The bistatic synthetic aperture radar (SAR) system based on the Five-Hundred-Meter Aperture Spherical Radio Telescope (FAST) has successfully acquired multiple high-quality Moon surface images. However, the system errors introduced by noncooperative transmitting and receiving radars prevent traditional geometric correction methods based on geographic data and radar parameters from accurately transforming delay-Doppler format images into geographic coordinates, making it impossible to further utilize these images for scientific applications. In this letter, a novel geometric correction method is proposed for transforming delay-Doppler images to the Moon’s geographic coordinate images. This approach utilizes a geometric positioning model to generate ideal delay-Doppler coordinates corresponding to the Moon’s geographic coordinates. These coordinate pairs are used to fit the rational polynomial coefficient (RPC). Subsequently, based on the RPC model, localization offsets are corrected through an affine transformation and selective coefficient optimization. An optical-SAR image registration method is used to determine the localization offsets and evaluate the reliability of the geometric correction method. We demonstrate this approach using Moon SAR images obtained by the bistatic SAR system based on FAST and other transmitting radars. This method can effectively integrate multisource Moon SAR data to address specific scientific challenges.

This letter proposes a novel self-calibration method for channel phase error (CPE) in antenna arrays by leveraging the geometric properties of non-collinear configurations. The CPE can be decomposed into linear and orthogonal components relative to the baseline length vector, which cause direction-of-arrival (DOA) bias and manifold distortion, respectively. However, for self-calibration methods, only the manifold distortion can be perceived and corrected. In collinear arrays, the DOA bias is independent of manifold distortion, so the linear component of CPE can't be self-calibrated. Fortunately, in non-collinear arrays, we can couple the DOA bias into the manifold distortion by reformulating the slant range formula with the Fresnel and virtual collinear array (VCA) approximations, making it possible to estimate the entire CPE without external references. We then propose an iterative least-squares algorithm that corrects for CPE using multiple snapshots collected from a non-collinear array. Simulations under various SNRs, distances, linear components of CPE, degrees of non-collinearity, and fields of view demonstrate the effectiveness and robustness of the proposed method.

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.

SAR Tomography can provide three-dimensional (3D) image of the observed scenes, becoming an important technology for urban mapping, target detecting and many other fields. When imaging urban scenes, multipath signals are always considered as noise in the previous research on 3D SAR imaging. However, the signals experienced multiple bounces hold massive information about the target scenario. In this paper, we aim to turn "waste" into treasure by digging for the hidden information in multipath. Based on the TomoSAR mechanism, we complete the restoration of the 3D imaging result. Firstly, we introduce the TomoSAR imaging model under the low-altitude case, which is more suitable for airborne or UAV- borne platform in urban area. We analyze the multipath model and propose the multipath detection and restoration methods. The virtual targets caused by multipath can be restored to the correct positions in the image by relocating. To verify the validity of our method, we conducted a series of real-data experiments, the results show that our method makes full use of multipath signals to retrieve non-line-of-sight targets missing in traditional direct-path 3D imaging.

The rapid development in SAR three-dimensional imaging technology and the feasibility of employing lightweight and convenient platforms have increased the attention of low-altitude UAV-borne TomoSAR. However, the building obstructions in urban area and the low altitude cause multipath phenomenon, which is challenging in imaging interpretation and accurate reconstruction. While previous research is mainly about the removal of multipath effects, we focus on exploiting multipath information within the framework of low-altitude TomoSAR. This paper aims to explore effective strategies for determining the viability of multipath exploitation in urban canyons. Firstly, we introduce the low-altitude TomoSAR imaging model under the spherical wavefront model. Through a comprehensive analysis of the range profile of the multipath mechanism, our objective is to provide detection and mechanism determination before relocating or restoring the multipath for TomoSAR. This algorithm was applied in real-data experiments to investigate the multipath mechanisms of different cases, validating practical value.

Synthetic Aperture Radar (SAR) is extensively employed in earth remote sensing, including both civilian and military sectors. Currently, establishing the correspondence between strong scattering regions in SAR images and the 3D geometry of the target has become a research hotspot. Traditional methods based on scattering center modeling overly rely on prior knowledge and manual annotation. However, neural network-based approaches often lack the constraints of SAR imaging principles when performing feature extraction. In this paper, we propose a method for analyzing strong scattering in SAR images based on a differentiable simulator to visualize the correspondence between strong scattering in SAR images and the 3D geometry of the target. Specifically, the differentiable simulator accumulates the scattering intensity in the scattering simulation stage to generate simulated SAR images at multi-pose. By computing the loss with the SAR image in the dataset (ground truth), the differentiable simulator can provide a direct mapping from the strong scattering in SAR images to the target's 3D structures. The effectiveness of the method is validated on a dataset of simulated SAR images based on the MSTAR. From the result we obtained, the correspondence between the 3D structures of the target model and the strong scattering in the image can be intuitively and clearly determined.

Synthetic Aperture Radar Tomography (TomoSAR) has proven to be a powerful technique in urban mapping, disaster assessment, and counter-terrorism operations. However, existing inversion algorithms face challenges in balancing accuracy and efficiency. This paper introduces a novel gridless three-dimensional inversion method based on Weighted Least Squares (WLS) for TomoSAR, aiming to address issues related to accuracy, efficiency, and the lack of analytical expressions for the scatterer positions and backscattering coefficients. The validity of the proposed method is verified on the measured data.

With the development of synthetic aperture radar (SAR) 3-D imaging technology, urban 3-D imaging has been realized by unmanned aerial vehicle (UAV)-borne array interferometric SAR (array-InSAR), which enables simpler access to SAR data. However, in low-altitude SAR 3-D imaging of urban scenes, multipath effect is significant. In past studies, multipath signals have been mostly considered as a hindrance in interpretation, but they provide important clues for imaging hidden targets in non-line-of-sight (NLOS) regions. Based on this idea, this article studies the multipath exploitation in low-altitude UAV-borne array-InSAR 3-D imaging and realizes accurate imaging of NLOS targets. An improved 3-D imaging model suitable for low-altitude cases is introduced to achieve preliminary 3-D imaging. Subsequently, key planes are extracted from the original results and the 3-D multipath reachable area is analyzed. After obtaining the multipath mechanism of the NLOS targets, they can eventually be accurately relocated and imaged. Both the simulation and real-data experiments show that the proposed method can effectively 3-D imaging and relocate NLOS targets in urban canyons, with errors typically below 0.5 m. The imageable range in the direction of the ground range can be expanded by 41.62%, and the imageable 3-D area can be expanded by 76.57%, which realizes the NLOS information acquisition.

Semantic comprehension aims to reasonably reproduce people's real intentions or thoughts, e.g., sentiment, humor, sarcasm, motivation, and offensiveness, from multiple modalities. It can be instantiated as a multimodal-oriented multitask classification issue and applied to scenarios, such as online public opinion supervision and political stance analysis. Previous methods generally employ multimodal learning alone to deal with varied modalities or solely exploit multitask learning to solve various tasks, a few to unify both into an integrated framework. Moreover, multimodal-multitask cooperative learning could inevitably encounter the challenges of modeling high-order relationships, i.e., intramodal, intermodal, and intertask relationships. Related research of brain sciences proves that the human brain possesses multimodal perception and multitask cognition for semantic comprehension via decomposing, associating, and synthesizing processes. Thus, establishing a brain-inspired semantic comprehension framework to bridge the gap between multimodal and multitask learning becomes the primary motivation of this work. Motivated by the superiority of the hypergraph in modeling high-order relations, in this article, we propose a hypergraph-induced multimodal-multitask (HIMM) network for semantic comprehension. HIMM incorporates monomodal, multimodal, and multitask hypergraph networks to, respectively, mimic the decomposing, associating, and synthesizing processes to tackle the intramodal, intermodal, and intertask relationships accordingly. Furthermore, temporal and spatial hypergraph constructions are designed to model the relationships in the modality with sequential and spatial structures, respectively. Also, we elaborate a hypergraph alternative updating algorithm to ensure that vertices aggregate to update hyperedges and hyperedges converge to update their connected vertices. Experiments on the dataset with two modalities and five tasks verify the effectiveness of HIMM on semantic comprehension.

Non-line-of-sight (NLOS) target imaging is a challenging issue in Synthetic Aperture Radar (SAR), because obstacles may block the direct line of sight between the radar and the target. In SAR tomographic (TomoSAR)3D imaging, the wrong presence of NLOS points can impact the accuracy and interpretability of the results. This paper presents an approach that not only detects but also relocates NLOS targets to their actual positions. The method relies on multipath signal analysis within a geometric model. The simulation and experimental results on a drone-borne system with 4 channels demonstrate that the proposed approach can effectively image NLOS targets, and has the potential for practical applications.

The traditional CNN-based methods usually employ the spatial information in the amplitude of complex Synthetic Aperture Radar (SAR) images. Several studies have started to concentrate on merging the unique physical properties of SAR images, such as DSN-v1, extracting the backscattering characteristic from the frequency domain. Although DSN-v1 has obtained impressive classification ability, there is some room for improvement. In this letter, DSN-v2 is proposed to boost the classification ability of man-made and natural objects in SAR images. The improvement is reflected in two aspects. First, a multi-scale sub-band feature extraction (MSFE) component is designed for natural objects. Since we observe their multi-scale sub-band spectrum is significantly different, multiple encoders are used to extract effective features. Second, the additive angular margin (AAM) loss is introduced to distinguish man-made objects more clearly by manually adding a margin to the decision boundary. The experimental results on the Sentinel-1 (S1) dataset show DSN-v2 achieves superior classification performance and model training speed compared with DSN-v1.

SAR tomography (TomoSAR) has the three-dimensional (3-D) resolving ability. Most existing 3D imaging methods for TomoSAR consider the layovers for different channels are the same. However, in the low-altitude airborne cases, the variation of layovers between channels becomes unneglectable. In this letter, we studied the channel migration phenomenon of sparse TomoSAR under low-altitude scenarios. We derived the model of the differential range from a particular target to different antennas, which is nearly a linear function along the array dimension. Therefore, we applied Keystone Transform on the array axis to correct this channel migration, and then the traditional compressed sensing-based 3D imaging methods for TomoSAR can be applied to get the final 3D reconstruction results. The approach was tested with simulation data and also the real data of the MV3DSAR system, which is a mini drone-borne TomoSAR system. The results demonstrate that the proposed method can provide a more accurate solution to the low-altitude sparse TomoSAR reconstruction problem.

This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot and sparse signals, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrificing the range and/or azimuth resolution, or adopt the sparsity constraint and utilize compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high; thus it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least squares (LS) estimation problem and solves the corresponding objective function via gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, as well as performance analysis for evenly distributed antenna array case are discussed in this paper. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g., CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experiment data. Results show great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect.

The existence of Radio Frequency (RF) Interference will cause an adverse effect on the interpretation of Synthetic Aperture Radar (SAR) images. There are various types of interference, and their pattens in images vary in different situations. Previous algorithms have disadvantages of low precision and large amount of computation. In this paper, we propose a prior-induced deep neural network. Based on the sparse and low-rank properties of interference signals in the time-frequency domain, an interference suppression network is designed to reconstruct useful signals. At the same time, a new loss function is designed, which integrates the sparse and low-rank properties with the training of network. The network combines the idea of semi-parametric interference suppression and the deep learning method, which can make good use of the characteristics of SAR echoes, making it more suitable for the field of signal processing and having a better effect. The proposed algorithm is applied to real SAR data with interference to validate its effect and efficiency.

Synthetic aperture radar automatic target recognition (SAR ATR) has been suffering from the insufficient labeled samples as the annotation of SAR data is time-consuming. Thus, adding unlabeled samples into training has attracted the attention of researchers. In this letter, a semi-supervised method based on consistency criterion, domain adaptation and Top-k loss is proposed to alleviate the need for labeled samples. According to consistency criterion that samples generated by the weak and strong augmentations from the same sample belong to the same category, we use the weak and strong augmented unlabeled samples to predict pseudo labels and train the model respectively. Then, to overcome the issue caused by the domain discrepancy between labeled and unlabeled samples especially when labeled samples concentrate on a narrow azimuth range, a domain adaptation component is designed to reduce their discrepancy. Besides, considering the incorrect pseudo labels will hamper the model training, the Top-k loss is adopted for unlabeled samples to mitigate the negative effects. The experimental results on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the superiority of our method in semi-supervised SAR ATR. Specifically, we achieve about a 14.29% improvement in recognition accuracy compared to the state-of-the-art when the labeled samples concentrate on a narrow azimuth range.

Building segmentation of synthetic aperture radar (SAR) images is a challenging task that has not been solved well. High-resolution interferometric SAR (InSAR) images can provide delicate textures and interferometric phase images useful for building segmentation. However, current semantic segmentation networks in computer vision cannot be directly applied in InSAR building segmentation tasks to get good results because of the InSAR images’ particularity. In this article, we present a novel complex-valued convolutional and multifeature fusion network (CVCMFF Net) specifically for building semantic segmentation of InSAR images. This CVCMFF Net not only learns from the complex-valued SAR images but also considers multiscale and multichannel feature fusion. It can effectively segment the layover, shadow, and background on both the simulated InSAR building images and the real airborne InSAR images. The segmentation performance of CVCMFF Net is significantly improved compared with those of other state-of-the-art networks. By feature visualization, the feature extraction rule and feature fusion mechanism of the network are explored. We hope that the proposed network can be beneficial to InSAR phase filtering, phase unwrapping, and information extraction in urban areas.

In recent years, deep learning has been successfully applied in the field of synthetic aperture radar (SAR) image object detection. However, unlike ships and tanks targets, the oil tank targets in SAR image are usually dense and compact with more overlaps and discrete scattering centers, which greatly increases the difficulty of extracting location and structural parameters. Most of the existing methods transfer the methods suitable for natural image to the SAR image field, without considering the unique characteristics of SAR image. Therefore, in this article, we propose an improved model based on the end-to-end transformer network, which is the first model introducing transformer network to 3-D detection of oil tank targets from single SAR image. We input the incidence angle into the transformer model as a priori token. Then, we propose a feature description operator (FDO) based on the scattering centers that are used as an aid to improve the precision of predictions. In addition, we also propose a cylinder IOU as a more suitable evaluation metric for 3-D detection of oil tank. Finally, we evaluate our model on an SAR image dataset that contains SAR images from RADARSAT-2, TerraSAR-X, and GF-3 with different incidence angles. Our experiments demonstrate that our proposed model achieves the AP of 77.6% compared with 60.8% of baseline, which proves the effectiveness of the introduction of observation conditions, cylinder IOU loss (CI Loss), and the FDO based on the scattering centers in our model and is appealing for 3-D detection of oil tank.

In airborne array interferometric SAR (Array-InSAR) tomography, the measurements acquired by conventional uniform sampling array are always restricted by the number of physical baseline elements and the size of baseline aperture. It is desirable to capture new acquisitions and enlarge the aperture with virtual signal processing instead of actually adding array baselines. For this motivation, we utilize the disparity of a pair of coprime sampling sub-arrays to enlarge the baseline aperture and construct new observations virtually. The generation of virtual measurements is equal to estimating cross-correlation matrices in real SAR data. Due to the spatial target variation, we adopted an adaptive filtering method to estimate the cross-correlation matrix. We call the above-mentioned processing of generating virtual measurements as acoprime sensing technique. The newly generated virtual measurements have more degrees of freedom, a larger baseline aperture, and a higher signal-to-noise ratio (SNR) than the physical measurements. These advantages offer the possibility to obtain competitive three-dimensional (3-D) imaging results without increasing the hardware cost of the Array-InSAR. We demonstrate the effectiveness of the proposed method by the coprime acquisitions selected from AIRCAS Array-InSAR data.

In the complex electromagnetic environment, radio frequency interference (RFI) from other radiation sources often conflicts with synthetic aperture radar (SAR) systems, which overlaps and destroys the useful data in the same frequency band, causing adverse impact to the quality of SAR imaging. When faced with wideband or mixed complicated RFI, traditional methods inevitablely damage the original signal, and cannot effectively protect and reconstruct the useful information. Besides, the current semi-parametric algorithms have large computations and limited generalization ability. To address these issues, this paper proposes a prior-induced-learning framework (PISNet) to achieve RFI suppression and useful signal recovery in time-frequency domain. Both narrowband and wideband interference are uniformly modeled as a sparse distribution in time-frequency domain, and the stationarity of SAR echoes determines its low-rank characteristic. These properties of RFI and SAR data are treated as prior knowledge to inject into our PISNet. An iterative reconstruction module is raised to achieve low-rank reorganization of the fused residual features. Meanwhile, a novel loss function is put forward to induce the network training to ensure that each component conform to the prior. The proposed approach innovatively integrates deep learning with semi-parametric methods for RFI suppression, which achieves superior performance on simulated and real data. Compared to existing learning-based methods, the image quality of the restored Sentinel-1 data is improved by 9.37% AG. The code and dataset will be available online (https://github.com/JyuanShen/PISNet).

Unsupervised methods play an essential role in Polarimetric SAR (PolSAR) image classification, where labeled data are difficult to obtain. However, there is still a large gap between existing unsupervised learning methods and supervised learning methods. Without the semantic constraints of labeled data, pixels within the same category are often misclassified into different categories, leaving the output to be messy. To address the previous issue, this paper proposes a fully unsupervised pipeline for training CNNs. The pipeline combines low-level superpixels and high-level CNN semantic features for high-quality pseudo label generation. It effectively eliminates the misclassified pixels by voting within the superpixel blob, while preserving the sharpness of edges. With the training process of the model, the quality of the generated labels is getting improved. Experiments on airborne (ESAR/AIRSAR) and spaceborne (RadarSat2) PolSAR images prove the effectiveness of the proposed method (measured with OA, AA, and Kappa metrics). Our method outperforms the previous unsupervised methods (H/alpha-Wishart,SM-Wishart, FDD-H, DEC, and VQC-CAE) with a large margin, and even has comparable performance to the supervised CNN model (FCN).

Deep learning algorithms are widely used in SAR target detection. At present, most detection methods based on neural networks treat SAR images as optical images for processing, and do not fully exploit the characteristics of SAR images. In this paper, a method for feature enhancement using sub-aperture decomposition is proposed. Considering the advantages of the rotate box in eliminating interference, we conducted experiments on the rotating frame detection network Rotate RetinaNet using the Complex SAR images Rotation Ship Detection Dataset (CSRSDD). Compared with the base method, AP improves by 2.4% without bells and whistles. The results confirm the effectiveness of the proposed method.

Synthetic aperture radar (SAR) is widely used in many applications due to its all weather and all day observation capabilities. With the development of many spaceborne and airborne SAR systems, the amount of SAR data and products is increasing rapidly. Meanwhile, how to ensure the quality of data and products becomes more important. Calibration and validation are the essential processes to improve and evaluate the quality of the data and products. Standardization of SAR calibration and validation is a critical step towards higher product quality and better comparability and interoperability. This paper presents recent efforts and future work of SAR calibration and validation of ISO/TC 211.

High-resolution (HR) SAR images contain rich details thus offer better capabilities on describing the target spatial properties. Besides, Polarimetric SAR (PolSAR) data can convey physical characteristics embedded in the polarimetric information. However, HR and polarimetric are two conflicting properties since acquiring PolSAR data will limit the pulse repetition frequency (PRF) for each polarization hence limit the resolution. Tackling this issue, recovering HR full-polarized (FP) data from HR single-polarized (SP) data using DNN is a meaningful approach. This paper studies the ability of DNN to recover FP information from SP data of different resolutions and different polarization channels in various areas. We use UAVSAR L-band data for experiments. Results demonstrate the feasibility of using DNN trained by lower-resolution FP data to recover HR FP data from HR SP data. It is showed that the performance is more notable over natural surfaces than in built-up areas, and HH polarization has a better reconstruction result compared with other channels. Besides, the recovery capability in different regions is also compared, which provides some inspirations for FP and HR SAR data applications.

Three-dimensional imaging with tomographic SAR is a hot research topic in the field of SAR. The existing methods usually solve the question based on elevation discretization. When the target point has an off-grid deviation, the accuracy will decrease and the number of spurious points will increase. In this paper, a position-first step-by-step 3D inversion method is proposed. By constructing the observation equation with only an unknown elevation position, the height of the scattering center is directly solved in the continuous domain based on the least square criterion, and then the scattering coefficient is calculated based on the position. Simulation results verify the effectiveness of the method compared with the classical OMP algorithm. The proposed method has higher estimation accuracy under the same noise level and observation number and is less affected by the scattering center spacing.

This article proposes a novel synthetic aperture radar visual-inertial odometry (SAR-VIO) consisting of an SAR and an inertial measurement unit (IMU), which aims to enable the observation platform to complete successfully a continuous observation mission in the context of low-cost demand and lack of enough navigation information. First, we establish the observation models of the SAR in a continuous observation process based on the SAR frequency-domain imaging algorithm and the SAR time-domain imaging algorithm, respectively. With the preintegrated IMU data, we then propose a method for estimating the geographic locations of the matched targets in the SAR images and verify the condition and correctness of the method. The optimization of the track and the locations of the targets is achieved by bundle adjustment according to the minimum reprojection error criterion, and a sparse point-cloud map can be obtained. Finally, these methods and models are organized into a complete SAR-VIO framework, and the feasibility of the framework is verified through experiments.

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.

Due to multipath (MP) scattering, many special phenomena and details about the targets in high-resolution synthetic aperture radar (SAR) images are hard to understand. In this letter, a more elaborate improved model combined with the synthetic aperture progress is presented, to analyze the MP scattering. We explore the defocusing and displacement phenomena of MP scattering through signal modeling and time-frequency relationship analysis. The theoretical equations for the displacement of double scattering are derived, and the equation of phase error which causes the defocusing is also given. The SAR raw data simulation of MP scattering and range-Doppler (RD) imaging results validates the correctness of the analysis. From the equations, the effects of MP scattering of different height and different orientation angle in high-resolution SAR images are given. Finally, based on the simulation results, the new interpretation about the relationship between the moustache effect in high-resolution TerraSAR-X SAR images and the height of the structure is obtained.

Synthetic aperture radar (SAR) image simulation can provide SAR target images under different scenes and imaging conditions at a low cost. These simulation images can be applied to SAR target recognition, image interpretation, 3-D reconstruction, and many other fields. With the accumulation of high-resolution SAR images of targets under different imaging conditions, the simulation process should be benefited from these real images. Accurate simulation parameters are one of the keys to obtain high-quality simulation images. However, it takes a lot of time, energy, and resources to get simulation parameters from actual target measurement or adjusting manually. It is difficult to derive the analytical form of the relation between a SAR image and its simulation parameter, so nowadays the abundant real SAR images can hardly help the SAR simulation. In this article, a framework is proposed to obtain the relationship between SAR images and simulation parameters by training the deep neural network (DNN), so as to extract the simulation parameters from the real SAR image. Two DNNs, convolutional neural network (CNN), and generative adversarial network (GAN) are used to implement this framework. By modifying the network structures and setting reasonable training data, our DNNs can learn the relationship between image and simulation parameters more effectively. Experimental results show that the DNNs can extract the simulation parameters from the real SAR image, which can further improve the similarity of the simulation image while automating the setting of simulation parameters. Compared with CNN, the simulation parameters extracted by GAN can achieve better results at multiple azimuth angles.

In the shadow and water areas of InSAR data, the coherence is low and the interferometric phase is noisy. In these areas, phase unwrapping is a difficult problem as the phase error is prone to spread to other areas while integrating path crosses the noisy area. In order to avoid the unwrapping error, interferometric phase characteristics of these areas are analysed in theory according to their terrain in this paper. Based on the analysis, other than the usual strategy of setting integral path to restrict discontinuities spreading, phase compensation methods are proposed to avoid wrong unwrapping phase. The experiment using airborne InSAR data verifies the effectiveness of this method.

Obtaining the three-dimensional data of the target is very useful for the interpretation and application of the SAR target. This paper proposes a deep learning framework to recover the three-dimensional structure of the target from a single SAR image, which is expressed in the form of 3D point cloud. Due to the small data set of SAR images, the network is combined by two parts. First, the two-dimensional image in the optical perspective is predicted from the SAR target image, and then the 3D points of the target is reconstructed based on the pre-trained 3D reconstruction network model from the optical images. The experiment is based on the MSTAR datasets. The results confirm the effectiveness of the three-dimensional reconstruction method.

High-resolution SAR has large transmitting bandwidth and wide synthetic aperture. How to understand and take advantage of the variation characteristics of SAR scattering characteristics with angle and frequency is a topic that worth studying. This article establishes a coherence matrix of sub-band and sub-aperture SAR images, and analyzes its ability to classify scattering mechanism. Experiments are conducted using the TerraSAR-X high-resolution data of different scenarios, and some meaningful results are got, which may provide some support to the analysis and application of high-resolution SAR data.

Accumulation of target features from multi-angle observation data is of great significance for SAR Automatic Target Recognition. How to make correspondence matching with multi-angle data effectively for man-made targets? Due to the intensities and positions of the scattering centers varies with the aspect angle strongly, it is difficult to register SAR images using traditional matching methods. In this work, an approach for feature matching of man-made targets is proposed from complex SAR data. And it is based on the mechanisms of the dominate scattering to build up the relationship of the different multi-aspect images. Scattering map is employed to make scattering center extractions. Then the scattering centers are matching by using the physical model. Terra-SAR data of an aircraft test the validity of the approach.

The stability characteristics of scattering are of great significance for achieving radiometric calibration without calibration fields. This letter used dual-polarization synthetic aperture radar (SAR) data to explore the temporal and spatial stability of microwave scattering. Based on Sentinel-1 SAR data, we mainly studied the stability of the median value of SAR image slices in urban areas, under the HH- and HV-polarization modes. The urban slices with stable scattering were extracted by an improved deep neural network designed herein. Based on 33 images of the Houston region, the results show that this suitable network, which is trained by dual-polarization data, can effectively distinguish the stable slices from the unstable ones. In addition, some meaningful characteristics about SAR scattering stability of dual-polarization data are also obtained, which can provide a good reference for SAR nonfield radiometric calibration.

The theoretical modeling and analysis of SAR location error play an important role in SAR system design and error source budget. Existing SAR geolocation error models are mainly implicit, which are not easy to do analysis, especially in the curved-path case. In this paper, a theoretical explicit model of the relationship between image geolocation error and the path measurement error is established for curved-path SAR, based on BP imaging algorithm. Simulations are given which verify the correctness of the model. The explicit model and the analysis results provide an effective reference for understanding and budgeting the system-level geometric location error for curved-path SAR, such as GeoSAR.

Polarization converter is used in the applications of the polar SAR observations. There exists coupling between TE and TM modes when plane wave is oblique incident on the surface of dielectric periodic structure, the single TE or TM polarized wave incident will cause TE and TM mixed transmission wave. In some proper incident conditions, complete polarization conversion can be realized between TE and TM mode. In this work, a design of complete polarization converter by using dielectric periodic structure is designed and it is carefully investigated by a method which combines the multimode network theory with the rigorous mode matching method. We revealed TE/TM complete polarization conversion characteristics of dielectric periodic structure, and also analyzed the effects of structure parameters. These investigations provide important guideline for accurate designing new millimeter wave polarization converters.

SAR images of aircrafts usually consist of several discrete scattering centers. For targets like aircrafts having relatively smooth surfaces, understanding and modeling about these scattering centers is of great use for the detection and the recognition in SAR images. In this work, an approach for modeling of the features about the dominant scattering centers of the aircrafts is proposed. The approach generates a scattering map based on the analytic scattering model of some typical unit structures. According to scattering maps, the equivalent parameters are adjusted for detail feature modeling. Through this approach, the main mechanisms of aircrafts can be interpreted and the main features are modeling too. This approach provides a way to characterize the features of aircrafts in SAR images without the large computational amount. Furthermore, it sets up a frame based on typical structures to model the local scattering features of the aircrafts in SAR images. The experiments based on the Terra-SAR data and airborne data test the validity of the approach. The work would be useful for SAR Automatic Target Recognition.

A new image formation algorithm is proposed for processing very high resolution spaceborne sliding-spotlight synthetic aperture radar (SAR) data. Because of along-track antenna steering, the Doppler bandwidth of the received SAR data is expanded significantly beyond one pulse repetition frequency interval. Furthermore, the range histories become spatially dependent in both dimensions and cannot be expressed exactly by a hyperbolic model. In our approach, we first reduce the Doppler bandwidth by a novel azimuth dechirp processing method in the range frequency domain. The data are then processed by the standard ω-κ algorithm with a fixed effective velocity. Thereafter, the chirp modulation concept is imported to rebuild new data with much shorter apertures. Finally, a standard back-projection algorithm is employed to accumulate the signal pixel by pixel along the newly built aperture. Thus, the balance between processing efficiency and precision can be controlled by adjusting the length of the new apertures. In addition, a more accurate 2-D spectrum derivation is employed to enhance the processing precision, and a novel range-splitting method is presented to accommodate the range dependence of effective velocities. Furthermore, when implementing the back projection, the image grid-the region and granularity level of which are user defined-is placed on the earth's surface instead of on the slant-range plane, and the routine geometry projection processing thus becomes dispensable.

Synthetic aperture radar (SAR) systems in forthcoming surveillance and reconnaissance tasks have to meet increasingly severe demands. The next generation of toplevel SAR systems will comprise high resolution wide swath (HRWS) imaging capability, highly sensitive ground moving target indication (GMTI) and a multitude of sophisticated operational modes. Multi-input multi-output (MIMO) radar systems, which can acquire more degrees of freedom, offer new potential solutions to future missions. In this paper, we introduce a novel airborne MIMO-SAR system built in IECAS. The proposed system contains a reconfigurable phased array, two transmit channels, and four receive ones. Thereby, it can realize 0.3m resolution for 30km swath, multimodal operation of wide swath imaging, spotlighting, ground-moving target indication (GMTI) et al. The system design and the orthogonal waveform scheme are detailed, along with the flight experimental results.

The CNNs manifest outstanding performance in SAR ATR applications. The most widely used means for evaluation is to test them on a separate testing set. However, as there exists a strong correlation between the working conditions of the training and the testing sets, the reliability of the method degrades. We revealed the problem by training and testing the models with pure clutters, and found that the model could still achieve a high accuracy. Furthermore, we noticed that some models showed approximately the same performance when the working condition was altered, while others suffered significant degradation. Analysis of the two types of models were conducted via saliency maps. Finally, a new inner feature ratio (IFR) metric for evaluation was proposed. Results demonstrated that the new metric could effectively reject those with false high accuracies, and help choose the robust ones.

GF-3 is the Chinese Synthetic Aperture Radar (SAR) satellite mission with scientific and commercial applications, which was launched in August, 2016. In this paper, various error sources about system position are analyzed based on real data of GF-3 satellite. The results show that satellite positioning accuracy is less than 4m.

This paper proposed a new approach to focus the very high resolution spaceborne sliding spotlight synthetic aperture radar (SAR) data. The main singularity of the approach is accommodating the space dependent range histories by a back projection method on reduced apertures achieved by chirp modulation method. Besides, in the range spectrum domain, a dechirp method is employed to reduce the Doppler bandwidth to be within one PRF interval. Additionally, range splitting method is employed to mitigate the range dependence of effective velocities. Furthermore, the image grid is placed on the earth surface instead of the slant range plane, and the routine geometry projection processing becomes dispensable. The proposed approach was validated by simulation results of nine point targets.

The edges of shadows are blurred in Synthetic Aperture Radar (SAR) images due to the moving of the radar when data are collected. This phenomenon becomes obvious in High Resolution (HR) SAR images. In this work, an adaptive approach for shadow enhancements is proposed. The performance of the shadow enhancement has some relationship with the precision of the estimation of the height and this rule is used in this work. The Height-Variant Phase Compensation (HVPC) and golden section algorithm are employed. The adaptive method is built by iterative progress of calculating the quantities of pixels corresponding to the shadow region. This method provides an automatic way for shadow enhancements and it is suitable for objects mainly composed of flat-like structures. The experiments based on the Mini-SAR of a helicopter are implemented to test the validity of the approach. The work in this paper provides a way for shadow enhancement for HR SAR images and would be useful for SAR Automatic Target Recognition.

Synthetic aperture radar (SAR) image simulators based on computer-aided drawing models play an important role in SAR applications, such as automatic target recognition and image interpretation. However, the accuracy of such simulators is due to geometric error and simplification in the electromagnetic calculation. In this letter, an end-to-end model was developed that could directly synthesize the desired images from the known image database. The model was based on generative adversarial nets (GANs), and its feasibility was validated by comparisons with real images and ray-tracing results. As a further step, the samples were synthesized at angles outside of the data set. However, the training process of GAN models was difficult, especially for SAR images which are usually affected by noise interference. The major failure modes were analyzed in experiments, and a clutter normalization method was proposed to ameliorate them. The results showed that the method improved the speed of convergence up to 10 times. The quality of the synthesized images was also improved.

Doppler walk is introduced by high speed motion of targets in passive radar, which decreases the signal-to-noise ratio. In this paper, a Doppler walk rectification method based on keystone-Wigner-Ville transform (KWT) is proposed, and the interference, weights and noise impact are analyzed. No prior information and parameters searching are unnecessary.

The edges of the shadow region are blurred in the SAR image due to the moving of the radar during data collection. This phenomenon becomes obvious in the High Resolution SAR images. Shadow enhancement is of great value for ATR especially when the scattering centers of the target itself are not clear. In this paper, an approach for shadow enhancement in the SAR images for targets with plat structures is presented. And experiments on the Mini-SAR data test the validity of the approach.

Multichannel synthetic aperture radar promises high-resolution and wide-swath imaging simultaneously. Channel error estimation is a critical step in signal processing before imaging. This letter mainly derives the Cramér-Rao lower bounds (CRLBs) for phase error estimates of three commonly used error estimators. Furthermore, to comprehensively evaluate the estimators, this letter compares both their accuracy and effectiveness. The accuracy is assessed by the maximum estimation deviation among channels, and the effectiveness is assessed by the proximity of the mean square errors (MSEs) to CRLB for phase error estimates. Finally, simulation is conducted to compare the maximum deviation as well as the MSE versus CRLB among the three estimators, under different clutter distributions and signal-to-noise ratios. Combined with the estimation accuracy and effectiveness, this letter aims to provide justifications for the proposed algorithms and gives recommendations for method selection in engineering applications.

The mystery of the asteroid (4179) Toutatis was revealed by Chang'e-2 spacecraft during a close flyby on December 13, 2012. Optical imaging and navigation of the probe during the flyby were performed entirely under ground-based radio tracking and default sequence built on ground. This paper establishes a set of estimation algorithms of the relative trajectory between Chang'e-2 and Toutatis based on dynamics, optical, and radio constraints that are determined by the unique flyby mode. This study is the first time to precisely reproduce the core process of Chang'e-2's encounter with Toutatis based on several optical images. In addition to constructing a strict photogrammetric model, the shadowing effects caused by the illumination and the deviation of the center-of-mass (COM) from the center-of-figure (COF) in optical images are also considered. The spacecraft trajectory with regard to the COF of the body is estimated using images taken from 120 km or less. The formal one sigma uncertainty is (67, 20, and 11 m) in the principal axes frame of the position error ellipse, and the closest approaching distance between Chang'e-2 and Toutatis's COF is calculated as 1557 ± 11 m, which is more precise than previous results with an uncertainty of hundreds of meters. The spacecraft trajectory with regard to the COM of the body is estimated with an uncertainty of (211, 34, and 17 m), and the corresponding closest distance is estimated as 1451 ± 18 m based on the previously developed shape model of Toutatis. The algorithms and results in this study are important for evaluating the performance of this flyby mission and are also valuable for any similar optical navigation during a close approach. In addition, our results can help in precisely determining the axis of Toutatis and sizes of impact craters, which are critical for understanding the formation and evolution of Toutatis.

The precision of the mutilsquint methods effects by several factors. This paper deduces the effects of these factors on the multisquint estimation accuracy. Expression of the estimation accuracy is deduced, which provides theoretical bases for the parameter choice and system design of the airborne interferometric SAR.

The boundary of the shadow region is blurred in SAR images for the moving of the radar during the collection of data. This phenomenon gets particularly obvious for tall and narrow targets in High Resolution (HR) SAR images. In this work, based on the Height-Variant Phase Compensation Algorithm (HVPC) according to the property of the target like poles, an approach for shadow enhancement about tall and narrow targets in SAR images is presented. This approach uses a group of a compensation filters in azimuth direction to focus the edge of the shadow. In addition, the experiments for Terra-SAR and Mini-SAR data are implemented and the results prove the validity of the approach.

In passive radar, the waveform is not controlled by the system, so strong echoes usually produce high sidelobes in the correlation function. Since the sidelobes can mask the weak targets, strong echo cancellation methods are required. The generalized adaptive notch filter (GANF) is an efficient method compared with the extensive cancellation algorithm. However, the GANF estimates different frequencies separately (in parallel or series), and a point-by-point iterative operation is adopted, which leads to heavy computational burden. This letter presents a multifrequency estimation notch filter which is a simplified GANF based on the signal model. Furthermore, an adaptive block notch filter is proposed to reduce the processing time. The efficiency of the block notch filter is verified by simulations.

Existing low-Earth-orbit synthetic aperture radar (SAR) algorithms generally assume that the data are azimuth invariant. However, this assumption does not hold for the medium-Earth-orbit (MEO) SAR systems due to the significantly longer azimuth integration time and complex imaging geometries. As a result, the MEO SAR data cannot be processed accurately and efficiently using the existing algorithms. To solve this problem, this letter proposes a two-step azimuth perturbation (AP) method that uses the first-step AP to remove the bulk azimuth variance at the range processing stage and the second-step AP to remove the residual variance at the azimuth processing stage. As an example, an improved range Doppler algorithm with the integrated two-step AP is discussed in this letter. Simulations of an L-band MEO SAR with 5-m resolution at 10 000-km orbit height are used to demonstrate the validity and accuracy of this algorithm.

Compared with current low-Earth-orbit synthetic aperture radar (SAR), geosynchronous SAR (GEO SAR) is featured with its ultrahigh orbit and ultralong integration time. In this letter, we answer the question whether the orbital perturbation items have effects on GEO SAR imaging during the long integration time and, if so, how they produce errors. To achieve these goals, we first develop a perturbing orbital elements errors model based on perturbation analysis. Then, we propose an accurate analytical expression of first to fourth Doppler parameters for GEO SAR and analyze the effects of perturbing orbital elements errors on the Doppler parameters. Furthermore, the relationship between the perturbing Doppler errors and the phase coherence of GEO SAR signal is deduced. Experiment and simulation results demonstrate the image defocusing caused by orbital perturbation. The conclusions are that the orbital perturbation does affect GEO SAR imaging by producing nonnegligible phase errors and that the required accuracy of the orbit determination should be at centimeter level in the radial direction.

Interferometric phase filtering is an indispensable step to obtain accurate measurement of digital elevation model and surface displacement. In the case of low-correlation or complicated topography, traditional phase filtering methods fail in balancing noise elimination and phase preservation, which leads to inaccurate interferometric phase. A new nonlocal interferometric phase filtering method taking advantage of higher order singular value decomposition (HOSVD) is proposed in this letter. For each pixel of the interferometric phase, a 3-D data array is established, and shrinkage is applied after HOSVD. A Wiener filter is used to improve the denoising performance in the end. Simulated and real data are employed to validate that the proposed method outperforms other traditional methods and some of the state-of-the-art nonlocal methods.

Attention has been devoted to multi-input multioutput (MIMO) synthetic aperture radar (SAR) systems in recent years. The applications of MIMO-SAR systems which involve high-resolution wide-swath remote sensing, 3-D imaging, and multibaseline interferometry are seriously limited to the orthogonal waveforms. This restriction is mainly caused by the imperfect orthogonal waveform-introduced ambiguous energy. For a single point target, the impact of the ambiguous energy can be neglected. However, when it comes to the spatially distributed scattering scenarios, the ambiguous energy from closely spaced scatterers, which would be accumulated, degrades the SAR image seriously. In order to suppress the ambiguous energy, a novel energy cancellation scheme using the Sequence CLEAN technique in the range direction is proposed in this letter. By employing the proposed novel scheme on the raw data of MIMO-SAR systems, the ambiguous energy from both point and distributed targets can be suppressed. The proposed scheme is validated by simulations and practical experiments.

In the area of airborne synthetic aperture radar (SAR), motion compensation (MOCO) is a crucial technique employed to correct the SAR data affected by nonlinear platform trajectory during data acquisition. Due to range-azimuth coupling and computational burden consideration, some approximations, which are valid for SAR systems of moderate aperture length, are usually adopted in commonly used MOCO approaches. However, a much more accurate SAR data processing approach is appealing to process the low-frequency SAR systems with large aperture length, such as P-band. In this paper, a new MOCO approach with high precision and high efficiency is proposed. After the ω - κ processing and the range-dependent MOCO, the analytical expression of a 2-D spectrum of a partially focused SAR image is given. Afterward, aperture reduction is achieved by a chirp modulation technique. Finally, with high precision and less computation cost, back projection along the new built short apertures (affected by the residual motion errors) is employed to yield a fairly well-focused SAR image. Experimental results on simulated and actual P-band SAR data are presented to verify the performance of the proposed approach.

Attention has been devoted to multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems in recent years. The applications of MIMO SAR systems, which involve high-resolution wide-swath remote sensing, 3-D imaging, and multibaseline interferometry, are seriously limited by the available sets of orthogonal waveforms. Although orthogonal frequency-division multiplexing (OFDM) chirp waveforms are proposed to avoid intrapulse interferences, this waveform scheme has not been investigated for practical implementation. In this paper, challenges in implementing the OFDM chirp waveforms on practical systems are analyzed and solved. First, the small extra carrier frequency between the mutually orthogonal waveforms, which renders the OFDM chirp waveforms not strictly on common spectral support, is avoided by improving the modulation of the OFDM chirp waveform. Second, the tedious demodulation, which is realized by circular-shift addition in the time domain and subcarrier extraction in the frequency domain, is improved. Third, the radar systematic error and the Doppler shift, which introduce bandwidth leakage and degrade the waveform orthogonality significantly, are compensated. Finally, taking all these challenges into consideration, a novel signal processing algorithm along with a MIMO SAR system model is proposed. Theoretical analysis is validated by simulations and systematic calibration measurements based on a C-band system.

Multiple scattering may render synthetic aperture radar (SAR) image interpretation difficult, particularly when it comes to imaging of man-made structures, which can be modeled as composite scatterers. To isolate different scattering mechanism, we designed an airborne SAR experiment based on the high resolution, sub-metric to decimetric range, capabilities full-polarimetric SAR system, CARSS (Chinese Airborne Remote Sensing System), developing by IECAS. The imaging results are quite accord with the theoretical analysis. With polarimetric target decomposition, we can simply distinguish the different scattering mechanism. The idea to interpret the man-made targets as the combination of simple scattering mechanisms is supported by the experiment results.

The geometry distortion phenomenon of SAR imaging arises in the mountainous scenarios due to the presence of strong terrain slopes. Because of phase discontinuities or the absence of valid phase, for single pass interferometric SAR (InSAR), it is difficult to recover accurate digital elevation model (DEM) in such areas. Fusion of two or more different aspects of InSAR data is practicable to deal with this problem. In this paper, the processing procedures of airborne InSAR data are presented. In order to decrease the processing error of every single aspect data, an iterative motion compensation (MOCO) method is used. Besides, the interferometric phase of shadow area is linearly complemented before phase unwrapping to avoid error spreading. Experimental results using two anti-parallel aspects of airborne InSAR data validate the feasibility of fusion.

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.

In the area of airborne synthetic aperture radar (SAR), motion compensation (MOCO) is a crucial technique employed to correct the SAR data affected by nonlinear platform trajectory during data acquisition. Due to range-azimuth coupling and computational burden consideration, MOCO is usually implemented only with respect to range dependent motion errors, which is adequate for SAR systems of moderate aperture length. However, a significantly more accurate SAR processing approach is desired for processing low frequency SAR systems with large aperture lengths, such as P-band. In this paper, we propose a new airborne SAR processing algorithm that considers both the high efficiency of the ω-κ algorithm and the high precision of the back projection algorithm. Experimental results on simulated and actual P-band SAR data are presented to verify the effectiveness of the proposed approach.

In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function (CAF) and the method based on matched filtering are the most common approaches. The method based on matched filtering is an approximation to CAF and the procedure is: (1) divide the signal into snapshots; (2) perform matched filtering on each snapshot; (3) perform fast Fourier transform (FFT) across the snapshots. The matched filtering method is computationally affordable and can offer savings of an order of 1000 times in execution speed over that of CAF. However, matched filtering suffers from severe energy loss for high speed targets. In this paper we concentrate mainly on the matched filtering method and we use keystone transform to rectify range migration. Several factors affecting the performance of coherent integration are discussed based on the matched filtering method and keystone transform. Modified methods are introduced to improve the performance by analyzing the impacts of mismatching, precision of the keystone transform, and discretization. The modified discrete chirp Fourier transform (MDCFT) is adopted to rectify the Doppler expansion in a multi-target scenario. A novel velocity estimation method is proposed, and an extended processing scheme presented. Simulations show that the proposed algorithms improve the performance of matched filtering for high speed targets.

A novel method of interferometric synthetic aperture radar phase filtering that combines empirical mode decomposition (EMD) with Hölder exponent adjustment is presented in this letter. First, intrinsic mode functions (IMFs) of different levels are obtained by decomposing the real and imaginary parts of the noisy interferometric phase in complex formulation respectively employing EMD, which is a totally data-driven method without parameters to be selected. Then, we increase the Hölder exponents of every IMF to appropriate extent according to the features of the signal and noise contained in them to realize different filtering effects. Thus, noise can be efficiently filtered without the loss of detailed information of the interferogram. Finally, the filtered IMFs are reconstructed to form the denoised interferogram. The experiments of simulated data with various correlation coefficients and real data verify the effectiveness and adaptability of the method.

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.

In synthetic aperture radar (SAR) images, the edge of the shadow is blurred because the radar is moving while the data are collected. In this letter, this problem is expanded on by using the imaging formation perspective. First, an approximate method to represent the imaging quality of the boundary of the shadow region based on the quadratic phase errors (QPEs) is provided for the first time, which built up the relationship between the parameters of the shadow caster and the behavior of the shadow in the SAR image. We notice that the QPE is approximately a linear function of the height of the caster. Second, we deduced the height-dependent phases due to the synthetic aperture process to the raw data, and a novel algorithm called height-variant phase compensation (HVPC) on the complex SAR image data is proposed by compensating the unexpected phases in the azimuth to sharpen the shadow. Compared with the traditional approach called fixed-focus shadow enhancement (FFSE), HVPC removes twice as much of the QPE as FFSE approximately. Experiments on simulation and real data demonstrate the precision and the better effect on shadow enhancement of our work. It is expected that the work in this letter could be some help for the SAR image understanding and application.

Due to the significant azimuth variance property in medium-Earth-orbit (MEO) synthetic aperture radar (SAR) echo, it is difficult for the conventional SAR algorithms to achieve a good compromise between accuracy and efficiency. A novel algorithm based on Keystone transform (KT) and azimuth perturbation (AP) is introduced in this paper to handle this problem. The function of KT is to correct the range walk and thus to mitigate the azimuth variance effect on range processing. The function of AP is to equalize the Doppler histories in each range gate and thus to mitigate the azimuth variance effect on azimuth compressing. Simulation results of an L-band MEO SAR with 5 m resolution at 10,000 km altitude demonstrate the capability of our algorithm.

The layover phenomenon of SAR imaging arises when different height contributions collapse in the same range-azimuth resolution cell, due to the presence of strong terrain slopes or discontinuities in the scenarios. Because of the phase discontinuities, for single baseline interferometric SAR, it is difficult to recover the accurate unwrapped phase in layover areas by traditional phase unwrapping methods. In this paper, according to the phase characteristic of layover areas, we propose a new method to retrieve unwrapped phase based on local frequency estimate. It can avoid the unwrapping error resulted from the phase jump at the edge of layover areas. As a result, relative accurate DEM of layover areas can be reconstructed, which is beneficial to afterward geocoding in InSAR topographic mapping.

In this letter, a stationary-wavelet-based despeckling algorithm based on the two-sided generalized gamma distribution (GΓD) model is proposed. We first introduce the two-sided GΓD as a flexible and efficient model for the wavelet coefficients of logarithmically transformed synthetic aperture radar intensity or amplitude. The strength of the model is highlighted in terms of its fit to the data, its low computational cost, and the ease of parameter estimation. By empirical results, we then motivate the GΓD as model for the wavelet coefficients of the noise-free signal. The GΓD model parameters are estimated with moment methods, using both absolute central moments for the wavelet coefficients of the noisy signal and the noise. Finally, we exploit the prior information contained in the model by designing a Bayesian maximum a posteriori estimator for estimating the noise-free wavelet coefficients. Experimental results demonstrate the superiority of our method in terms of simultaneously reducing speckle and preserving structural details.

In this letter, a novel synthetic aperture radar (SAR) imaging simulator is proposed based on analytical electromagnetic and geometric models for urban structures. The backscatter contributions are evaluated by an analytical electromagnetic model based on the Kirchhoff approach (KA) in either physics or geometrical optics approximations rather than specular and Lambertian models. In addition, the position vectors of object facets in the SAR imaging plane are evaluated by a closed-form analytical geometrical model based on the ray-tracing model. These models are expressed in terms of few and basic parameters. Compared with other numerical methods, they are helpful for improving the efficiency of the simulation, but more importantly, they are significant for direct understanding of and further interpreting the SAR image features. Some experiments validate the geometric and electromagnetic models and demonstrate the efficiency of the simulator.

A novel parameter estimation algorithm of fast moving targets using single-antenna airborne synthetic aperture radar (SAR) databased on desampling and Radon transform (RT) is introduced in this letter. First, the dual-channel data are constructed by desampling the single-antenna airborne SAR data in the azimuth direction. Then, the clutter and the spectrum aliasing of the moving target can be cancelled by coherent subtracting. As a result, the moving target trajectory exhibits a single curve in both the range-compressed and the range-Doppler domains. Second, range cell migration correction is adopted to eliminate the range curve and parts of the range walk. Owing to the Doppler ambiguity, the moving target trajectory becomes a straight line. Third, the desampled Doppler ambiguity number and the Doppler rate of the moving target can be calculated by the slope of the line, which is measured by RT. Finally, along- and across-track velocities of the moving target are further obtained. The effectiveness of the proposed scheme is validated by the simulated and real data.

The signal processing of the medium-Earth-orbit synthetic aperture radar (SAR) is more challenging than that of the current low-Earth-orbit SAR because the imaging geometry is more complicated, and the range and azimuth variances are more severe. This paper deals with these imaging problems in three aspects. First, an advanced hyperbolic range equation (AHRE) is proposed for the first time, which is more precise for a spaceborne SAR than the conventional hyperbolic range equation (CHRE). Second, the point target spectrum based on the AHRE is analytically derived, which is useful for developing efficient SAR processing algorithms. Third, the well-known nonlinear chirp scaling (NLCS) algorithm is modified according to this new spectrum, and the so-called AHRE-based advanced NLCS (A-NLCS) algorithm is established. The simulation results validate the correctness of our method for L-band SAR systems at altitudes from 1000 to 10 000 km with an azimuth resolution around 3 m. It is also shown that the A-NLCS algorithm has better performance than the CHRE-based algorithms in longer integration time cases. Therefore, we recommend the A-NLCS algorithm for a spaceborne SAR with a lower frequency, finer resolution, and higher satellite altitude.

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.

Processing bistatic SAR image all through numerical calculation is the concept of NuSAR. In this paper, the block scheme of NuSAR is modified to handle the translational variant case. Then a new calculation method of NuSAR is provided, which saves half of the memory compared with the existing calculation method. The provided new NuSAR is practical and can handle the spaceborne-airborne configuration.

In this paper, the effects of multi-path scattering mechanisms on SAR image is deduced through range Doppler algorithms (RDA). The conclusion that the cloud phenomenon appeared due to the multi-path scattering mechanisms is detained. Through the analysis, the cloud caused by the multi-path in the down range is corresponding to focus mechanisms and the cloud appeared azimuth is non-focus. At last, the shooting and bouncing ray (SBR) technique is employed to calculate the scattering of the cylinder cavity and by combining with range Doppler algorithms (RDA), the SAR image of a cylinder cavity with underside closed is precisely given, considering the effects of the multi-path scattering mechanisms in different azimuth.

As the Doppler history depends both on the transmit range and the receive range in bistatic SAR, those targets, who have the same bistatic range but different locations, will have different doppler history. So the unknown terrain undulation will cause defocusing in bistatic SAR imaging. This paper firstly analyzes which parameters in bistatic configuration affect the defocusing severely, and then shows the simulation results to testify the analysis. Besides, it points out the 3D imaging ability (though very weak) of bistatic SAR based on the defocusing phenomena. And Finally the 3D imaging results are also exhibited.

Forward-looking imaging has many potential applications, but it is impossible with the usual monostatic synthetic aperture radar (SAR) principle. Through the bistatic SAR configuration, forward-looking imaging can be realized for one of the bistatic platforms. This letter designs a bistatic configuration with a stationary transmitter and a forward-looking airborne receiver. It then analyzes the 2-D resolution and finds out which geometric parameter affects the imaging ability mostly. Besides, it gives out the signal formulation in the frequency domain and shows its imaging characteristics. Then, an imaging method is chosen for this special configuration, and the simulation results are exhibited, which validate the correctness of the analysis and prove the 2-D imaging ability of forward-looking bistatic SAR.

This paper first shows the 3D property of bistatic synthetic aperture radar (BiSAR) geometry, which clarifies that the algorithms for bistatic SAR should be deduced in 3D space. It then models the bistatic echo according to the 3D geometry and obtains the signal spectrum in the wavenumber domain. Based on the spectrum, the formula for the wavenumber-domain interpolation of the omega-K algorithm is deduced, and the residual phase is obtained. Then, the impacts of the residual phase, including position displacement, range, and azimuth defocusing, and a constant phase for each pixel, are explicated. Finally, the simulating results exhibited at the end of this paper validate the correctness of the analysis and the feasibility of the algorithm.

This paper deals with the imaging problem of one-stationary bistatic SAR (BiSAR) with large bistatic angle. An improved nonlinear chirp scaling (NLCS) algorithm is proposed for this BiSAR. The main work here includes three aspects. First, a range chirp scaling function for correcting the differential range cell migration correction is derived. Then, the azimuth perturbation is generated by local fit method, which makes the NLCS algorithm suitable for the large bistatic angle case. Furthermore, the negative effects introduced by the perturbation (including phase error and locality error) are discussed, and some compensation methods are proposed to enhance the capability of the algorithm. The simulating results exhibited at the end of this paper validate the correctness of the analysis and the feasibility of the algorithm.

Because of active and side-look imaging and speckles in the SAR image, the road edge is blur and it is difficult to determinate the road edge. Therefore, extraction of the road from high-resolution SAR image can't use the methods that were used to the optical image. In this paper, we research how to extract the information of zonal road and how to get the vector road from the high-resolution SAR image. Usually, urban road is regular. In remote sensing image, the road has many characters, such as functional character, spectral character, geometric character and texture character so on. These characters consist of the knowledge of road extraction. After accurately expressing the character knowledge with mathematical equations, we can extract the road information more accurately. Therefore, we must construct road model after analyzing the road characters in the high-resolution SAR image. Based on the above analysis, we may accurately extract road from SAR image. By our experiment, the result proves that our method is a good idea.