Xie Dongri, Hong Shaohua, Yao Chaojun
Sichuan Aerospace Electronic Equipment Research Institute, Chengdu 610100, China.
School of Informatics, Xiamen University, Xiamen 316005, China.
Entropy (Basel). 2021 Apr 22;23(5):503. doi: 10.3390/e23050503.
The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications.
复杂多变的海洋环境中充斥着各种噪声，包括海洋动物的声音、工业噪声、交通噪声以及分子运动形成的噪声，这不仅干扰了港口附近居民的正常生活，还对船舶辐射噪声（S-RN）的特征提取产生了重大影响。本文提出了一种基于优化变分模态分解（OVMD）、排列熵（PE）和归一化斯皮尔曼相关系数（NSCC）的船舶辐射噪声信号特征提取新技术。首先，通过反向加权排列熵（RWPE）确定模态数，OVMD将目标信号分解为一组固有模态函数（IMF）。然后分别计算所有IMF的PE以及每个IMF与原始信号之间的SCC。随后，通过NSCC加权的PE之和为IMF提取特征参数。最后，将得到的特征向量输入支持向量机多类分类器（SVM）中以区分各类船舶。实验结果表明，所提方案能够准确识别5种船舶辐射噪声样本，识别率达到94%，高于其他已发表的方法。因此，所提方法在实际应用中更具优势。
