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周芳芳, 曾媛, 赵颖, 张蓉, 王劲松, 金雷, 郑伟, 汪云海. 无线电频谱与无线电信号数据协同可视分析方法[J]. 计算机辅助设计与图形学学报, 2017, 29(1): 27-37.
引用本文: 周芳芳, 曾媛, 赵颖, 张蓉, 王劲松, 金雷, 郑伟, 汪云海. 无线电频谱与无线电信号数据协同可视分析方法[J]. 计算机辅助设计与图形学学报, 2017, 29(1): 27-37.
Zhou Fangfang, Zeng Yuan, Zhao Ying, Zhang Rong, Wang Jinsong, Jin Lei, Zheng Wei, Wang Yunhai. A Joint Visual Analysis on Radio Frequency Spectrum and Radio Signal Data[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29(1): 27-37.
Citation: Zhou Fangfang, Zeng Yuan, Zhao Ying, Zhang Rong, Wang Jinsong, Jin Lei, Zheng Wei, Wang Yunhai. A Joint Visual Analysis on Radio Frequency Spectrum and Radio Signal Data[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29(1): 27-37.

无线电频谱与无线电信号数据协同可视分析方法

A Joint Visual Analysis on Radio Frequency Spectrum and Radio Signal Data

  • 摘要: 无线电频谱数据和无线电信号特征数据是无线电管理人员了解复杂电磁环境的主要信息来源,将两者协同起来分析是无线电管理领域的新发展方向,但频谱数据结构化程度低且频谱分析专业门槛高,信号数据在时频上离散、量大且信号特征提取不稳定,这些问题会严重影响分析效率.为此,提出了一种面向无线电频谱数据与无线电信号数据协同工作的可视分析方法,将经典的频谱可视化与新颖的信号可视化方法相结合,提供多种交互手段实现2种数据的联动分析和相互验证.文中采用了信号投影图作为人机交互接口,帮助分析人员可视交互地判别信号数量并聚类信号特征数据.同时还设计了一种新颖的信号流图,用于同时展示不同无线电信号的多特征时变模式.通过实验和用户评估,文中原型系统能较好地完成信号特征记录聚类、信号时变特征分析和信号数据提取准确性分析等需要两种数据密切配合才能完成的分析任务.

     

    Abstract: Radio spectrum monitoring data and radio signal feature data are the main sources for understanding complicated radio environment. Faced with unstructured spectrum data and unstable signal feature data, we need perform a joint analysis on these two types of data. This paper proposes a visual analysis method for the cooperative work of radio spectrum data and radio signal data. First, we integrate signal data visualization methods into conventional radio spectrum visualization techniques. Secondly, we combine both automatic clustering and visual clustering together for helping analysts discern the number of radio signals. Thirdly, we design a novel flow-style visualization that can smoothly visualize multi-featured and time-varying patterns in dispersed radio signal data. During the experiment, our prototype presented good results in the clustering of radio signal data, the analysis of time-varying features of radio signals, and signal extraction precision analysis. In final, we invited some experts for the evaluation of the prototype and discussed towards the future work.

     

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