李国正,张波.基于Duffing振子检测频率未知微弱信号的新方法[J].仪器仪表学报,2017,(1):181-189
基于Duffing振子检测频率未知微弱信号的新方法
Novel method for detecting weak signal with unknown frequency based on duffing oscillator
  
DOI:
中文关键词:  微弱信号  Duffing振子  噪声  遗传算法
英文关键词:weak signal  Duffing oscillator  noise  genetic algorithm
基金项目:国家自然科学基金青年科学基金(51505154)项目资助
作者单位
李国正 华南理工大学电力学院广州510640 
张波 华南理工大学电力学院广州510640 
AuthorInstitution
Li Guozheng The School of Electric Power, South China University of Technology, Guangzhou 510640, China 
Zhang Bo The School of Electric Power, South China University of Technology, Guangzhou 510640, China 
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中文摘要:
      针对现有混沌振子难以检测频率未知微弱信号这一难点,提出利用Duffing振子输出值的方差峰值结合遗传算法检测淹没在强噪声背景中频率未知微弱信号的一种新方法。从分析混沌系统结构参数的阈值入手,讨论了周期策动力的频率、初始相位和噪声对系统运行状态的影响;研究系统输出值方差与系统状态的对应关系,探讨待测信号频率以及与周期策动力之间相位差对状态变量方差和状态转换时间的影响。由此,提出采用具有相位偏移的Duffing振子阵列覆盖全相位,并结合遗传算法,优化求解不同频率输入信号下系统输出值方差的极值,以此得到待测信号频率的方法。该方法解决了现有混沌振子类检测方法必须已知信号频率的限制。实验结果证明了本方法能准确、快速地检测待测信号频率。新方法的状态判定简便、检测精度高、更为灵活、适应性强,为微弱信号的检测提供了新的手段。
英文摘要:
      In order to detect weak signal with unknown frequency, this paper presents a novel method for detecting the unknown frequency signal by combining the variance peak value of the Duffing oscillator and genetic algorithm (GA). Firstly, the impact of periodic driving signal with different frequencies, initial phase angle and noise on the system operating state are analyzed. Second, the corresponding relationship between the system output variance and the system running state is studied. The effect of the test signal frequency, as well as the phase difference between the periodic driving signal and the test signal on the state variables variance and the state transition time are discussed. Finally, the new method is proposed, which using a number of Duffing oscillators with different initial phases to cover all phase signal detection. Moreover, the proposed method combines with genetic algorithm to obtain the detected signal frequency by optimizing the calculation of peak value of system output varaince under different input frequency signals. This method resolves the limitation of existing weak signal detection method based on the chaotic oscillator, in which the signal frequency needs to be known. Experimental results show that the method can accurately and flexibly detect the frequency of the test signal and has strong adaptability. This workt provides a novel approach for the weak signal detection.
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