电子测量与仪器学报

江莉,焦予栋,向世召,尚文擎,王燕妮.用于多分量信号分析的自适应广义 S 变换算法研究[J].电子测量与仪器学报,2022,36(12):136-143

用于多分量信号分析的自适应广义 S 变换算法研究

Research on adaptive generalized S-transform algorithm formulti-component signal analysis

DOI：

英文关键词:signal processing time-frequency analysis instantaneous frequency adaptive generalized S transform

基金项目:国家自然科学基金（61803294）、陕西省自然科学基础研究计划(2020JQ 684, 2020JM 499)项目资助

作者	单位
江莉	1.西安建筑科技大学信息与控制工程学院
焦予栋	1.西安建筑科技大学信息与控制工程学院
向世召	1.西安建筑科技大学信息与控制工程学院
尚文擎	1.西安建筑科技大学信息与控制工程学院
王燕妮	1.西安建筑科技大学信息与控制工程学院

Author	Institution
Jiang Li	1.College of Information and Control Engineering, Xi′an University of Architecture and Technology
Jiao Yudong	1.College of Information and Control Engineering, Xi′an University of Architecture and Technology
Xiang Shizhao	1.College of Information and Control Engineering, Xi′an University of Architecture and Technology
Shang Wenqing	1.College of Information and Control Engineering, Xi′an University of Architecture and Technology
Wang Yanni	1.College of Information and Control Engineering, Xi′an University of Architecture and Technology

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中文摘要:

时频分析是处理非平稳信号强有力的工具,S 变换作为传统的时频分析方法之一,其窗函数的尺度可以随频率改变。但是,其时频窗函数尺度变化是固定的,无法适用不同信号的局部特性,导致能量聚集性较差。本文提出了一种自适应的广义 S 变换算法,设计了由 4 个调节参数控制的广义高斯窗函数,采用浓度测量自适应优化调节参数,以寻求最佳的时频表征效果。并针对时频分析结果,采用瞬时频率重组和分量重构方法,得到各个分量的瞬时频率,同时进行平滑处理,最终实现多分量信号的参数估计。仿真实验说明,本文提出的自适应广义 S 变换算法,结合瞬时频率重组和分量重构信号方法,极大地提升了多分量信号的时频分辨率和信号分离的准确性。

英文摘要:

Time-frequency analysis is a powerful tool to deal with non-stationary signals. As one of the traditional time-frequency analysis methods, s-transform can change the scale of its window function with frequency. However, the scale variation of time-frequency window function is fixed, which cannot be applied to the local characteristics of different signals, resulting in poor energy aggregation. In this paper, an adaptive generalized S-transform algorithm is proposed, and a generalized Gaussian window function controlled by four adjusting parameters is designed. The adjustment parameters are optimized by the adaptive concentration measurement to seek the best time-frequency characterization effect. According to the results of time-frequency analysis, instantaneous frequency recombination and component reconstruction are used to obtain the instantaneous frequency of each component, and at the same time, smooth processing is carried out to achieve the parameter estimation of multi-component signals. Simulation results show that the proposed adaptive generalized S-transform algorithm, combined with instantaneous frequency recombination and component reconstruction signal method, greatly improves the time-frequency resolution of multi-component signals and the accuracy of signal separation.

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