In this paper, an automatic system is presented for target recognition using target echo signals of High
Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and
classification from measured real target echo signal waveforms by using X–band pulse radar. The past studies in
the field of radar target recognition have shown that the learning speed of feedforward neural networks is in
general much slower than required and it has been a major disadvantage. There are two key reasons forth is
status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to
train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning
algorithms [1-25]. To resolve these disadvantages of feedforward neural networks for automatic target
recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for
single-hidden layer feedforward neural networks (SLFNs) [1-25] which randomly choose hidden nodes and
analytically determines the output weights of SLFNs. The experimental results show that the new algorithm can
produce good generalization performance in most cases and can learn thousands of times faster than
conventional popular learning algorithms for feedforward neural networks.
2nd Mosharaka International Conference on Communications and Signal Processing (MIC-CSP 2012)
Congress
2012 Global Congress on Communications and Signal Processing (GC-CSP 2012), 6-8 April 2012, Barcelona, Spain
Pages
13-18
Topics
Wavelet Processing Neural Networks
ISSN
2227-331X
DOI
BibTeX
@inproceedings{310CSP2012,
title={Radar Target Recognition Based on Wavelet - ELM},
author={Engin Avci},
booktitle={2012 Global Congress on Communications and Signal Processing (GC-CSP 2012)},
year={2012},
pages={13-18},
doi={}},
organization={Mosharaka for Research and Studies}
}