水下目标信号的结构化稀疏特征提取方法

A structured sparse feature extraction method of acoustic signal emitted from underwater target

为了提取具有噪声鲁棒性的水下目标信号特征用于水下目标识别,通过分析实测水下目标辐射噪声的时频谱,发现其时频谱中往往存在稀疏分布的具有目标区分性信息的强能量窄带线谱。结合稀疏分解理论,利用窄带线谱的结构化稀疏特点,提出一种稀疏特征提取方法。该特征提取方法借助稀疏贝叶斯学习模型,利用相邻帧样本间的相关性信息,能够有效增强窄带线谱成分,提高特征的噪声鲁棒性。并用一组实测数据对该特征的分类性能进行了测试,结果表明该特征在训练样本和测试样本噪声条件不匹配的情况下,能够保持较高的识别正确率,是一种具有噪声鲁棒性的特征。

To extract noise-robust feature of underwater target signal for target recognition,the time-frequency spectrum analysis was used,and the high-energy narrow band line spectra which provide distinguishable information in underwater target recognition were found in most cases,and they were sparsely distributed in the time-frequency spectra of the noise emitted from the underwater targets.On the basis of sparse decomposition theory,a sparse feature extraction method is proposed,which combines the structured sparse characteristics.This method adopts sparse Bayesian learning model,which can utilize the correlation information between adjacent frame samples to strengthen the narrow band line spectra and improve the noise robustness of the proposed feature extraction method.An experiment based on a measured dataset was conducted,and its result showed that the proposed feature is robust to noise.Moreover,it achieved a high recognition performance when the test samples and the train samples were in mismatched noise conditions.