Ship target detection using large-scale synthetic aperture radar (SAR) images has important application in military and civilian fields. However, ship targets are difficult to distinguish from the surrounding background and many false alarms can occur due to the influence of land area. False alarms always occur with ship target detection because most of the area in large-scale SAR images are treated as background and clutter, and the ship targets are considered unevenly distributing small targets. To address these issues, a ship detection method in large-scale SAR images via CenterNet is proposed in this article. As an anchor-free method, CenterNet defines the target as a point, and the center point of the target is located through key point estimation, which can effectively avoid the missing detection of small targets. At the same time, the spatial shuffle-group enhance (SSE) attention module is introduced into CenterNet. Through SSE, the stronger semantic features are extracted while suppressing some noise to reduce false positives caused by inshore and inland interferences. The experiments on the public SAR-ship-data set show that the proposed method can detect all targets without missed detection with dense-docking targets. For the ship targets in large-scale SAR images from Sentinel 1, the proposed method can also detect targets near the shore and in the sea with high accuracy, which outperforms the methods like faster R-convolutional neural network (CNN), single-shot multibox detector (SSD), you only look once (YOLO), feature pyramid network (FPN), and their variations.

Scanning radar is of notable importance for ground surveillance, terrain mapping and disaster rescue. However, the angular resolution of a scanning radar image is poor compared to the achievable range resolution. This paper presents a deconvolution algorithm for angular super-resolution in scanning radar based on Bayesian theory, which states that the angular super-resolution can be realized by solving the corresponding deconvolution problem with the maximum a posteriori (MAP) criterion. The algorithm considers that the noise is composed of two mutually independent parts, i.e., a Gaussian signal-independent component and a Poisson signal-dependent component. In addition, the Laplace distribution is used to represent the prior information about the targets under the assumption that the radar image of interest can be represented by the dominant scatters in the scene. Experimental results demonstrate that the proposed deconvolution algorithm has higher precision for angular super-resolution compared with the conventional algorithms, such as the Tikhonov regularization algorithm, the Wiener filter and the Richardson–Lucy algorithm.

Airborne forward-looking radar (AFLR) imaging has raised many concerns in fields of Earth observation, independent of weather and daytime. Constrained by imaging principles, conventional high-resolution radar imaging techniques such as synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) are incapable of AFLR imaging. The real aperture radar (RAR) can obtain AFLR images using a scanning antenna, but suffers from coarse cross-range resolution. Recently, there has been much attention paid to the iterative adaptive approach (IAA), which draws from the benefits of RAR imaging and provides improved cross-range resolution. However, earlier work on the IAA imposed a convolution model on the received azimuth echo, neglecting the effect of the Doppler phase. This model mismatch degrades the imaging performance for moving platforms. To settle this problem, this paper first establishes a Doppler-convolution model of AFLR imaging, where both Doppler phase and antenna convolution effects are considered, allowing more accurate reconstruction when applying the IAA to formulate a super-resolution image. Then, a data-depended approach for Doppler centroid estimation is proposed to circumvent the problem of low estimation precision using platform motion parameters delivered by navigational devices mounted on the radar platform. Simulation results demonstrate that the proposed implementation of the IAA based on the Doppler-convolution model and Doppler centroid estimation can overcome the deficiencies of the SAR and DBS techniques in the forward-looking imaging direction, and present a noticeably superior performance as compared with conventional AFLR imaging methods.

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.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of the receiver. Current literature and reports about BFSAR mainly concentrate on the stationary scene and ground-moving target imaging. Unlike stationary and ground-moving targets, the translational and rotational movements of ship targets usually lead to complicated range cell migration (RCM) and Doppler frequency migration (DFM). Moreover, the characteristics of RCM and DFM for different scattering points of the ship target are significantly different, i.e., the characteristics of the RCM and DFM are 2-D spatial variation, ultimately leading to severe defocusing of ship target in the SAR image. To solve these problems, a kind of hybrid SAR-ISAR imaging formation is proposed for BFSAR ship target imaging. First, to solve the problem of the Doppler ambiguity caused by the forward-looking mode of the receiver, an efficient ambiguity estimation method based on the minimum entropy criterion is presented. Then, keystone transform and range alignment processing can be applied to correct the spatial variant range walk and higher order RCM, respectively. Moreover, in order to obtain a high-resolution and well-focused image after translational compensation, a new method based on the fractional Fourier transform (FrFT) and the Wigner–Ville distribution (WVD) is proposed, where FrFT is applied to separate the multiple main scattering points in each range cell, and WVD is applied to obtain the high-resolution time–frequency distribution of each scattering point. Compared with the conventional ISAR range-Doppler (RD) algorithm and time–frequency estimation-based imaging methods, this method not only has no cross terms but also has high processing accuracy and better antinoise performance.

Due to the division of the transceiver, bistatic synthetic aperture radar (SAR) gains many advantages, such as forward-looking imaging and powerful anti-interference capabilities. In the meantime, information sharing (e.g., positions, status) between the transmitter and receiver is required for SAR imaging. This paper addresses the co-use waveform design for SAR-dual functional radar communication (SAR-DFRC). To this end, we embed information into time-frequency spectrum of the phase coded signal, which can be a feasible solution to SAR-DFRC, bringing the possibility of realizing a light-weighted, miniaturized, low-costed, spectrum reusable, and more confidential system. A novel time-frequency spectrum shaping (TFSS) SAR-DFRC architecture based on short time Fourier transform (STFT) is proposed for the first time. The weighted peak sidelobe level (WPSL) is considered a figure of merit for SAR imaging performance. Information is embedded by nulling the time-frequency spectrum of the waveform. Here, we develop the majorization-minimization peak sidelobe level-time frequency spectrum shaping (MMPSL-TFSS) algorithm to solve the resulting non-convex NP-hard optimization problem. The designed waveform can ensure imaging performance and obtain high communication capacity in the meantime. Experimental and numerical results highlight the effectiveness of the proposed SAR-DFRC framework for imaging performance and the secure transition to communication information.