电子学报 ›› 2017, Vol. 45 ›› Issue (8): 1864-1872.DOI: 10.3969/j.issn.0372-2112.2017.08.009

• 学术论文 • 上一篇    下一篇

基于人工鱼群算法的自适应随机共振方法研究

孔德阳, 彭华, 马金全   

  1. 解放军信息工程大学 信息系统工程学院, 河南郑州 450002
  • 收稿日期:2016-05-23 修回日期:2016-11-21 出版日期:2017-08-25
    • 通讯作者:
    • 孔德阳
    • 作者简介:
    • 彭华,男,1976出生,江西萍乡人,解放军信息工程大学教授、博士生导师,主要研究方向为软件无线电、通信信号处理等.E-mail:pengh139@139.com;马金全,男,1975出生,甘肃天水人,解放军信息工程大学副教授,主要研究方向为软件无线电、信号逆向处理与分析等.
    • 基金资助:
    • 国家自然科学基金 (No.61401511)

Adaptive Stochastic Resonance Method Based on Artificial-fish Swarm Optimization

KONG De-yang, PENG Hua, MA Jin-quan   

  1. PLA Information Engineering University, Zhengzhou, Henan 450002, China
  • Received:2016-05-23 Revised:2016-11-21 Online:2017-08-25 Published:2017-08-25

摘要: 随机共振为微弱通信信号的检测提供了新途径.本文提出一种基于人工鱼群算法的自适应随机共振新方法,重点研究基于随机共振的微弱周期信号检测技术,将人工鱼群算法和归一化处理结合增强随机共振,适当添加噪声并设定自适应步长策略及迭代停止条件.理论分析和仿真结果表明,对比传统群智能算法处理随机共振其在算法适应性及稳定性、最佳共振精确度、寻优收敛速度、精度方面有明显提升,并为信噪比增益带来3-5dB的提升,运算时间复杂度降低逾70%.

关键词: 随机共振, 人工鱼群算法, 归一化处理, 自适应步长

Abstract: Stochastic resonance provided new ways detecting weak communication signals.A new method based on artificial-fish swarm optimization is proposed whose emphasis is on weak periodic signal detection based on stochastic resonance.Artificial-fish swarm optimization is combined with normalization of stochastic resonance.Noise is utilized by adding it to the signal while new adaptive step strategy and iteration stopping strategy are also used.The results of theoretical analysis and simulation experiments show this proposed method have great promotions on adaptability,stability,precision of the optimal resonance and the convergence speed,as well as a great promotion of 3-5dB on SNR gain compared with traditional stochastic resonance based on swarm optimization.Meanwhile,the time complexity of calculation is decreased by 70%.

Key words: stochastic resonance, artificial-fish swarm optimization, normalization method, adaptive step

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