A Model for Generating Synthetic VHF SAR Forest Clutter Images

We propose a model for generating low-frequency synthetic aperture radar (SAR) clutter that relates model parameters to physical characteristics of the scene. The model includes both distributed scattering and large-amplitude discrete clutter responses. The model also incorporates the SAR imaging process, which introduces correlation among image pixels. The model may be used to generate synthetic clutter for a range of environmental operating conditions for use in target detection performance evaluation of the radar and automatic target detection/recognition algorithms. We derive a statistical representation of the proposed clutter model's pixel amplitudes and compare with measured data from the CARABAS-II SAR. Simulated clutter images capture the structure and amplitude responses seen in the measured data. A statistical analysis shows an order of magnitude improvement in model fit error compared with standard maximum-likelihood (ML) density fitting methods.     

Layover solution in multibaseline SAR interferometry

In this work, spectral estimation techniques are used to exploit baseline diversity of a multichannel interferometric synthetic aperture radar (SAR) system and overcome the layover problem. This problem 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 sensed scene. We propose a multilook approach to counteract the presence of multiplicative noise, which is due to the extended nature of natural targets; to this purpose we extend the RELAX algorithm to the multilook data scenario (M-RELAX). A thorough performance analysis of nonparametric (beamforming and Capon) and parametric (root MUSIC and M-RELAX) techniques is carried out based on Monte Carlo simulations and Cramer-Rao lower bounds (CRLB) calculation. The results suggest the superiority of parametric methods over nonparametric ones.     

SAR幅度图像统计模型及其参数估计

对适用于复杂场景SAR幅度图像的乘积模型统计特征进行了深入的分析,导出了其概率分布的一般形式,在单视情况下讨论了三种参数估计方法。MSTAR数据的统计分析结果表明该模型对于高分辨力的SAR图像有较好的适用性。     

一种新颖的超高分辨率SAR频率域反投影成像算法

超高分辨率条件下,机载合成孔径雷达（SAR）发射信号带宽大,合成孔径时间比较长,对成像处理算法的精度和效率要求较高。现有近似频率域处理和时间域滤波反投影（FBP）算法聚焦SAR数据时均存在诸多问题。基于微局部分析方法,提出了一种新颖的频率域滤波反投影（FD-FBP）成像处理方案。首先,利用Keystone变换简化了数据距离多普勒（RD）域徙动表达式。然后,在RD域进行反投影操作,对参考位置处反投影数据进行移位、相位补偿和FFT等操作即可以得到图像,从而在保证算法精确性的前提下有效降低了运算效率,实现了频率域方法的高效率和时间域方法的精确性特点的结合。最后,点目标仿真和实测数据处理以及与FBP等算法的对比验证了该方法的有效性。     

Performance complexity study of several approaches to automatictarget recognition from SAR images

A framework which allows for the direct comparison of alternate approaches to automatic target recognition (ATR) from synthetic aperture radar (SAR) images is described and applied to variants of several ATR algorithms. This framework allows comparisons to be made on an even footing while minimizing the impact of implementation details and accounts for variation in image sizes, in angular resolution, and in the sizes of orientation windows used for training. Alternate approaches to ATR are characterized in terms of the best achievable performance as a function of the complexity of the model parameter database. Several approaches to ATR from SAR images are described and the performance achievable by each for a range of database complexities is studied and compared. These approaches are based on a likelihood test under a conditionally Gaussian model, log-magnitude least squared error, and quarter power least squared error. All approaches are evaluated for a wide range of parameterizations and the dependence on these parameters of both the resulting performance and the resulting database complexity is explored. Databases for all of the approaches are trained using identical sets of images and their performance is assessed under identical testing scenarios in terms of probability of correct classification, confusion matrices, and orientation estimation error. The results indicate that the conditionally Gaussian approach outperforms the other two approaches on average for both target recognition and orientation estimation, that accounting for radar power fluctuation improves performance for all three methods, and that the conditionally Gaussian approach normalized for power delivers average performance that is equal or superior to all other considered approaches     

An improved imaging algorithm for spaceborne MAPs sliding spotlight SAR with high-resolution wide-swath capability

Conventional synthetic aperture radar(SAR) systems cannot achieve both highresolution and wide-swath imaging simultaneously.This problem can be mitigated by employing multiple-azimuth-phases(MAPs) technology for spaceborne sliding spotlight SAR systems.However, traditional imaging algorithms have met challenges to process the data accurately, due to range model error, MAPs data reconstruction problem, high-order cross-coupling phase error and variation of Doppler parameters along the azimuth direction.Therefore, an improved imaging algorithm is proposed for solving the above problems.Firstly, a modified hyperbolic range equation(MHRE) is proposed by introducing a cubic term into the traditional hyperbolic range equation(THRE).And two curved orbit correction methods are derived based on the proposed range model.Then, a MAPs sliding spotlight data reconstruction method is introduced, which solves the spectral aliasing problem by a de-rotation operation.Finally, high-order cross-coupling phases and variation of Doppler parameters are analyzed and the corresponding compensation methods are proposed.Simulation results for point-target scene are provided to verify the effectiveness of the proposed algorithm.     

SAR image formation via semiparametric spectral estimation

A new algorithm, referred to as the SPAR (Semiparametric) algorithm, is presented herein for target feature extraction and complex image formation via synthetic aperture radar (SAR). The algorithm is based on a flexible data model that models each target scatterer as a two-dimensional (2-D) complex sinusoid with arbitrary unknown amplitude and constant phase in cross-range and with constant amplitude and phase in range. By attempting to deal with one corner reflector, such as one dihedral or trihedral, at a time, the algorithm can be used to effectively mitigate the artifacts in the SAR images due to the flexible data model. Another advantage of SPAR is that it can be used to obtain initial conditions needed by other parametric target feature extraction methods to reduce the total amount of computations needed. Both numerical and experimental examples are provided to demonstrate the performance of the proposed algorithm     

基于先验误差模型的机载高分宽幅DBF-SAR自聚焦算法

高分宽幅（HRWS）数字波束形成（DBF）合成孔径雷达（SAR）利用多通道空间采样代替部分时域采样,可以有效缓解SAR成像时高分辨率与宽测绘带间的矛盾,具有重要的军用和民用价值。现有常规DBF-SAR成像算法都假设雷达传感器相对位置精确已知,实际应用中受传感器位置测量误差影响,由位置不精确导致的相位误差会严重影响DBF-SAR高精度成像能力。在极坐标格式算法（PFA）框架下,推导了DBF-SAR成像处理后,残留相位误差的解析模型,分析了该误差对成像质量的影响。依据推导的先验相位误差解析结构模型,提出了一种基于图像对比度最优化准则的自聚焦算法。新算法通过引入先验相位结构信息,极大降低了待估参数的空间维数,可以同时改善自聚焦算法的参数估计精度和计算效率。数据处理结果验证了理论分析的正确性和所提算法的有效性。     

基于AR模型参数双谱估计的超分辨ISAR成像

利用双谱分析方法，用非高斯AR模型对雷达目标回波信号进行参数化双谱估计，并在此基础上对数据进行外推处理，然后对数据进行ISAR成像。仿真和实测数据实验表明，本文的处理方法比传统的ISAR成像分辨力有明显的提高，且运算量不大，满足ISAR的实时或者准实时成像要求。     

基于稀疏表示的多雷达信号二维融合处理

多雷达信号融合通过对多视角和多频带雷达信号进行相干融合,可以提高图像的距离和方位向分辨率.为了克服基于谱估计的多雷达信号融合方法稳健性严重依赖于散射点个数估计精度和二维极点配对精度的问题,在深入研究逆合成孔径雷达(ISAR)信号的基础上,构造了多雷达信号二维融合的线性表示模型,将融合处理转化为一个信号表示问题;充分挖掘...     

Efficient implementation of Capon and APES for spectral estimation

Both the Capon and APES estimators can be shown to belong to the class of matched-filterbank spectral estimators and can be used to obtain complex spectral estimates that have more narrow spectral peaks and lower sidelobe levels than the fast Fourier transform (FFT) methods. It can also be shown that APES has better statistical performance than Capon. In this paper, we address the issue of how to efficiently implement Capon and APES for spectral estimation     

Resolution of signal sources via spectral moment estimation

In different practical situations it is desired to estimate the number of signal sources and their positions in space or in frequency domain. The first problem is known as the detection or the order estimation and the second one as the resolution. For the resolution problem techniques such as nonlinear least squares (NLSM), high-order Yule-Walker method (HOYW), multiple signal classification (MUSIC), Pisarenko harmonic retrieval method, min-norm method, estimation of signal parameters by rotational invariance technique (ESPRIT), were proposed (Marple, 1987 and Stoica and Moses, 1997). All these high-resolution methods are based on the analysis of the signal covariance matrix. But the covariance matrix is not the only choice to represent the signal spectrum. In different applications (weather radars, synthetic aperture radar (SAR) signal processing, ultrasound imaging in medicine, atmospheric turbulence measurements) the signal spectrum can be modeled through its algebraic moments. Recently a number of efficient nonparametric methods have been proposed to estimate the algebraic spectral moments (Monakov, 1999). The presented paper is an attempt to solve the direction of arrival (DOA) problem via estimation of the algebraic spectral moments. A method proposed in the article is comparable in its accuracy with the MUSIC method. At the same time its computational burden is much lower. The method permits to estimate the signal power of sources easily to complete the full spectral line analysis. Additionally the method shows good robustness in situations when signal sources have noticeable spatial extend     

基于2－D熵的机载SAR图像分割迭代算法研究

机载合成孔径雷达(SAR)图像可用于检测识别机场跑道.由于SAR图像包含大量噪声,算法抗噪性能对于SAR图像处理很重要.2-D阈值方法的分割效果比传统的一维方法要好,特别是对受噪声影响的图像.然而,2-D阈值方法耗时长,难以实用.因此,本文在现有的2-D熵迭代分割算法的基础上提出了一种基于二维直方图统计特点的最大熵阈值分割算法.通过大量试验证明,本文提出的方法不仅获得了理想的分割效果,而且进一步提高了二维阈值选取速度.     

Parametric Methods for Strip-Map SAR Doppler Ambiguity Resolution

Physical modeling of the Doppler centroid (DC) can be used to predict synthetic aperture radar (SAR) Doppler ambiguity when antenna attitude is controlled or measured precisely enough. It is shown that the same model proves useful even in the cases of higher attitude uncertainty, if it is combined with suitable adaptive techniques. In this paper, Doppler ambiguity resolution is formulated as a hypothesis testing problem over a domain of integer values that are directly related to the attitude uncertainty. A test statistic is derived from the entire SAR scene using data adaptive processing. A broad class of such adaptive algorithms is analyzed in a unified way, starting from the range-azimuth coupling in the frequency domain and multilook techniques. The analysis includes two well-known and two new multilook methods for Doppler ambiguity resolution. A suitable test statistic is proposed for each of these methods and its dependency on the scene spatial correlation is discussed. Experimental results confirm the robustness of the combined scheme.     

A technique for enhanced slant range resolution for SAR systems inknowledge-based environment

Synthetic Aperture Radar (SAR) is an airborne (or spaceborne) radar mapping technique for generating high resolution maps of surface target areas including terrain. High resolution is achieved by coherently combining the returns from a number of radar transmissions. The resolution of the images is determined by the parameters of the emissions, with more data giving greater resolution. A requirement of the Microwave Radar Division's SAR radar is to provide classification of targets. This paper presents a technique for enhancing slant range resolution in SAR images by dithering the carrier centre frequency of the transmitted signal. The procedure controls the radar waveforms so they will optimally perform the classification function, rather than provide an image of best quality. It is shown that a Knowledge-Based engineering approach to determining the waveform of the radar gives considerably improved performance as a classifier of targets (of large radar cross-section), even though the corresponding image is degraded     

SAR ATR performance using a conditionally Gaussian model

A family of conditionally Gaussian signal models for synthetic aperture radar (SAR) imagery is presented, extending a related class of models developed for high resolution radar range profiles. This signal model is robust with respect to the variations of the complex-valued radar signals due to the coherent combination of returns from scatterers as those scatterers move through relative distances on the order of a wavelength of the transmitted signal (target speckle). The target type and the relative orientations of the sensor, target, and ground plane parameterize the conditionally Gaussian model. Based upon this model, algorithms to jointly estimate both the target type and pose are developed. Performance results for both target pose estimation and target recognition are presented for publicly released data from the MSTAR program     

High-resolution SAR imaging with angular diversity

We propose to use the APES (amplitude and phase estimation) approach for the spectral estimation of gapped data and synthetic aperture radar (SAR) imaging with angular diversity. A relaxation-based algorithm, referred to as GAPES (Gapped-data APES), is proposed, which includes estimating the spectrum via APES and filling in the gaps via a least squares (LS) fitting. For SAR imaging with angular diversity data fusion, we perform one-dimensional (1-D) windowed fast Fourier transforms (FFTs) in range, use the GAPES algorithm to interpolate the gaps in the aperture for each range, apply 1-D inverse FFTs (IFFTs) and dewindow in range, and finally apply the two-dimensional (2-D) APES algorithm to the interpolated matrix to obtain the 2-D SAR image. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm     

Role of processing geometry in SAR raw data focusing

Synthetic aperture radar (SAR) systems require that a focusing operation be performed on the received backscattered echoes (raw data) to generate high-resolution microwave images. Either due to platform attitude instabilities, or to Earth rotation effects, the SAR raw data may be acquired in "squinted" geometries, i.e., with the radar beam directed with an offset angle (squint angle) from the broadside direction. This research investigates the impact of the focusing operation carried out on squinted raw data acquisitions performed by SAR sensors operating in the stripmap mode. To this end the 2D frequency SAR processing approach is generalized with respect to conical, i.e., nonorthogonal, reference systems. This allows analysis of the geometric, spectral, and phase aberrations introduced in the images by the chosen processing geometry with respect to the acquisition, and identification of the focusing procedure that minimizes these aberrations. The whole theory is validated by experimental results carried out on simulated data. Moreover, the extension of this analysis to the interferometric case where these aberrations may have a significant role is also investigated     

一种横向机动弹道下SAR成像算法

针对横向机动弹道下SAR成像回波方位向和距离向严重耦合、弹载SAR平台实时性要求高的特点,提出了一种基于频谱分析的扩展SAR成像算法。首先,以初始距离相同的目标作为成像处理对象,建立了横向机动下弹载SAR成像模型,分析了回波相位和瞬时距离的泰勒展开;然后,采用包含水平面速度和偏航加速度参数的相位因子依次进行距离徙动校正、二次相位补偿和多普勒中心频率补偿,实现了SAR图像的精确聚焦。该算法处理流程简单、实时性高,适合横向机动弹道下的中等分辨率的大斜视成像,给出了算法流程,仿真验证了算法有效性。     

VSAR: a high resolution radar system for ocean imaging

The velocity synthetic aperture radar (VSAR) is a conceptual synthetic aperture radar (SAR)-based sensor system for high resolution ocean imaging. The VSAR utilizes data collected by a multielement SAR system, to extract information not only about the radar reflectivity of the observed area, but also about the radial velocity of the scatterers in each pixel. This is accomplished by making use of the phase information contained in multiple SAR images, and not just the magnitude information as in conventional SAR. Using this velocity information, the VSAR attempts to compensate for the velocity distortion inherent in conventional SAR and to reconstruct the ocean reflectivity. We present the basic theory of the VSAR system and its performance. We also provide an analysis of the VSAR imaging mechanism for a statistical model of the radar returns, designed to capture the effects of speckle and of resolution degradation due to the decorrelation of the radar returns