为了完成对实测水下冲击信号的建模,充分提取信号声学特性是非常有必要的。本文以水池实测信号为研究对象,采用不同分析方法处理信号,最终发现希尔伯特黄变换(HHT)时频分析方法优势明显,在HHT时频谱图上可以看到非常清晰的时间、频率分布信息。在此基础上,使用多段线性调频(LFM)信号模拟实测水下冲击信号。比较实测信号与建模信号的HHT谱图可知,建模信号的频谱走势与实测信号相同,具有与实测水声信号非常相似的时频特性,验证了HHT时频分析的优势与建模效果。
In order to complete the modeling of the measured underwater impact signal, it is necessary to fully extract the acoustic characteristics of the signal. In this paper, the measured signal of the pool is taken as the research object, and different analysis methods are used to process the signal. Finally, it is found that the Hilbert Huang transform (HHT) time-frequency analysis method has obvious advantages, and very clear time and frequency distribution information can be seen on the HHT time-frequency spectrum. On this basis, the multi - segment linear frequency modulation (LFM) signal is used to simulate the measured underwater impact signal. Comparing the HHT spectra of the measured signal and the modeled signal, it can be concluded that the spectrum trend of the modeled signal is the same as the measured signal, and has the time-frequency characteristics very similar to the measured underwater acoustic signal, which verifies the advantages of HHT time-frequency analysis and the modeling effect.
2023,45(23): 122-126 收稿日期:2022-11-11
DOI:10.3404/j.issn.1672-7649.2023.23.021
分类号:TB561
作者简介:朱拥勇(1981-),男,助理研究员,研究方向为装备试验指挥与管理
参考文献:
[1] 陈博涛. EMD分解联合时频分析在阵列声波信号中的应用[D]. 长春: 吉林大学, 2010.
[2] 肖祺. 低信噪比信号调制识别中的时频分析与应用[D]. 杭州: 杭州电子科技大学, 2021.
[3] 黄会婷. 基于时频分析的超声信号处理方法研究[D]. 荆州: 长江大学, 2020.
[4] 曹晓勇. 基于时频分析的砼脱空声频信号特征研究[D]. 西安: 长安大学, 2014.
[5] 马妍. 基于时频分析的地震信号瞬时参数提取方法研究[D]. 大庆: 东北石油大学, 2013.
[6] BABA T. Time-frequency analysis using short time Fourier transform[J]. The Open Acoustics Journal, 2012, 5(1): 245–253.
[7] DJEBBARI A, REGUIG F B. Short-time Fourier transform analysis of the phonocardiogram signal[C]. Proceedings of ICECS, IEEE, 2000, 2: 844–847.
[8] ZHONG J, HUANG Y. Time-frequency representation based on an adaptive short-time Fourier transform[J]. IEEE Transactions on Signal Processing, 2010, 58(10): 5118–5128
[9] MATEO C, TALAVERA J A. Short-time Fourier transform with the window size fixed in the frequency domain[J]. Digital Signal Processing, 2018, 77: 13–21
[10] PORTNOFF M. Time-frequency representation of digital signals and systems based on short-time Fourier analysis[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1980, 28(1): 55–69
[11] 王红萍. 非平稳信号时频分析方法性能研究[D]. 南京: 南京航空航天大学, 2008.
[12] 张旗. 非平稳信号的改进时频分析方法研究[D]. 西安: 西安电子科技大学, 2020.
[13] AALAM M K,. Shubhanga k n. emd based detrending of non-linear and non-stationary power system signals[C]//Proceedings of INDICON, IEEE, 2021: 1–6.
[14] HUANG N E, CHERN C C, HUAng K, et al. A new spectral representation of earthquake data: Hilbert spectral analysis of station TCU129, Chi-Chi, Taiwan, 21 September 1999[J]. Bulletin of the Seismological Society of America, 2001, 91(5): 1310–1338
[15] HUANG N E, SHEN Z, LONG S R. A new view of nonlinear water waves: the Hilbert spectrum[J]. Annual Review of Fluid Mechanics, 1999, 31: 417–457
[16] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A:mathematical, physical and engineering sciences, 1998, 454(1971): 903–995
[17] 王黎黎. 基于希尔伯特—黄变换的时频分析算法研究[D]. 西安: 西安电子科技大学, 2009.
[18] 宋倩倩. 基于Hilbert-Huang变换的语音信号时频分析[D]. 无锡: 江南大学, 2009.
[19] 孙涛, 刘晶璟, 孔凡, 等. 小波变换和希尔伯特—黄变换在时频分析中的应用[J]. 中国水运(理论版), 2006(11): 111–113
[20] 杨培杰, 印兴耀, 张广智. 希尔伯特-黄变换地震信号时频分析与属性提取[J]. 地球物理学进展, 2007(5): 1585–1590
[21] HUANG N E. Review of empirical mode decomposition[J]. Proceedings of SPIE - The International Society for Optical Engineering, 2001, 4391: 71–80
[22] HUANG N E. New method for nonlinear and nonstationary time series analysis: empirical mode decomposition and Hilbert spectral analysis[J]. Proceedings of SPIE - The International Society for Optical Engineering, 2000, 4056: 197–209