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Real Aperture Radar (RAR) observes wide-scope target information by scanning its antenna. However, because of the limited antenna size, the angular resolution of RAR is much lower than the range resolution. Angular super-resolution methods can be applied to enhance the angular resolution of RAR by inverting the low-rank steering matrix based on the convolution relationship between the antenna pattern and target scatterings. Because of the low-rank characteristics of the antenna steering matrix, traditional angular super-resolution methods suffer from manual parameter selection and high computational complexity. In particular, these methods exhibit poor super-resolution angular resolution at low signal-to-noise ratios. To address these problems, an iterative adaptive approach for angular super-resolution imaging of scanning RAR is proposed by combining the traditional Iterative Adaptive Approach (IAA) with a deep network framework, namely IAA-Net. First, the angular super-resolution problem for RAR is transformed into an echo autocorrelation matrix inversion problem to mitigate the ill-posed condition of the inverse matrix. Second, a learnable repairing matrix is introduced into the IAA procedure to combine the IAA algorithm with the deep network framework. Finally, the echo autocorrelation matrix is updated via iterative learning to improve the angular resolution. Simulation and experimental results demonstrate that the proposed method avoids manual parameter selection and reduces computational complexity. The proposed method provides high angular resolution under a low signal-to-noise ratio because of the learning ability of the deep network.

The imaging of aerial targets using Inverse Synthetic Aperture Radar (ISAR) is affected by micro-Doppler effects resulting from localized micromotions, such as rotation and vibration. These effects introduce additional Doppler frequency modulation into the echo, leading to spectral broadening. Under ultrahigh-resolution conditions, these micromotions interfere with the focusing process of subject scatterers, resulting in images with poor focus showing significantly reduced quality. Furthermore, micro-Doppler signals exhibit temporal variability and nonstationary characteristics, posing difficulties in their estimation and differentiation from the echo. To address these challenges, this paper proposes a nonparametric method based on Variational Mode Decomposition (VMD) and mode optimization to separate the echo of the subject from micro-Doppler components. This separation is achieved by utilizing differences in their respective time-frequency distributions. This methodology mitigates the effect of micro-Doppler signals on the echo and obtains imaging results of a drone with ultrahigh-resolution. The VMD algorithm is introduced and subsequently extended to the complex domain. The method entails the decomposition of the ISAR echo along the azimuth direction into several mode functions distributed uniformly across the Doppler sampling bandwidth. Subsequently, image entropy indices are employed to optimize the decomposition parameters and select the imaging modes. This ensures the effective suppression of micro-Doppler signals and preservation of the subject echo. Compared to existing methods based on Empirical Mode Decomposition (EMD) and Local Mean Decomposition (LMD), the proposed method exhibits superior performance in suppressing image blurring caused by micro-Doppler effects while ensuring complete retention of fuselage details. Furthermore, the effectiveness and advantages of the proposed method are validated through simulations and processing of ultrawideband microwave photonic data obtained from drone measurements.

Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the Total Least Square (TLS) criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the super-resolution algorithm.

Microwave Photonic (MWP) radars have remarkably improved traditional microwave radar hardware architectures using photonics devices. With the exceptional physical properties of photonic devices, MWP radars can emit ultra-wideband, high-linearity, high-quality linear frequency modulation signals, allowing ultra-high-resolution target imaging and detection. Different target regions exhibit distinct responses to different frequency signals during target imaging and detection due to their diverse structures and characteristics. Therefore, MWP radars have the potential to generate pseudo-color images based on scattering differences, further enhancing the information retrieval capability of MWP Synthetic Aperture Radar (MWP-SAR). Pseudo-color images generated using traditional remote sensing techniques cannot achieve centimeter-level resolution. Therefore, we propose a method for generating pseudo-color images while maintaining the resolution of MWP-SAR. The algorithm first determines an optimal sub-band echo search model and subsequently employs the optimal sub-band search algorithm to process the ultra-wideband echoes to obtain sub-band echo channels with the largest scattering characteristic differences. The multi-sub-band images are then color-composited to generate pseudo-color images that best describe the target scattering characteristics. However, to ensure the high resolution of MWP-SAR, a fusion model is established to combine the full-resolution SAR image with the multi-sub-band image. Finally, full-resolution pseudo-color images are successfully synthesized using the measured airborne MWP-SAR data, validating the effectiveness of the algorithm. This algorithm enables MWP-SAR to obtain more target information during imaging, offering assistance in implementing imaging radar and microwave vision.

With the widespread application of deep learning methods in Synthetic Aperture Radar (SAR) image interpretation, the explainability of SAR target recognition deep networks has gradually attracted the attention of scholars. Class Activation Mapping (CAM), a commonly used explainability algorithm, can visually display the salient regions influencing the recognition task through heatmaps. However, as a post hoc explanation method, CAM can only statically display the salient regions during the current recognition process and cannot dynamically show the variation patterns of the salient regions upon changing the input. This study introduces the concept of perturbation into CAM, proposing an algorithm called SAR Clutter Characteristics CAM (SCC-CAM). By introducing globally distributed perturbations to the input image, interference is gradually applied to deep SAR recognition networks, causing decision flips. The degree of change in the activation values of network neurons is also calculated. This method addresses the issue of perturbation propagation and allows for dynamic observation and measurement of variation patterns of salient regions during the recognition process. Thus, SCC-CAM enhances the explainability of deep networks. Experiments on the MSTAR and OpenSARShip-1.0 datasets demonstrate that the proposed algorithm can more accurately locate salient regions. Compared with traditional methods, the algorithm in this study shows stronger explainability in terms of average confidence degradation rates, confidence ascent ratios, information content, and other evaluation metrics. This algorithm can serve as a universal method for enhancing the explainability of networks.

In recent years, millimeter-wave radar has been widely used in safety detection, nondestructive detection of parts, and medical diagnosis because of its strong penetration ability, small size, and high detection accuracy. However, due to the limitation of hardware transmission bandwidth, achieving ultra-high two-dimensional resolution using millimeter-wave radar is challenging. Two-dimensional high-resolution imaging of altitude and azimuth can be realized using radar platform scanning to form a two-dimensional aperture. However, during the scanning process, the millimeter-wave radar produces sparse tracks in the height dimension, resulting in a sparse sampling of the altitude echo, thus reducing the imaging quality. In this paper, a high-resolution three-dimensional imaging algorithm for millimeter-wave radar based on Hankel transformation matrix filling is proposed to solve this problem. The matrix filling algorithm restores the sparse sampling echo, which guarantees the imaging accuracy of the millimeter-wave radar in the scanning plane. First, the low-rank prior characteristics of the millimeter-wave radar's elevation-range section were analyzed. To solve the problem of missing whole rows and columns of data during sparse trajectory sampling, the echo data matrix was reconstructed using the Hankel transform, and the sparse low-rank prior characteristics of the constructed matrix were analyzed. Furthermore, a matrix filling algorithm based on truncated Schatten-p norm combining low-rank and sparse priors was proposed to fill and reconstruct the echoes to ensure the three-dimensional resolution of the sparse trajectory millimeter-wave radar. Finally, using simulation and several sets of measured data, the proposed method was proved to achieve high-resolution three-dimensional imaging even when only 20%–30% of the height echo was used.

Multi-Radar Collaborative Surveillance (MRCS) technology enables a geographically distributed detection configuration through the linkage of multiple radars, which can fully obtain detection gains in terms of spatial and frequency diversity, thereby enhancing the detection performance and viability of radar systems in the context of complex electromagnetic environments. MRCS is one of the key development directions in radar technology and has received extensive attention in recent years. Considerable research on MRCS has been conducted, and numerous achievements in system architecture design, signal processing, and resource scheduling for MRCS have been accumulated. This paper first summarizes the concept of MRCS technology, elaborates on the signal processing-based closed-loop mechanism of cognitive collaboration, and analyzes the challenges faced in the process of MRCS’s implementation. Then, the paper focuses on cognitive tracking and resource scheduling algorithms and implements the technical summary regarding the connotation characteristics, system configuration, tracking model, information fusion, performance evaluation, resource scheduling algorithm, optimization criteria, and cognitive process of cognitive tracking. The relevance between multi-radar cognitive tracking and its system resource scheduling is further analyzed. Subsequently, the recent research trends of cognitive tracking and resource scheduling algorithms are identified and summarized in terms of five aspects: radar resource elements, information fusion architectures, tracking performance indicators, resource scheduling models, and complex task scenarios. Finally, the full text is summarized and future technology in this field is explored to provide a reference for subsequent research on related technologies.

Bi/multistatic Synthetic Aperture Radar (SAR) using spaceborne illuminator utilizes spaceborne platforms as transmitters and satellites, near-space vehicles, aircraft, and ground platforms as receivers, which allows high-resolution imaging of ground and marine scenes and targets. The system’s benefits include a broad imaging field of view, high concealment, and potent antijamming abilities. By using beam steering methods, a variety of imaging modes can be achieved, such as staring spotlight and sliding spotlight modes, which obtain abundant information about the imaging scene and offer broad application prospects both in civil and military fields. Thus far, the bi/multistatic SAR using spaceborne illuminator has been intensively investigated, and several research findings have been reported. This paper examines the technology from the aspects of system components, configuration design, echo model, imaging techniques, bistatic synchronization, and experimental verification and systematically reviews the state-of-the-art research progress. Finally, it is anticipated that spaceborne illuminator technology will be used to develop bi/multistatic SAR in the future.

A geosynchronous (GEO) satellite can provide continuous illumination with broad beam coverage for a Low Earth Orbit (LEO) receiver, used as the transmitting station of bistatic Synthetic Aperture Radar (SAR). Meanwhile, because the bistatic SAR system comprises a separate transmitter and receiver, the LEO receiver can realize multiview imaging such as downward-, forward-, and backward-looking. Therefore, GEO-LEO bistatic SAR is widely used in earth surveying and mapping to reconnaissance and surveillance application. To realize large-scene imaging, the pulse repetition rate of the GEO SAR transmitter should be low. Meanwhile, the LEO SAR receiver introduces a wide Doppler bandwidth, resulting in the azimuth undersampling of the GEO-LEO bistatic SAR. Although the multichannel technology in the receiver can suppress the ambiguity, the multichannel unambiguous recovery method requires numerous channels, resulting in the undersampling of the GEO-LEO bistatic SAR, and hindering the miniaturization of the receiving system. To address the problem of ambiguous imaging of complex observation scenes under the condition of severe azimuth subsampling condition, a sequential joint multiframe and multireceiving channel recovery unambiguous imaging method is proposed. The unambiguous imaging is recovered jointly from the correlation between sequential multiframe observation scenes and multireceiving channel sampling information. First, the unambiguous imaging problem of the GEO-LEO bistatic SAR is modeled as a joint low rank and sparse tensor optimization problem. Second, in the iterative solution of the alternating direction multiplier method, the multireceiving channel information is used to realize the unambiguous imaging of the GEO-LEO bistatic SAR for complex observation scenes. The proposed method can significantly reduce the number of receiving channels required for unambiguous imaging compared with the imaging method based on traditional multichannel The results obtained by the proposed method are validated by simulations and experiments.

Multiscale object detection in Synthetic Aperture Radar (SAR) images can locate and recognize key objects in large-scene SAR images, and it is one of the key technologies in SAR image interpretation. However, for the simultaneous detection of SAR objects with large size differences, that is, cross-scale object detection, existing object detection methods are difficult to extract the features of cross-scale objects, and also difficult to realize cross-scale object simultaneous detection. In this study, we propose a multiscale object detection method based on the Feature-Transferable Pyramid Network (FTPN) for SAR images. In the feature extraction stage, the feature migration method is used to obtain an effective mosaic of the feature images of each layer and extract feature images with different scales. Simultaneously, the void convolution method is used to increase the receptive field of feature extraction and aid the network in extracting large object features. These steps can effectively preserve the features of objects of different sizes, to realize the simultaneous detection of cross-scale objects in SAR images. The experiments based on the GaoFen-3 SAR dataset, SAR Ship Detection Dataset (SSDD), and high-resolution SSDD-2.0 show that the proposed method can detect cross-scale objects, such as airports and ships in SAR images, and the mean Average Precision (mAP) can reach 96.5% on the existing dataset, which is 8.1% higher than that of the characteristic pyramid network algorithm. Moreover, the overall performance of the proposed method is better than that of the latest YOLOv4 and other object detection algorithms.

Objective:To observe the effect of ischemic oppression on trigger points of patients with myofascial pain syndrome (MPS).Methods:A total of 20 patients with cervical and shoulder myofascial pain syndrome were randomly divided into control group and treatment group, with 10 patients in each group. The control group was treated with healthy education, and the treatment group was treated with the therapy of ischemic oppression on trigger points based on the control group. Firstly, we release the upper trapezius bundle of patients by gradually increasing the vibration frequency through Booster Pro3 fascia gun to relax and activate the upper trapezius muscle, and then we treat the patients with MPS by the method of ischemic compression on pain trigger points, once time per day, continuous treatment for two weeks. Before and after treatment, the cervical joint mobility and muscle strength were measured by the tester instrument of muscle and spinal mobility, the pain degree of neck and shoulder was evaluated by visual analogue scale (VAS), the level of cervical dysfunction was evaluated by neck disability index (NDI).Results:Compared with before treatment, the left and right muscle strength increased significantly in the treatment group after the first treatment, the range of motion (forward flexion), left and right muscle strength increased significantly in the treatment group after treatment for two weeks, VAS scores at the left flexion, right flexion and right rotation and NDI scores decreased significantly, with statistically significant differences (PP

The problem of high-dimensional data collection satisfying local differential privacy was studied.A new locally differentially private algorithm called Multi-RPHM was proposed based on the random projection technology,which achieved the high utility of the collected high-dimensional numeric data while satisfying the local differential privacy.The algorithm was formally proved to meet ε-local differential privacy.The effectiveness of the algorithm was comfirmed through experiments on synthetic datasets.