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http://dx.doi.org/10.9717/kmms.2016.19.10.1775

Multi-aspect Based Active Sonar Target Classification  

Seok, Jongwon (Dept. of Information & Communication, Changwon National University)
Publication Information
Journal of Korea Multimedia Society / v.19, no.10, 2016 , pp. 1775-1781 More about this Journal
Abstract
Generally, in the underwater target recognition, feature vectors are extracted from the target signal utilizing spatial information according to target shape/material characteristics. In addition, various signal processing techniques have been studied to extract feature vectors which are less sensitive to the location of the receiver. In this paper, we synthesized active echo signals using 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to echo signals to extract signal features. For the performance verification, classification experiments were performed using backpropagation and probabilistic neural network classifiers based on single aspect and multi-aspect method. As a result, we obtained a better recognition result using proposed feature extraction and multi-aspect based method.
Keywords
Active Sonar; Multi-aspect; Classification; Highlight Model; Fractional Fourier Transform; Neural Network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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