Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/LSP.2012.2221698 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
Gaussian distribution,probability,radar clutter,radar resolution,CG class,CG distribution,amplitude cumulative distribution function,closed-form expression,complex K -distribution,complex t-distribution,compound-Gaussian clutter modeling,compound-Gaussian distribution,distributional properties,heavy-tailed CG model,high-resolution radar,inverse Gaussian texture distribution,kurtosis parameter,nonGaussian clutter modeling,probability density function,real-world radar lake-clutter,sea clutter modeling,$K$-distribution,$t$ -distribution,Compound-Gaussian distribution,inverse Gaussian texture,radar clutter | Inverse Gaussian distribution,Artificial intelligence,Kurtosis,Statistical physics,K-distribution,Pattern recognition,Clutter,Generalized inverse Gaussian distribution,Gaussian,Constant false alarm rate,Statistics,Probability density function,Mathematics | Journal |
Volume | Issue | ISSN |
19 | 12 | 1070-9908 |
Citations | PageRank | References |
21 | 1.30 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esa Ollila | 1 | 351 | 33.51 |
| David E. Tyler | 2 | 186 | 21.79 |
| Visa Koivunen | 3 | 1917 | 187.81 |
| H. V. Poor | 4 | 25411 | 1951.66 |