电子学报 ›› 2019, Vol. 47 ›› Issue (11): 2323-2329.DOI: 10.3969/j.issn.0372-2112.2019.11.013

• 学术论文 • 上一篇    下一篇

低信噪比通信信号的自适应调参随机共振方法

张政, 马金全   

  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
  • 收稿日期:2018-12-27 修回日期:2019-05-10 出版日期:2019-11-25 发布日期:2019-11-25
  • 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副教授,主要研究方向为软件无线电、信号分析和处理等.

Adaptive Parameter-Tuning Stochastic Resonance Method for Communication Signals Under Low SNR

ZHANG Zheng, MA Jin-quan   

  1. PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan 450002, China
  • Received:2018-12-27 Revised:2019-05-10 Online:2019-11-25 Published:2019-11-25

摘要: 参数调节随机共振系统中参数的选择对输出信号的效果优劣具有决定性作用.本文针对目前随机共振无法通用地处理多类微弱通信信号的问题,提出基于自适应调参随机共振的信号增强方法.首先,从信号的特征子空间角度阐释了随机共振的能量转移本质,提出将基于奇异值分解的测度函数作为评价函数进行寻优.其次,在分析了两个不同系统参数的作用后,利用幅度归一化对单参数优化,降低了复杂度,并将滑动平均滤波器加入随机共振模块来防止幅度漂移.最后,以人工鱼群算法为基础,模块化设计出方法的整体框架和具体步骤.仿真结果表明,针对四类共九种信号,该方法能够以平均4至5次的迭代收敛速度实现带噪声的信号和非线性系统的匹配.

关键词: 通信信号处理, 信号去噪, 随机共振, 双稳态系统, 低信噪比

Abstract: The selection of the parameters of the stochastic resonance system plays a decisive role in the effect of the output signal.In this paper,aiming at the problem that the stochastic resonance cannot process many kinds of weak communication signals in general,an enhancement method based on adaptive parameter-tuning stochastic resonance is proposed.Firstly,the energy transfer nature of the stochastic resonance is explained from the characteristic subspace of the signal,and a measuring function based on singular value decomposition is presented as the evaluation function.Secondly,after analyzing the effects of two different system parameters,the amplitude normalization is utilized to optimize one single parameter,which reduces the complexity.And the sliding average filter is added to the module to prevent the phenomenon of the amplitude drift.Finally,based on the artificial fish swarm algorithm,the overall framework and specific steps of the method are modularly designed.The simulation result demonstrates that the proposed method can match the noisy signal and the nonlinear system with an average of 4 to 5 iteration convergence speeds for four kinds of signals.

Key words: communication signal processing, signal denoising, stochastic resonance, bistable system, low Signal-Noise Ratio (SNR)

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