摘要
这份报纸用生产并且放射的声音为轮船识别和分类建议一个新方法在水下。到那么，一个三步的过程被建议。首先，预处理操作被利用减少噪音效果并且为特征抽取提供信号。第二，一幅二进制图象，用信号分割的频率光谱做了，被形成提取有效特征。第三，一个神经分类器被设计分类信号。二条途径，建议方法和基于分数维图形的方法在真实数据上被比较并且测试。比较结果比基于分数维图形的方法显示了更好的识别能力和建议方法的更柔韧的性能。因此，建议方法能改进识别精确性在水下声学的目标。
This paper proposes a new method for ship recognition and classification using sound produced and radiated underwater. To do so, a three-step procedure is proposed. First, the preprocessing operations are utilized to reduce noise effects and provide signal for feature extraction. Second, a binary image, made from frequency spectrum of signal segmentation, is formed to extract effective features. Third, a neural classifier is designed to classify the signals. Two approaches, the proposed method and the fractal-based method are compared and tested on real data. The comparative results indicated better recognition ability and more robust performance of the proposed method than the fractal-based method. Therefore, the proposed method could improve the recognition accuracy of underwater acoustic targets.
