Analysis of foliage-induced synthetic pattern distortions

Phase and amplitude fluctuations induced by wave propagation through foliage limit the ability of a synthetic aperture radar (SAR) system to image a target under foliage. One-way measurements of these fluctuations were done at C-, L-, UHF band during the July 1990 Foliage Penetration Experiment using single frequency CW signal sources and the NASA/JPL SAR receiver. The phase and amplitude data are coherently integrated to create the synthetic azimuthal patterns that would result when attempting to image a point target obscured by foliage. The effect of synthetic aperture length frequency, and polarization on the attenuation and azimuthal response of foliage obscured targets is investigated     

Measured and predicted synthetic aperture radar target comparison

The DARPA Image Understanding program publicly released measured and predicted synthetic aperture radar (SAR) targets were compared by means of correlation. The training set consisted of three classes (BMP-2, T-72, and BTR-70) at 17 deg depression and 233 azimuths. The test set consisting of seven different serial-numbered targets at 15 deg depression was tested at 196 azimuths. After segmentation and normalization, each test image was correlated with all the training images to generate correlation and classification statistics. Measured correlation scores were higher and more consistent for same serial number training than variant training. The average in-class (0.837) and between-class (0.734) means for measured correlations were higher than both the average in-class (0.707) and between-class (0.675) means for predicted correlations; however, the corresponding variances for in-class (0.056) and between-class (0.048) predicted correlations were higher than in-class (0.026) and between-class (0.036) measured variances. The measured training data declared the target correctly almost 100% of the time; the T-72 and BTR-70 model-predicted data declared the target correctly 80% of the time. The correlation scores varied approximately sinusoidally with aspect. Correlation plots between a single orientation target and the entire training sets showed that a target was highly correlated at both the correct aspect angle and the correct angle rotated 180 deg     

Superresolution HRR ATR with high definition vector imaging

A new 1-D template-based automatic target recognition (ATR) algorithm is developed and tested on high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. In this work, a superresolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through ATR classification. The new I-D ATR system using HDVI demonstrates significantly improved target recognition compared with previous I-D ATR systems that use conventional image processing techniques. This improvement in target recognition is quantified by improvement in probability of correct classification (PCC). More importantly, the application of HDVI to HRR profiles helps to maintain the same ATR performance with reduced radar resource requirements     

High resolution reconstruction of ISAR images

A 2-D high-resolution spectral analysis algorithm with application to an inverse synthetic aperture radar (ISAR) imaging, is presented. This algorithm is based on two-dimensional linear prediction using autoregressive (AR) coefficients. Stability is guaranteed by AR process pole adjustment. An ISAR target is modeled for a complex scatterer geometry. Computer simulation results are provided for the high-resolution reconstruction of ISAR images     

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     

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     

SAR imaging of moving targets

A method of forming synthetic aperture radar (SAR) images of moving targets without using any specific knowledge of the target motion is presented. The new method uses a unique processing kernel that involves a one-dimensional interpolation of the deramped phase history which we call keystone formatting. This preprocessing simultaneously eliminates the effects of linear range migration for all moving targets regardless of their unknown velocity. Step two of the moving target imaging technique involves a two-dimensional focusing of the movers to remove residual quadratic range migration errors. The third and last step removes cubic and higher order defocusing terms. This imaging technique is demonstrated using SAR data collected as part of DARPA's Moving Target Exploitation (MTE) program     

Precision tracking algorithms for ISAR imaging

The simultaneous imaging and tracking of a moving target is one of the most difficult problems in inverse synthetic aperture radar (ISAR) signal processing. The problem is addressed here using a two-antenna imaging radar. The target range shift is sensed with an exponentially averaged envelope correlation algorithm while the angle change is measured with a differential phase shift algorithm. The three state Kalman filters are used in range and azimuth dimensions to provide both a filtered estimate and a one-sample-ahead prediction for the purpose of target tracking. The filtered range shift provides an accurate information for range bin alignment, and the target angle change provides the angular positions of the synthetic array elements. Therefore, the ISAR imaging simply becomes processing of a circular-arc aperture. The algorithms are verified both by computer simulations and also with experimental data processing     

An efficient 3-D near-field ISAR algorithm

This paper presents a new three-dimensional (3-D) near-field inverse synthetic aperture radar (ISAR) imaging technique. A 3-D ISAR image can be obtained by processing coherently the backscattered fields as a function of the frequency and two rotation angles about axes which are mutually orthogonal. Most of the existing ISAR algorithms are based on the Fourier transform and as such can tolerate only small amounts of wavefront curvature. Wavefront curvature must be taken into account when imaging an object in the near-field. Near-field ISAR imaging of large objects using a direct Fourier inversion may result in images which are increasingly unfocused at points which are more distant from the center of rotation. An algorithm based on an azimuth convolution between a near-field focusing function and the frequency domain backscattered fields is discussed. This convolution is efficiently implemented by using fast Fourier transform (FFT) techniques. Furthermore, in order to further alleviate the computational load of the algorithm, the discrete Fourier transform (DFT) of the focusing function is evaluated by means of the stationary phase method. Experimental results show that this technique is precise and virtually impulse invariant     

Dynamic imaging and RCS measurements of aircraft

Results on radar cross section (RCS) measurements and inverse synthetic aperture radar images of a Mooney 231 aircraft using a ground-to-air measurement system (GTAMS) and a KC-135 airplane using an airborne radar are presented. The Mooney 231 flew in a controlled path in both clockwise and counterclockwise orbits, and successively with the gear down, flaps in the take-off position and with the speed brakes up. The data indicates that RCS pattern measurements from both ground-based and airborne radar of flying aircraft are useful and that the inverse synthetic aperture radar (ISAR) images obtained are valuable for signature diagnostics     

多干扰机InSAR欺骗干扰方法

单航过的干涉合成孔径雷达（InSAR）有很强的抗干扰能力,单部干扰机所发射干扰信号的干涉相位在不同快时间是完全相同的,不同慢时间也近似相等,不能产生具有高程信息的干扰图像。基于此,提出了一种利用双（多）干扰机产生InSAR三维图像欺骗干扰的方法,分析了InSAR的成像机理,给出了假目标模板设置方法,讨论了聚焦后复图像和干涉相位要求的干扰信号调制参数以及干涉相位的估计方法。产生的干扰信号在雷达接收机中可独立实现SAR二维像干扰,经干涉后能生成虚假高程信息,理论分析和仿真结果表明,该方法能实现对SAR二维成像和InSAR三维成像的有效干扰。     

Phase quantized SAR signal processing: theory and experiments

A complete theory of synthetic aperture radar (SAR) processing from phase-only data is presented, for both analogue and coded raw signals and reference functions. Appropriate processing architectures are presented as well as quality indexes to judge performance of the selected coding. The analysis encompasses both images and interferometric fringes for interferometry SAR (IFSAR) application. Theoretical results are validated by a large number of experiments, comparing the performance of the suggested coding levels, and their combinations between raw and reference functions. Experiments are extended until the comparison of final digital elevation models     

A Velocity Estimation Algorithm of Moving Targets using Single Antenna SAR

A new algorithm is proposed for velocity estimation of moving targets in single antenna synthetic aperture radar (SAR). Based on the fact that different velocity vectors cause different geometrical figures of the two-dimensional (2-D) signature in the range-Doppler (RD) domain, this algorithm estimates the azimuth and range velocities by a 2-D search such that the range cell migration correction (RCMC) and the second range compression (SRC) are correctly performed. It is shown that, using the proposed algorithm, the Doppler ambiguity problem can be avoided and satisfactory accurate velocity estimation can be obtained in high signal-to-clutter ratio (SCR) scenarios.     

Target detection with synthetic aperture radar

Target detection with synthetic aperture radar (SAR) is considered. We derive generalized likelihood ratio (GLR) detection algorithms that may be used with SAR images that are obtained with coherent subtraction or have Gaussian distributions. We analytically compare the performance of (1) a single pixel detector, (2) a detector using complete knowledge of the target signature information and known orientation information, (3) a detector using incomplete knowledge of the target signature information and known orientation information (4) a detector using unknown target signature information and known orientation information, and (5) a detector using unknown target signature information and unknown orientation information     

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.     

Super resolution SAR imaging via parametric spectral estimationmethods

Super resolution synthetic aperture radar (SAR) image formation via sophisticated parametric spectral estimation algorithms is considered. Parametric spectral estimation methods are devised based on parametric data models and are used to estimate the model parameters. Since SAR images rather than model parameters are often used in SAR applications, we use the parameter estimates obtained with the parametric methods to simulate data matrices of large dimensions and then use the fast Fourier transform (FFT) methods on them to generate SAR images with super resolution. Experimental examples using the MSTAR and Environmental Research Institute of Michigan (ERIM) data illustrate that robust spectral estimation algorithms can generate SAR images of higher resolution than the conventional FFT methods and enhance the dominant target features     

Iterative MMSE method and recurrent Kalman procedure for ISAR imagereconstruction

This work presents a novel approximate iterative and recurrent approach for image reconstruction from inverse synthetic aperture radar (ISAR) data. Mathematical models of the quadrature components of the ISAR signal, reflected by an object with a complex geometry, are devised. Approximation matrix functions are used to describe deterministic signals reflected by point scatterers located at nodes of the uniform grid (model) during inverse aperture synthesis. Minimum mean square error (MMSE) equations and Kalman equations are derived. To prove the validity and correctness of the developed iterative MMSE method and recurrent Kalman procedure, numerical experiments were performed. The computational results demonstrate high resolution images, unambiguous and convergent estimates of the point scatterers' intensities of a target from simulated ISAR data     

High Range Resolution Profiles as Motion-Invariant Features for Moving Ground Targets Identification in SAR-Based Automatic Target Recognition

A quantitative model analysis is presented to justify the extraction of high range resolution (HRR) profiles from synthetic aperture radar (SAR) images as motion-invariant features for identifying moving ground targets. A comparative study is conducted to assess the effectiveness in the identification process between using HRR profiles and SAR images as target signatures. The results indicate that HRR profiles are just as viable as SAR image for identification. Furthermore, a score-level multi-look fusion identification method has been investigated. It is found that a correct accurate identification rate of greater than 99 percent, a low false alarm rate, and a high level of identification confidence can be achieved, providing very robust performance.     

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.     

A Generalized Approach to Multiresolution Complex SAR Signal Processing

Multiresolution synthetic aperture radar (SAR) image formation has been proven to be beneficial in a variety of applications such as improved imaging and target detection as well as speckle reduction. SAR signal processing traditionally carried out in the Fourier domain has inherent limitations in the context of image formation at hierarchical scales. We present a generalized approach to the formation of multiresolution SAR images using biorthogonal shift-invariant discrete wavelet transform (SIDWT) in both range and azimuth directions. Particularly in azimuth, the inherent subband decomposition property of wavelet packet transform is introduced to produce multiscale complex matched filtering without involving any approximations. This generalized approach also includes the formulation of multilook processing within the discrete wavelet transform (DWT) paradigm. The efficiency of the algorithm in parallel form of execution to generate hierarchical scale SAR images is shown. Analytical results and sample imagery of diffuse backscatter are presented to validate the method.