Computer generation of correlated non-Gaussian radar clutter

Rangaswamy, Muralidhar; Weiner, D.D.; Öztürk, A.

doi:10.1109/7.366297

Cited by 155 publications

(70 citation statements)

References 14 publications

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“…The PDF of Mahalanobis distance d is given by f(d) = 2p/2r(/2)dhP(d) (7) and can be used to uniquely reduce the multidimensional PDF modeling problem to an equivalent one-dimensional one. If x(n), .…”

Section: Random Vectors With Elliptically Contoured (Ec) Distributionsmentioning

confidence: 99%

<title>Statistics of hyperspectral imaging data</title>

et al. 2001

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Characterization of the joint (among wavebands) probability density function (pdf) of hyperspectral imaging (HSI) data is crucial for several applications, including the design of constant false alarm rate (CFAR) detectors and statistical classifiers. HSI data are vector (or equivalently multivariate) data in a vector space with dimension equal to the number of spectral bands. As a result, the scalar statistics utilized by many detection and classification algorithms depend upon the joint pdf of the data and the vector-to--scalar mapping defining the specific algorithm. For reasons of analytical tractability, the multivariate Gaussian assumption has dominated the development and evaluation of algorithms for detection and classification in HSI data, although it is widely recognized that it does not always provide an accurate model for the data. The purpose ofthis paper is to provide a detailed investigation ofthejoint and marginal distributional properties of HSI data. To this end, we assess how well the multivanate Gaussian pdf describes HSI data using univariate techniques for evaluating marginal normality, and techniques that use unidimensional views (projections) of multivanate data. We show that the class of elliptically contoured distributions, which includes the multivariate normal distribution as a special case, provides a better characterization of the data. Finally, it is demonstrated that the class of univariate stable random variables provides a better model for the heavy-tailed output distribution of the well known matched filter target detection algorithm.

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Section: Random Vectors With Elliptically Contoured (Ec) Distributionsmentioning

confidence: 99%

<title>Statistics of hyperspectral imaging data</title>

et al. 2001

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“…44 29 Statistical analysis of the mixed scene background to evaluate model fit, band decorrelation, and normality. 48 Geometrical illustration of the operation of subspace matched filter detection.…”

mentioning

confidence: 99%

Detection Algorithms for Hyperspectral Imaging Applications

Manolakis¹

2002

133

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Detection and identification of military and civilian targets from airborne platforms using hyperspectral sensors is of great interest. Relative to multispectral sensing, hyperspectral sensing can increase the detectability of pixel and subpixel size targets by exploiting finer detail in the spectral signatures of targets and natural backgrounds. A multitude of adaptive detection algorithms for resolved and subpixel targets, with known or unknown spectral characterization, in a background with known or unknown statistics, theoretically justified or ad hoc, with low or high computational complexity, have appeared in the literature or have found their way into software packages and end-user systems. The purpose of this report is twofold. First, we present a unified mathematical treatment of most adaptive matched filter detectors using common notation, and we state clearly the underlying theoretical assumptions. Whenever possible, we express existing ad hoc algorithms as computationally simpler versions of optimal methods. Second, we present a comparative performance analysis of the basic algorithms using theoretically obtained performance characteristics. We focus on algorithms characterized by theoretically desirable properties, practically desired features, or implementation simplicity. Sufficient detail is provided for others to verify and expand this evaluation and framework. A primary goal is to identify best-of-class algorithms for detailed performance evaluation. Finally, we provide a taxonomy of the key algorithms and introduce a preliminary experimental framework for evaluating their performance.

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“…The AML estimator of C can be computed using (8). However, as the texture PDF is known (23) and the PDF generating function, h p (q) (13), reduces to an analytical form given in (34), it is desirable to find the ML estimator of normalized covariance matrix. In the case of the G distribution, the…”

Section: Parameter Estimationmentioning

confidence: 99%

“…The data is generated using a simulation procedure similar to the one detailed in [34], [35] for known PDFs. A summary of steps for simulated PolSAR data generation are listed here: 1) Compute C 1/2 for a given covariance matrix C, where C 1/2 (C 1/2 ) * t = C, by using a unitary transformation U to diagonalize C,…”

Section: Convergence Of G Distribution Parameters Using Simulated Polmentioning

confidence: 99%