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     

Coherent spatial filtering for SAR detection of stationary targets

Analysis of UHF synthetic aperture radar (SAR) images has revealed a spatially correlated phase structure over a resolved target such as a long wire or a large truck. This phase variation, approximated by a simple, range-dependent factor 4πr/λ, results in a line-shaped image spatial spectrum. Such image phase and spectral features can be exploited by coherent spatial filtering to improve target-to-clutter ratios of large targets and stationary target detectability in a strong clutter environment     

Detecting moving targets in SAR imagery by focusing

A new method for detecting moving targets in a synthetic aperture radar (SAR) image is presented. It involves segmenting a complex-valued SAR image into patches, focusing each patch separately, and measuring the sharpness increase in the focused patch. The algorithm is sensitive to azimuth velocities and is exquisitely sensitive to radial accelerations of the target, allowing it to detect motion in any direction. It is complementary to conventional Doppler-sensing moving target indicators, which can sense only the radial velocity of rapidly moving targets.     

一种基于MLS的SAR图像点特征提取方法

针对合成孔径雷达图像点特征提取问题,提出了一种基于MLS的SAR图像点特征提取方法.首先利用提出的基于超像素关联分析的SAR图像分割算法得到二值分割图像,再把该二值图像和SAR图像进行点乘运算,获取到含有强度信息的目标区域,然后采用移动最小二乘法(MLS)对目标区域进行曲面拟合,根据设定的点特征判决规则,最后提取出SAR图像的峰值、脊和谷等多种点特征.基于MSTAR数据的实验结果表明了该方法的有效性和准确性.     

SAR data focusing using seismic migration techniques

The focusing of synthetic-aperture-radar (SAR) data using migration techniques quite similar to those used in geophysics is treated. The algorithm presented works in the ω-k_x domain. Because time delays can be easily accommodated with phase shifts that increase linearly with ω, range migration poses no problem. The algorithm is described in plane geometry first, where range migration and phase history can be exactly matched. The effects of the sphericity of the Earth, of the Earth's rotation, and of the satellite trajectory curvature are taken into account, showing that the theoretically achievable spatial resolution is well within the requirements of present day and near future SAR missions. Terrestrial swaths as wide as 100 km can be focused simultaneously with no serious degradation. The algorithm has been tested with synthetic data, with Seasat-A data, and with airplane data (NASA-AIR). The experimental results fully support the theoretical analysis     

Object-based SAR image compression using vector quantization

A simple and elegant algorithm is presented to encode images with rich content, which allows easy access to various objects. An object-plane-based encoding method for compression of synthetic aperture radar (SAR) imagery is developed, with different object planes for target classes and background. A variable-rate residual vector quantization (VQ) algorithm is developed to encode the background information. This algorithm is very powerful as indicated by the experimental results. The proposed coding scheme allows compression matched to the final application of the images, which in this case is target recognition and classification.     

Ambiguity resolution of multiple targets using pulse-Dopplerwaveforms

To cancel clutter, both medium-PRF waveforms which are ambiguous in both range and Doppler and high-PRF waveforms which are ambiguous in range but unambiguous in ambiguities, a previous paper has shown that superior results for a single target can be achieved by using a clustering algorithm. Here, the problem of multiple targets is considered. A maximum likelihood (ML) technique which incorporates the clustering algorithm is developed for the multiple target problem. Simulation results show that four targets which have the same speed but are at different ranges can be resolved by using a medium-PRF waveform and employing the ML resolution technique     

Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random feld modeling

Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.     

Tracking targets using adaptive Kalman filtering

A simple algorithm for estimating the unknown process noise variance of an otherwise known linear plant, using a Kalman filter is suggested. The process noise variance estimator is essentially dead beat, using the difference between the expected prediction error variance, computed in the Kalman filter, and the measured prediction error variance. The estimate is used to adapt the Kalman filter. The use of the adaptive filter is demonstrated in a simulated example in which a wildly maneuvering target is tracked     

Accuracy of source localization using multipath delays

The depth and range of underwater source can be estimated from measurements of propagation delay differences along multiple propagation paths. The accuracy of depth and range estimation using the Cramer-Rao lower bound is studied. The formulas derived can be used in conjunction with a propagation medium (nonconstant velocity profile). Explicit formulas for the bounds are derived for the case of homogeneous medium (constant velocity profile). Numerical examples are presented to illustrate the behavior of these bounds     

Automatic target recognition using enhanced resolution SAR data

Using advanced technology, a new automatic target recognition (ATR) system has been developed that provides significantly improved target recognition performance compared with ATR systems that use conventional synthetic aperture radar (SAR) image-processing techniques. This significant improvement in target recognition performance is achieved by using a new superresolution image-processing technique that enhances SAR image resolution (and image quality) prior to performing target recognition. A computationally efficient two-level implementation of a template-based classifier is used to perform target recognition. The improvement in target recognition performance achieved using superresolution image processing in this new ATR system is quantified     

Remote data sensing using SAR and harmonic reradiators

In sensor networks distributed over large areas, communication by means of active transmitters on sensor nodes is inherently energy expensive and poses a significant bottleneck to achieve a long battery life. We propose modulated reradiation of radar illumination as a means to transmit information from a group of sensors to an airborne radar. This puts the communications energy burden on the radar transmitter rather than on the sensor nodes, thus increasing their battery lifetimes. To distinguish the sensor return from the clutter return, the modulation on the sensors is done by switching a nonlinear load on the sensor antenna and processing the harmonic reradiation. We present techniques to transmit information from the sensors, which use stripmap mode synthetic aperture radar (SAR) ideas to decode the information and to simultaneously obtain a geographic map of the sensor locations.     

Filtering of moving targets using SBIR sequential frames

In this paper three-dimensional (3-D) finite-impulse response (FIR) filters are proposed for moving target detection and tracking from multiframe space-based infrared (SBIR) data. An optimal, in the l_p sense, 3-D FIR filter design technique is proposed which is suitable for the above application. This technique is the first 3-D FIR design of its kind presented in the open literature. Directional, matched, and adaptive 3-D filtering techniques are proposed. Prior to the filtering, clutter mean estimation and mean subtraction are required. Real time implementation of directional and/or matched filters for processing maneuvering targets is discussed and filter design methods are proposed. Finally, performance comparisons of the proposed and other available 3-D FIR and infinite-impulse response (IIR) filters, on real SBIR data, are presented in which the advantages of the proposed 3-D filters are shown     

Classification of radar targets using synthetic neural networks

Radar target classification performance of neural networks is evaluated. Time-domain and frequency-domain target features are considered. The sensitivity of the neural network algorithm to changes in network topology and training noise level is examined. The problem of classifying radar targets at unknown aspect angles is considered. The performance of the neural network algorithms is compared with that of decision-theoretic classifiers. Neural networks can be effectively used as radar target classification algorithms with an expected performance within 10 dB (worst case) of the optimum classifier     

FOPEN SAR imaging using UWB step-frequency and random noisewaveforms

The detection and identification of targets obscured by foliage have been topics of great interest. Several synthetic aperture radar (SAR) experiments have demonstrated promising images of terrain and man-made objects obscured by dense foliage, by using either linear frequency modulation (LFM) or step-frequency waveforms. We present here the methodology and results of a comparative study on foliage penetration (FOPEN) SAR imaging using ultrawideband (UWB) step-frequency and random noise waveforms. A statistical-physical foliage transmission model is developed for simulation applications. The foliage obscuring pattern is analyzed by means of the technique of paired echoes. The results of the comparative study demonstrates the ability of a UHF band UWB random noise radar to be used as a FOPEN SAR. Advantages of the random noise radar system include covert detection and immunity to radio frequency interference (RFI)     

Doppler aliasing reduction in SAR imagery using stepped-frequency waveforms

A new technique for eliminating Doppler aliasing artifacts in synthetic aperture radar (SAR) imagery is presented. The technique uses continuous transmission of stepped-frequency (SF) waveforms to generate a simple, but effective, approximation of a two-dimensional |sinc|² ambiguity function. This approach ensures aliasing artifacts are placed at nulls in a Doppler filter, effectively removing them from the imagery. Various methods of generating SF waveforms are introduced and their performance is quantified. Finally, simulated SAR imagery shows elimination of more than 99% of the aliased energy     

Monopulse-radar tracking of swerling III-IV targets using multiple observations

This paper proposes a novel statistical prediction of monopulse errors (Levanon, 1988) for a radar Swerling III-IV target embedded in noise or noise jamming where multiple observations are available. First, the study of the maximum likelihood estimator (MLE) of the complex monopulse ratio for a Swerling III-IV target embedded in spatially white noise allows us to extend the use of the MLE practical approximate form introduced by Mosca (1969) for Swerling 0-I-II cases. Afterward, we derive analytical formulas for both the mean and variance of the MLE in approximate form conditioned by the usual detection step performed on the sum channel of a monopulse antenna. Last, we provide a comparison of target direction of arrival (DOA) estimation performance based on monopulse ratio estimation as a function of the Swerling model in the context of a multifunction radar.     

图像融合技术检测低可观测目标评述

阐明了利用图像信息空间覆盖范围大的特点,运用图像融合的手段来对低可观测目标进行检测.通过综合处理来自多个传感器图像包含的检测对象信息、环境特性信息、运动信息、空间信息、时间信息,获得的融合信息包含任何单一传感器无法提供的信息,进而提高检测性能.扼要介绍了像素层图像融合、特征层图像融合以及符号层图像融合的基本概念.列举了图像融合技术一些应用实例,阐述了用图像融合技术检测低可观测目标的优点.着重强调了检测低可观测目标需要进一步研究的问题.