刘奇,冯冬冬. 一种面向宽带频谱的信噪分离方法[J]. 电波科学学报,2021,36(2):201-207. DOI: 10.13443/j.cjors.2020031601
      引用本文: 刘奇,冯冬冬. 一种面向宽带频谱的信噪分离方法[J]. 电波科学学报,2021,36(2):201-207. DOI: 10.13443/j.cjors.2020031601
      LIU Q, FENG D D. A signal-to-noise separation method for broadband spectrum[J]. Chinese journal of radio science,2021,36(2):201-207. (in Chinese) DOI: 10.13443/j.cjors.2020031601
      Citation: LIU Q, FENG D D. A signal-to-noise separation method for broadband spectrum[J]. Chinese journal of radio science,2021,36(2):201-207. (in Chinese) DOI: 10.13443/j.cjors.2020031601

      一种面向宽带频谱的信噪分离方法

      A signal-to-noise separation method for broadband spectrum

      • 摘要: 自动化频谱监测中,信噪分离为宽带频谱序列进一步信号识别和统计提供算法支撑,而现有的频域信噪分离方法存在准确性较低的问题,为提高算法准确性,提出了一种面向宽带频谱的信噪分离方法. 首先,分析实测宽带频谱样本数据特征,采用爱泼斯-普利方法验证了频谱噪声数据样本满足正态性;其次,结合标准差理论,采用邻值比较和窗口划分方法确定信噪分离阈值;最后,以信噪分离准确性为目标,通过迭代优化邻值比较判别值及窗口划分宽度,提高了算法的准确性. 数据验证和对比分析表明,本文方法的信噪分离阈值与频谱噪声吻合较佳,准确性高至90.53%,明显高于现有信噪分离方法,且运行速度快,能够较好地应用于自动化频谱监测的实时信号识别与统计.

         

        Abstract: Signal-to-noise separation provides algorithmic support for further signal identification and statistics of wide-band spectrum sequences for automated spectrum monitoring. However, the existing methods of frequency-domain signal-to-noise separation have low accuracy issues. In order to improve the accuracy of the algorithm, a signal-to-noise separation method for wideband spectrum is proposed. Firstly, the characteristics of the measured broadband spectral sample data are analyzed, and the Epps-Pulley method is employed to verify that the spectral noise data samples satisfy the normality. Secondly, along with the standard deviation theory, the signal-to-noise separation threshold is obtained by using neighbor value comparison and window partitioning methods. Finally, the key parameters is iteratively optimizes for improving the accuracy of the algorithm. Data verification and comparative analysis show that the signal-to-noise separation threshold of this method agrees well with the spectral noise, and the accuracy is as high as 90.53%, which is significantly higher than the existing methods. Furthermore, the algorithm runs fast and can be better applied to real-time signal identification and statistics.

         

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