一种基于谱图SIFT的同源频谱监测数据判定方法

Method for Homologous Spectrum Monitoring Data Identification Based on Spectrum SIFT

随着各类无线电应用的普及,在一定空间范围内,超短波监测过程中的监测数据易受到非同源的同频或邻频信号的影响,仅依靠常规监测中的频谱数据是无法判定信号是否同源的,因而不同监测站点获得的数据缺乏关联性,数据分析结果可能产生误导,降低工作效率。依据人工监测的经验,尝试用计算机视觉等技术分析监测数据的频谱图和时频谱图,结合谱图特性引入角度阈值改进SIFT算法的特征点匹配模式,以适应无线电监测数据分析的需要,并提出以图像特征点检测匹配率为前提,利用卡帕值综合评价两种谱图同源判定结果一致性的方法。通过实验模拟和实例验证,两种谱图同源判定结果的卡帕值为0.7605,达到高度一致;同时,所提方法在实践过程中有提高工作效率的作用,具备操作可行性和实际意义。

With the popularity of various radio applications,different kinds of monitoring data in the process of ultra-short wave monitoring is susceptible to the influence of non-homologous signals of the same frequency or adjacent frequency within a limited space.It is impossible to determine whether the signals are homologous or not merely relying on the frequency spectrum data in conventional monitoring,so that the data obtained from different monitoring stations lack of correlation and the data analysis results may be misleading,even affecting work efficiency.Based on the experience of manual monitoring,this paper attempts to analyze the frequency spectrum and time-frequency spectrum with computer vision technology,and introduces angle threshold to improve the feature point matching mode of SIFT algorithm in combination with the spectrum characteristics,so as to meet the needs of radio monitoring data analysis.Meanwhile,this paper puts forward a method to comprehensively evaluate the consistency of the homologous determination results of two kinds of spectra by using the Kappa on the premise of the matching rate of image feature point detection.Through experimental simulation and case validation,the Kappa of the homologous result is 0.7605,which is highly consistent.At last,the proposed methodcan improve work efficiency in practice,and has operational feasibility and practical significance.