Department of Information and Communication, School of Informatics, Xiamen University, Xiamen 316005, China.
Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt.
Sensors (Basel). 2021 Dec 24;22(1):112. doi: 10.3390/s22010112.
Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.
由于水声信道的复杂性和独特性，被动声纳检测到的船舶辐射噪声（SRN）往往会发生较大的失真。因此，提出了 SRN 特征提取方法来改善检测到的被动声纳信号。然而，当前用于 SRN 特征提取的方法存在许多缺点。考虑到这一点，本文提出了一种新的多阶段特征提取方法，该方法基于增强变分模态分解（EVMD）、加权排列熵（WPE）、局部切空间排列（LTSA）和基于粒子群优化的支持向量机（PSO-SVM），对当前的 SRN 特征提取进行增强。在提出的方法中，首先，通过将 SRN 信号分解为有限个固有模态函数（IMF）来增强传统 VMD 的分解操作，然后计算每个 IMF 的 WPE。然后，使用 LTSA 方法将获得的高维特征降维到二维特征。最后，将特征向量输入到 PSO-SVM 多类分类器中，以实现不同类型的 SRN 样本的分类。仿真和实验结果表明，所提出的方法的识别率优于传统的 SRN 特征提取方法，识别率高达 96.6667%。
