Showing papers by "Mengdao Xing published in 2020"

InSAR Phase Denoising: A Review of Current Technologies and Future Directions.

Abstract: Nowadays, interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition. During the InSAR processing, phase denoising of interferogram is a mandatory step for topography mapping and deformation monitoring. Over the last three decades, a large number of effective algorithms have been developed to do efforts on this topic. In this paper, we give a comprehensive overview of InSAR phase denoising methods, classifying the established and emerging algorithms into four main categories. The first two parts refer to the categories of traditional local filters and transformed-domain filters, respectively. The third part focuses on the category of nonlocal (NL) filters, considering their outstanding performances. Latter, some advanced methods based on new concept of signal processing are also introduced to show their potentials in this field. Moreover, several popular phase denoising methods are illustrated and compared by performing the numerical experiments using both simulated and measured data. The purpose of this paper is intended to provide necessary guideline and inspiration to related researchers by promoting the architecture development of InSAR signal processing.

A Novel Image Fusion Method of Multi-Spectral and SAR Images for Land Cover Classification

Abstract: The fusion of multi-spectral and synthetic aperture radar (SAR) images could retain the advantages of each data, hence benefiting accurate land cover classification. However, some current image fusion methods face the challenge of producing unexpected noise. To overcome the aforementioned problem, this paper proposes a novel fusion method based on weighted median filter and Gram–Schmidt transform. In the proposed method, Sentinel-2A images and GF-3 images are respectively subjected to different preprocessing processes. Since weighted median filter does not strongly blur edges while filtering, it is applied to Sentinel-2A images for reducing noise. The processed Sentinel images are then transformed by Gram–Schmidt with GF-3 images. Two popular methods, principal component analysis method and traditional Gram–Schmidt transform, are used as the comparison methods in the experiment. In addition, random forest, a powerful ensemble model, is adopted as the land cover classifier due to its fast training speed and excellent classification performance. The overall accuracy, Kappa coefficient and classification map of the random forest are used as the evaluation criteria of the fusion method. Experiments conducted on five datasets demonstrate the superiority of the proposed method in both objective metrics and visual impressions. The experimental results indicate that the proposed method can improve the overall accuracy by up to 5% compared to using the original Sentinel-2A and has the potential to improve the satellite-based land cover classification accuracy.

A Frequency-Domain Imaging Algorithm for Translational Variant Bistatic Forward-Looking SAR

Abstract: Bistatic forward-looking synthetic aperture radar (BFSAR) breaks through the limitations of the conventional monostatic SAR on the forward-looking imaging. However, the problems of range cell migration (RCM) caused by the linear range walk and 2-D spatial variability of Doppler parameters become more serious and complicated in translational variant BFSAR. In this article, a keystone transform is introduced to correct the linear RCM. Based on the characteristics of a small aperture, the nonlinear chirp scaling (NCS) is discussed in the frequency domain to equalize the azimuth-range-dependent Doppler parameters. The improved NCS in our newly proposed BFSAR imaging algorithm, especially the re-definition of range direction and the model of spatial variant phase, differentiates this article from all the existing studies in the literature on BFSAR signal processing. Simulation results and real data processing further validate the effectiveness of the proposed algorithm.

Cooperative Multitask Learning for Sparsity-Driven SAR Imagery and Nonsystematic Error Autocalibration

Abstract: Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the sensitivity of nonsystematic errors and highly computational load. In this article, a cooperative multitask learning algorithm is proposed based on an autocalibrated alternating direction method of multipliers (AutoCal-ADMM) framework, by which the sparse feature of the scenes/targets-of-interests can be enhanced, and simultaneously the nonmodeled motion errors of either airborne platform or moving target can be autocalibrated in a synergistic manner. By leveraging the entropy and sparsity regularizers in the AutoCal-ADMM framework, the proposed algorithm is particularly tailored to obtain focused SAR images with enhanced sparsity. A reasonable surrogate function is designed for a convex objective function, so that an analytical proximal mapping of the entropy regularizer can be derived. Both nonsystematic range cell migration (NsRCM) and azimuthal phase errors (APEs) are concerned and coherently compensated. A linear and complex soft-thresholding operator is introduced for the sparse solution. The proposed algorithm is capable of greatly alleviating “error propagation” between multiple tasks, where an optima balance between the sparse and focusing features can be achieved. Superior performance in terms of convergence and efficiency can be guaranteed. Both raw SAR and canonical ground moving target imaging (GMTIm) data sets are processed and comparisons with conventions are performed, where the effectiveness and superiority of the proposed AutoCal-ADMM algorithm are validated.

An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images

Abstract: With the improvement of image resolution in synthetic aperture radars (SARs), sea clutter characteristics become more complex, which poses new challenges to traditional ship target detection missions. In this paper, to detect ship targets quickly and efficiently in a complex background, we propose an adaptive hierarchical detection method based on a coarse-to-fine mechanism. This method constructs a new visual attention mechanism to strengthen ship targets and obtain the candidate targets adaptively by the means dichotomy method. On this basis, the precise detection results of the targets are obtained using the speed block kernel density estimation method, which maintains constant false alarm characteristics. Compared with existing methods, the adaptive hierarchical detection method has simple, fast, and accurate characteristics. Experiments based on GF-III satellite and airborne SAR datasets are presented to demonstrate the effectiveness of the proposed method.

InSAR Phase Denoising: A Review of Current Technologies and Future Directions

Abstract: Interferometric synthetic aperture radar (InSAR) is a powerful remote sensing tool that enhances information acquisition. During InSAR processing, the phase denoising of the interferogram is a mandatory step for topography mapping and deformation monitoring. During the past three decades, a large number of effective algorithms have been developed for efforts related to this topic. In this article, we give a comprehensive overview of InSAR phase-denoising methods, classifying the established and emerging algorithms into four main categories. The purpose is to provide the necessary guidelines and inspiration to related researchers by promoting the architecture development of InSAR signal processing.

Antenna Beampattern With Range Null Control Using Weighted Frequency Diverse Array

Abstract: The range-angle-dependent beampattern of frequency diverse array (FDA) makes it capable of dealing with range-dependent interference, which cannot be handled by conventional phased array. However, current publications pay more attention to the dot-shaped beam forming, which is not suitable for the range-dependent jamming signal suppression. To address this problem, this paper proposes a weight designing method to control the nulls distribution among range bins in the mainbeam. With the additional degrees of freedom provided by FDA, it is possible to flexibly adjust the positions and notch widths of the range nulls. The performance and effectiveness of the proposed weighted FDA are validated with numerical simulations.

A Special Issue on Synthetic Aperture Radar Interferometry [From the Guest Editors]

Abstract: The articles in this special section focus on inSAR (synthetic aperture radar interferometry) technology. This technology provides the unique ability to quantitatively map Earth’s elevation and surface deformation with high spatial resolution and precision. For this reason, it is used in many remote sensing applications (e.g., landslides, wetland water-level observation, and mining subsidence). Since Seasat-A, the very first SAR satellite, was launched by NASA in 1978, many InSAR missions have been completed, are in progress, or will be launched soon. These include the current GF-3 and Advanced Land Observing Satellite 2 (ALOS-2) SAR satellites and the nearfuture COSMO/Skymed 2nd Generation and Surface Water and Ocean Topography (SWOT) mission. This has created a new class of radar data that has evolved significantly in recent decades. In the meantime, hundreds of research articles have been published exploring algorithms for InSAR signal processing and demonstrating related applications across many Earth observation fields of interest. It is fair to say that InSAR has evolved from its initial development as a new and pioneering radar remote sensing technique into a mature technology that can provide crucial constraints for a broad and diverse range of Earth-science processes.

A Novel Feature Extension Method for the Forest Disaster Monitoring Using Multispectral Data

Abstract: Remote sensing images classification is the key technology for monitoring forest changes. Texture features have been demonstrated to have better effectiveness than spectral features in the improvement of the classification accuracy. The accuracy of extracting texture information by window-based method depends on the choice of the window size. Moreover, the size should ideally match the spatial scale of the object or class under consideration. However, most of the existing texture feature extraction methods are all based on a single window and do not adequately consider the scale of different objects. Our first proposition is to use a composite window for extracting texture features, which is a small window surrounded by a larger window. Our second proposition is to reinforce the performance of the trained ensemble classifier by training it using only the most important features. Considering the advantages of random forest classifier, such as fast training speed and few parameters, these features feed this classifier. Measures of feature importance are estimated along with the growth of the base classifiers, here decision trees. We aim to classify each pixel of the forest images disturbed by hurricanes and fires in three classes, damaged, not damaged, or unknown, as this could be used to compute time-dependent aggregates. In this study, two research areas—Nezer Forest in France and Blue Mountain Forest in Australia—are utilized to validating the effectiveness of the proposed method. Numerical simulations show increased performance and improved monitoring ability of forest disturbance when using these two propositions. When compared with the reference methods, the best increase of the overall accuracy obtained by the proposed algorithm is 4.77% and 2.96% on the Nezer forest data and Blue Mountain forest data, respectively.

A High-Squint TOPS SAR Imaging Algorithm for Maneuvering Platforms Based on Joint Time-Doppler Deramp Without Subaperture

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

Clutter Suppression via Subspace Projection for Spaceborne HRWS Multichannel SAR System

Abstract: Traditional clutter suppression methods are mainly studied under the condition that the pulse repetition frequency (PRF) of the system is not less than the Nyquist frequency. Whereas in the high-resolution and wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system, a low PRF is used to break through the minimum antenna area constraint. The low PRF case brings new challenges to the traditional clutter suppression methods. In this letter, a subspace projection clutter suppression method is proposed based on the fact that moving targets and the clutter consist in different signal subspaces. This method can be directly applied to the HRWS multichannel SAR system, and it shows better performance compared to the space-time adaptive processing (STAP) when the moving target components cannot be ignored in the clutter covariance matrix calculation. Simulated data and airborne measured data are processed to verify its effectiveness.

Time-Varying Baseline Error Estimation and Compensation in UAV SAR Interferometry Based on Time-Domain Subaperture of Raw Radar Data

Abstract: The rigid oscillation and flexible deformation baseline errors occur in dual-antenna unmanned aerial vehicle (UAV) SAR interferometry (InSAR). The errors caused by airflow disturbances and UAV platform mechanical oscillation will lead to interferometric phase undulation. Measuring the errors has high requirements for length and time accuracy for the equipment. In this paper, a time-varying baseline error (TBE) estimation and compensation method based on continuous time-domain subaperture data is proposed. Firstly, we model the TBE and derive its expression in each subaperture image focused by the chirp scaling dechirp (CS-dechirp) algorithm. Then it is possible to extract the estimated differential TBE (D-TBE) from the differential interferogram of overlapping scenes in subaperture images. Further, the full-aperture TBE can be obtained through an integration of the estimated D-TBE. Finally, the full-aperture compensation can be accomplished by a phase correction after the range variation estimation. Taking advantage of the time-domain subaperture, the D-TBE phases are sampled at each subaperture center time, and the proposed method can be well combined with a motion compensation algorithm in the processing flow for UAV InSAR. Furthermore, the case of low coherence is overcome. The results of simulation and real measured airborne single-pass dual-antenna data validate the proposed approach.

Focusing of MEO SAR Data Based on Principle of Optimal Imaging Coordinate System

Abstract: The curved trajectory and long synthetic aperture time of medium-Earth-orbit (MEO) synthetic aperture radar (SAR) lead to a 2-D spatial variation in the signals. Traditional methods treat the range and azimuth variations separately and usually suffer from high computational complexities. In this article, we investigate the Doppler rate distribution across a large scene and exploit an optimal imaging coordinate system, in which the MEO SAR signals satisfy the azimuth-shift-invariant property. Thus, the additional processing of the azimuth spatial variation in MEO SAR imaging algorithms can be avoided, and the efficiency of the image formation processor can be obviously improved. The Doppler linearization is used to address the higher-order Doppler parameters to achieve more precise focusing, and at the same time, addresses the azimuth time shift caused by the changes of signal distribution. Finally, processing results of simulated stripmap-mode data with the 2-m resolution are presented to validate the proposed algorithm.

A Two-Step Processing Method for Diving-Mode Squint SAR Imaging With Subaperture Data

Abstract: Due to the existence of vertical velocity in diving-squint synthetic aperture radar (SAR) imaging, the azimuth-shift invariance along the horizontal direction is not satisfied. This will lead to a big approximation error and influence the imaging results when the existing imaging processing methods are directly applied. In order to solve these problems, an equivalent model is first introduced to describe the motion characteristic in the diving-squint mode. By adopting this approach, the diving-squint SAR imaging can be treated as the conventional one, i.e., the height remains unchanged, with the azimuth-shift invariance satisfied along the flight direction. Based on the equivalent model, a two-step processing method for the diving-squint SAR imaging with subaperture data is proposed in this article. First, a time-scaling approach is adopted to obtain the well-focused 2-D image in the slant plane. Since the equivalent model will cause the rotation of the imaging projection plane and introduce the severe distortion in the ground imagery, a rapid geometric correction method based on inverse projection is further performed to get the ground imagery with little distortion through 2-D sinc interpolation. Simulation and real-data results validate the effectiveness of the proposed approach.

Focusing of Maneuvering High-Squint-Mode SAR Data Based on Equivalent Range Model and Wavenumber-Domain Imaging Algorithm

Abstract: Due to the complexity of high-squint synthetic aperture radar (SAR) mounted on maneuvering platforms, the traditional geometric model and imaging algorithms cannot be directly applied in the diving or climbing stage for the existence of vertical velocity. Aiming at this issue, an equivalent geometric model of maneuvering high-squint-mode SAR is constructed, and a modified wavenumber-domain imaging algorithm combined with the proposed equivalent range model is proposed in this article. First, the disadvantages of the conventional range model are analyzed in detail and an equivalent range model is proposed to describe the motion characteristic of squint SAR in maneuvering mode, which maintains the azimuth-shift invariance along the flight direction in the new slant range plane. Then, to achieve the requirement of maneuvering SAR real-time processing, a modified wavenumber-domain imaging algorithm with a high usage of the spectrum by axis rotation for high-squint SAR data is proposed. Further, since the equivalent model may introduce the severe distortion in the imaging plane, a novel geometric correction method based on inverse projection is performed to obtain the ground imagery with a little distortion. Finally, simulation and real-data processing results validate the superiority of the proposed algorithm.

Validating GEV Model for Reflection Symmetry-Based Ocean Ship Detection with Gaofen-3 Dual-Polarimetric Data

Abstract: The spaceborne synthetic aperture radar (SAR) is quite powerful in worldwide ocean observation, especially for ship monitoring, as a hot topic in ocean surveillance. The launched Gaofen-3 (GF3) satellite of China can provide C-band and multi-polarization SAR data, and one of its scientific applications is ocean ship detection. Compared with the single polarization system, polarimetric systems can be used for more effective ship detection. In this paper, a generalized extreme value (GEV)-based constant false alarm rate (CFAR) detector is proposed for ship detection in the ocean by using the reflection symmetry metric of dual-polarization. The reflection symmetry property shows big differences between the metallic targets at sea and the sea surface. In addition, the GEV statistical model is employed for reflection symmetry statistical distribution, which fits the reflection symmetry probability density function (pdf) well. Five dual-polarimetric GF3 stripmap ocean data sets are introduced in the paper, to show the contrast in enhancement by using reflection symmetry and to investigate the GEV model fit to the reflection symmetry metric. Additionally, with the detection experiments on the real GF3 datasets, the effectiveness and efficiency of the GEV model for reflection symmetry and the model-based ocean ship detector are verified.

Spectral-Spatial Feature Extraction based CNN for Hyperspectral Image Classification

Abstract: Convolutional neural networks (CNN) can automatically learn features from the hyperspectral image data, which could avoid the difficulty of manually extracting features. However, the number of training set for the classification of hyperspectral images is always limited, making it difficult for CNN to obtain effective features and resulting in low classification accuracy. In this paper, a spectral-spatial feature (SSF) extraction based CNN method is proposed for an accurate classification with a small training set. Experimental results based on two standard hyperspectral images demonstrate the effectiveness of the proposed method.

Unambiguous Signal Reconstruction Algorithm for High Squint Multichannel SAR Mounted on High Speed Maneuvering Platforms

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

GF-3 data real-time processing method based on multi-satellite distributed data processing system

Abstract: Due to the limited scenes that synthetic aperture radar (SAR) satellites can detect, the full-track utilization rate is not high. Because of the computing and storage limitation of one satellite, it is difficult to process large amounts of data of spacebome synthetic aperture radars. It is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing. A multi-satellite distributed SAR real-time processing method based on Chirp Scaling (CS) imaging algorithm is studied in this paper, and a distributed data processing system is built with field programmable gate array (FPGA) chips as the kernel. Different from the traditional CS algorithm processing, the system divides data processing into three stages. The computing tasks are reasonably allocated to different data processing units (i.e., satellites) in each stage. The method effectively saves computing and storage resources of satellites, improves the utilization rate of a single satellite, and shortens the data processing time. Gaofen-3 (GF-3) satellite SAR raw data is processed by the system, with the performance of the method verified.

A Sidelobe Reduction Algorithm for SAR Imagery Formed by Fast Back Projection Algorithm Based on Spectrum Compression

Abstract: Fast back projection algorithm (FBPA) is commonly used for image formation of complex SAR mode. However, traditional sidelobe reduction algorithm is not applicable to remove the image sidelobes because the spectrum of the image formed by FBPA is aliased. In this paper, a novel sidelobe reduction algorithm is proposed based on spectrum compression (SC). The spectrum aliasing is eliminated by SC first. Then a modified spatial variant apodization (SVA) is used for sidelobe suppression. The mainlobe preserves without widening and the sidelobe is suppressed. Simulation and measured data processing verify the effectiveness of the proposed method.

A Modified Range Model and Doppler Resampling Based Imaging Algorithm for High Squint SAR on Maneuvering Platforms

Abstract: There are two technical difficulties to overcome before obtaining a well-focused image from high squint (HS) synthetic aperture radar (SAR) with constant acceleration. One is effective range modeling and the other is the correction of space-variant (SV) Doppler parameters. Based on the imaging characteristics analysis, an orthogonal expansion range model (OERM) is proposed which can handle the coordinate rotation caused by range walk correction (RWC). Then a modified spectral analysis (SPECAN) with the Doppler resampling method is designed to correct the SV Doppler parameters. Finally, the proposed algorithm is verified by both simulated and real SAR data. Meanwhile, it shows an improvement in azimuth focusing quality over the reference one.

Long Synthetic Aperture Passive Localization Using Azimuth Chirp-Rate Contour Map

Abstract: A long synthetic aperture passive localization method for two Frequency shift keying (2FSK) signal via azimuth chirp-rate contour is proposed in this paper. By introducing synthetic aperture radar (SAR) imaging technology into passive localization, Doppler frequency change rate of received signal, which is called as azimuth chirp-rate in this paper, is estimated by azimuth focusing. Then, a grid map is formed on the ground and azimuth chirp-rate of each point is calculated to get an azimuth chirp-rate contour map. In the contour map, signal emitter is located in an azimuth chirp-rate curve in which the azimuth chirp-rate value is equal to its estimate. The azimuth chirp-rate contour map of a ground area varies with position of sensor. Therefore, two different azimuth chirp-rate curves can be obtained through different periods of a trajectory and the intersection of the two curves gives estimate of the emitter location.

Ship Positioning and Radial Velocity Estimation for Spaceborne SAR Based on Energy Center Extraction

Abstract: Spaceborne synthetic aperture radar (SAR) has a high application value in the observation of ship targets. After the ship is detected, the actual observation position of the moving ship and its motion parameters are worthy of concern, especially for some medium and large size valuable ships. In this paper, we propose a method of extracting the energy center of the ship signal trajectory to locate the ship first. Then according to the difference between the imaging position and the positioning position of the ship, the radial velocity estimation can be calculated. The proposed method does not need to construct a reference data box, and can directly locate the moving ship. The processing of the Gaofen-3 (GF-3) complex data verifies the effectiveness of the proposed method.

Correction of “A Frequency-Domain Imaging Algorithm for Translational Variant Bistatic Forward-Looking SAR”

Abstract: In [1] , the result of Fig. 13(b) was incorrectly provided. Now, we provide the corrected result, as shown in Fig. 1 .

Interferometric SAR Phase Filtering With SURE-Based Non-Local Method

Abstract: As the phase of the interferometric synthetic aperture radar (InSAR) contains abundant information for many earth observation activities, the interferometric phase denoising is an important step before InSAR processing and application because of its significant influence on the following steps. And this paper focused on the interferometic phase filtering and proposed a new filter based on the Stein’s unbiased risk estimate (SURE)-based nonlocal means method. The SURE formula is more convenient for estimating the mean square error (MSE) from the noisy image only. Due to advantages of both the nonlocal means and the Stein’s unbiased risk estimate, the proposed filter is exploited to the interferometric phase in the complex domain in the paper. Except for the denoising performance, the proposed method is more focused on preserving more useful information in the InSAR phase fringe patterns, such as the phase jumps utilized for urban information retrieval. The experiments on both the simulated data sets and the real data sets were implemented and analyzed to demonstrate the effectiveness of the proposed filtering method.

A Two-Step Ship Target Detection Method in High-Resolution Sar Image Based on Coarse-to-Fine Mechanism

Abstract: With the development of synthetic aperture radar technology, the resolution of SAR images becomes higher accompanied by more complex clutter characteristics, which poses challenges to the detection of ship targets. To achieve efficient ship detection in high-resolution synthetic aperture radar images, a two-step ship target detection method based on a coarse-to-fine mechanism is proposed in this paper. First, the SAR image is filtered through the gravitational field method to enhance ship targets. Then the candidate targets are obtained by the improved mean dichotomy method. Finally, the kernel density estimation method is utilized to complete precise detection results. Compared with the conventional ship target detection method, the proposed method is fast and accurate. Experimental results demonstrate the effectiveness of the proposed method.

ISAR Image Matching and Three-Dimensional Scattering Imaging Based on Extracted Dominant Scatterers

Abstract: This paper studies inverse synthetic aperture radar (ISAR) image matching and three-dimensional (3D) scattering imaging based on extracted dominant scatterers. In the condition of a long baseline between two radars, it is easy for obvious rotation, scale, distortion, and shift to occur between two-dimensional (2D) radar images. These problems lead to the difficulty of radar-image matching, which cannot be resolved by motion compensation and cross-correlation. What is more, due to the anisotropy, existing image-matching algorithms, such as scale invariant feature transform (SIFT), do not adapt to ISAR images very well. In addition, the angle between the target rotation axis and the radar line of sight (LOS) cannot be neglected. If so, the calibration result will be smaller than the real projection size. Furthermore, this angle cannot be estimated by monostatic radar. Therefore, instead of matching image by image, this paper proposes a novel ISAR imaging matching and 3D imaging based on extracted scatterers to deal with these issues. First, taking advantage of ISAR image sparsity, radar images are converted into scattering point sets. Then, a coarse scatterer matching based on the random sampling consistency algorithm (RANSAC) is performed. The scatterer height and accurate affine transformation parameters are estimated iteratively. Based on matched scatterers, information such as the angle and 3D image can be obtained. Finally, experiments based on the electromagnetic simulation software CADFEKO have been conducted to demonstrate the effectiveness of the proposed algorithm.

A Novel Reconstruction Method of K-Distributed Sea Clutter with Spatial-Temporal Correlation.

Abstract: The reconstruction of sea clutter plays an important role in target detection and recognition in a maritime environment. Reproducing the temporal and spatial correlations of real data simultaneously is always a problem in the reconstruction of sea clutter due to the complex coupling between them. In this paper, the spatial–temporal correlated proportional method (STCPM), based on a compound model, is proposed to reconstruct K-distributed sea clutter with correlation characteristics obtained from the real data. The texture component with spatial–temporal correlation is generated by the proportional method and the speckle component with temporal correlation is generated by matrix transformation. Compared with previous methods, the biggest innovation of the STCPM is that it can more accurately generate K-distributed sea clutter with both temporal and spatial correlations. The comparison of the reconstructed and real data demonstrates that the method can reproduce the characteristics of real sea clutter well.

InSAR Signal and Data Processing.

Abstract: We present here the recent advances in exploring new techniques related to interferometric synthetic aperture radar (InSAR) signal and data processing and applications

Achieving Millimetre Wave Seeker Performance Evaluation Based on the Real-Time Kinematic

Abstract: The millimetre wave (MMW) seeker can realize target detection under all weather conditions, the performance of which directly determines the design of the control algorithms. To guarantee the hitting accuracy and damaging effect of the expensive MMW guidance missile, assessing the performance of the seeker is indispensable before the launching of the missile. Real tactical environment of the seeker cannot be simulated comprehensively by indoor laboratories, and high-precision evaluation method outdoor is desperately needed. Focusing on the problem, a method for outdoor MMW seeker performance evaluation is proposed via the real-time kinematic (RTK) technology in this paper, which has the advantages of high-precision orientation and working ability under all climates. Firstly, the geometry of the seeker performance evaluation system is constructed, guaranteeing the effective working of the RTK. And then, the key parameters associated with the guidance control are calculated on the basis of the global position system (GPS) measurements. Finally, comparisons are made between the parameters obtained based on the RTK and the seeker outputs. Besides, for the performance assessment of the MMW seeker towards moving targets, a time synchronization method for different GPS carrier platforms is presented. The effectiveness of the proposed method is validated by the mooring test-fly experiments. Experimental results demonstrate that the performance of the MMW seeker can be evaluated effectively by using the proposed RTK-based method.