• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2137-2142
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• 2012

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target classification technique has been considered as a difficult technique. And it has a difficult in collecting actual underwater data. In this paper, we implemented the simulator to synthesize the active target signal, to extract feature and to classify the target in the underwater environment. In target signal synthesis, highlight and three-dimensional model are used and multi-aspect based hidden markov model is used for target classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.418-424
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• 2018

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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.1878-1883
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• 2012

Generally, in the underwater target recognition, feature vectors are extracted from the target signal utilizing spatial information according to target shape/material characteristics. And, various signal processing techniques have been studied to extract feature vectors which is less sensitive to the location of the receiver. In this paper, we analyzed the characteristics of synthesized underwater objects using canonical correlation analysis method which is relatively less sensitive to the location of receiver. Canonical correlation analysis is applied to two consecutive backscattered sonar returns at different aspect angles to analyze the correlation characteristics in multi-aspect environment.