In recent years, the back-projection (BP) algorithm has shown good potentials in SAR focusing with nonideal conditions like nonideal trajectory and nonuniform sampling. However, when applying the original BP algorithm directly to periodic non-uniform sampling data, ghostings can be introduced into the imaging results, leading to image quality distortion. To address this issue, a novel reconstruction method based on joint sub-channel BP images is proposed. We first analyze the causes and properties of Doppler grating lobes and BP ghostings in the periodic non-uniform signals. Based on the analysis, a reconstruction process of the joint sub-channel BP images is established to achieve the purpose of suppressing ghosting. The proposed method can be smoothly combined with the Ground Cartesian back-projection (GCBP) algorithm to realize high computational efficiency. Finally, the simulation and measured data are presented to verify the effectiveness of the algorithm.

Due to the constraint of minimum antenna area, 2-D ambiguity resolving is a challenging task in ship focusing of single-channel SAR mounted on high-speed maneuvering platforms. In order to accommodate the issues, a ship focusing and positioning algorithm based on 2-D ambiguity resolving is proposed. First, we analyze the constraint of minimum antenna area and the distribution of the 2-D ambiguity area. An optimal-PRF SAR concept is proposed, where the signal ambiguity is evenly distributed to the range and azimuth directions. In this concept, the timing sequence of the orthogonal phase-coded waveform is designed to make the target echoes in different regions orthogonal. Then, an orthogonal matching filter is used to suppress the signal energy in range ambiguity regions. Aiming at the target defocus and position shift caused by the Doppler ambiguity, we propose an azimuth ambiguity resolving method. The Doppler ambiguity number can be estimated by residual envelope inclination. Subsequently, the target can be relocated and accurately focused by the estimated Doppler parameters. After the operation of each target is completed, the focusing SAR image of the whole scene can be obtained. Finally, simulation results and real data processing are presented to validate the proposed algorithm.

For ultra-high resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultra-high resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation for ultra-high resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm.

As an important remote sensing means, synthetic aperture radar (SAR) has many superiorities to other sensors. How to effectively detect and locate ships in SAR images is also a popular field. In previous ship detection research, most algorithms focus on detecting the horizontal bounding box of ship targets, which ignore the rotation angle of each ships. Thus, too much background noise in the horizontal detection results makes them difficult to describe each ship accurately. Inspired by the powerful feature representation ability of convolutional neural networks (CNNs), a novel anchor-free and keypoint-based deep learning method is proposed for oriented ship detection in multiresolution SAR images. Our detector first extracts multilevel features from the input SAR image with a backbone network and feature pyramid network. Next, considering multiscale ships in multiresolution SAR images, we detect different sizes of ships on different levels of feature maps with identical head network structures. In each head network, the classification subnetwork determines each pixel in feature maps as the central pixel of this ship or not, and the regression subnetwork regresses the oriented bounding box for each ship. In the training process, the proposed oriented nonnormalized Gaussian function is used to describe the center point of ship targets, while the nonuniform weighting of the different level loss functions is used to suppress the imbalanced sample distribution. Experimental results on two authoritative SAR-oriented ship detection datasets and two Gaofen-3 images demonstrate the effectiveness and robustness of the proposed methods.

Due to the constraint of minimum antenna area, azimuth ambiguity resolving is a challenging task in the ship focusing for single-channel synthetic aperture radar (SAR) mounted on high-speed maneuvering platforms. In order to accommodate the issues, a ship focusing algorithm based on azimuth ambiguity resolving is proposed in this paper. For ship SAR imaging with small-aperture data, the energies of different targets are separated in Doppler domain with different Doppler ambiguity numbers. Thus, the Doppler ambiguity number of a single target can be estimated by residual envelope inclination. Then, the target can be accurately focused and located at the correct position by the known Doppler ambiguity number. After the operation of each target is completed, the focusing SAR image of the whole scene can be obtained. Finally, simulation results are presented to validate the proposed algorithm.

High squint multichannel (HSMC) synthetic aperture radar (SAR) mounted on high speed maneuvering platforms is an available mode to achieve wide swath imaging. However, the traditional multichannel reconstruction methods are not suitable because of range-dependent and time-variant steering vector caused by the nonlinear trajectory. To address the issue, a novel unambiguous signal reconstruction algorithm is proposed in this paper. According to the geometry model, the properties of range-dependent and time-variant steering vector are analyzed. Then, a range-dependent and time-variant inter-channel phase compensation method is proposed to correct the space time spectrum, and the constant steering vector is obtained. Before the reconstruction, the range walk correction (RWC) is performed to remove the mismatch between the reconstruction filters and the squinted signal. Furthermore, a modified spatial domain filter is proposed to reconstruct the unambiguous Doppler spectrum. Finally, simulation results are presented to validate the proposed approach.

The beam steering of high-squint terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) mounted on maneuvering platforms causes azimuth spectrum aliasing and nonlinear variation of the Doppler center with target azimuth position. A joint time-Doppler deramp (JTDD) based method is proposed and mainly contains two parts. First, for the azimuth spectrum aliasing, the unfolded 2-D spectrum is obtained by a modified linear deramp function in the azimuth time domain constructed from the 3-D motion parameters. After range cell migration correction (RCMC), the data supporting area in the azimuth time domain is expanded, and thus, aliased because of the nonlinear variation of Doppler center. Then, a nonlinear deramp operation in the Doppler domain is further proposed to obtain a nonaliasing signal. The proposed algorithm is efficient with less zero-padding due to the consideration of nonlinear components of Doppler center variation. Simulation and real SAR data processing are presented to validate the proposed algorithm.