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Simulation of correlated Pareto distributed sea clutter | IEEE Conference Publication | IEEE Xplore

Simulation of correlated Pareto distributed sea clutter


Abstract:

The memoryless nonlinear transformation method is used to simulate Pareto distributed sea clutter with a specified correlation function for the clutter power. The Pareto ...Show More

Abstract:

The memoryless nonlinear transformation method is used to simulate Pareto distributed sea clutter with a specified correlation function for the clutter power. The Pareto distribution is formed from a compound model with a negative exponential distribution for the speckle intensity and an inverse gamma distribution for the clutter power. An estimator based on the expectation value of z log z is obtained for the Pareto shape parameter, which has comparable accuracy to the maximum likelihood estimator and the advantage of also being applicable to the compound gamma distribution which arises for multiple looks.
Date of Conference: 09-12 September 2013
Date Added to IEEE Xplore: 04 November 2013
ISBN Information:
Print ISSN: 1097-5764
Conference Location: Adelaide, SA, Australia
References is not available for this document.

I. Introduction

Analysis of the detection performance of maritime surveillance radars requires a good model of the sea clutter returns. The K distribution is a well established model for sea clutter which is formed by compounding a negative exponential distribution for the speckle intensity with a gamma distribution for the clutter power [1]. The K distribution often does not match the tail of the clutter distribution very well, so another component is introduced in the KA [2] and KK [3] models. Recent work has shown that the Pareto distribution is able to fit the observed clutter distribution, including the tail, without introducing another component [4], [5], [6]. The Pareto distribution can be formulated as a compound model with a negative exponential distribution for the speckle intensity and an inverse gamma distribution for the clutter power.

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1.
K. D. Ward, R. J. A. Tough and S. Watts, Sea Clutter: Scattering, the K Distribution and Radar Performance, The Institution of Engineering and Technology, London 2006.
2.
D. Middleton, "New physical-statistical methods and models for clutter and reverberation: the KA-distribution and related probability structures", IEEE J. Ocean Eng.,1999, 24, (3), pp. 261-284
3.
Y. Dong, "Distribution of X-band high resolution and high grazing angle sea clutter," Research Report DSTO-RR-0316, DSTO, 2006.
4.
M. Farshchian and F. L. Posner, "The Pareto distribution for low grazing angle and high resolution X-band sea clutter," in IEEE Radar Conference, 2010, pp. 789-793.
5.
G. V. Weinberg, "Assessing Pareto fit to high-resolution high-grazingangle sea clutter", Electronics Letters, 2011, 47 (8), pp. 516-517.
6.
L. Rosenberg and S. Bocquet, "The Pareto distribution for high grazing angle sea clutter", submitted to IEEE International Geoscience and Remote Sensing Symposium, 2013.
7.
R. J. A. Tough and K. D. Ward, "The correlation properties of gamma and other non-Gaussian processes generated by memoryless nonlinear transformation", J. Phys D 32, 1999, pp. 3075-3084.
8.
D. Blacknell and R. J. A. Tough, "Parameter estimation for the Kdistribution based on [z log(z)]", IEEE Proc. Radar, Sonar Navig., 2001, 148, (6), pp. 309-312.
9.
S. D. Dubey, "Compound gamma, beta and F distributions," Metrika, 1970, 16, pp. 27-31.
10.
A. R. Didonato and A. H. Morris, "Computation of the incomplete gamma function ratios and their inverse", ACM Trans. Math. Software 12, 1986, pp. 377-393.
11.
J. Venier and D. Serachitopol, "Library of routines for cumulative distribution functions, inverses, and other parameters (DCDFLIB)", 2003, URL: https://biostatistics. mdanderson. org/SoftwareDownload/Sin gleSoftware. aspx?Software-Id=21
12.
G. Davidson, gleninjapan@hotmail. com (code available at http://www. codeforge. com/article/195648).

References

References is not available for this document.