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Kullback-Leibler divergence and the Pareto-Exponential approximation.

G V Weinberg1

  • 1Defence Science and Technology Group, Edinburgh, Australia.

Springerplus
|June 2, 2016
PubMed
Summary

Researchers analyzed the Pareto distribution for X-band maritime radar clutter. They found the Kullback-Leibler divergence determines when an Exponential distribution provides a valid and optimal approximation for simpler radar detection schemes.

Area of Science:

  • Radar systems engineering
  • Statistical signal processing
  • Information theory

Background:

  • The Pareto distribution is increasingly used to model X-band maritime surveillance radar clutter.
  • Approximating Pareto clutter with an Exponential distribution simplifies radar detection algorithms.
  • Understanding the conditions for this approximation is crucial for effective radar design.

Purpose of the Study:

  • To investigate the validity and optimality of approximating Pareto clutter with an Exponential distribution.
  • To introduce an information theory approach for analyzing this approximation.
  • To determine the optimal Exponential approximation for a given Pareto model.

Main Methods:

  • Analysis of the asymptotic behavior of the Pareto distribution.

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  • Application of information theory, specifically Kullback-Leibler divergence.
  • Comparative analysis of Pareto and Exponential distributions.

    • Main Results:

    • The Kullback-Leibler divergence quantifies the accuracy of the Pareto-Exponential approximation.
    • Conditions for the validity of the Exponential approximation were established.
    • A method to find the optimal Exponential approximation for a given Pareto model was developed.

    Conclusions:

    • The information theory approach provides a robust method for assessing Pareto-Exponential approximation in radar clutter.
    • Accurate approximation enables the use of simpler, more efficient radar detection schemes.
    • This research facilitates improved performance in maritime surveillance radar systems.