The waveforms used in Multiple-Input Multiple-Output (MIMO) Synthetic Aperture Radar (SAR) should have a large time-bandwidth product and good ambiguity function performance. A scheme to design multiple orthogonal MIMO SAR Orthogonal Frequency Division Multiplexing (OFDM) chirp waveforms by combinational sparse matrix and correlation optimization is proposed. First, the problem of MIMO SAR waveform design amounts to the associated design of hopping frequency and amplitudes. Then a iterative exhaustive search algorithm is adopted to optimally design the code matrix with the constraints minimizing the block correlation coefficient of sparse matrix and the sum of cross-correlation peaks. And the amplitudes matrix are adaptively designed by minimizing the cross-correlation peaks with the genetic algorithm. Additionally, the impacts of waveform number, hopping frequency interval and selectable frequency index are also analyzed. The simulation results verify the proposed scheme can design multiple orthogonal large time-bandwidth product OFDM chirp waveforms with low cross-correlation peak and sidelobes and it improves ambiguity performance.

This study proposes a novel Multiple Input Multiple Output (MIMO) microwave imaging mode based on arc antenna array, which is mounted on the belly of platform. In this mode, an arc aperture is quickly synthesized using an MIMO. Consequently, high space and time resolution images of the illuminated scene around the platform are acquired. First, an imaging principle model based on arc antenna array is described, and its signal model is developed. Then, an imaging algorithm based on confocal projection is discussed and the performance of the mode is analyzed. Finally, the feasibility of the imaging mode and the validity of the proposed algorithm are demonstrated with a numerical simulation.

Downward-looking Linear Array Synthetic Aperture Radar (LASAR) has many potential applications in the topographic mapping, disaster monitoring and reconnaissance applications, especially in the mountainous area. However, limited by the sizes of platforms, its resolution in the linear array direction is always far lower than those in the range and azimuth directions. This disadvantage leads to the blurring of Three-Dimensional (3D) images in the linear array direction, and restricts the application of LASAR. To date, the research on 3D SAR image enhancement has focused on the sparse recovery technique. In this case, the one-to-one mapping of Digital Elevation Model (DEM) brakes down. To overcome this, an optimal DEM reconstruction method for LASAR based on the variational model is discussed in an effort to optimize the DEM and the associated scattering coefficient map, and to minimize the Mean Square Error (MSE). Using simulation experiments, it is found that the variational model is more suitable for DEM enhancement applications to all kinds of terrains compared with the Orthogonal Matching Pursuit (OMP)and Least Absolute Shrinkage and Selection Operator (LASSO) methods.

To deal with the difficulty for target outlines extracting precisely due to neglect of target scattering characteristic variation during the processing of high-resolution space-borne SAR data, a novel fusion imaging method is proposed oriented to target feature extraction. Firstly, several important aspects that affect target feature extraction and SAR image quality are analyzed, including curved orbit, stop-and-go approximation, atmospheric delay, and high-order residual phase error. Furthermore, the corresponding compensation methods are addressed as well. Based on the analysis, the mathematical model of SAR echo combined with target space-time spectrum is established for explaining the space-time-frequency change rule of target scattering characteristic. Moreover, a fusion imaging strategy and method under high-resolution and ultra-large observation angle range conditions are put forward to improve SAR quality by fusion processing in range-doppler and image domain. Finally, simulations based on typical military targets are used to verify the effectiveness of the fusion imaging method.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful technique for precise topographic mapping. However, owing to the side-looking SAR imaging geometry, geometry distortions appear in mountainous scenarios. Because of phase discontinuities or the absence of a valid phase, it is difficult to recover accurate DEM in such areas with single-aspect InSAR data. Fusion of two or more different aspects of InSAR data can deal with this problem in practice. Experiments using two antiparallel aspects of airborne InSAR data are carried out based on this idea. To decrease the processing error in single-aspect data and fuse them seamlessly, a MOtion COmpensation (MOCO) method using iterative DEM is used to reduce the MOCO error. Besides, phase-unwrapping methods based on terrain characteristics are proposed to avoid phase-unwrapping error owing to phase discontinuities in areas of shadow and layover. Experimental results verify the effectiveness of the processing methods.

Millimeter-wave Interferometric Synthetic Aperture Radar (InSAR) has smaller size, lower weight, and higher resolution compared with other bands. Thus, it has become a hot research topic. However, owing to its shorter wavelength, millimeter-wave InSAR data processing requires high-precision measurements of platform motion. For nonideal trajectories, traditional methods face difficulties in echo imaging and interferogram extraction. In addition, existing methods mainly produce SAR images based on plane projection. When the terrain changes abruptly, these methods may cause strong interferometric phase unwrapping and geometric distortion in SAR images. To overcome the abovementioned disadvantages of conventional methods in millimeter-wave InSAR imaging, an approach based on terrain surface projection is proposed. The echoes of different antennas are projected on the same terrain surface space for data imaging and interferogram extraction. In addition, the relation between terrain elevation and interferometric phase is derived. Simulations and experimental results verify the effectiveness of the proposed method; furthermore, the proposed approach improves the precision of interferometric phase extraction in complex motion conditions, while minimizing geometric distortion and phase wrapping in rough terrain, which is more conducive to terrain description and elevation inversion.

Owing to the platform instability and precision limitations of motion sensors, motion errors negatively affect the quality of synthetic aperture radar (SAR) images. The autofocus Back Projection (BP) algorithm based on the optimization of image sharpness compensates for motion errors through phase error estimation. This method can attain relatively good performance, while assuming the same phase error for all pixels, i.e., it ignores the spatial variance of motion errors. To overcome this drawback, a high-precision motion error compensation method is presented in this study. In the proposed method, the Antenna Phase Centers (APC) are estimated via optimization using the criterion of maximum image intensity. Then, the estimated APCs are applied for BP imaging. Because the APC estimation equals the range history estimation for each pixel, high-precision phase compensation for every pixel can be achieved. Point-target simulations and processing of experimental data validate the effectiveness of the proposed method.

This paper focuses on a novel squint spotlight SAR mode, where the PRI variation is employed to increase the range swath width, especially for high-resolution imaging. The spotlight SAR is developed to increase the azimuth resolution via steering the antenna beam to always illuminate the same area on the ground during the whole data acquisition interval. However, requirement of high resolution and large squint angle leads to large Range Cell Migration (RCM). Therefore, to ensure the scattered echoes along the azimuth to be completely received within the fixed reception window, the range swath has to be much narrower than the reception window. In order to increase the range swath, we can change the PRI along the azimuth to shift the reception window according to the variation of instantaneous slant range. This paper first derives the PRI variation scheme. Afterward, a modified time-domain Back-Projection Algorithm (BPA) is presented to implement the focusing. Finally, simulation results are given to validate the presented SAR mode and corresponding imaging processor.

An approach for simulation and analysis on the high resolution Synthetic Aperture Radar (SAR) images is provided and bridges are chosen for analysis. According to the principle of SAR, the Geometry Optics (GO) is employed to calculate the direct scatterings and multi-path scatterings of the bridge in this approach. In this study, the Sydney Bridge is chosen for analysis, and the Terra-SAR data with 1 m resolution is used. The results show that, the main properties of the bridge with the simulation and the real data are consistent, which tests the validity of the approach. Furthermore, according to the simulation process, the main scattering characteristics of the SAR image of the Sydney Bridge are interpreted. The experiments show that this approach can provide the main scattering characteristics of the target, and also show that it can be helpful for SAR images interpretations.

Aiming to remove the false alarm caused by azimuth ambiguity in SAR imagery during the process of ship target detection, a method based on the improved H/α/Wishart unsupervised classification is proposed. First, the scattering echo peak zone of target is extracted and the H/α classification results are treated as the initial cluster centers. Second, the ship target and azimuth ambiguity are identified by comparing and analyzing the structure of each region. It is showed by experiment that the method can detect ships based on the azimuth ambiguity; thus the false alarm rate in SAR-based ship detection is reduced.

In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR) images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM). Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

The spaceborne sodium Doppler lidar can be developed to measure global wind, temperature and sodium number density in the mesopause and lower thermosphere region. In order to analyze the feasibility of the lidar, simulation calculations about backscattering signals and measurement accuracy have been done in this paper. The analyzed result shows that the line-of-sight wind accuracy, horizontal wind accuracy and temperature accuracy are 0.8 m/s, 1.5 m/s and 2.5 K when the height of satellite orbit is 400 km, observational angle is 30.0°, laser power is 9.0 W, receiver diameter is 1.0 m, vertical range resolution is 2.0 km, and signal integrated time is 30.0 s.

Synthetic Aperture Radar (SAR) is widely used in ship surveillance. High-Resolution Wide-Swath (HRWS) SAR data are simultaneously collected, which introduces challenges and offers new research opportunities. SAR-based ship-surveillance technologies and the performance requirements of SAR systems are reviewed and summarized. Furthermore, the characteristics of HRWS SAR imaging and ship surveillance technologies are considered in tandem, and preliminary research results on ship detection, feature extraction, and classification are discussed. Finally, we point out issues to be addressed in future work.