摘要
由于卫星信号较为微弱,易受到各种有意和无意的干扰,使得全球导航卫星系统(Global Navigation Satellite System,GNSS)不能安全、可靠的提供服务,GNSS干扰监测方法的研究对于保障民航飞行安全具有重要意义。本文利用GNSS射频干扰所导致的广播式自动相关监视(ADS-B)数据中导航完好性指标的变化特性,给出结合受干扰影响航班粗监测以及精细监测的多级GNSS射频干扰监测方法。首先对ADS-B数据导航完好性级别(Navigation Integrity Category,NIC)进行统计分析实现受干扰影响航班的粗监测,再依据干扰发生时多个航班在干扰区域会同时受到影响,具有空间上的聚集性,利用MeanShift聚类方法实现GNSS射频干扰的精细监测,提取干扰发生的起始与终止点所对应的时间与位置信息。实验结果与QAR(Quick Access Recorder,QAR)数据得到的干扰监测结果进行对比分析,验证所提方法的有效性。
Since satellite signals are very weak and vulnerable to all kinds of intentional and unintentional interference,the Global Navigation Satellite System(GNSS)cannot provide services safely and reliably.The research on GNSS interference monitoring method is of great significance to civil aviation flight safety.Based on the variation characteristics of navigation integrity index in ADS-B data caused by GNSS interference,a multi-level GNSS interference monitoring method is presented.Firstly,statistical analysis is conducted on the Navigation Integrity Category(NIC)of ADS-B data to realize crude monitoring of flights affected by interference.Then,based on the fact that multiple flights will be affected simultaneously in the interference area when the interference occurs,MeanShift clustering method was used to realize the fine monitoring of GNSS interference,and the time and location information corresponding to the start and end of the interference area were extracted.The experimental results are compared with the interference monitoring results obtained from QAR(Quick Access Recorder)data to verify the effectiveness of the proposed method.
作者
何炜琨
李志强
王晓亮
HE Weikun;LI Zhiqiang;WANG Xiaoliang(Tianjin Key Lab for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300,China)
出处
《信号处理》
CSCD
北大核心
2023年第3期472-481,共10页
Journal of Signal Processing
基金
国家自然基金与民航局联合资助重点项目(U2133204)
中国民航大学国家自然科学基金配套专项(3122022PT01)
天津市教委科研计划重点项目(2022ZD005)。
关键词
全球导航卫星系统
干扰监测
ADS-B
导航完好性指标
统计特性
聚类分析
global navigation satellite system
interference monitoring
ADS-B
navigation integrity index
statistical characteristics
cluster analysis