航空甚高频频率使用与航班总量关联及预测分析  

Correlation and predictive analysis of aviation VHF frequency usage and total flight volume

作　　者：赵国栋 刘播阳 程思备 蒋博筹 骆骁 邓丽莉 ZHAO Guodong;LIU Boyang;CHENG Sibei;JIANG Bochou;LUO Xiao;DENG Lili(Chongqing Radio Monitoring Station,Chongqing 400020,P.R.China;Chongqing Academy of Information and Communications Technology,Chongqing 401336,P.R.China)

机构地区：[1]重庆市无线电监测站,重庆400020 [2]重庆信息通信研究院,重庆401336

出　　处：《重庆邮电大学学报（自然科学版）》2022年第2期193-200,共8页Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)

摘　　要：目前,国家对频率使用率测试数据的统计汇总已形成规范化要求,但是如何分析原始数据,挖掘其应用价值尚缺乏进一步探索。依照国家电信联盟及国家无线电管理机构相关测试标准,采用重庆市无线电监测站固定监测站木耳站对重庆江北国际机场航空甚高频频段的信道占用度进行专项测试,结合认知无线电领域频谱占用模型相关理论向量自回归(vecter autoregression, VAR)模型对频率使用与航班起降的关联性进行分析与预测。计算结果表明,信道时间占用度与航班进出港总量构成格兰杰因果关系,即信道时间占用度可用于预测航班进出港总量,且基于VAR模型的向量误差修正(vector error correction, VEC)模型的预测准确度比基于差分整合移动平均自回归模型(autoregressive integrated moving average model, ARIMA)预测的准确度更高,为无线电监测数据的应用价值挖掘提供了新思路。At present, China has established standardized requirements for the statistical collection of frequency utilization test data, but how to analyze the original data and tap its application value still lacks further exploration. In accordance with the relevant test standards of the ITU and China’s radio regulatory agencies, this paper uses the fixed monitoring station Fungus Station of Chongqing Radio Monitoring Station to conduct a special test on the channel occupancy of the aviation VHF band of Chongqing Jiangbei International Airport. Combining the spectrum occupancy of the cognitive wireless field Model related theory uses vector autoregressive model(VAR model), this paper analyzes the correlation between spectrum usage and flight take-off and landing, and predicts the number of flights in the next 9 days. The calculation results show that the channel time occupancy and the total number of flights in and out of the port constitute a Granger causal relationship, that is, the channel occupancy can be used to predict the total number of flights in and out of the port, and the error correction model based on the VAR model is more accurate than the VEC model based on ARIMA. The prediction accuracy of the model is higher, which provides a new idea for the efficient use of radio monitoring data.

关 键 词：频率使用 航班起降 向量自回归(VAR)模型 信道时间占用度 

分 类 号：TN92[电子电信—通信与信息系统]