Recently, a split sparse iterative covariance-based estimation (Split SPICE) method has been proposed for synthetic aperture radar (SAR) imaging to enhance the azimuth resolution. However, this method suffers the significant high computational complexity, which is caused by the high-dimensional matrix inversion. To this end, a fast Split SPICE approach is proposed to reduce the tricky complexity of the traditional ones for airborne scanning radar super-resolution imaging. It takes advantage of the low displacement rank feature of the Toeplitz matrix to solve the matrix inversion by Gohberg–Semencul (GS) representation efficiently. Compared with traditional methods, the proposed method offers higher computational efficiency for scanning radar super-resolution imaging with marginal resolution loss. The simulation results verify the effectiveness of the proposed method.

Automatic target recognition (ATR) is the key of synthetic aperture radar (SAR) image interpretation. Due to the superior feature extraction and target classification capabilities, deep learning has been widely used in SAR ATR fields. Most of state-of-the-art SAR ATR methods are proposed for single-view input, however, multi-view SAR images include more abundant classification features. In order to improve the SAR ATR performance, it is necessary to carry out an effective method to extract and discriminate useful features from multi-view SAR images. In this paper, we propose a new SAR ATR method based on multi-view feature extraction and discrimination network, which includes two main components: multi-view feature extraction and multi-view feature discrimination. Multi-view features can be effectively extracted from input SAR images with the feature extraction component. After that, the extracted features are fed into the multi-view feature discrimination component, which aims to gather the features of the same class and separate the features of different classes. Therefore, the proposed method can achieve good multi-view SAR target recognition performance. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the effectiveness of our method.

In this paper, the constrained waveform design of multiple-input multiple-output (MIMO) radar is considered to achieve transmit beampattern assignment. Firstly, we construct a framework that minimizes the spatial integrated sidelobe level ratio (ISLR) as the objective function and constrains the transmit waveforms in terms of amplitude fluctuations and similarity. To solve the resulting non-convex problem, an iterative optimization method based on coordinate descent (CD) is developed by transforming the multivariate problem into multiple univariate problems. Finally, numerical simulation results demonstrate the effectiveness of the proposed method in beampattern assignment and waveform similarity.

Synthetic aperture radar (SAR) imaging for moving target can obtain complete situational awareness information of the detection area, and can realize the monitoring and control for moving target in the region of interest, which has important military and civilian dual-use value. However, due to the complex motion of target, the processing results of the existing SAR imaging methods severly defocused. In this paper, a high resolution SAR imaging method for moving target is proposed. First, we eliminate the coupling induced by linear range cell migration (RCM) by keystone transform. Then, the particle swarm optimization algorithm (PSO) is utilized to estimate the Doppler frequency rate, which can solve the problem of Doppler frequency rate mismatching when azimuth compression. Simulation results verifies the effectiveness of the proposed method.

Short-time pulse train signal has narrow pulse width and high peak power, which enables it with great potential for airborne radar target detection. Clutter interference always exists in complex detection environment, thus the design of the anticlutter emission waveform decides the accuracy of target detection. In this paper, a secondary optimization method based on maximum signal-clutter-to-noise ratio (SCNR) criterion and minimum squared error criterion is proposed for short-time pulse train signal form. Firstly, a maximum SCNR model solved by the Lagrange multiplier method is constructed based on prior information. Secondly, according to the short-time train signal form, the time-domain form of the optimal transmission signal is obtained by minimizing the weighted square error of the optimal spectrum and the train spectrum and the integrated side lobe of the train signal. Finally, simulation results show that the designed waveform is capable of anti-clutter interference and can effectively improve the SCNR in the clutter environment.

Multistatic SAR is able to observe targets from different angles simultaneously, which enhances the information acquiring capability. However, multistatic SAR can still be affected by electromagnetic jamming, resulting in the misinterpretation of multistatic SAR images. This article proposes a method to locate multiple range-deception jammers and suppress jamming signals. First, the echo model of multistatic SAR under a multijammer environment is established. Second, the detection of interested targets in multistatic SAR images can be achieved through visual saliency detection methods based on spectral residual. Third, location distribution features of false targets in multistatic SAR images are analyzed, and the Euclidean distance criteria are used to effectively distinguish false targets. Accurate localization is then achieved by combing multistatic SAR configuration information. Finally, using a linear constrained minimum variance beamforming algorithm to suppress jamming signals, multistatic SAR images without jamming signals can be obtained. Simulation results validate the effectiveness of the proposed method in this article.

Real-aperture radar can be utilized to realize forward-looking imaging by antenna scanning the imaging region. However, low azimuth resolution seriously affects its practical application. Although traditional super-resolution methods could enhance azimuth resolution to a certain extent, effective preservation of contour information for important targets still remains to be a problem. In this article, a method of total variation-sparse (TV-sparse) multiconstraint deconvolution is proposed to improve azimuth resolution of forward-looking imaging as well as preserve contour information of important targets. Since our interested targets usually appear to be sparse, the sparse constraint of the target is introduced first to achieve high resolution of forward-looking images, which may cause the loss of target contour information in the meantime. Second, total variation (TV) constraint is introduced based on the sparse constraint, converting traditional single-constraint super-resolution problem to a multiconstraint problem. We then use the split Bregman algorithm (SBA) to solve the multiconstraint problem, whose solution is the super-resolution image of radar forward-looking region. Compared with traditional super-resolution methods, the proposed method can improve the azimuth resolution of radar forward-looking imaging as well as better restore target contour information by adjusting respective weights of sparse constraint and TV constraint. Finally, the performance of the proposed method is validated with the simulation and measured data.

Doppler parameters, range cell migrations (RCMs), and higher order coupling terms in the raw data of translational variant bistatic synthetic aperture radar (TV-BiSAR) exhibit 2-D spatial variations. The 2-D spatial variations result in significant performance degradation of TV-BiSAR imaging. To solve these problems, an Omega- k imaging algorithm based on linearization theory is proposed in this letter. Compared with the traditional Omega- k algorithm that uses the 1-D Stolt transformation to eliminate only the spatial variations in the range direction, the proposed algorithm applies the 2-D Stolt transformations to achieve the goal of both 2-D frequency linearization and 2-D spatial-domain linearization. After the 2-D Stolt transformations, a focused image can be obtained by performing a 2-D inverse fast Fourier transform (IFFT). In the proposed Omega- k imaging algorithm, the 2-D spatial variations of the Doppler parameters, RCM, and higher order coupling terms for TV-BiSAR can be simultaneously eliminated. However, in previous publications about BiSAR imaging algorithms, the spatial variations in the azimuth direction are barely considered, which reduces the imaging accuracy. Numerical simulations verify the effectiveness of the proposed method.

A new ship imaging and tracking method using linear frequency modulated continuous wave (LFMCW) scanning radar in shipping lane management application is proposed. It aims at solving the problem that it is difficult for a ship to find the right shipping lane when it first enters an unknown port in bad weather conditions. A theoretical analysis is presented, demonstrating the imaging process of LFMCW radar. An image can be got after the radar scans 360°. Multi-frame images of a ship can be obtained by continuous scanning the ship area. Changes between images are used for ship tracking by multi-frame joint detection algorithm. It is then validated on an experiment. The experiment results show the efficiency of the proposed method.

The generation of SAR echo is very important for both the system design and the validity test of imaging algorithm. Common time-domain-based echo generation methods are usually time-consuming and inefficient. Hence, this article will focus on the research of efficient echo generation methods. In translational variant bistatic mode, two-dimensional (2D) spatial variability is the major problem to be faced with in efficient echo generation. To solve this problem, this paper proposes an efficient echo generation method of translational variant bistatic SAR based on 2D inverse Stolt mapping. The proposed method first uses 2D FFT to generate 2D linear spectrum, and then completes nonlinearized operation of the spectrum by 2D inverse Stolt mapping. This process fully considers the 2D spatial variability in translational variant bistatic mode, which guarantees the accuracy of the generated echo. Finally, simulation results are presented to verify the validity of the proposed method.

A novel ground moving target detection and imaging model called Near-Space Slow SAR (NSS-SAR) is introduced in this paper. It is not only effective for fast-moving targets but also slow-moving and micro-moving targets, which is hardly possible for conventional airborne and spaceborne SAR. Meanwhile, this model only needs mono-channel to distinguish Doppler signature of moving targets from the competing ground clutter returns via Doppler filtering. In addition, following analysis demonstrates that the NSS-SAR also has the potential to simultaneously achieve high-resolution and wide-swath (HRWS) imaging. Simulations given at the end of this paper verify the validity of the new NSS-SAR mono-channel moving target detection and imaging model.