Sum-difference Reduced-dimensional Processing in SAR Image Domain and Statistical Analysis for Ground Moving Target Indication

In this article, a new reduced-dimensional adaptive processing algorithm based on joint pixels sum-difference data for clutter rejection is proposed. The sum-difference data are obtained by orthogonal projection of the joint pixels data of different synthetic aperture radar (SAR) images generated by a multi-satellite radar system. In the sense of statistical expectation, the sum-differ- ence data contain the common and different information of the SAR images. Therefore, the objective of clutter cancellation can be achieved by adaptive processing. Moreover, based on the residual image after clutter rejection, statistical analysis of constant false-alarm rate (CFAR) detection of moving targets is also presented. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm even with heterogeneous clutter and image co-registration error.     

基于杂波对消-自聚焦的多通道SAR-GMTI

对超高频(UHF)波段多通道合成孔径雷达(SAR)动目标检测技术进行研究,解决了长相干积累时间导致动目标在方位向散焦严重的问题。采用分块自聚焦技术对多通道SAR地面移动目标指示(GMTI)系统自适应杂波抑制后的SAR图像进行处理,改善杂波抑制后的SAR图像中动目标的聚焦情况,增强动目标与周围剩余杂波的对比度,进而提高恒虚警率(CFAR)检测的性能。与传统杂波抑制后直接进行CFAR检测方法相比较,该方法降低了检测虚警概率。实测数据处理结果显示动目标的信杂比明显提高,动目标方位向聚焦成功,证明了该方法的有效性。     

Data fusion for ground moving target tracking

The aim of ground surveillance is the large scale, continuous and near real time determination of a dynamical ground picture. This task comprises detection and tracking of moving single targets and convoys, mobile weapon systems, and military equipment. The sensors of choice are airborne Ground Moving Target Indicator (GMTI) radar and synthetic aperture radar (SAR). As ground target tracking often suffers from dense target situations, high clutter, and low visibility, the integration and fusion of external background information is essential for providing precise and continuous tracks. We present Multi Hypotheses techniques for tracking several targets in complex ground situations with clutter. Methods to incorporate topographic information, in particular digital road maps, are described and demonstrated.     

Effects of polarization and resolution on SAR ATR

Lincoln Laboratory is investigating the detection and classification of stationary ground targets using high resolution, fully polarimetric, synthetic aperture radar (SAR) imagery. A study is summarized in which data collected by the Lincoln Laboratory 33 GHz SAR were used to perform a comprehensive comparison of automatic target recognition (ATR) performance for several polarization/resolution combinations. The Lincoln Laboratory baseline ATR algorithm suite was used, and was optimized for each polarization/resolution case. Both the HH polarization alone and the optimal combination of HH, HV, and VV were evaluated; the resolutions evaluated were 1 ft/spl times/1 ft and 1 m/spl times/1 m. The data set used for this study contained approximately 74 km/sup 2/ of clutter (56 km/sup 2/ of mixed clutter plus 18 km/sup 2/ of highly cultural clutter) and 136 tactical target images (divided equally between tanks and howitzers).     

HRR Detector for Slow-Moving Targets in Sea Clutter

The radar detection of targets in the presence of sea clutter has historically relied upon the radial velocity of targets with respect to the radar platform either by exploiting the relative target Dopplers (for targets with sufficient radial velocity) or by discerning the paths targets traverse from scan to scan. For targets with little to no radial velocity component, though, it can become quite difficult to differentiate targets from the surrounding sea clutter. This paper addresses the detection of slow-moving targets in sea clutter using a high resolution radar (HRR) such that the target has perceptible extent in range. Under the assumption of completely random sea clutter spikes based on an epsiv-contaminated mixture model with the signal and clutter powers known, optimal detection performance results from using the likelihood ratio test (LRT). However, for realistic sea clutter, the clutter spikes tend to be a localized phenomenon. Based upon observations from real radar data measurements, a heuristic approach exploiting a salient aspect of the idealized LRT is developed which is shown to perform well when applied to real measured sea clutter.     

Predicting an upper bound on SAR ATR performance

We present a method for predicting a tight upper bound on performance of a vote-based approach for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. In such an approach, each model target is represented by a set of SAR views, and both model and data views are represented by locations of scattering centers. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, as well as model factors such as structural similarity. Firstly, we calculate a measure of the similarity between a given model view and each view in the model set, as a function of the relative transformation between them. Secondly we select a subset of possible erroneous hypotheses that correspond to peaks in similarity functions obtained in the first step. Thirdly, we determine an upper bound on the probability of correct recognition by computing the probability that every selected hypothesis gets less votes than those for the model view under consideration. The proposed method is validated using MSTAR public SAR data, which are obtained under different depression angles, configurations, and articulations     

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     

Moving target detection via airborne HRR phased array radar

We study moving target detection in the presence of temporally and spatially correlated ground clutter for airborne high range resolution (HRR) phased array radar. We divide the HRR range profiles into large range segments to avoid the range migration problems that occur in the HRR radar data. Since each range segment contains a sequence of HRR range bins, no information is lost due to the division and hence no loss of resolution occurs. We show how to use a vector autoregressive (VAR) filtering technique to suppress the ground clutter. Then a moving target detector based on a generalized likelihood ratio test (GLRT) detection strategy is derived. The detection threshold is determined according to the desired false alarm rate, which is made possible via an asymptotic statistical analysis. After the target Doppler frequency and spatial signature vectors are estimated from the VAR-filtered data as if a target were present, a simple detection variable is computed and compared with the detection threshold to render a decision on the presence of a target. Numerical results are provided to demonstrate the performance of the proposed moving target detection algorithm     

Prescreening during image formation for ultrawideband radar

Standard radar image formation techniques waste computational resources by full resolving all areas of the scene, even regions of benign clutter. We introduce a multiscale prescreener algorithm that runs as part of the image formation processing step for ultrawideband (UWB) synthetic aperture radar (SAR) systems. The prescreener processes intermediate radar data generated by a quadtree backprojection image former. As the quadtree algorithm iterates, it is resolving increasingly finer subpatches of the scene. After each quadtree stage, the prescreener makes an estimate of the signal-to-background ratio of each subpatch and applies a constant false alarm rate (CFAR) detector to decide which ones might contain a target of interest. Whenever the prescreener determines that a subpatch is not near a detection, it cues the image former to terminate further processing of that subpatch. Using a small database of UWB radar field data, we demonstrate that the prescreener is able to decrease the overall computational load of the image formation process. We also show that the new multiscale prescreener method produces fewer false alarms than the conventional two-parameter CFAR prescreener applied to the completely formed image     

Target prescreening based on a quadratic gamma discriminator

This work presents the development, analysis and validation of a new target discrimination module for synthetic aperture radar (SAR) imagery based on an extension of gamma functions to 2-D. Using the two parameter constant false-alarm rate (CFAR) stencil as a prototype, a new stencil based on 2-D gamma functions is used to estimate the intensity of the pixel under test and its surroundings. A quadratic discriminant function is created from these estimates, which is optimally adapted with least squares in a training set of representative clutter and target chips. This discriminator is called the quadratic gamma discriminator (QGD). The combination of the CFAR and the QGD was tested in realistic SAR environments and the results show a large improvement of the false alarm rate with respect to the two-parameter CFAR, both with high resolution (1 ft) fully polarimetric SAR and with one polarization, 1 m SAR data     

Detection of moving targets in wideband SAR

A likelihood ratio is proposed for moving target detection in a wideband (WB) synthetic aperture radar (SAR) system. WB is defined here as any systems having a large fractional bandwidth, i.e., an ultra wide frequency band combined with a wide antenna beam. The developed method combines time-domain fast backprojection SAR processing methods with moving target detection using space-time processing. The proposed method reduces computational load when sets of relative speeds can be tested using the same clutter-suppressed subaperture beams. The proposed method is tested on narrowband radar data.     

Optimal speckle reduction in polarimetric SAR imagery

Speckle is a major cause of degradation in synthetic aperture radar (SAR) imagery. With the availability of fully polarimetric SAR data, it is possible to use the three complex elements (HH, HV, VV) of the polarimetric scattering matrix to reduce speckle. The optimal method for combining the elements of the scattering matrix to minimize image speckle is derived, and the solution is shown to be a polarimetric whitening filter (PWF). A simulation of spatially correlated, K-distributed, fully polarimetric clutter is then used to compare the PWF with other, suboptimal speckle-reduction methods. Target detection performance of the PWF, span, and single-channel |HH|² detectors is compared with that of the optimal polarimetric detector (OPD). A novel, constant-false-alarm-rate (CFAR) detector (the adaptive PWF) is as a simple alternative to the OPD for detecting targets in clutter. This algorithm estimates the polarization covariance of the clutter, uses the covariance to construct the minimum-speckle image, and then tests for the presence of a target. An exact theoretical analysis of the adaptive PWF is presented; the algorithm is shown to have detection performance comparable with that of the OPD     

New SAR Processor Based on Matched Subspace Detectors

This paper deals with a new synthetic aperture radar (SAR) Processor based on a subspace detector designed for man-made target (MMT) detection. As MMTs are more accurately decribed by a set of canonical elements than with isotropic points, we develop a new algorithm which aims at using new models, instead of the isotropic point model commonly used in SAR processors. A subspace detector matched to canonical elements is included in the SAR processing. The implementation and the optimization of subspace detector SAR (SDSAR) algorithm is described. Simple examples of MMT detection in simulations and real data with a target hidden in a forest show the power of our approach. The SDSAR algorithm is shown to be the first robust and tractable algorithm relying on realistic scattering assumptions about the target.     

海杂波条件异方差特性分析与波动信息提取

由于低擦地角、高海况等易引起雷达海杂波序列的局部剧烈波动,传统的统计分布模型难以描述突然出现的具有冲激特性的强回波,因此,针对这一问题,将广义自回归条件异方差(Generalized Auto Regressive Conditional Heteroskedasticity,GARCH)模型引入海杂波建模中,通过 GARCH模型阶数步进搜索结合残差序列方差齐性检验,实现了海杂波数据的波动信息提取。经 X波段雷达实测数据验证,所提出的波动信息提取方法,可以很好地提取实测海杂波数据在局部区域或时间段内的波动信息,为特征检测方法设计提供有效的特征支撑。     

Adaptive Radar Detection in Doubly Nonstationary Autoregressive Doppler Spread Clutter

The problem of adaptive radar detection in clutter which is nonstationary both in slow and fast time is addressed. Nonstationarity within a coherent processing interval (CPI) often precludes target detection because of the masking induced by Doppler spreading of the clutter. Across range bins (i.e., fast time), nonstationarity severely limits the amount of training data available to estimate the noise covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in complex multipath propagation conditions where path lengths can change abruptly in dynamic scenarios. To mitigate nonstationary Doppler spread clutter, an approximation to the generalized likelihood ratio test (GLRT) detector is presented wherein the CPI from the hypothesized target range is used for both clutter estimation and target detection. To overcome the lack of training data, a modified time-varying autoregressive (TVAR) model is assumed for the clutter return. In particular, maximum likelihood (ML) estimates of the TVAR parameters, computed from a single snapshot of data, are used in a GLRT for detecting stationary targets in possibly abruptly nonstationary clutter. The GLRT is compared with three alternative methods including a conceptually simpler ad hoc approach based on extrapolation of quasi-stationary data segments. Detection performance is assessed using simulated targets in both synthetically-generated and real radar clutter. Results suggest the proposed GLRT with TVAR clutter modeling can provide between 5–8 dB improvement in signal-to-clutter plus noise ratio (SCNR) when compared with the conventional methods.     

Modified Difference Change Detector for Small Targets in SAR Imagery

One of the most straightforward techniques for detecting changes in an image involves forming the difference between a test image and a reference image. Unfortunately, such a technique can give rise to a large number of false alarms due to the statistical variability of the underlying pixel values, as has been well established within the radar community over the years. One method for dealing with this large number of false alarms involves forming the ratio, rather than the difference, of two synthetic aperture radar (SAR) images. We introduce a modified version of the standard differencing technique to overcome problems associated with pixel value variability. The new (modified) differencing approach utilizes assumptions about the statistics of the image background and the object being sought (target) to reduce the number of false alarms due to highly variable background (clutter) regions, and it includes the standard ratio test as a special case. In fact, we find that the modified difference approach can also be viewed as a modified version of the ratio test with a threshold that varies as a function of the background clutter radar cross section (RCS). We also present an abridged, albeit suboptimal, version of this approach that eliminates assumptions regarding the target's probability distribution, and we analyze both of the approaches. We then compare these results with those obtained with a standard ratio test, and illustrate how the modified difference test reduces to the ratio test under certain operating conditions. The abridged version of the modified approach is applied to high resolution synthetic aperture radar imagery and compared with results obtained with the classical differencing technique, and following this, the modified difference technique is compared with the standard ratio test. Results suggest that under appropriate conditions the abridged, modified technique can successfully detect changes without the need for any image segmentation.     

Statistical analysis of real clutter at different range resolutions

A statistical analysis is presented of real radar clutter data collected using the McMaster I FIX radar in 1998 and stored in the Grimsby database. We first show the deviations of the amplitude statistics from the Rayleigh model and the suitability of the K- and Weibull-distribution for the first-order amplitude statistical characterization. Thus we focus on the I and Q components of the available data and study their statistical compatibility with the compound Gaussian model. Towards this goal it has been necessary devising appropriate testing procedures; in particular, with reference to the higher order statistics agreement, we have designed a validation procedure involving the clutter representation into generalized spherical coordinates. Remarkably the results have confirmed the suitability of the spherically invariant random processes (SIRPs) for the correct modeling of the radar clutter. Finally we have performed a spectral analysis highlighting the close matching between the estimated clutter spectral density and the exponential model.     

海杂波谱的机理及时变特性分析

针对海杂波背景下目标检测问题的实际需求,在机理分析的基础上,利用实测数据对海杂波谱的时变特性进行了分析。首先,分析了波束照射区域内由海表面波浪运动引起的多普勒频移和展宽现象,以及随雷达工作参数之间的依赖关系;然后,对已有海杂波谱机理研究结果进行了分析和总结。在此基础上,利用加窗的周期图法估计得到海杂波的时间-多普勒谱,进而从相关时间的统计特性、谱宽与时域海杂波拖尾程度的依赖关系两个层面对海杂波谱的时变特性进行了分析。分析结果可为海杂波谱建模、海杂波抑制及目标检测方法设计提供理论指导。     

弹载合成孔径雷达自适应重频分析与设计

弹载合成孔径雷达(Synthetic Aperture Radar,SAR)的目标距离、视线角由于高速逼近目标而快速变化,这导致传统的固定脉冲重复频率(Pulse Repetition Frequency,PRF)(简称重频)波形难以兼顾弹载SAR雷达在成像各方面的约束条件,故需要根据当前弹体运动和弹目关系变化情况实时计算重频。详细分析了影响重频选择的各项因素,包括避免距离模糊、方位模糊、高度杂波、发射遮挡影响及SAR成像分辨率、系统相参性要求等影响因素,并设计了自适应重频计算的工作流程。某SAR雷达系统实验表明,该设计能够在实际飞行弹道条件下根据实际弹目关系自适应调整脉冲重复频率,从而更好地实现SAR雷达系统的工作性能,有效解决了固定重频波形不能适应弹载SAR工作条件的难题。     

机动目标的逆合成孔径雷达成像

 逆合成孔径雷达(ISAR)和合成孔径雷达 (SAR)都是利用目标 (场景)与雷达的相对运动,提高横向分辨率,实现对目标 (场景 )的成像。SAR的运动方是雷达平台,可控制作平稳飞行,且用仪器测校其偏离误差;ISAR的运动方通常是非合作目标,运动不受控制,且难以精确测量。当目标作机动飞行时,以目标作固定基准,雷达等效地在空间形成流形复杂的逆合成孔径 (阵列 ),对这种情况下成像的问题进行了系统的研究。