基于稀疏表示的水声信号分类识别
发布时间：2019-05-06 20:12
【摘要】：传统的水声信号分类都是直接使用原信号进行处理的,特征提取耗时长,数据量大,针对这两个缺点,提出了一种压缩感知理论中基于稀疏表示的水声信号特征提取方法;该方法利用了水声信号在DCT变换域的稀疏特性,将信号的稀疏表示作为目标特征,并采用SVM分类算法进行分类识别。仿真结果表明,该方法不仅减少了特征向量的计算时间,还提高了目标分类识别率,还降低了水声信号的传输数据量,压缩率可达96%,在实际工程应用中具有较高的实用价值。
[Abstract]:The traditional classification of underwater acoustic signal is directly processed by the original signal, and the feature extraction is time-consuming and large amount of data. In view of these two shortcomings, a feature extraction method of underwater acoustic signal based on sparse representation in compression sensing theory is proposed. In this method, the sparse representation of underwater acoustic signal in DCT transform domain is used as the target feature, and the SVM classification algorithm is used to classify and recognize the underwater acoustic signal. The simulation results show that this method not only reduces the computing time of eigenvector, but also improves the recognition rate of target classification, and also reduces the amount of data transmitted by underwater acoustic signals, and the compression ratio can reach 96%. It has high practical value in practical engineering application.
【作者单位】： 西北工业大学航海学院;
【分类号】：TN911.7
[Abstract]:The traditional classification of underwater acoustic signal is directly processed by the original signal, and the feature extraction is time-consuming and large amount of data. In view of these two shortcomings, a feature extraction method of underwater acoustic signal based on sparse representation in compression sensing theory is proposed. In this method, the sparse representation of underwater acoustic signal in DCT transform domain is used as the target feature, and the SVM classification algorithm is used to classify and recognize the underwater acoustic signal. The simulation results show that this method not only reduces the computing time of eigenvector, but also improves the recognition rate of target classification, and also reduces the amount of data transmitted by underwater acoustic signals, and the compression ratio can reach 96%. It has high practical value in practical engineering application.
【作者单位】： 西北工业大学航海学院;
【分类号】：TN911.7
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