Holographic synthetic aperture radar (HoloSAR) tomography is an attractive imaging mode that can retrieve the 3-D scattering information of the observed scene over 360° azimuth angle variation. To improve the resolution and reduce the sidelobes in elevation, the HoloSAR imaging mode requires many passes in elevation, thus decreasing its feasibility. In this article, an imaging method based on iterative adaptive approach (IAA) and generalized likelihood ratio test (GLRT) is proposed for the HoloSAR with limited elevation passes to achieve super-resolution reconstruction in elevation. For the elevation reconstruction in each range-azimuth cell, the proposed method first adopts the nonparametric IAA to retrieve the elevation profile with improved resolution and suppressed sidelobes. Then, to obtain sparse elevation estimates, the GLRT is used as a model order selection tool to automatically recognize the most likely number of scatterers and obtain the reflectivities of the detected scatterers inside one range-azimuth cell. The proposed method is a super-resolving method. It does not require averaging in range and azimuth, thus it can maintain the range-azimuth resolution. In addition, the proposed method is a user parameter-free method, so it does not need the fine-tuning of any hyperparameters. The super-resolution power and the estimation accuracy of the proposed method are evaluated using the simulated data, and the validity and feasibility of the proposed method are verified by the HoloSAR real data processing results.

The convolutional neural network (CNN) is widely used in synthetic aperture radar (SAR) target recognition, but conventional CNN mainly adopts a single-scale convolutional kernel, resulting in losing part of the feature information of targets and does not pay enough attention to significant features. On the other hand, conventional CNN approaches only assign fine-class labels to SAR targets, ignoring the high-level semantics information of similar categories, which reduces the feature differences between categories and the generalization ability of the model. Therefore, this article proposes a multiscale attention super-class CNN (MSA-SCNN) for SAR target classification. First, MSA-SCNN combines multiscale feature fusion with the attention module to improve the integrity of SAR target feature representation. The attention module includes channel and spatial attention modules, which realize the weighted enhancement of different scale features. Additionally, MSA-SCNN introduces super-class labels to increase the feature difference between categories. The classification stage consists of a fine-class branch and a super-class branch, and the features trained on the super-class branch are fused to the fine-class branch to improve the network's fine classification ability. Experiments on the moving and stationary target acquisition and recognition dataset and the FUSAR-Ship dataset show that the proposed MSA-SCNN outperforms many current existing state-of-the-art methods.

In the last few years, interest in circular synthetic aperture radar (CSAR) acquisitions has arisen as a consequence of the potential achievement of 3D reconstructions over 360° azimuth angle variation. In real-world scenarios, full 3D reconstructions of arbitrary targets need multi-pass data, which makes the processing complex, money-consuming, and time expending. In this paper, we propose a processing strategy for the 3D reconstruction of vehicle, which can avoid using multi-pass data by introducing a priori information of vehicle’s shape. Besides, the proposed strategy just needs the single-pass single-polarization CSAR data to perform vehicle’s 3D reconstruction, which makes the processing much more economic and efficient. First, an analysis of the distribution of attributed scattering centers from vehicle facet model is presented. And the analysis results show that a smooth and continuous basic outline of vehicle could be extracted from the peak curve of a noncoherent processing image. Second, the 3D location of vehicle roofline is inferred from layover with empirical insets of the basic outline. At last, the basic line and roofline of the vehicle are used to estimate the vehicle’s 3D information and constitute the vehicle’s 3D outline. The simulated and measured data processing results prove the correctness and effectiveness of our proposed strategy.

Circular synthetic aperture radar (CSAR) has attracted much attention in the field of high-resolution SAR imaging. The spatial resolution is usually considered as the most significant parameter for SAR image quality evaluation and has been widely applied for different monostatic/bistatic SAR operation modes. However, it is hard to get an analytical solution to calculate exactly the value of spatial resolution, especially for the circumstance of CSAR noncoherent imaging. In this paper, based on general impulse response function (IRF) of CSAR noncoherent imaging, a method to obtain the quasi-analytical result of the spatial resolution is proposed, which takes the fractional bandwidth and the subaperture angle into consideration. First, the IRF for CSAR is introduced. Second, based on the IRF, the spatial resolution of CSAR noncoherent imaging is derived. Moreover, the half-power beam-width factor is presented, which allows obtaining the form of spatial resolution in an easier way. Finally, the effectiveness of the proposed method is verified with the simulated and experimental CSAR data.

Since the trajectory deviations of a radar platform cause serious phase errors that degrade the focusing quality of synthetic aperture radar (SAR) imagery, an autofocus method is very important for high-resolution airborne SAR imaging. In this letter, an extended autofocus backprojection (EABP) algorithm is developed to accommodate the phase errors. Under the criterion of maximum image sharpness, the traditional ABP algorithm supports a broader class of collection and imaging geometries. However, it neglects the influence of SAR image energy distribution on the estimation of phase errors that make it inapplicable for SAR imaging, which has high dynamic range, such as low-frequency SAR imaging. By choosing regions and balancing the energy distribution of the data, the EABP algorithm is more efficient and useful to avoid the estimation error caused by the unbalanced energy distribution. Its performance has been demonstrated by using the experimental data that are acquired by a P-band airborne SAR system with a low-accuracy global positioning system.

Synthetic aperture radar ground moving target indication (SAR-GMTI) is an attractive mode for radar system to obtain high-resolution regional images and detection of moving targets simultaneously. Moreover, the 360° observation capability of circular SAR (CSAR) makes it possible for moving target trajectory reconstruction. In order to address the problem that slow-moving targets, which are buried in strong stationary ground clutter, are difficult to be detected, a framework of multichannel SAR-GMTI is presented in this article. For ground moving target detection in the presence of stationary clutter, the proposed method first adopts clutter suppression interferometry and relaxation-based cyclic algorithm to suppress clutterand retrieve parameters (e.g., radial velocity and Doppler information). Then, to further reduce the false alarm rate, multiple moving target tracking algorithm is performed in range–Doppler domain. Finally, the moving target trajectory is reconstructed by using the proposed two-stage parameter estimation method based on Doppler characteristic and CSAR geometry, and the mathematical analysis of this method is performed. The proposed method is robust and insensitive to the signal-to-noise ratio. It does not rely on priori road information, so it has wide applicability, especially in military application. In addition, the framework is performed in echo domain and independent of imaging processing. The experimental results on simulated data are presented to evaluate the estimation accuracy of the proposed method, and the real data processing results are provided to demonstrate the validity and feasibility of the proposed method.