| 基于电磁环境数据的信息挖掘与关联分析 |
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| 引用本文： | 李爽,刘海鹏,郭兰图.基于电磁环境数据的信息挖掘与关联分析[J].太赫兹科学与电子信息学报,2022,20(1):8-15. |
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| 作者姓名： | 李爽 刘海鹏 郭兰图 |
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| 作者单位： | School of Information and Communication Engineering，Harbin Engineering University，Harbin Heilongjiang 150000，China;Military Representative Office of Equipment Development Department in Jinan Area，Jinan Shandong 250100，China; China Research Institute of Radio Wave Propagation，Qingdao Shandong 266107，China |
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| 摘 要： | 城市通信技术的改革换代和用频设备的逐渐增多使得电磁环境变得越来越复杂.充分了解频谱资源利用的特性是提高频谱管理效率的关键.为了更全面地探索频谱利用的特点,提出一套完整的对复杂多样电磁环境大数据进行详细数据质量分析和处理的流程,分别对处于同一服务的不同信道、处于不同服务的不同信道进行频谱相关性分析,证明了频谱之间的相关性...
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| 关 键 词： | 关联分析 属性构造 图像处理 多维混合高斯模型 电磁环境数据 |
| 收稿时间： | 2021/4/21 0:00:00 |
| 修稿时间： | 2021/5/1 0:00:00 |
Information mining and association analysis based on electromagnetic environment data |
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| LI Shuang,LIU Haipeng,GUO Lantu.Information mining and association analysis based on electromagnetic environment data[J].Journal of Terahertz Science and Electronic Information Technology,2022,20(1):8-15. |
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| Authors: | LI Shuang LIU Haipeng GUO Lantu |
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| Affiliation: | 1.School of Information and Communication Engineering, Harbin Engineering University, Harbin Heilongjiang 150000, China;2.Military Representative Office of Equipment Development Department in Jinan Area, Jinan Shandong 250100, China;3.China Research Institute of Radio Wave Propagation, Qingdao Shandong 266107, China |
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| Abstract: | With the development of urban communication technology and the increase of frequency equipment, the electromagnetic environment becomes more and more complex. Fully understanding the characteristics of spectrum resource utilization in the past is the key to improve the efficiency of spectrum management. A complete process about detailed data quality analysis for big data in complex and diverse electromagnetic environment is proposed, in order to explore the characteristics of spectrum utilization more comprehensively. The spectrum correlation for different channels in the same service, and for different channels in different services, is performed. Attribute construction is carried out for big data of electromagnetic environment, including the attributes of frequency dimension occupancy and time dimension occupancy. The multi-dimensional Gaussian mixture model in the field of image processing is introduced to remove the background noise of the electromagnetic signal and extract the electromagnetic signal, which can lay the foundation for the subsequent information mining and association analysis. |
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| Keywords: | association analysis attribute construction image processing multi-dimensional Gaussian mixture model electromagnetic environment data |
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