Journal ArticleDOI
Non-Gaussian random vector identification using spherically invariant random processes
TLDR
E elegant and tractable techniques are presented for characterizing the probability density function (PDF) of a correlated non-Gaussian radar vector and an important result providing the PDF of the quadratic form of a spherically invariant random vector (SIRV) is presented.Abstract:
With the modeling of non-Gaussian radar clutter in mind, elegant and tractable techniques are presented for characterizing the probability density function (PDF) of a correlated non-Gaussian radar vector. The need for a library of multivariable correlated non-Gaussian PDFs in order to characterize various clutter scenarios is discussed. Specifically,. the theory of spherically invariant random processes (SIRPs) is examined in detail. Approaches based on the marginal envelope PDF and the marginal characteristic function have been used to obtain several multivariate non-Gaussian PDFs. An important result providing the PDF of the quadratic form of a spherically invariant random vector (SIRV) is presented. This result enables the problem of distributed identification of a SIRV to be addressed. >read more
Citations
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Journal ArticleDOI
Detection algorithms for hyperspectral imaging applications
Dimitris G. Manolakis,G. Shaw +1 more
TL;DR: This work focuses on detection algorithms that assume multivariate normal distribution models for HSI data and presents some results which illustrate the performance of some detection algorithms using real hyperspectral imaging (HSI) data.
Journal ArticleDOI
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
L. Sendur,Ivan Selesnick +1 more
TL;DR: This work proposes new non-Gaussian bivariate distributions, and corresponding nonlinear threshold functions (shrinkage functions) are derived from the models using Bayesian estimation theory, but the new shrinkage functions do not assume the independence of wavelet coefficients.
Journal ArticleDOI
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
TL;DR: This work suggests a two-stage separation process: a priori selection of a possibly overcomplete signal dictionary in which the sources are assumed to be sparsely representable, followed by unmixing the sources by exploiting the their sparse representability.
Journal ArticleDOI
Attributed scattering centers for SAR ATR
Lee C. Potter,Randolph L. Moses +1 more
TL;DR: This paper presents a framework for feature extraction predicated on parametric models for the radar returns, and presents statistical analysis of the scattering model to describe feature uncertainty, and provides a least-squares algorithm for feature estimation.
Journal ArticleDOI
Covariance matrix estimation for CFAR Detection in correlated heavy tailed clutter
Fulvio Gini,Maria Greco +1 more
TL;DR: Numerical results show that the AML estimator can be calculated with a very small number of iterations, it has a negligible performance loss with respect to the ML and less computational complexity, and guarantees the desired CFAR property to the detector.
References
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Journal ArticleDOI
A representation theorem and its applications to spherically-invariant random processes
TL;DR: The form of the unit threshold likelihood ratio receiver in the detection of a known deterministic signal in additive sirp noise is shown to be a correlation receiver or a matched filter.
Characterisation of radar clutter as a spherically invariant random process
Ernesto Conte,Maurizio Longo +1 more
TL;DR: In this article, a statistical characterisation of clutter as a complex random process is needed in the design of optimum detection schemes, and the model is modeled as a spherically invariant random process (SIRP), assuming that its PDFs can be expressed as non-negative definite quadratic forms, a generalisation of a Gaussian process.
Journal ArticleDOI
Modelling and simulation of non-Rayleigh radar clutter
TL;DR: Modelling the clutter as a compound Gaussian or a spherically invariant random process allows a complete specification of the clutter suitable for use in radar design and lends itself readily to computer simulation procedures.
Journal ArticleDOI
Detection in the presence of spherically symmetric random vectors
TL;DR: A theorem characterizing the form of SS random vectors X is proved and the problem of detecting a known signal vector in the presence of X + N when \rho =I is looked at.