Huaping Xu

Bio: Huaping Xu is an academic researcher from Beihang University. The author has contributed to research in topics: Synthetic aperture radar & Radar imaging. The author has an hindex of 7, co-authored 39 publications receiving 165 citations.

Extended Wavenumber Domain Algorithm for Highly Squinted Sliding Spotlight SAR Data Processing

Abstract: Image formation from squinted sliding spotlight synthetic aperture radar (SAR) is limited by azimuth spectral folding and severe two dimension coupling. This paper presents an Extended Wavenumber Domain Algorithm (WDA) for highly squinted sliding spotlight SAR data processing. This algorithm adopts azimuth deramping approach to overcome the azimuth spectral folding phenomenon. The chirp rate for azimuth deramping and the principle of choosing pulse repetition frequency (PRF) is presented to accommodate the characteristic of Doppler history. Subsequently, the full focusing is implemented by WDA. Instead of the conventional Stolt mapping in WDA, a modifled Stolt mapping is introduced in order to enlarge the range extension of focused image and enable to update the Doppler parameters along range. To conflrm the correctness of the implementation of modifled Stolt mapping and the azimuth position of target in focused image, related compensation terms are developed. Point target simulation results are presented to validate the efiectiveness of extended WDA to process highly squinted sliding spotlight SAR data.

SAR ATR based on dividing CNN into CAE and SNN

Abstract: As for the problem of too long training time of convolution neural network (CNN), this paper proposes a fast training method for CNN in SAR automatic target recognition (ATR). The CNN is divided into two parts: one that contains all the convolution layers and sub-sampling layers is considered as convolutional auto-encoder (CAE) for unsupervised training to extract high-level features; the other that contains fully connected layers is regarded as shallow neural network (SNN) to work as a classifier. The experiment based on MSATR database shows that the proposed method can tremendously reduce the training time with little loss of recognition rate.

Oil tank detection in synthetic aperture radar images based on quasi-circular shadow and highlighting arcs

Abstract: Oil tanks are one of the most important targets in remote sensing. Oil tank detection using optical images has been developed in recent years, but few methods have been studied for oil tank detection in synthetic aperture radar (SAR) images. Optical methods suffer incorrect assessments or false alarms when they are applied in SAR imagery. A method that combines the quasi-circular shadows and highlighting arcs is proposed to detect oil tanks with higher precision and lower false alarm. In general, a highlighting arc caused by the double reflection exists exactly at the bottom of each cylinder tank in a SAR image, so it can be employed to detect the oil tanks. However, it is very difficult to detect those arcs directly. Additionally, each cylinder tank has a quasi-circular shadow area in SAR image, which is near the highlighting arc and is easy to be detected. Cylinder tank can be detected by taking advantages of a corresponding quasi-circular shadow area in SAR image, instead of detecting a highlighting arc directly. This research proposes to detect the quasi-circular shadow first, then find the strong scattering point around the shadow areas, and finally shift the edge of the detected circle to its corresponding strong scattering point. This leads to low false alarm oil tank detection in SAR imagery. Analysis of TerraSAR-X images allows a limited validation of the method proposed.

Deceptive jamming suppression for SAR based on time-varying initial phase

Abstract: Deceptive jamming, covering the true targets and producing false targets in the SAR image, has been widely applied in electronic warfare. To effectively suppress the deceptive jamming generated by detecting parameters of target echoes, this work proposes a novel anti-jamming approach. Firstly, the chirp signal with varying initial phase is predesigned and transmitted. Then, the random initial phases of received echoes are removed before imaging. The imaging outputs of the deceptive jamming and real signal are derived through theoretical analysis. By comparing those signal models, we obtain that the proposed method induces the defocusing of false signal while the real signals are well focused. Finally, the performance of deceptive jamming suppression method is validated by simulation.

A Novel Non-Interpolation Polar Format Algorithm Using Non-Lineal Flight Trajectories and Auto-Adaptive Prf Technique

Abstract: The classical interpolation-based Polar Format Algorithm (PFA) for spotlight synthetic aperture radar (SAR) results in numerous computation load, which, reduces processing speed and increase system complexity. To decrease computation load, this paper proposes a novel non-interpolation PFA algorithm for sensor ∞ying along non-lineal ∞ight trajectories, which are specially designed curves in conical surface. Then an innovative auto-adaptive Pulse Repetition Frequency (PRF) technique is put forward to uniformly sample signal in azimuth direction. The computation load of the new PFA is merely left to azimuth chirp z-transforms (CZTs) and range fast Fourier transforms (FFTs) after dechirp processing and residual video phase (RVP) compensation. Two ∞ight modes (ellipse trajectory mode and hyperbola trajectory mode) are analyzed. A lineal approximation method is proposed to simplify non-lineal sensor trajectory analysis. Computer simulation results for multiple point targets validate the presented approach. Comparison of computation load between this PFA and traditional PFA is represented in Appendix B.

Radar interferometry

Abstract: Since its inception 30 years ago perhaps no innovation in radar technology has made such a tremendous impact on the field as that of radar interferometry. Radar interferometry uses two or more observations separated in either time or space to measure fraction of a wavelength scale range differences between the two observations. Radar interferometry is used by scientific, commercial and government institutions for numerous applications including topographic map generation, surface deformation mapping, landslide monitoring, current velocity measurement, vegetation structure determination and change detection. Radar interferometers can be flown on either spaceborne or airborne platforms or be fixed observing systems. This course is designed to provide an overview of the basic concepts of radar interferometry and an introduction to some of its applications. The course will cover basic radar imaging principles, a geometric and imaging signal perspective of the interferometric phase, interferometric correlation, basic sensitivity equations, phase unwrapping, topographic mapping and repeat pass interferometry for deformation measurements. The principles will be illustrated with examples from both spaceborne and airborne interferometric data sets. An overview of some of the major applications of radar interferometry will be presented with an emphasis on topographic and deformation mapping. The course will also briefly touch upon permanent scatter methods and polarimetric interferometry.

A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

Abstract: In an attempt to exploit the automatic feature extraction ability of biologically-inspired deep learning models, and enhance the learning of target features, we propose a novel deep learning algorithm. This is based on a deep convolutional neural network (DCNN) trained with an improved cost function, and combined with a support vector machine (SVM). Specifically, class separation information, which explicitly facilitates intra-class compactness and inter-class separability in the process of learning features, is added to an improved cost function as a regularization term, to enhance the DCNN’s feature extraction ability. The enhanced DCNN is applied to learn the features of Synthetic Aperture Radar (SAR) images, and the SVM is utilized to map features into output labels. Simulation experiments are performed using benchmark SAR image data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Comparative results demonstrate the effectiveness of our proposed method, with an average accuracy of 99% on ten types of targets, including variants and articulated targets. We conclude that our proposed DCNN method has significant potential to be exploited for SAR image target recognition, and can serve as a new benchmark for the research community.

Linear Array SAR Imaging via Compressed Sensing

Abstract: In recent years, various attempts have been undertaken to obtain three-dimensional (3-D) re∞ectivity of observed scene from synthetic aperture radar (SAR) technique. Linear array SAR (LASAR) has been demonstrated as a promising technique to achieve 3-D imaging of earth surface. The common methods used for LASAR imaging are usually based on matched fllter (MF) which obeys the traditional Nyquist sampling theory. However, due to limitation in the length of linear array and the \Rayleigh" resolution, the standard MF- based methods sufier from low resolution and high sidelobes. Hence, high resolution imaging algorithms are desired. In LASAR images, dominating scatterers are always sparse compared with the total 3- D illuminated space cells. Combined with this prior knowledge of sparsity property, this paper presents a novel algorithm for LASAR imaging via compressed sensing (CS). The theory of CS indicates that sparse signal can be exactly reconstructed in high Signal-Noise- Ratio (SNR) level by solving a convex optimization problem with a very small number of samples. To overcome strong noise and clutter interference in LASAR raw echo, the new method flrstly achieves range focussing by a pulse compression technique, which can greatly improve SNR level of signal in both azimuth and cross- track directions. Then, the resolution enhancement images of sparse targets are reconstructed by L1 norm regularization. High resolution properties and point localization accuracies are tested and verifled by simulation and real experimental data. The results show that the CS method outperforms the conventional MF-based methods, even if very small random selected samples are used.

Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity, by Jean-Luc Starck, Fionn Murtagh, and Jalal M. Fadili

Abstract: This PDF file contains the editorial “Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity," by Jean-Luc Starck, Fionn Murtagh, and Jalal M. Fadili for JEI Vol. 19 Issue 04

Suppression Approach to Main-Beam Deceptive Jamming in FDA-MIMO Radar Using Nonhomogeneous Sample Detection

Abstract: Suppressing the main-beam deceptive jamming in traditional radar systems is challenging. Furthermore, the observations corrupted by false targets generated by smart deceptive jammers, which are not independent and identically distributed because of the pseudo-random time delay. This in turn complicates the task of jamming suppression. In this paper, a new main-beam deceptive jamming suppression approach is proposed, using nonhomogeneous sample detection in the frequency diverse array-multiple-input and multiple-output radar with non-perfectly orthogonal waveforms. First, according to the time delay or range difference, the true and false targets are discriminated in the joint transmit–receive spatial frequency domain. Subsequently, due to the range mismatch, the false targets are suppressed through a transmit–receive 2-D matched filter. In particular, in order to obtain the jamming-plus-noise covariance matrix with high accuracy, a nonhomogeneous sample detection method is developed. Simulation results are provided to demonstrate the detection performance of the proposed approach.