基于多尺度Chirplet稀疏分解和Wigner-Ville變換的時頻分析方法

張?zhí)祢U; 全盛榮; 強幸子; 江曉磊

doi:10.11999/JEIT160750

基于多尺度Chirplet稀疏分解和Wigner-Ville變換的時頻分析方法

doi: 10.11999/JEIT160750

基金項目:

國家自然科學基金(61671095, 61371164, 61275099)，信號與信息處理重慶市市級重點實驗室建設(shè)項目(CSTC2009 CA2003)，重慶市教育委員會科研項目(KJ130524, KJ1600427, KJ1600429)

計量
- 文章訪問數(shù): 1422
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-07-14
- 修回日期: 2017-03-30
- 刊出日期: 2017-06-19

Time-frequency Analysis Method Based on Multi-scale Chirplet Sparse Decomposition and Wigner-Ville Transform

Funds:

The National Natural Science Foundation of China (61671095, 61371164, 61275099), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003), The Research Project of Chongqing Educational Commission (KJ130524, KJ1600427, KJ1600429)

摘要

摘要: 針對多分量多項式相位信號(mc-PPS)的Wigner-Ville分布存在的時頻干擾問題，該文提出一種基于多尺度Chirplet稀疏分解和Wigner-Ville變換的時頻分析方法。該方法采用多尺度的Chirplet基函數(shù)對信號進行投影分解，通過延時相關(guān)解調(diào)的分數(shù)階傅里葉變換(FRFT)搜索投影系數(shù)最大的基函數(shù)，將搜索得到的基函數(shù)通過Wigner-Ville變換和最佳路徑連接方法，逐次獲得使分解信號能量最大的信號分量及其時頻分布。仿真結(jié)果表明，該方法能在低信噪比條件下有效抑制等振幅mc-PPS的自交叉項和互交叉項的干擾，具有最佳的時頻聚集性，克服了全局搜索基函數(shù)計算量大的問題，適用于非平穩(wěn)信號的分析和處理。
- 多尺度Chirplet /
- Wigner-Ville變換 /
- 分數(shù)階傅里葉變換 /
- 時頻干擾 /
- 信噪比
Abstract: To solve the problem of time-frequency interference existing in the multicomponent Polynomial Phase Signal (mc-PPS) Wigner-Ville distribution, a new time-frequency analysis method based on the multi-scale Chirplet sparse decomposition and Wigner-Ville transform is proposed. This method projects mc-PPS onto the multi-scale Chirplet base functions, searching best base functions by the improved FRactional Fourier Transform (FRFT). Through the Wigner-Ville transform and best path pursuit algorithm, the base functions constitute largest energy signals component and power distribution in turns. Simulation results verify that the proposed method can restrain effectively the cross-interference of constant mc-PPS in low Signal-to-Noise Ratio condition, maintain a high time-frequency aggregation, and overcome the large computation of global searching algorithm. Furthermore, this method is suitable for non-stationary signals analysis and processing.
- Multi-scale Chirplet /
- Wigner-Ville transform /
- FRactional Fourier Transform (FRFT) /
- Time-frequency interference /
- Signal-to-Noise Ratio

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參考文獻(21)

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