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ABSTRACT: We consider processing of SAR data collected on multiple complete circular apertures at different elevation angles, for three dimensional target reconstruction. Multipass circular SAR provides wide-angle information about the anisotropic reflectivity of the scattering centers in the scene and provides for three dimensional imaging capability. The 3-D resolution of circular SAR systems is constrained by two factors: the sparse sampling in elevation and the limited azimuthal persistence of the reflectors in the scene. In addition, real flight paths exhibit nonuniform elevation spacing and non-constant elevation throughout the circular pass. We first develop parametric spectral estimation methods that extend standard IFSAR method of height estimation to apertures at more than two elevation angles. We then present a new sparsity regularized interpolation algorithm to preprocess nonuniform elevation samples to create a virtual uniform linear array geometry. We illustrate the performance of the proposed method using simulated backscatter data.
Synthetic Aperture Radar (EUSAR), 2008 7th European Conference on; 07/2008
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ABSTRACT: We study circular synthetic aperture radar (CSAR) systems collecting radar backscatter measurements over a complete circular aperture of 360 degrees. This study is motivated by the GOTCHA CSAR data collection experiment conducted by the Air Force Research Laboratory (AFRL). Circular SAR provides wide-angle information about the anisotropic reflectivity of the scattering centers in the scene, and also provides three dimensional information about the location of the scattering centers due to a non planar collection geometry. Three dimensional imaging results with single pass circular SAR data reveals that the 3D resolution of the system is poor due to the limited persistence of the reflectors in the scene. We present results on polarimetric processing of CSAR data and illustrate reasoning of three dimensional shape from multi-view layover using prior information about target scattering mechanisms. Next, we discuss processing of multipass (CSAR) data and present volumetric imaging results with IFSAR and three dimensional backprojection techniques on the GOTCHA data set. We observe that the volumetric imaging with GOTCHA data is degraded by aliasing and high sidelobes due to nonlinear flightpaths and sparse and unequal sampling in elevation. We conclude with a model based technique that resolves target features and enhances the volumetric imagery by extrapolating the phase history data using the estimated model.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
04/2007;
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ABSTRACT: "Feature Extraction using Attributed Scattering Center Models for Model-Based Automatic Target Recognition." The primary research goal of the program was to develop fundamental understanding and advanced signal processing techniques for feature extraction to support feature-based automatic target recognition (ATR) systems employing synthetic aperture radar. This report summarizes the major technical accomplishments that were realized. We developed a set of attributed scattering center models for SAR ATR whose model primitives that balance between modeling fidelity and estimation accuracy. We developed computationally-efficient algorithms for automatic feature extraction of attributed scattering center features from complex SAR image-domain data. We analyzed feature uncertainty and derived analytical uncertainty bounds. We implemented stand-alone match scoring methods to evaluate target discriminability and feature estimation tradeoffs. We developed STAP/SFAP-Based Adaptive Antennas. We developed techniques for understanding rough surface scattering. We developed ultrawide bandwidth antennas, and slot array antennas with wide scan angles. Finally, we increased the U.S. technology base by training of graduate students and by disseminating research through technical publications and presentations.
09/2005;
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IEEE Transactions on Information Theory. 01/2000; 46:1842-1854.
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IEEE Transactions on Image Processing. 01/1997; 6:79-91.
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ABSTRACT: We present an algorithm for model order determination and simultaneous maximum likelihood parameter estimation for complex exponential signal modeling. The algorithm exploits initial nonparametric (i.e., FFT) frequency location estimates and Cram'er-Rao Bound (CRB) resolution limits to significantly reduce the search space for the correct model order and parameter estimates. The algorithm initially overestimates the model order. After iterative minimization to obtain maximum likelihood (ML) parameter estimates for that order, a post-processing step eliminates the extraneous sinusoidal modes using CRB resolution limits and statistical detection tests. Because the algorithm searches on only a limited set of model orders and parameter regions, it is computationally tractable even for large data lengths and model orders. In this paper we analyze the performance of the algorithm and compare with other existing approaches. 1. INTRODUCTION A parametric modeling problem can be divided into t...
10/1996;
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ABSTRACT: Using a point scatterer assumption, high-frequency synthetic aperture radar (SAR) phase histories can be modeled as a sum of two-dimensional (2D) complex exponentials in additive noise. This paper summarizes our SAR signal modeling experience using the XPatch simulated scattering data. We apply several 2D parametric estimation techniques including 2D TLS-Prony, MEMP, 2D IQML, and 2D CLEAN to estimate the complex exponential model parameters. From the estimation results, we discuss the engineering trade-offs among memory requirement, computation requirement, and estimation accuracy. Keywords: 2D exponential modeling, SAR imaging, performance evaluation. 1. INTRODUCTION Over narrow viewing angles, high-frequency synthetic aperture radar (SAR) phase histories can be modeled as a sum of two-dimensional (2D) complex exponentials in additive noise. 1;2;3;4;5;6 The 2D exponential terms correspond to scattering centers on the object, and the exponential amplitude taper in angle and frequen...
10/1996;
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ABSTRACT: In this paper we consider linear correlation filters for image pattern recognition, with particular application to Synthetic Aperture Radar (SAR). We investigate the statistical properties of several popular Synthetic Discriminate Function (SDF) based linear correlation filters, including SDF, MVSDF, and MACE filters. We compare these statistical properties both qualitatively and analytically for SAR applications. We also develop modifications to these SDF-type filters which have particular utility for Synthetic Aperture Radar (SAR) image classification. We compare the performance of the modified filters to the standard filters using X-patch generated SAR images with both white and colored noise. We also investigate effects of performance degradation caused by mis-estimated noise statistics, and the effects of image normalization on the target detection rates. Keywords: linear correlators, SDF, pattern classification, pattern recognition, image classification, SAR images 1 INTRODUCTIO...
10/1996;
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ABSTRACT: Polarimetric diversity can be exploited in synthetic aperture radar (SAR) for enhanced target detection and target description. Detection statistics and target features can be computed from either polarimetric imagery or parametric processing of SAR phase histories. We adopt an M- ary Bayes classification approach and derive Bayes-optimal decision rules for detection and description of scattering centers. Scattering centers are modeled as one of M canonical geometric types with unknown amplitude, phase and orientation angle; clutter is modeled as one of M canonical geometric types with unknown amplitude, phase and orientation angel; clutter is modeled as a spherically invariant random vector. For the Bayes optimal decision rules, we provide a simple geometric interpretation and an efficient computational implementation. Moreover, we characterize the certainty of decisions by deriving an approximate posteriori probability.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
06/1996;
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IEEE Transactions on Signal Processing. 01/1995; 43:77-84.
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ABSTRACT: We present a Bayesian approach for model-based classification from unordered, attributed feature sets. A set of features is estimated from measured data and is matched with a set predicted for each candidate hypothesis using a feature model. Both extracted and predicted feature sets have uncertainty, and some features may not be present in one set or the other. Computation of the match likelihoods requires a correspondence between estimated and predicted features, and two Bayesian correspondence methods are discussed. The proposed procedure is used to predict classification performance as a function of sensor parameters for a 10-vehicle target recognition problem using X-band synthetic aperture radar imagery.
Pattern Recognition.
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ABSTRACT: This paper considers processing and visualization of radar scattering measurements from one or more wide-angle syn-thetic apertures. We consider both two-dimensional imag-ing and three-dimensional interferometric reconstructions. A point-scattering assumption is poorly suited to wide-angle scattering; scattering objects within a target typically have narrow beam patterns, resulting in limited angles of persistence. We revisit traditional imaging in 2D or 3D to propose image formation techniques that use nonlinear, non-coherent combinations of subaperture data. The pro-posed techniques can be interpreted as approximations to a generalized likelihood ratio test. For sufficiently high resolutions, individual reflectors are resolved, and hence the phase and amplitude of their responses are stable under small changes in viewing angle. This stability is in contrast to the scintillation observed in low-resolution data. Three processing techniques benefit as a result: interferometric measurement of 3D location is possible without wide-area averaging; polarimetric fea-tures may be used to describe local geometry; and, band-width enhancement is possible. We illustrate processing and visualization techniques using X-band scattering predictions of a backhoe computed using X-patch. The reconstructions permit an approxima-tion to the literal interpretation afforded by optical imag-ing, but with millimeter wave sensing. In addition, angle-dependent and polarization-dependent behaviors are dis-played for enhanced recognition.
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ABSTRACT: We consider imaging strategies for synthetic aperture radar data collections that span a wide angular aperture. Most traditional radar imaging techniques are predicated on the assumption of isotropic point scattering mech-anisms, which does not hold for wide apertures. We investigate point scattering center images for narrowband, wide angle data, and consider the effect of limited persistence on the resulting images. We investigate imaging strategies that apply to wide angle apertures. We show that coherent processing of the entire wide angle aperture may not be the best image formation strategy for objects of practical interest. Finally, we present initial results on resolution enhancement techniques for wide angle apertures.
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ABSTRACT: We present a parametric attributed scattering model for Synthetic Aperture Radar imagery. The model characterizes both frequency and aspect dependence of scattering centers. We present algorithms for estimating the model pa-rameters from SAR image chips, and propose model order estimation algorithms that exploit nested model structures. We develop a Bayes classiier for the extracted model parameterss the classiier uses uncertainty in both extracted and predicted features. Numerical results on synthetic and measured SAR data validate the model and show encouraging results in both the ability to accurately extract scattering at-tributes and the utility of these attributes for improved discriminability of target classes.
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ABSTRACT: We describe an acoustic array project and testbed for use in undergraduate instruction and research. The testbed consists of eight high-quality acoustic micro- phones connected to a programmable data acquisition system controlled by a laptop. The system is portable, allowing for outdoor data collections. The system can be used for collecting data that is processed off-line and can also perform real-time signal processing tasks. The project and hardware provide hands-on signals and sys- tems experience for undergraduate students.
