电子学报 ›› 2019, Vol. 47 ›› Issue (12): 2647-2652.DOI: 10.3969/j.issn.0372-2112.2019.12.026

• 科研通信 • 上一篇    下一篇

一种随机共振联合小波变换的符号速率估计方法

张政1, 马金全1,2   

  1. 1. 中国人民解放军战略支援部队信息工程大学, 河南郑州 450002;
    2. 信号分析与处理国家级实验教学示范中心, 河南郑州 450002
  • 收稿日期:2019-02-26 修回日期:2019-06-03 出版日期:2019-12-25 发布日期:2019-12-25
  • 作者简介:张政 男,1995年生,陕西宝鸡人.中国人民解放军战略支援部队信息工程大学硕士生,主要研究方向为微弱信号处理等.E-mail:zz2018zheng@gmail.com;马金全 男,1975年生,甘肃张掖人.中国人民解放军战略支援部队信息工程大学与信号分析与处理国家级实验教学示范中心副教授,主要研究方向为软件无线电、信号分析和处理等.
  • 基金资助:
    国家自然科学基金(No.61401511)

A Symbol Rate Estimation Algorithm Based on Stochastic Resonance Combined with Wavelet Transform

ZHANG Zheng1, MA Jin-quan1,2   

  1. 1. PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan 450002, China;
    2. National Signal Analysis & Processing Experiment Education Demonstration Center, Zhengzhou, Henan 450002, China
  • Received:2019-02-26 Revised:2019-06-03 Online:2019-12-25 Published:2019-12-25

摘要: 在非合作通信中,很多情况下由于信道恶化,使得接收信号的信噪比偏低,导致无法对符号速率这一重要参数进行准确估计.随机共振能够在一定程度上利用噪声能量,使其转移并增强微弱信号,小波变换则可以有效检测相位和幅度的瞬变,利用二者各自优势,提出了一种将随机共振与小波变换联合进行MPSK(Multiple Phase Shift Keying,多进制数字相位调制)和MQAM(Multiple Quadrature Amplitude Modulation,多进制正交幅度调制)信号符号速率的估计方法.先利用自适应参数调节随机共振为带噪信号匹配最佳系统参数,之后利用Haar小波变换进一步提取突变信息,不仅弥补了单独使用随机共振效果不佳及其作为非线性系统易发散的缺点,还降低了小波最佳尺度难以确定的影响.仿真实验表明,该方法能够在一定程度上提高输出峰值,降低信噪比门限,适合于低信噪比下的符号速率估计.

关键词: 微弱信号处理, 符号速率, 参数调节随机共振, 小波变换

Abstract: In non-cooperative communications,due to the deterioration of the channel,the Signal-Noise Ratio (SNR) of the receiving signal is very low in many cases,resulting in the inability to accurately estimate the symbol rate.Stochastic resonance can use noise energy to transfer and amplify the weak signals to some extent,and wavelet transform can effectively detect the instantaneous variation of phase and amplitude of the signals.By using the advantages of both methods,a combination algorithm for estimating the symbol rate of MPSK and MQAM is proposed.First,the adaptive parameter-tuning stochastic resonance is used to match the optimal system parameters for noisy signals,and then the transient information is further extracted by Haar wavelet transform,which not only compensates for the shortcomings of the poor effect of using stochastic resonance alone and its easy divergence as a non-linear system,but also reduce the influence of the optimal scale of the wavelet.The simulation result shows that this method can improve the output peak and reduce the SNR threshold,which is suitable for the symbol rate estimation under low SNR.

Key words: weak signal processing, symbol rate, parameter-tuning stochastic resonance, wavelet transform

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