基于非线性动态特征的海洋背景噪声特征提取的比较研究
Guanni Ji1, Yu Wang1, Fei Wang1
1School of Zhongxing Communication, Xi'an Traffic Engineering Institute, Xi'an 710300, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
提取海洋背景噪声 (MBN) 特性是一项挑战. 这项研究表明非线性动态特征,包括和Lempel-Ziv复杂性 (LZC),有效地捕获MBN复杂性,以改善环境参数反转.
科学领域:
- 海洋学 海洋学 海洋学
- 信号处理 信号处理
- 复杂性科学 复杂性科学
背景情况:
- 海洋背景噪声 (MBN) 对于了解海洋环境至关重要.
- 由于海洋环境的复杂性,提取MBN特征是复杂的.
研究的目的:
- 为了研究有效的MBN特征提取的非线性动态特征.
- 为了比较各种基于和Lempel-Ziv复杂性 (LZC) 的方法的性能.
主要方法:
- 基于的方法的比较分析:分散 (DE),偶数 (PE),模糊 (FE) 和样本 (SE).
- 基于Lempel-Ziv复杂性 (LZC) 的方法的比较分析:LZC,分散性LZC (DLZC),排列性LZC (PLZC) 和基于分散性的LZC (DELZC).
- 使用模拟实验进行评估,以评估时间序列复杂性检测和实际的MBN数据,以评估特征提取性能.
主要成果:
- 所有测试的非线性动力学特征都有效地检测时间序列复杂性的变化.
- 基于和基于LZC的特征提取方法都显示出MBN的卓越性能.
结论:
- 非线性动态特征为MBN特征提取提供了强大的方法.
- 开发的方法提高了使用MBN.准确的海洋环境参数逆转的潜力.
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