宽带声呐湖底沉积物分类研究

Classification of lake bottom sediments using a wideband active sonar system

本文论述如何运用实际宽带主动声呐的回波信号识别湖底沉积物介质类型。五类湖底沉积层介质分别为：淤泥、岩石、孵石、沙、沙／砾石混合层。比较了等Ｑ和等带宽两种子带能量频域特征提取方法，确认了等Ｑ方法的优点。应用标准前馈ＢＰ人工神经网络结构，对五类湖底沉积层介质的平均正确识别率达到８２．１％。

The paper presents a method of lake bottom sediment classification using echo signals provided by a wideband active sonar. The five types of sediments are silt. rocks. pebbles. sand. and mixture of sand and gravel. Different sub-band divisions for frequency domain feature extraction are compared and it is shown that the contant Q method provides better results in comparison with the constant bandwidth method. Using a standard feedforward BP network, 82. 1% correct classification in average has been achieved for the five classes of sediments. The advantages of using wideband sonar signals to improve the classification Performance are also shown.