Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks

TitleImproved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks
Publication TypeJournal Article
Year of Publication2016
AuthorsMachado-Fernández, J. R., and J. C. Bacallao-Vidal
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume4
Number2
Date Published12/2016
Pagination7-11
Abstract

The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE). The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications.

KeywordsArtificial Neural Networks, Estimation, Pareto Distributed Clutter
DOI10.9781/ijimai.2016.421
URLhttp://www.ijimai.org/journal/sites/default/files/files/2016/11/ijimai20164_2_1_pdf_46986.pdf
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