袁力, 刘奇, 孙正文, 刘晔, 王玥, 刘烽, 陈卯蒸. 现场环境下设备区域电磁干扰检测与识别方法[J]. 电波科学学报, 2017, 32(6): 650-656. doi: 10.13443/j.cjors.2017082301
      引用本文: 袁力, 刘奇, 孙正文, 刘晔, 王玥, 刘烽, 陈卯蒸. 现场环境下设备区域电磁干扰检测与识别方法[J]. 电波科学学报, 2017, 32(6): 650-656. doi: 10.13443/j.cjors.2017082301
      YUAN Li, LIU Qi, SUN Zhengwen, LIU Ye, WANG Yue, LIU Feng, CHEN Maozheng. Detection and identification for electromagnetic interference of equipment area on-site[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2017, 32(6): 650-656. doi: 10.13443/j.cjors.2017082301
      Citation: YUAN Li, LIU Qi, SUN Zhengwen, LIU Ye, WANG Yue, LIU Feng, CHEN Maozheng. Detection and identification for electromagnetic interference of equipment area on-site[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2017, 32(6): 650-656. doi: 10.13443/j.cjors.2017082301

      现场环境下设备区域电磁干扰检测与识别方法

      Detection and identification for electromagnetic interference of equipment area on-site

      • 摘要: 现场环境下电磁环境复杂,设备区域电磁干扰的有效识别可为射电天文台站频谱分析提供重要依据.文章基于现场电磁干扰测量频谱,提出了一种电子设备区域干扰信号检测与识别方法.首先,针对对着设备区域和隔过设备区域两种测量状态多组频谱数据进行预处理,实现信噪分离,运用二值法检测信号边界,提取频谱中干扰信号.然后,依据一元回归算法和通道占用统计方法对多组测量频谱中干扰信号进行相关性分析,识别设备区域内电磁辐射频谱.所提方法对新疆天文台南山站内多个设备区域电磁辐射频谱检测结果与人工识别结果对比表明,本文方法94%以上的识别结果与人工识别结果相符,验证了现场环境下设备电子区域电磁干扰检测与识别方法的准确性和通用性.

         

        Abstract: It is very difficult to measure and identify the electromagnetic interference on-site due to the transient signals. Effective identification of the electromagnetic interference for electronic equipment area provides an important basis for the spectral analysis of the radio astronomy site. Base on electromagnetic interference measurement spectrum, an electromagnetic interference (EMI) signal detection and identification method for electronic equipment area on-site is proposed. The multi-group environment spectrum and equipment area spectrum are obtained and preprocessed to realize separation of signal and noise. Additionally, a two-value method is presented to determine the signal boundary and extract interference signal from spectrum. Then, the regression algorithm and the channel occupancy statistical methods are employed to analyze the correlation of signals between equipment area and environment spectrum, then the electromagnetic radiation spectrum of equipment area are identified. The effectiveness and accuracy of this method are verified with tested spectrum coming from Nan Shan 26 m telescope site. More than 94% of the recognition results are in conformity with the artificial recognition results, indicating that this method has high versatility and accuracy.

         

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