Noise reduction in a non-homogenous ground penetrating radar problem by multiobjective neural networks

X. L. Travassos, D. A G Vieira, V. Palade, A. Nicolas

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper applies artificial neural networks (ANNs) trained with a multiobjective algorithm to preprocess the ground penetrating radar data obtained from a finite-difference time-domain (FDTD) model. This preprocessing aims at improving the target's reflected wave signal-to-noise ratio (SNR). Once trained, the NN behaves as an adaptive filter which minimizes the cross-validation error. Results considering both white and colored Gaussian noise, with many different SNR, are presented and they show the effectiveness of the proposed approach.

Original languageEnglish
Article number4787489
Pages (from-to)1454-1457
Number of pages4
JournalIEEE Transactions on Magnetics
Volume45
Issue number3
DOIs
Publication statusPublished - Mar 2009
Externally publishedYes

Keywords

  • Ground penetrating radar
  • Inverse problems
  • Multiobjective training algorithms
  • Neural networks (NNs)
  • Noise
  • Regularization methods

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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