRACT FROM AUTHOR]

Radar-based dynamic hand gesture classification has been an active research field in recent years. Deep learning methods using radar sensors are widely used to classify dynamic hand gestures. The existing deep learning methods need the overhead of performing the slow- and fast-time Fourier transforms to obtain the various spectrum images as the input of the deep convolutional neural network (DCNN). In this article, a dynamic hand gesture classification method based on the multichannel radar using a multistream fusion 1-D convolutional neural network (MSF-1-D-CNN) is proposed. The proposed MSF-1-D-CNN has four branches in parallel, and each branch has the inception modules to extract features from the raw echo data of each receiving antenna. Then, the extracted features from each branch are concatenated, and the long short-term memory (LSTM) layer is utilized to extract the temporal characteristic of the concatenated features. Finally, the dense layer with the softmax function is utilized to obtain the classification result of hand gestures. The experimental results show that, compared with existing methods, the proposed method using multichannel radar data can provide improved classification accuracy when the hand has a large incident angle and distance from the radar. Moreover, the proposed method can also reduce the network parameters and computational complexity, which has the potential to be implemented on commercial embedded systems. [ABSTRACT FROM AUTHOR]

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

A ${k}$ -space and Doppler velocity decomposition based on an accurate millimeter wave (mmW) 3-D target localization method is presented, using the range points’ migration (RPM) method to provide multi-information associated with point clouds. The incoherent method, known as RPM, has a number of advantages over coherent localization, including avoiding a false response due to phase uncertainty or the necessity for highly accurate phase calibration in multiple arrays. However, various concerns must be addressed to retain the benefit of high-frequency mmW radar. In order to bring some benefits of high-frequency mmW radar into RPM scheme, this study introduces the ${k}$ -space and Doppler velocity decomposition schemes for multiple objects moving at different speeds into the RPM scheme, where a new weighted term is introduced. In addition, the RPM point cloud is incorporated with the incoherent Doppler velocity estimation method known as the weighted kernel density (WKD) method, which provides a multifunctional 3-D localization. The mmW radar experiment in the 79-GHz band demonstrates that our proposed method achieves accurate 3-D Doppler-associated localization, even with a small aperture array. [ABSTRACT FROM AUTHOR]

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

Dynamic identification is crucial for space target investigation and awareness, which is especially challenging for uncooperative and uncontrolled targets. This article presents a dynamic identification algorithm using the time-variant geometric feature flow extracted from an inverse synthetic aperture radar (ISAR) image sequence. The range-Doppler (RD) projection, along with the sequential radar light of slight of the target component, intrinsically relies on the targets’ dynamic states. With the accommodation of a long short-time memory (LSTM) network, reliable dynamic identification of space targets can be implemented by exploiting the geometrical feature flow from the ISAR sequence. The superiority of the proposed method is demonstrated through comparison experiments. [ABSTRACT FROM AUTHOR]

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]

The realization of nondestructive detection and classification of pavement distress is of great significance for putting forward a reasonable maintenance scheme and prolonging the service life of the road. In this paper, the advanced air-coupled 3D ground-penetrating radar (3D-GPR) was used to detect Li-Ma Expressway (Jiangsu Province, China) to achieve the purpose of rapid, accurate, and nondestructive testing. Through on-site coring at the corresponding radar anomaly signal, it is verified that the 3D-GPR can identify the characteristics of pavement distress. To address the issue of a large amount of 3D-GPR images required to be processed and low efficiency of current manual classification, VGG16 and ResNet50 models were established based on deep convolutional neural network to classify four pavement distresses automatically. The results showed that the maximum accuracy, precision, and recall obtained from the VGG16 model training results were 98.73%, 97.98%, and 96.17%, respectively. In addition, The overall processing time of VGG16 to identify 2525 radar images with $200 \times 200$ pixels is 232 s. And VGG16 was selected as the automatic classification model of 3D-GPR image for pavement distress of Li-Ma Expressway. [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]

With radar networks, the resolution of critical radar parameters such as Doppler and angle can be improved compared to a single radar sensor. As the network’s aperture is considerably larger than that of a single radar, a much higher angular resolution is achieved. However, with a large aperture, range-dependent phase deviations, that is, near-field effects, occur and affect the angle estimation. In this work, these near-field effects are evaluated exemplarily for a coherent network. Furthermore, a new strategy to compensate for those network near-field effects is proposed and demonstrated based on measurements. The benefits of the near-field compensation are emphasized by comparing the network’s angle-estimation capabilities with and without compensated near-field effects. [ABSTRACT FROM AUTHOR]

The respiratory rate (RR) and waveform play an important role in reflecting health conditions. Some diseases such as Biot’s breathing and Chyne–Stokes respiration require long-term continuous respiration monitoring, which is normally conducted by hospitals or sleep centers. Portable sensor devices, such as piezoelectric sensors, watch-type monitoring devices, ultrasound technologies, radar sensors, and so on, are required to help patients that are difficult to attend to such settings. In terms of these choices, the radar sensor can provide a more comfortable and contactless experience. Most of the existing methods analyze the 1-D radar signal. The 2-D radar signal, such as the radar image, contains more spatial information that is easier to be interfaced with further pose or gesture recognition functions to extract useful information. This article presents an image processing approach with radar images generated from an ultrawideband (UWB) radar sensor to produce respiration waveforms and estimate RR in real-time. The raw signals from the radar sensor are converted to a 2-D heatmap image, and then an image-processing algorithm is employed to extract respiration information including the respiration waveform from the heatmap. Experimental results show that the proposed system can reliably measure respiration motion in static situations such as lying in the supine or lateral position with an average precision of 95.02%. [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]

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]

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]