高聚焦時(shí)頻分析算法研究

喬麗紅 衛(wèi)彬 楊鐵軍 秦瑤

摘? 要： 時(shí)頻分析技術(shù)是研究非平穩(wěn)信號(hào)時(shí)頻分布的重要手段，但傳統(tǒng)的時(shí)頻分析技術(shù)無(wú)法精確地反映信號(hào)的時(shí)頻分布特點(diǎn)。文中主要介紹了三種高聚焦時(shí)頻分析技術(shù)：小波變換（WT）、同步擠壓小波變換（SSWT）、CWT?based ConceFT 。首先分別闡述了以上三種高聚焦時(shí)頻分析技術(shù)的原理，然后將這三種高聚焦時(shí)頻分析方法應(yīng)用于非平穩(wěn)信號(hào)，并比較它們的時(shí)頻分析效果。結(jié)果表明，SSWT和CWT?based ConceFT明顯提高了小波變換的時(shí)頻分辨率，小波變換和同步擠壓小波變換的噪聲魯棒性較差，CWT?based ConceFT的噪聲魯棒性較好。

關(guān)鍵詞： 時(shí)頻分析; 小波變換; 同步擠壓小波變換; CWT?based ConceFT; 時(shí)頻分辨率; 噪聲魯棒性

中圖分類號(hào)： TN911.6?34? ? ? ? ? ? ? ? ? ? ? ? 文獻(xiàn)標(biāo)識(shí)碼： A? ? ? ? ? ? ? ? ? ? ? ? ?文章編號(hào)： 1004?373X（2020）13?0040?04

Study on well?focusing time?frequency analysis algorithm

QIAO Lihong1， 2， WEI Bin1， YANG Tiejun1， QIN Yao1

（1. College of Information Science and Engineering， Henan University of Technology， Zhengzhou 450001， China;

2. Key Laboratory of Food Information Processing and Control， Ministry of Education， Zhengzhou 450001， China）

Abstract： The time?frequency analysis technology is an important means to study the time?frequency distribution of non?stationary signals， but the traditional time?frequency analysis technology cannot accurately reflect the characteristics of time?frequency distribution of signals. In this paper， three well?focusing time?frequency analysis techniques， e.g. wavelet transform （WT）， synchrosqueezed wavelet transform （SSWT）， CWT?based concentration of frequency and time （CWT?based ConceFT） are introduced. The principles of the three well?focusing time?frequency analysis techniques are expounded respectively. The three technologies were applied to non?stationary signals for comparing their time?frequency analysis effects. The experiment results show SSWT and CWT?based ConceFT have improved the time?frequency resolution of wavelet transform obviously， and the noise robustness of CWT?based ConceFT is better than that of WT and SSWT.

Keywords： time?frequency analysis; wavelet transform; synchrosqueezed wavelet transform; CWT?based ConceFT; time?frequency resolution; noise robustness