Open AccessResearch on Discovery of Radio Communication Relationship Based on Correlation Analysis
Author(s) -
Changkun Liu,
Xinrong Wu,
Changhua Yao,
Jun Guo,
Haoren Fan,
Tongyan Pan,
Wei Kefeng
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/440/4/042006
Subject(s) - computer science , data mining , wireless , cluster analysis , relation (database) , electromagnetic spectrum , correlation , telecommunications , artificial intelligence , mathematics , geometry , physics , quantum mechanics
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.