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Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target’s motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target’s effective rotation angle and the equivalent rotation angular velocity and then define the target’s optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm.

Abstract Synthetic aperture radar tomography (TomoSAR) baseline optimization method is capable of reducing system complexity and improving the temporal coherence of data, which has become important research in the field of TomoSAR. A nested TomoSAR technique is proposed, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co‐array through the nested array to increase the degrees of freedom of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co‐array, covariance matrix of the echo needs to be obtained. Therefore, a TomoSAR sparse reconstruction algorithm based on the nested array is proposed, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. The effectiveness of the proposed method through simulated and real data experiments is demonstrated. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of this proposed method have better anti‐noise performance and retain more image information.

Abstract Owing to the time variance of the inverse synthetic aperture radar (ISAR) imaging plane of space targets, scatterers with large heights relative to the imaging plane migrate through several Doppler cells. This defocussing in the azimuth direction of each scatterer cannot be compensated via the traditional migration through the resolution cell (MTRC) compensation method because the height of each scatterer is quite different and independently distributed in the ISAR image. In this study, a parametric azimuth‐defocussing model‐based CLEAN method is proposed for the space‐target ISAR image. First, high‐order defocus parameters and their influences on the image of space targets were analysed at different arc segments. Subsequently, a mathematical azimuth‐defocussing expression of the ISAR image scatterers was derived after MTRC and azimuth high‐order phase compensation, and an approximate defocus expression based on the Fresnel integral was provided for computational efficiency. This model can estimate the complex amplitude, coordinates and defocus parameters of each scatterer in the image domain by applying a sequential quadratic programing algorithm. Finally, the validity and stability of the proposed algorithm for the isolated and coupled scatterer extraction were verified at different signal‐to‐noise ratios, and different defocus parameters were verified through simulations.

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]

Establishing effective feature descriptors is a crucial step in 3D point cloud registration task. Existing manual‐based methods are noise‐susceptible and time‐consuming when running on low‐cost edge computing devices. To this end, the authors proposed a Local Reference Frame (LRF) based approach that can quickly and robustly register point clouds by using a novel lightweight local‐spherical grid weighted descriptor (LSGWD). Firstly, the LRF of the proposed algorithm is established by the covariance matrix eigenvector of KeyPoint's spherical support set and the centroid vector's projection on its orthogonal plane. Then the spherical support is grided to 32 bins, and the 4D geometric features of each subset are constructed by the centroid moment and the cosine value of the angles between the centroid vector and axes of LRF. Secondly, to restrain the insufficient discriminative information presented in the purely geometric features, the Gaussian projection and gradient mapping are proposed to calculate the smooth density and the correlation of structural characteristics, which are obtained as the distribution information of each bin to weigh the feature representation. Finally, the 32 × 4‐dimensional KeyPoint descriptor is obtained and used in the 3D point cloud registration framework. Experiments are carried out on three test datasets and real scene data. Compared to previous baselines, our descriptor achieves the state‐of‐the‐art performance in terms of efficiency and accuracy owing to its compact structure and noise robustness. The proposed method enhances the recognition and registration performance of 3D point cloud matching in low‐cost edge computing applications. [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]

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]

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]

The attributed scattering center (ASC) can provide a concise physical description of radar targets, which has broad applications for physical-induced radar recognition. However, the ASC extraction is of great difficulty due to its high-dimensional parametric modeling. In this letter, a modified alternative direction method of multipliers (ADMM) unfolded net is proposed for ASC decomposition of synthetic aperture radar (SAR) targets. By transforming the ASC decomposition challenge into a sparse reconstruction problem, the modified ADMM-Net can fast attain ASC parameters and reconstruct a complete signal from sparse observations. Instead of tremendous iterations with fixed hyperparameters as traditional methods, the modified ADMM-Net unrolls several iterations into a few layers with learned hyperparameters. Therefore, the proposed algorithm can realize fast and precise ASC decomposition. Experimental results confirm its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Two-dimensional (2-D) water surface elevations (WSEs) can be measured for the first time using a new generation of radar altimeter, which uses the interferometric synthetic aperture radar (InSAR) technique on near-nadir swath to measure elevations of both the inland surface waters and the ocean with unprecedented swath and spatial resolution. The interferometric imaging radar altimeter (InIRA) in the Tiangong-2 space laboratory of China is the first spaceborne interferometric radar altimeter, which was launched in 2016. Calibration is essential to a guarantee of the measurement accuracy. Not only the cross-track errors, but also the along-track errors have to be estimated and corrected. This letter proposes a correction method to remove the azimuth time shift of the radar system using geographic positions of the surrounding lakeshores and riverbanks. Real acquisitions of InIRA over the largest inland lake of China, i.e., the Qinghai Lake, have been selected to validate the method. Both azimuth and range system time errors, the media delays, and the cross-track phase errors have been estimated and corrected on the level-1 complex products. The derived 2-D WSEs over an area of ~400 square kilometers have been compared with the high-resolution data of Sentinel-3A synthetic aperture radar altimeter (SRAL). The validation results have shown good agreements in both along-track and cross-track directions with a root mean square error of 28.1 cm and a mean absolute error of 22.7 cm. [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]

In practical applications, airborne terahertz synthetic aperture radar (THz-SAR) echo signal is not only affected by range amplitude and phase errors introduced by nonideal THz device but also affected by motion error in azimuth induced by the nonideal trajectory of platform, resulting in the 2-D defocus of image. To address the above errors, a novel 2-D autofocusing algorithm for real airborne stripmap THz-SAR imaging is proposed. In range autofocusing, the amplitude and phase errors are estimated simultaneously in range frequency domain based on the dominant point target selected in the preliminary focused images. To avoid dividing more subapertures in azimuth error estimate, the autofocusing method based on the maximization of image contrast is adopted. Then, range-Doppler algorithm (RDA) integrated with the proposed 2-D autofocusing is performed to obtain the focused images. The real measured data, acquired by a 0.22-THz airborne SAR system, are used to demonstrate the validity of the proposed algorithm. By comparing the imaging results, the proposed method can effectively and efficiently focus airborne stripmap THz-SAR images. [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]

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]

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]

Deep learning (DL) has succeeded in the field of target detection and has been introduced into the research works of ground-moving target indication (GMTI) for synthetic aperture radar (SAR) recently. Due to the lack of labeled data in SAR/GMTI, simulated data are usually employed to support the training of networks, which has proved to be a feasible way in practice. Although some simulated data are very close to the real radar data, the fact is that distribution differences between them are inevitable and always lead to a performance loss of the network. Motivated by recent advances in transfer learning, this letter proposes a new method for ground-moving target detection of multichannel SAR systems, namely, subdomain adaptive residual network (SARN). It is built on the basis of ResNet18, and subdomain adaptation is introduced. During the network training, multikernel local maximum mean discrepancy (MK-LMMD) is minimized as well as classification error. Experiments on three-channel SAR data show that the proposed method significantly improves the detection performance as compared with CA-CFAR and the DL method. [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]

Aiming at solving the clutter flooding problem in the traditional cross correlation reverse time migration (RTM) of ground penetrating radar (GPR), we proposed an improved RTM method based on multitraces cross correlation window (MCW) imaging condition. The main difference between the proposed method and the traditional direct stacking is that it can effectively enhance the effective signal while weakening the clutter by performing MC calculation on the single trace imaging results, avoiding the enhancement of both clutter interference and effective signal concurrently by direct stacking. Second, the window threshold is set according to the GPR observation accuracy, and the effective signal in the MC result is retained as the abnormal region window, while the imaging results in the nonabnormal region are discarded, so as to suppress the clutter and retain the abnormal region information. Numerical experiments show that compared with the traditional RTM and total variation (TV) denoising method with cross correlation imaging conditions, the MCW imaging condition can accurately locate abnormal region, suppress clutter interference, and have the advantage of no loss of effective information, which greatly improves the imaging quality. Finally, the proposed method is applied to the measured data to verify the practicability and effectiveness in practical engineering applications. [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]

Recent studies revealed that the time-domain synthetic aperture radar (SAR) processors are more appropriate for future innovative SAR missions (e.g., radar observing system for Europe in L-band (ROSE-L) and Harmony) not only due to their ability to form the SAR image on user-defined regions of interest (ROIs) but also for the straightforward accommodation to configurations wherein the azimuth spectrum folding occurs [e.g., terrain observation with progressive scans SAR (TOPSAR)]. In this letter, we propose an accelerated back-projection (BP) SAR processor working in conjunction with a fast routine for generating the elliptical grid (laying on arbitrary planes) necessary for the subaperture (SA)-based BP speed-up. The proposed workflow forms all the SA SAR images on the same coarse elliptical grid which before the coarse-to-fine grid interpolation are translated in the azimuth baseband. The fine-resolution SAR image is obtained by coherently integrating the SA images together with the concatenation of the corresponding fraction from the azimuth spectrum (unfolding) making the single look complex (SLC) outcome proper for further Doppler-based processing. Our validation experiments indicate that the most suitable family of imaging planes is the one containing the receiver (fan-like grid) on bistatic scenarios with relatively large transmitter–receiver separation. The processing gain has been enhanced by one order of magnitude under low amplitude and phase distortions. [ABSTRACT FROM AUTHOR]

Retrieval of building height from synthetic aperture radar (SAR) image is of great significance for damage assessment after natural disasters, urban development and planning, and dynamic time series monitoring in urban areas. Based on the prior information of the building and the radar platform, the building height can be estimated by iteratively simulating the SAR image and matching it with the measured SAR image. However, the estimation accuracy would be affected inevitably when there is error of prior information introduced by instable platform or inaccuracy of measuring instruments. In this letter, the beam incident angle and building height are both used as search variables for iterative simulation and matching (ISM), and normalized mutual information (NMI) is used to measure the similarity between the simulated image and the measured image to achieve an accurate estimation of building height. At last, measured data experiments are provided to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) images typically depict underground targets as hyperbolas, which pose a challenging detection task due to their low amplitude and resolution. To address this, we propose a robust and efficient feature descriptor based on a modified phase symmetry (PS) model. Specifically, we enhance the PS model to better represent hyperbolas in GPR images and introduce a weighted PS histogram descriptor (WPSHD) as a local structure descriptor. The proposed descriptor is used as the feature input to the classifier to realize the hyperbola recognition. The proposed method is compared with two baselines and state-of-the-art (SOTA) methods, such as histogram of oriented gradient (HOG), edge histogram descriptor (EHD), and histogram of oriented vector PS (HOVPS). Our validation experiments on both public datasets and real-world data show that our proposed algorithm improves hyperbola detection in GPR images, as demonstrated by qualitative and quantitative analyses. [ABSTRACT FROM AUTHOR]

The performance of traditional translational motion compensation methods is degraded when migration through range cell (MTRC) occurs or the echo amplitude is time-varying. In this letter, an effective translational motion compensation approach is proposed for high-resolution inverse synthetic aperture radar (ISAR) imaging with time-varying amplitude. The proposed algorithm based on the minimum mean square error (MMSE) criterion and the energy extraction of prominent points can realize the translational motion compensation accurately. First, the observation time is divided into multiple subapertures, and the range alignment of the echoes within each subaperture is achieved. Then, the range alignment between subapertures is realized based on the MMSE criterion. The Radon transform is performed on the range-aligned echoes to extract the prominent point with the maximum energy, and the envelope’s fine alignment and phase correction are realized. After rotation compensation, a well-focused ISAR image is obtained. Simulation experiments and real measured data demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The inverse synthetic aperture radar (ISAR) images are often afflicted by boundary-blurring, discontinuity, sidelobe effects of strong scattering points, a large dynamic range of gray values, and azimuth defocus, which pose significant challenges to image segmentation. This letter proposes a novel semantic segmentation method for ISAR images of space targets. The method is based on contrastive learning (CL) and nonlocal Unet (NL-Unet). First, the method roughly segments the target contour using binary semantic tags to remove sidelobe interference and image noise. Then, the nonlocal (NL) self-attentive mechanism with a global perceptual field is used to exploit the structural symmetry of the ISAR image. Finally, to improve the segmentation ability of target small parts, the method adopts a training method based on CL to overcome the relatively weak problem of the supervised learning model. The proposed method outperforms existing methods on the simulated ISAR dataset. Moreover, it is practical and can be directly generalized to the real-measured dataset without retraining. [ABSTRACT FROM AUTHOR]

In inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, traditional high-order phase compensation methods are usually limited by the heavy computational burden of parameter estimation, making it hard to apply in real-time cases. In this letter, an efficient ISAR motion compensation method is proposed based on fast parameter estimation. A novel analytic expression of the image entropy is derived with the help of a compensation matrix based on sinc function interpolation, which can eliminate the high-order phase term. Hence, the parameter estimation is transformed into an optimization problem where the gradient descent algorithm (GDA) can be adopted to accelerate the computation speed. Compared with other recently proposed methods, our method is superior in high robustness and low computing cost. Experiments with simulated and real data have verified that without affecting the image quality, the computing time of our method can be reduced to about 1/4 of the traditional search algorithm. [ABSTRACT FROM AUTHOR]

The false targets generated by interrupted sampling repeater jamming (ISRJ) bring serious threats to radar target detection and imaging. Transmitted waveform design with joint processing has become a promising way to deal with them. In this letter, the ambiguity function (AF) of the integral power frequency modulated (PFM) waveform is derived. Meanwhile, the time-frequency characteristic of the ISRJ signal received by the radar is analyzed on the range-Doppler plane. In consideration of the range-Doppler coupling characteristic, an improved Doppler filtering processing method is proposed to suppress the jamming of false targets. Simulation results demonstrate that the selected PFM waveform with the proposed joint processing method can effectively suppress the false targets resulting from ISRJ. [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]

Forward scatter radar (FSR) can achieve target detection by capturing the signal disturbance caused by targets crossing the baseline, which provides a countermeasure to stealth/small targets. First, the FSR net model based on the Global Navigation Satellite System (GNSS) is constructed. Then, the statistical features in the time and frequency domains are used to describe the fluctuation characteristics of the forward scatter signal. Moreover, the influences of the relative position of the target and baseline on the statistical features are analyzed through dynamic simulations, and the change regulation of time and frequency features is revealed. Finally, an FSR experiment is carried out in an anechoic chamber and the results validate the theoretical analysis. This work provides a meaningful reference for the detection and parameter estimation of targets crossing the baseline in an FSR. [ABSTRACT FROM AUTHOR]

This letter presents an imaging approach for multiple input–multiple output ground penetrating radar (MIMO GPR) systems working in down-looking contactless mode. The approach exploits a linear approximation of the scattering phenomenon and is based on a ray-based propagation model, which takes into account the presence of the air–soil interface. Accordingly, the interface reflection point (IRP) concept is extended to the case of MIMO GPR. The proposed approach performs the imaging in the 2-D scalar case and applies the truncated singular value decomposition regularization scheme to perform the inversion. The effectiveness of the approach is assessed by processing synthetic and real data. The real data are referred to the lunar soil and have been collected by means of the Lunar Regolith Penetrating Radar (LRPR), installed on the Chang’E-5 (CE-5) lander. [ABSTRACT FROM AUTHOR]

Height estimation from a single synthetic aperture radar (SAR) imagery has shown a great potential in real-time scene understanding and environment detection. It is a mathematical ill-posed problem for that a single 2-D image may be projected from multiple 3-D scenes. Then, the problem is that the accuracy of height estimation from a single SAR image is not high enough without prior knowledge, especially in mountain areas. Thus, we propose a geometry-aware consistent constraint (GACC) to improve the performance of single SAR height estimation. The simulated SAR images generated from height maps are used to establish high-precision transformation between map and radar coordinates in mountain areas. The main idea of GACC is that the simulated SAR image generated from estimated height map should be consistent with the ground-truth simulated SAR image. A sparse height (SH) map is included as the supplementary inputs to further improve the accuracy of estimated height maps. Comparison experiments are performed in Geermu, Huangshan, and Guiyang datasets, and the results show that the root-mean-square error (RMSE) of the estimated height map by U-shaped convolution neural network (Unet) with GACC and 0.0434% SH gets reduced by about 98%, 78%, and 94% compared with that by Unet in the three datasets. [ABSTRACT FROM AUTHOR]

This letter aims at investigating the potential of high-resolution (up to $0.7\times0.5\,\,\text{m}^{2}$) synthetic aperture radar (SAR) images in generating digital elevation models (DEMs) using the radargrammetry technique. In this work, we process two SAR images recorded by the Capella Space X-band satellite-borne radar sensor on two consecutive days, October 2 and 3, 2021, over La Palma (Canary Islands, Spain) during the Cumbre Vieja volcanic eruption. We adopt an iterative point-aggregation algorithm to identify matching pixels between the two images; then, the height estimation is performed using a distance minimization routine over the previously identified pairs of matching points. The resultant radargrammetric DEM is validated against a lidar-based DEM for various land cover (LC) classes, showing a good agreement in the areas less affected by lava flow. An estimation of the lava thickness is performed, yielding profiles of the cone area, which are compared to the photogrammetry estimates obtained from the Pléiades mission data. [ABSTRACT FROM AUTHOR]

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Synthetic aperture radar (SAR) techniques are commonly used in spaceborne and airborne side-looking radar imaging applications, where the relatively high platform speeds enable the formation of very long synthetic apertures, which provide images with high angular resolution. However, the recent development of new autonomous ground vehicles, with relatively slow speeds, calls for new SAR imaging methods to obtain high angular resolution images. In this paper, the concept of forward-looking multiple-input multiple-output SAR (FL-MIMO-SAR) is analyzed and applied for short-range millimeter-wave radar forward-looking imaging in autonomous mobile robots (AMRs). Moreover, if the antenna inter-element spacing in the MIMO array is small, the FL-MIMO-SAR imaging system will provide low or no angular resolution refinement at the radar boresight region, i.e., Doppler beam sharpening (DBS) ineffective region. To solve that, the combination of FL-MIMO-SAR and sparse MIMO arrays with large inter-element spacing (FL-sparseMIMO-SAR) is proposed. Aspects such as the signal model, image reconstruction, complexity reduction, and the left-right ambiguity for FL-MIMO-SAR imaging systems are addressed. In addition, different imaging scenarios for AMRs are simulated using a frequency-modulated continuous wave radar simulator. Simulation results are validated with real measurements. It is shown that the proposed combination of FL-MIMO-SAR and MIMO arrays with large inter-element spacing manages to significantly suppress the grating lobes, which come from the sparsity in the antenna array. Moreover, the proposed FL-sparseMIMO-SAR helps to relax the requirements on the synthetic aperture length and solves the DBS-blind region problem, thanks to the larger real sparse MIMO array and the grating lobes suppression with the FL-SAR processing. Finally, the results show high potential for radar imaging systems, with FL-sparseMIMO-SAR capabilities, to be employed in the applications of AMRs towards smart factories and warehouses.

Reliable and convenient walk-through security scanning, which doesn't separate people or impede their movement, is an extremely challenging task. In this paper, a novel approach for a security check with an overhead observation and a polarimetric target decomposition is presented. The viewing angle of the scanner equals a side-looking airborne radar. However, it will be shown that the established polarimetric target decomposition methods of remote-sensing are not well suited for close-range radar imaging and need to be adapted due to the differences in the geometry of the imaging scenario. The usage of a multiple input and multiple output (MIMO) array and the shorter distance between array and target need a changed decomposition technique in order to distinguish between persons with or without worn threat objects. The differences between radar remote-sensing and close-range imaging scenarios are investigated. An optimized version of the Sato four-component polarimetric scattering decomposition is derived. The proposed close-range adjustment is applied to the model and investigated experimentally with a 4-to-12 GHz fully polarimetric MIMO imaging system. Polarimetric decomposition is carried out on defined test structures with known scattering mechanisms, as well as on mannequins and persons with different threat objects like guns or explosives. In test campaigns, promising results were achieved for a correct target decomposition in radar-based walk-through security scanning.

Abstract Establishing effective feature descriptors is a crucial step in 3D point cloud registration task. Existing manual‐based methods are noise‐susceptible and time‐consuming when running on low‐cost edge computing devices. To this end, the authors proposed a Local Reference Frame (LRF) based approach that can quickly and robustly register point clouds by using a novel lightweight local‐spherical grid weighted descriptor (LSGWD). Firstly, the LRF of the proposed algorithm is established by the covariance matrix eigenvector of KeyPoint's spherical support set and the centroid vector's projection on its orthogonal plane. Then the spherical support is grided to 32 bins, and the 4D geometric features of each subset are constructed by the centroid moment and the cosine value of the angles between the centroid vector and axes of LRF. Secondly, to restrain the insufficient discriminative information presented in the purely geometric features, the Gaussian projection and gradient mapping are proposed to calculate the smooth density and the correlation of structural characteristics, which are obtained as the distribution information of each bin to weigh the feature representation. Finally, the 32 × 4‐dimensional KeyPoint descriptor is obtained and used in the 3D point cloud registration framework. Experiments are carried out on three test datasets and real scene data. Compared to previous baselines, our descriptor achieves the state‐of‐the‐art performance in terms of efficiency and accuracy owing to its compact structure and noise robustness. The proposed method enhances the recognition and registration performance of 3D point cloud matching in low‐cost edge computing applications.

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 tomography (TomoSAR) baseline optimization method is capable of reducing system complexity and improving the temporal coherence of data, which has become important research in the field of TomoSAR. A nested TomoSAR technique is proposed, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co‐array through the nested array to increase the degrees of freedom of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co‐array, covariance matrix of the echo needs to be obtained. Therefore, a TomoSAR sparse reconstruction algorithm based on the nested array is proposed, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. The effectiveness of the proposed method through simulated and real data experiments is demonstrated. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of this proposed method have better anti‐noise performance and retain more image information. [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]

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 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]

Owing to the time variance of the inverse synthetic aperture radar (ISAR) imaging plane of space targets, scatterers with large heights relative to the imaging plane migrate through several Doppler cells. This defocussing in the azimuth direction of each scatterer cannot be compensated via the traditional migration through the resolution cell (MTRC) compensation method because the height of each scatterer is quite different and independently distributed in the ISAR image. In this study, a parametric azimuth‐defocussing model‐based CLEAN method is proposed for the space‐target ISAR image. First, high‐order defocus parameters and their influences on the image of space targets were analysed at different arc segments. Subsequently, a mathematical azimuth‐defocussing expression of the ISAR image scatterers was derived after MTRC and azimuth high‐order phase compensation, and an approximate defocus expression based on the Fresnel integral was provided for computational efficiency. This model can estimate the complex amplitude, coordinates and defocus parameters of each scatterer in the image domain by applying a sequential quadratic programing algorithm. Finally, the validity and stability of the proposed algorithm for the isolated and coupled scatterer extraction were verified at different signal‐to‐noise ratios, and different defocus parameters were verified through simulations. [ABSTRACT FROM AUTHOR]

We analyze synthetic aperture radar (SAR) imaging of complex ground scenesthat contain both stationary and moving targets. In the usual SAR acquisitionscheme, we consider ways to preprocess the data so as to separate thecontributions of the moving targets from those due to stationary backgroundreflectors. Both components of the data, that is, reflections from stationaryand moving targets, are considered as signal that is needed for target imagingand tracking, respectively. The approach we use is to decompose the data matrixinto a low rank and a sparse part. This decomposition enables us to capture thereflections from moving targets into the sparse part and those from stationarytargets into the low rank part of the data. The computational tool for this isrobust principal component analysis (RPCA) applied to the SAR data matrix. Wealso introduce a lossless baseband transformation of the data, which simplifiesthe analysis and improves the performance of the RPCA algorithm. Our maincontribution is a theoretical analysis that determines an optimal choice ofparameters for the RPCA algorithm so as to have an effective and stableseparation of SAR data coming from moving and stationary targets. This analysisgives also a lower bound for detectable target velocities. We show inparticular that the rank of the sparse matrix is proportional to the squareroot of the target's speed in the direction that connects the SAR platformtrajectory to the imaging region. The robustness of the approach is illustratedwith numerical simulations in the X-band SAR regime.

Abstract Passive radar systems use illuminators of opportunity to illuminate targets instead of dedicated radar transmitters. The signals of opportunity have lower bandwidth than dedicated active radar systems, leading to poor downrange resolution. Multiple signals of opportunity can be coherently combined to increase the overall bandwidth of the system, and therefore create finer resolution images. These signals are usually separated in the frequency domain (non‐contiguous), which causes large unwanted grating lobe artefacts in the image when using back‐projection or Fourier transform based imaging. Additionally, these signals may originate from transmitters not located at the same position. A calibration method is developed to align the downrange responses and coherently combine the two signals. A compressive sensing‐based algorithm is used to combine the calibrated non‐contiguous frequency data, and is shown to mitigate the grating lobe artefacts that occur when using a back‐projection algorithm. This approach is validated with simulated and experimental data from a passive radar system tracking a commercial airline using two digital television (DTV) broadcast transmitters.