期刊
IEEE TRANSACTIONS ON MAGNETICS
卷 45, 期 3, 页码 1454-1457出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMAG.2009.2012677
关键词
Ground penetrating radar; inverse problems; multiobjective training algorithms; neural networks (NNs); noise; regularization methods
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.
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