A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy.
A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy.
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基于自适应噪声、杜芬混沌振荡器和加权排列熵的完全系综经验模态分解的舰船无线电噪声线性频谱频率特征提取技术
DOI：
10.3390/e21050507
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发表时间：
2019-05-18
期刊：
影响因子：
--
通讯作者：
Yang X
中科院分区：
文献类型：
--
作者：
Li Y;Wang L;Li X;Yang X
军舰在现代海战场中发挥着重要作用。研究舰船无线电噪声信号的线谱特征，有助于实现不同类型舰船的分类识别，为海战场提供关键信息。提出了一种基于自适应噪声完全集成经验模式分解（CEEMDAN）、Duffing混沌振子（DCO）和加权排列熵（W-PE）的舰船无线电噪声线性谱频率特征提取方法。本文提出的线性谱频率特征提取技术CEEMDAN-DCO-W-PE与其他线性谱频率特征提取技术相比，具有以下优点：（i）CEEMDAN作为一种自适应数据驱动算法，具有比经验模式分解（EMD）和集成EMD（EEMD）更精确、更可靠的分解性能，且无需预先设定参数，（2）DCO利用其对微弱周期信号的敏感性和对噪声的免疫性，可以检测窄带周期性舰船信号的线性谱;首次将W-PE用于水声信号特征提取，并与传统的排列熵（PE）进行了比较，W-PE在一定程度上增加了振幅信息。首先，利用CEEMDAN将舰船无线电噪声信号分解为从高频到低频的若干个本征模态函数。然后，DCO用于检测低频IMF的线性谱。最后，我们可以确定的线性频谱频率的低频IMF使用W-PE。实验结果表明，该技术能够准确提取仿真信号的线谱频率，对真实的舰船无线电噪声信号具有比传统技术更高的分类识别率。
Warships play an important role in the modern sea battlefield. Research on the line spectrum features of warship radio noise signals is helpful to realize the classification and recognition of different types of warships, and provides critical information for sea battlefield. In this paper, we proposed a novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), duffing chaotic oscillator (DCO), and weighted-permutation entropy (W-PE). The proposed linear spectrum frequency feature extraction technique, named CEEMDAN-DCO-W-PE has the following advantages in comparison with other linear spectrum frequency feature extraction techniques; (i) as an adaptive data-driven algorithm, CEEMDAN has more accurate and more reliable decomposition performance than empirical mode decomposition (EMD) and ensemble EMD (EEMD), and there is no need for presetting parameters, such as decomposition level and basis function; (ii) DCO can detect the linear spectrum of narrow band periodical warship signals by way of utilizing its properties of sensitivity for weak periodical signals and the immunity for noise; and (iii) W-PE is used in underwater acoustic signal feature extraction for the first time, and compared with traditional permutation entropy (PE), W-PE increases amplitude information to some extent. Firstly, warship radio noise signals are decomposed into some intrinsic mode functions (IMFs) from high frequency to low frequency by CEEMDAN. Then, DCO is used to detect linear spectrum of low-frequency IMFs. Finally, we can determine the linear spectrum frequency of low-frequency IMFs using W-PE. The experimental results show that the proposed technique can accurately extract the line spectrum frequency of the simulation signals, and has a higher classification and recognition rate than the traditional techniques for real warship radio noise signals.
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影响因子：
3.4
作者：
Wang, Shuguang;Zeng, Xiangyang
通讯作者：
Zeng, Xiangyang
影响因子：
5.6
作者：
Bustos, Alejandro;Rubio, Higinio;Carlos Garcia-Prada, Juan
通讯作者：
Carlos Garcia-Prada, Juan
基于希尔伯特变换和间歇混沌的水声微弱信号检测
影响因子：
1
作者：
Chen Zhi-Guang;Li Ya-An;Chen Xiao
通讯作者：
Chen Xiao
DOI：
10.3390/s18030793
发表时间：
2018-03-06
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Bustos A;Rubio H;Castejón C;García-Prada JC
通讯作者：
García-Prada JC
影响因子：
3.3
作者：
Damasevicius, Robertas;Napoli, Christian;Wozniak, Marcin
通讯作者：
Wozniak, Marcin