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http://dx.doi.org/10.6109/jkiice.2018.22.3.418

Multiaspect-based Active Sonar Target Classification Using Deep Belief Network  

Kim, Dong-wook (School of Electronics Engineering, Kyungpook National University)
Bae, Keun-sung (School of Electronics Engineering, Kyungpook National University)
Seok, Jong-won (Department of Information and Communication, Changwon National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.3, 2018 , pp. 418-424 More about this Journal
Abstract
Detection and classification of underwater targets is an important issue for both military and non-military purposes. Recently, many performance improvements are being reported in the field of pattern recognition with the development of deep learning technology. Among the results, DBN showed good performance when used for pre-training of DNN. In this paper, DBN was used for the classification of underwater targets using active sonar, and the results are compared with that of the conventional BPNN. We synthesized active sonar target signals using 3-dimensional highlight model. Then, features were extracted based on FrFT. In the single aspect based experiment, the classification result using DBN was improved about 3.83% compared with the BPNN. In the case of multi-aspect based experiment, a performance of 95% or more is obtained when the number of observation sequence exceeds three.
Keywords
Active Sonar; Highlight model; Deep learning; Deep belief network; Fractional fourier transform;
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