Compound-Gaussian clutter modeling with an inverse gaussian texture distribution

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

Research output: Contribution to journalArticlepeer-review

121 Scopus citations


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 languageEnglish (US)
Article number6319356
Pages (from-to)876-879
Number of pages4
JournalIEEE Signal Processing Letters
Issue number12
StatePublished - 2012

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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


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