As the medium of wireless communication, the physical characteristics of electromagnetic spectrum signals and the connection between signals also potentially reflect the intelligence information related to the communication intention of communication individuals. For military communications, spec-trum management, communication security and other fields, it is of great significance to mine and analyze the communication behavior of communication individuals from spectrum data. This paper proposes a method for mining the communication relation-ship between stations in massive spectrum signals based on association analysis. Firstly, we improved the peak density clustering algorithm and used it to study the distribution rules and statistical information of spectrum signals. Then, we carried out correlation analysis on the time range of clustering data to mine of the communication relationship. The experimental results show that this method can effectively discover the communication relationship from chaotic and missing spectrum signals, which lays a foundation for further discovering and studying the commu-nication network structure between radio stations and provides ideas for the analysis of spectrum data.
The physical characteristics of the massive spectrum signals carrying the communication information and the statistical laws of these characteristics also potentially reflect the communication behavior of the communication individuals and the intelligence information related to the communication behavior. Intercepting and cracking signal content usually faces enormous difficulties and costs, and more often, we are not able to crack the encrypted signal content. However, by studying the physical features extracted from the spectrum monitoring signals and the statistical laws of these features, it is also possible to dig out the hidden relationships between communication individuals and even the communication network structure, so as to analyze the communication behaviors of the communication individuals. Based on the characteristics of carrier frequency, bandwidth, power, signal monitoring time and direction information of spectrum monitoring signals, this paper identifies each spectrum signal and studies the distribution characteristics and statistical laws of massive spectrum monitoring signals in the column coordinate system. Due to the clustering of the spectrum signals generated by the sources in the power, monitoring time and direction, and the correlation of the spectrum signals generated by the two parties in the communication process, based on the improved density clustering algorithm, this paper proposes a method for mining the communication relationship between communication individuals from the spectrum monitoring data, and guesses and constructs the communication network structure by matching the communication individual with the communication relationship. Finally, we analyze the communication network structure mined from the spectrum monitoring data.INDEX TERMS Spectrum monitoring data, communication network structure, communication relationship discovery, data mining, density clustering.
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