This paper introduced a three-dimensional reconstruction approach for Synthetic Aperture Radar (SAR) based on polarization-coherent optimization. It includes two-dimensional imaging of full-polarization tomographic SAR data, along with registration and amplitude-phase correction of the resultant images. Through the application of a polarization-coherent optimal strategy, a set of coherent-optimal two-dimensional complex images is derived for the full-polarization multi-channel tomographic SAR. Subsequently, a three-dimensional reconstruction is executed using a compressive sensing technique, yielding a refined three-dimensional point cloud of the target region.This innovation addresses challenges encountered in traditional single-polarization tomographic SAR systems, including issues such as low image coherence, subpar three-dimensional reconstruction quality, and inadequate detail in point cloud representation. By employing the polarization-coherent optimal method on two-dimensional complex images from multi-channel full-polarization tomographic SAR, a set of images with optimal coherence coefficients is obtained. This approach effectively enhances coherence among two-dimensional complex images of each channel in full-polarization TomoSAR, ultimately elevating the quality of three-dimensional imaging point clouds.The methodology involves initial two-dimensional imaging of full-polarization multi-channel tomographic SAR echo data, followed by registration and amplitude-phase correction of the obtained complex image set. The polarization-coherent optimal method is then applied to acquire a set of coherent-optimal two-dimensional complex images. These images are further processed using a compressive sensing-based three-dimensional reconstruction algorithm, yielding the original three-dimensional point cloud. Finally, post-processing steps such as point cloud filtering and coordinate transformation are applied to obtain a high-quality three-dimensional SAR image of the target area.The significance of this approach is demonstrated through its application in the field of Polarimetric TomoSAR (PolTomoSAR), a vital tool for acquiring three-dimensional information about urban structures. The proposed method addresses the challenges faced by PolTomoSAR, including the limited application of full-polarization data and the need to improve coherence among multi-channel complex images for effective three-dimensional reconstruction.Additionally, the effectiveness of the proposed method is validated through the acquisition and processing of real-world data from a fully polarized P-band TomoSAR radar system over the Dunhuang Quadrilateral residential area. The developed technique holds great potential in advancing the applications of polarimetric SAR data in three-dimensional imaging, urban mapping, city planning, and building monitoring.

Synthetic Aperture Radar Tomography (TomoSAR) thre-dimensional imaging technology is built upon the foundation of two-dimensional SAR imaging, utilizing multiple observations in the elevation direction to construct the synthetic aperture for the elevation imaging capability. The classic TomoSAR imaging algorithm typically refers to the third dimension as elevation, and research over the years has been based on this model. The process of three-dimensional imaging involves reconstructing the spatial distribution of scattering characteristics along the elevation direction. According to this model, for the same range-azimuth cell, discrimination of overlapping scatterers can be achieved based on different levels of sparsity. However, studies have found significant limitations of this model for urban buildings. Firstly, there is scatterer diffusion along the elevation direction, particularly at the junctions of building surfaces, such as the intersections between the facade and ground. Secondly, the maximum unambiguous range of the reconstruction model based on elevation is limited by the model. To address this issue, considering the characteristics of urban buildings, this paper proposes a novel imaging model based on the unique angle property. Specifically, the scatterer parameters to be estimated are transformed from the elevation coordinates to the off-nadir angle. Moreover, based on the non-penetrating property of electromagnetic waves for buildings, it is assumed that there is only one scatterer for each off-nadir angle, which is consistent with physical reality. Experiments were conducted based on measured data from two sites, and the results confirmed that the proposed model can effectively suppress clutter caused by multiple scattering, significantly improving the three-dimensional imaging quality.

A small-size and highly-integrated real-time processing system for small UAV borne SAR is presented in the paper. The system collects and processes radar echo data in real time, efficiently compresses the raw data without significantly compromising imaging quality, and transmits the compressed data to the ground center for fine imaging. Compared to existing SAR systems, this paper offers advantages of smaller size, lighter weight, and flexible application. A prototype preprocessing unit was developed, measuring 7×7×2cm³ and weighing 200g, with the capability of seamless three-dimensional integration with the antenna and RF system. A UAV-borne flight imaging test validated the real-time imaging effectiveness of this system.

The polarimetric interferometric synthetic aperture radar (Pol-InSAR) model under P-band observations exhibits vertical structure diversity. Compared with the exponential-based random volume over ground (RVoG) model, the Gaussian vertical backscatter volume over ground (GVBVoG) model expresses a more complex forest vertical structure via introducing more parameters. On account of the influence of topographic fluctuation on the model, this article establishes the sloped Gaussian vertical backscatter volume over ground (SGVBVoG) model by drawing into the terrain slope. Based upon the SGVBVoG model, this article develops the 2-D SGVBVoG (2-D-SGVBVoG) model by defining the structure factor, which effectively reduces the model complexity from three to two dimensions. In the 2-D-SGVBVoG model inversion, in view of the diversity of forest species, age, shape, density, etc., in the natural scene and the variation of specific radar systems, a structure factor prediction scheme relying on machine learning is proposed. In the machine-learning model training, the radar incidence angle and the PDHigh coherence acquired by coherence optimization with terrain phase removal are utilized as the variables for characterizing the structure factor. Ultimately, in the case of fixing structure factor, a geometric inversion process on the complex plane is put forward to extract the forest height. The BIOSAR 2008 P-band Pol-InSAR data validation shows that the proposed method achieves an RMSE of 3.07 m, which is 24.0% better than the three-baseline SRVoG inversion.

Superpixel segmentation approaches for polarimetric synthetic aperture radar (SAR) images have only been studied in recent years. Simple linear iterative clustering (SLIC) is a simple and efficient superpixel segmentation method, first proposed for optical images. It basically includes three implementation steps, i.e., initialization, local$k$-means clustering, and postprocessing. The challenge of applying SLIC to polarimetric SAR images lies in constructing the effective spatial and feature similarity and proposing the efficient segmentation procedure. In this study, to address both issues, we modify the SLIC clustering function to adapt the characteristics of polarimetric statistical measures. A new initialization method is proposed, which exploits the image gradient information to produce robust cluster centers. Furthermore, in an effort to give a comprehensive comparison and provide a fair assessment of the feature similarities for polarimetric SAR imagery, four classic statistical distances, among which two were not studied along with the SLIC previously, are embedded in the modified clustering function. The proposed method is validated by comparing with state-of-the-art SLIC-based algorithms and also the Ncut and TurboPixel algorithms. Experiments on extensive polarimetric SAR data sets show that the proposed method can significantly improve the segmentation results, with producing better boundary adherence and compact as well as uniform superpixels. We also obtain distinct conclusions that are different from the existing studies when investigating the performances of the statistical measures.

To solve the problem of dense vehicle target detection in polarimetric synthetic aperture radar (PolSAR) images from urban areas under complex scenarios, this paper proposes a target detection method that combines the superpixel segmentation and the Wishart classifier. Firstly, the buildings are detected based on the different polarimetric scattering characteristics of ground objects. Then, the morphological information of the target is obtained by the local Wishart classifier and the superpixel segmentation. After that, the center points of the target are obtained by the global Wishart classifier. Finally, the region growing procedure is used to fuse the information obtained by above-mentioned classifiers to complete the target detection task.

There are shadows existing in traditional SAR tomography (TomoSAR) 3D imaging results, which bring difficulties in application of TomoSAR. However, TomoSAR with multi-azimuth observations can be applied to address this problem. In this paper, a panoramic 3D reconstruction method for TomoSAR based on multi-azimuth observations will be introduced. Firstly, 2D images are achieved with backprojection (BP) algorithm on the ground plane. Secondly, 3D reconstruction of TomoSAR is realized with orthogonal matching pursuit (OMP). After coordinate transformation, the panoramic 3D reconstruction results of TomoSAR based on multi-azimuth observations are achieved with registration of point clouds, in which iterative closest point algorithm (ICP) is effectively applied. Panoramic 3D reconstruction results of airborne TomoSAR experimental data can validate correctness and effectiveness of our method.

Circular SAR (CSAR) is a typical 3-D imaging model of SAR. 3-D back-projection (BP) algorithm is a common time-domain 3-D imaging method for CSAR. However, 3-D imaging with back-projection algorithm has high algorithm complexity and low efficiency for processing pulse by pulse and grid by grid. This paper proposes a new fast 3-D imaging method for CSAR based on 3-D back-projection algorithm. In this method, 3-D interpolation and phase compensation operations can be transformed into 1-D interpolation and phase compensation operations and matrix searching operations with the construction of a geometric interpolation kernel. This proposed method can greatly improve imaging efficiency of CSAR when the error range allows. The simulated experimental results can prove the correctness and effectiveness of the proposed method.

Synthetic aperture radar (SAR) tomography (TomoSAR) systems have been widely used because of its capability of 3D reconstruction of urban area. However, due to the critical requirements of phase and amplitude accuracy, the channel imbalance of the airborne multi-baseline TomoSAR system must be estimated and compensated. In this paper, we proposed a channel imbalance calibration method with visual semantics. This method only uses the information from 2D SAR images and works well in the absence of ground control points (GCPs). Firstly, strong scatterer in 2D image is selected and the channel imbalances are estimated based on the SAR imaging geometry. Secondly, altitude of the scatterer is estimated with TomoSAR technique and the imaging geometry can be updated accordingly. After some iterations, the channel imbalance will converge and then be compensated. The estimated error is compared with the results based on GCPs and 3D point cloud are presented, which validates the feasibility of proposed method.

The micro-Doppler modulation of the radar echo of the drone's rotor reflects the micro-movement characteristics of the target. Accurate estimation of the length and rotation frequency of an unmanned aerial vehicle (UAV) rotor is of great significance for target identification and classification in radar echoes. Firstly, this paper proposes a method of optimal estimation based on concentration of time-frequency rotation domain (CTFRD), in the time-frequency rotation domain of a multi-component micro-Doppler signal, under the FMCW radar system. Secondly, in the scene where the drone rotor rotates at a constant speed or at a uniform acceleration, the proposed method realizes the accurate estimation for multicomponent micro-motion feature parameters. Compared to traditional methods, it is also very robust in low signal-to-noise ratio (SNR) environments. Finally, the effectiveness of the proposed method is verified by simulations and real-world scenarios. Index Terms- Micro-Doppler, Concentration of time-frequency rotation domain, Parameter estimation, Target identification.

One of the satellites of CAPS(Chinese Area Positioning System) can retransmit the dual-frequency signal, so it is possible for ionospheric TEC measurement based on CAPS. A new method of ionospheric TEC measurement is introduced in this paper. The experiment system was established and an experiment was executed. The ionospheric TEC was deduced and the accuracy was validated in two methods. It is proved that the accuracy of TEC measurement is better than 1TECu. Moreover, we give the point image of the satellite both before range compensation and after range compensation based on the TEC measured in this paper. The point characteristics is obvious better after range compensation.

SAR/INS navigation system utilizes SAR radar as an additional sensor to provide the platform trajectory position and compensate an aircraft drift due to Inertial Measurement Unit(IMU) errors. Images obtained by SAR radar are matched with a digital landmark Data. When no landmarks are available, this article presents a novel method of InSAR/INS integrated navigation system based on interferogram matching. Interferogram contains total terrain information and hardly affected by seasonal variations of features compared with SAR/INS system. Interferogram from actual InSAR real time processing and interferogram simulated by additional DEM are matched and platform position and attitude inversion model is constructed to compensate INS drift errors. Real-data experiments show that the approach can compensate INS drift errors well when GPS signal is lost.

The combination of interference and circular SAR (CSAR) can reduce the influence of high sidelobes in the vertical direction of CSAR while retaining the merits of CSAR, such as subwavelength resolution and 3D imaging capability. However, C-band repeated pass interference is difficult due to short wavelength and decorrelation. This paper introduces interferometric processing of CSAR using fully polarimetric C-band circular data which is acquired over a test site in Zhuhai, China. The phase of 2D CSAR images obtained at two different platform height in the same subaperture is different. And the interferogram is acquired based on the phase difference. The experiment verifies the feasibility of repeated pass interference model for CSAR at C-band.

This work proposes a forest model based on Gamma distribution to better describe the forest vertical structure for height inversion using P-band polarimetric synthetic aperture radar interferometry (Pol-InSAR) data. The proposed model takes into account the forest vertical heterogeneity and asymmetry, to which volume interferometric coherence is sensitive. The interferometric coherence associated with a volume where the vertical backscattered power varies following a Gamma distribution is derived. The effect of scattering center height standard deviation and mean elevation to the volume interferometric coherence is investigated. Finally, the strategy of multi-baseline on the proposed model for forest height inversion using P-band Pol-InSAR data is proposed.

A multi-frequency polarimetric SAR named “IECAS-SAR” has been developed in Institute of Electronics, Chinese Academy of Sciences (IECAS). It is composed of four subsystems, namely, P-, L-, C- and X-band SAR sensors. It would play the role of a powerful remote sensing instrument and support scientific researches on the mechanism of scattering. The first-stage test flights have been flown, validating the SAR sensors and getting valuable results. An overview of this system and processing results are presented in the paper.

Motion compensation (MoCo) is pivotal in the processing of airborne synthetic aperture radar (SAR) data. Motion errors are space variant according to the data acquisition geometry, which can be split into range-variant and azimuth-variant components. In the processing of wide-beam SAR data, the azimuth variance must be considered. Several approaches have been proposed, among which the subaperture algorithm based on frequency division (FD) is a good choice if motion errors involve high-frequency components. However, there are two drawbacks to using this algorithm in conditions where the magnitude of motion errors is large, i.e., the invalidation of the time-frequency relation in an FD with too many subapertures and paired echoes caused by periodic discontinuities in the azimuth phase history. Theoretical analysis and simulations are presented to demonstrate these two drawbacks. Improvements are then made on the traditional algorithm: implementing the FD after the subaperture MoCo instead of before it and adopting a nonuniform FD scheme rather than a uniform one. Finally, the validity of the improved algorithm is further demonstrated by experimental results with real data.

A synthetic aperture radar (SAR) raw data simulator is an important tool for testing the system parameters and the imaging algorithms. In this paper, a scene raw data simulator based on an inverse ω-kalgorithm for bistatic SAR of a translational invariant case is proposed. The differences between simulations of monostatic and bistatic SAR are also described. The algorithm proposed has high precision and can be used in long-baseline configuration and for single-pass interferometry. Implementation details are described, and plenty of simulation results are provided to validate the algorithm.