Compound-Gaussian clutter modeling with an inverse gaussian texture distribution

Esa Ollila; David E. Tyler; Visa Koivunen; H. Vincent Poor

doi:10.1109/LSP.2012.2221698

Compound-Gaussian clutter modeling with an inverse gaussian texture distribution

Esa Ollila, David E. Tyler, Visa Koivunen, H. Vincent Poor

Research output: Contribution to journal › Article › peer-review

139 Scopus citations

Abstract

The compound-Gaussian (CG) distributions have been successfully used for modelling the non-Gaussian clutter measured by high-resolution radars. Within the CG class, the complex K-distribution and the complex t-distribution have been used for modelling sea clutter which is often heavy-tailed or spiky in nature. In this paper, a heavy-tailed CG model with an inverse Gaussian texture distribution is proposed and its distributional properties such as closed-form expressions for its probability density function (p.d.f.) as well as its amplitude p.d.f., amplitude cumulative distribution function and its kurtosis parameter are derived. Experimental validation of its usefulness for modelling measured real-world radar lake-clutter is provided where it is shown to yield better fits than its widely used competitors.

Original language	English (US)
Article number	6319356
Pages (from-to)	876-879
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	19
Issue number	12
DOIs	https://doi.org/10.1109/LSP.2012.2221698
State	Published - 2012

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

Keywords

Compound-Gaussian distribution
K-distribution
inverse Gaussian texture
radar clutter
t-distribution

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10.1109/LSP.2012.2221698

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title = "Compound-Gaussian clutter modeling with an inverse gaussian texture distribution",

abstract = "The compound-Gaussian (CG) distributions have been successfully used for modelling the non-Gaussian clutter measured by high-resolution radars. Within the CG class, the complex K-distribution and the complex t-distribution have been used for modelling sea clutter which is often heavy-tailed or spiky in nature. In this paper, a heavy-tailed CG model with an inverse Gaussian texture distribution is proposed and its distributional properties such as closed-form expressions for its probability density function (p.d.f.) as well as its amplitude p.d.f., amplitude cumulative distribution function and its kurtosis parameter are derived. Experimental validation of its usefulness for modelling measured real-world radar lake-clutter is provided where it is shown to yield better fits than its widely used competitors.",

keywords = "Compound-Gaussian distribution, K-distribution, inverse Gaussian texture, radar clutter, t-distribution",

author = "Esa Ollila and Tyler, {David E.} and Visa Koivunen and Poor, {H. Vincent}",

note = "Funding Information: Manuscript received May 15, 2012; revised August 10, 2012; accepted August 28, 2012. Date of publication October 02, 2012; date of current version October 24, 2012. This work was supported in part by the U.S. Office of Naval Research under Grant N00014-12-1-0767. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Wing-Kin Ma.",

year = "2012",

doi = "10.1109/LSP.2012.2221698",

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T1 - Compound-Gaussian clutter modeling with an inverse gaussian texture distribution

AU - Ollila, Esa

AU - Tyler, David E.

AU - Koivunen, Visa

AU - Poor, H. Vincent

N1 - Funding Information: Manuscript received May 15, 2012; revised August 10, 2012; accepted August 28, 2012. Date of publication October 02, 2012; date of current version October 24, 2012. This work was supported in part by the U.S. Office of Naval Research under Grant N00014-12-1-0767. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Wing-Kin Ma.

PY - 2012

Y1 - 2012

N2 - The compound-Gaussian (CG) distributions have been successfully used for modelling the non-Gaussian clutter measured by high-resolution radars. Within the CG class, the complex K-distribution and the complex t-distribution have been used for modelling sea clutter which is often heavy-tailed or spiky in nature. In this paper, a heavy-tailed CG model with an inverse Gaussian texture distribution is proposed and its distributional properties such as closed-form expressions for its probability density function (p.d.f.) as well as its amplitude p.d.f., amplitude cumulative distribution function and its kurtosis parameter are derived. Experimental validation of its usefulness for modelling measured real-world radar lake-clutter is provided where it is shown to yield better fits than its widely used competitors.

AB - The compound-Gaussian (CG) distributions have been successfully used for modelling the non-Gaussian clutter measured by high-resolution radars. Within the CG class, the complex K-distribution and the complex t-distribution have been used for modelling sea clutter which is often heavy-tailed or spiky in nature. In this paper, a heavy-tailed CG model with an inverse Gaussian texture distribution is proposed and its distributional properties such as closed-form expressions for its probability density function (p.d.f.) as well as its amplitude p.d.f., amplitude cumulative distribution function and its kurtosis parameter are derived. Experimental validation of its usefulness for modelling measured real-world radar lake-clutter is provided where it is shown to yield better fits than its widely used competitors.

KW - Compound-Gaussian distribution

KW - K-distribution

KW - inverse Gaussian texture

KW - radar clutter

KW - t-distribution

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JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

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ER -

Compound-Gaussian clutter modeling with an inverse gaussian texture distribution

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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