Xie Dongri, Sun Haixin, Qi Jie
School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.
School of Informatics, Xiamen University, Xiamen 316005, China.
Entropy (Basel). 2020 Jun 3;22(6):620. doi: 10.3390/e22060620.
Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected signals the new focus in the field of underwater acoustic target recognition. In view of this, this paper presents a novel hybrid feature extraction scheme integrating improved variational mode decomposition (IVMD), normalized maximal information coefficient (norMIC) and permutation entropy (PE) for SRN signals. Firstly, the IVMD method is employed to decompose the SRN signals into a number of finite intrinsic mode functions (IMFs). The noise IMFs are then filtered out by a denoising method before PE extraction. Next, the MIC between each retained IMF and the raw SRN signal and PE of retained IMFs are calculated, respectively. After this, the norMICs are used to weigh the PE values of the retained IMFs and the sum of the weighted PE results is regarded as the classification parameter. Finally, the feature vectors are fed into the particle swarm optimization-based support vector machine multi-class classifier (PSO-SVM) to identify different types of SRN samples. The experimental results have indicated that the classification accuracy of the proposed method is as high as 99.1667%, which is much higher than that of other currently existing methods. Hence, the method proposed in this paper is more suitable for feature extraction of SRN signals in practical application.
由于海洋环境噪声的存在，再加上水声信道的不稳定性，传感器检测到的舰船辐射噪声（SRN）信号往往会受到噪声污染以及传输介质引起的失真影响，使得对原始检测信号进行去噪成为水声目标识别领域的新焦点。鉴于此，本文提出了一种针对SRN信号的新型混合特征提取方案，该方案集成了改进的变分模态分解（IVMD）、归一化最大信息系数（norMIC）和排列熵（PE）。首先，采用IVMD方法将SRN信号分解为多个有限固有模态函数（IMF）。然后在提取PE之前，通过去噪方法滤除噪声IMF。接下来，分别计算每个保留的IMF与原始SRN信号之间的MIC以及保留的IMF的PE。在此之后，用norMIC对保留的IMF的PE值进行加权，加权PE结果的总和被视为分类参数。最后，将特征向量输入基于粒子群优化的支持向量机多类分类器（PSO-SVM）以识别不同类型的SRN样本。实验结果表明，所提方法的分类准确率高达99.1667%，远高于其他现有方法。因此，本文提出的方法在实际应用中更适合于SRN信号的特征提取。
