This paper presents an efficient raw signal generation method based on equivalent scatterer and subaperture processing for the one-stationary bistatic synthetic aperture radar (SAR). It considers the moving platform's motion error to generate the precise raw signal for real SAR systems. First, the imaging geometry in elliptical polar coordinates is built, and then, the scene is divided into several equidistant elliptical rings. Based on the equivalent scatterer model, the approximate SAR system transfer function is derived; thus, each pulse's raw signal is calculated by the convolution of the transmitted signal and transfer function, via the fast Fourier transform (FFT). To further improve the simulation speed, the subaperture and subscene processing is used. The transfer function for the same subaperture's pluses is calculated simultaneously by the weighted sum of all subscenes' equivalent scattering coefficient in the same equidistant elliptical ring, which is performed by the nonuniform FFT (NUFFT). This method only involves the FFT, NUFFT, and complex multiplication, which means the easier operation and higher efficiency. The implementation, limitation, and evaluation of the proposed method are discussed. Simulation results are given to prove the correctness and validity of the proposed method.

In this paper, an extended fast factorized back projection (FFBP) algorithm is proposed to focus the missile-borne bistatic forward-looking synthetic aperture radar (BFSAR) data. It is also based on the subaperture processing technique to reduce the computational burden, but represents the subimages on the elliptical polar coordinates referenced to the tracks of both transmitter and receiver. First, the imaging geometry and signal model of the missile-borne BFSAR are established. Then, considering the special configuration of the missile-borne BFSAR, a novel elliptical polar coordinate system whose origin is determined by the positions of both transmitter and receiver is established. The subaperture processing approach based on this coordinate system is presented. Successively, based on the range error analysis, the sampling requirements of the elliptical polar subimages are derived to provide a tradeoff between the imaging quality and efficiency. Finally, the computational burden of the proposed algorithm is discussed, and the speed-up factor of the proposed algorithm with respect to the direct back projection algorithm is derived. The experimental results with simulated data prove the validity and feasibility of the proposed algorithm.

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.

Synthetic aperture radar (SAR) ground moving target indication (GMTI) has attracted a lot of interest from researchers for its ability of simultaneously obtaining high-quality images of stationary scenes and detection of moving targets. In recent years, the research on moving target tracking besides detection by using SAR-GMTI has also raised interest. Compared with standard SAR, the long observation time of circular SAR (CSAR) makes it possible for moving target trajectory reconstruction. By using a prior knowledge of the road information, an effective method for moving target trajectory reconstruction in single-channel CSAR is proposed in this paper. First, the radar echo is segmented according to subapertures, and the Doppler filtering is performed on each subaperture echo for clutter suppression. Second, the range walk induced by target motion is compensated by the keystone transform, and trajectories in range-Doppler domain are obtained. Last, the moving target trajectory in ground coordinate is reconstructed by using the dynamic programming algorithm with road information restrain. Experimental SAR data processing results show that the method is effective for the moving target trajectory reconstruction.

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.