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http://dx.doi.org/10.7471/ikeee.2018.22.4.1044

Target/non-target classification using active sonar spectrogram image and CNN  

Kim, Dong-Wook (School of Electronics Engineering, Kyungpook National University)
Seok, Jong-Won (Dept. of Information & Communication Eng., Changwon National University)
Bae, Keun-Sung (School of Electronics Engineering, Kyungpook National University)
Publication Information
Journal of IKEEE / v.22, no.4, 2018 , pp. 1044-1049 More about this Journal
Abstract
CNN (Convolutional Neural Networks) is a neural network that models animal visual information processing. And it shows good performance in various fields. In this paper, we use CNN to classify target and non-target data by analyzing the spectrogram of active sonar signal. The data were divided into 8 classes according to the ratios containing the targets and used for learning CNN. The spectrogram of the signal is divided into frames and used as inputs. As a result, it was possible to classify the target and non-target using the characteristic that the classification results of the seven classes corresponding to the target signal sequentially appear only at the position of the target signal.
Keywords
Sonar signal processing; Active sonar; Target classification; Convolutional Neural Networks; Spectrogram;
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Times Cited By KSCI : 1  (Citation Analysis)
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