Classification of Underwater Signals Using Neural Networks

Chin-Hsing  Chen; Jiann-Der Lee; Ming-Chi  Lin

doi:10.6180/jase.2000.3.1.04

Classification of Underwater Signals Using Neural Networks

Chin-Hsing Chen¹, Jiann-Der Lee² and Ming-Chi Lin¹

¹Department of Electrical Engineering National Cheng Kung University Taiwan 700, R. O. C.
²Department of Electrical Engineering Chang Gung University Tao-Yuan, Taiwan 333, R. O. C.

Received: November 10, 1999
Accepted: June 12, 2000
Publication Date: June 12, 2000

Download Citation: ||https://doi.org/10.6180/jase.2000.3.1.04

ABSTRACT

In this paper, four kinds of neural network classifiers have been used for the classification of underwater passive sonar signals radiated by ships. Classification process can be divided into two stages. In the preprocessing and feature extraction stage, Two-Pass Split-Windows (TPSW) algorithm is used to extract tonal features from the average power spectral density (APSD) of the input data. In the classification stage, four kinds of static neural network classifiers are used to evaluate the classification results, inclusive of the probabilistic based classifier-Probabilistic Neural Network (PNN), the hyperplane based classifier-Multilayer Perceptron (MLP), the kernel based classifierAdaptive Kernel Classifier (AKC), and the exemplar based classifierLearning Vector Quantization (LVQ). For comparison, the same classifiers but using Dyadic Wavelet Transform (DWT) in the feature extraction are used to evaluate the performance of the proposed method. Experimental results show that feature extraction using TPSW gives better classification performance than using DWT, but require more computation time. In neural network classifiers, exemplar classifiers give better performance than the others both in learning speed and classification rate. Moreover, the classifier using LVQ families with data extracted by DWT can reach the same correction rate (100%) as the classifier using various networks with data extracted by TPSW. Detail discussion for experimental results are also included.

Keywords: Underwater Signal Classification, Neural Networks, TPSW, PNN, MLP, AKC, LVQ

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