基于双树复小波的被动目标特征提取方法研究

Research on feature extraction for passive target based on dual-tree complex wavelet transform

针对水下目标辐射噪声特征提取困难的问题,提出一种基于双树复小波变换的特征提取方法。首先,采用一种改进的小波阈值去噪方法去除水下目标辐射噪声信号中的噪声成分,该方法可以获得优于经典小波阈值去噪方法的结果;其次,在特征提取过程中,采用具有近似平移不变性的双树复小波变换,该方法能够克服信号时移带来的影响,得到稳定的目标特征向量。仿真试验和实航数据处理结果表明,本文方法提取的目标特征向量比使用离散小波变换提取的结果更稳定。

Due to the difficulty in feature extraction of underwater target radiated noise, a novel method based on dualtree complex wavelet transform（DT-CWT） is presented. Firstly, a modified method based on wavelet threshold de-noising is applied to reduce the noise from the signal and shows a better de-noising performance. Secondly, DT-CWT is introduced during feature extraction to calculate the feature vectors and the results are almost invariant even if the signal is shifted in time domain. The experiment results of simulation signal and the real underwater acoustic signal show that the method shown in this paper can get more stable feature vectors than discrete wavelet transform（DWT）.