民航ADS-B数据多质量指标与多特征联合处理的GNSS干扰检测方法

陈敏; 朱彤晖; 何炜琨; 鲁建波; 周洪

doi:10.19818/j.cnki.1671-1637.2025.03.021

民航ADS-B数据多质量指标与多特征联合处理的GNSS干扰检测方法

doi: 10.19818/j.cnki.1671-1637.2025.03.021

1.
中国民航大学天津市智能信号与图像处理重点实验室，天津 300300
2.
中国民用航空华东地区管理局设备运行监控中心，上海 200335
3.
上海市无线电监测站，上海 201419

基金项目:

国家自然科学基金项目 U2133204

详细信息

作者简介:
陈敏(1987-)，男，湖北黄冈人，中国民航大学讲师，工学博士研究生，从事空管信号处理、异常检测与GNSS干扰检测研究

通讯作者:
何炜琨(1977-)，女，黑龙江龙江人，中国民航大学教授，博士

中图分类号: U8
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出版历程
- 收稿日期: 2024-06-27
- 录用日期: 2025-03-12
- 修回日期: 2025-01-06
- 刊出日期: 2025-06-28

GNSS interference detection method based on combined processing of civil aviation ADS-B multi-quality indicator and multi-features

1.
Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
2.
Equipment Operation Monitoring Center, CAAC East China Regional Administration, Shanghai 200335, China
3.
Shanghai Radio Monitoring Center, Shanghai 201419, China

Funds:

National Natural Science Foundation of China U2133204

More Information

Corresponding author: HE Wei-kun (1977-), female, professor, PhD, hwkcauc@126.com

Article Text (Baidu Translation)

摘要

摘要: 为解决导航卫星信号微弱，极易受到各种有意或无意的干扰，造成航空安全隐患、航班延误和运行效率下降的问题，利用全球导航卫星系统(GNSS)射频干扰导致的飞机航迹异常以及广播式自动相关监视(ADS-B)数据中导航质量指标的变化特性，在兼容DO-260、DO-260A/B不同质量指标的情况下，给出了一种基于ADS-B数据多质量指标多特征联合处理的GNSS干扰检测方法；在GNSS干扰环境下提取ADS-B数据变化特征，通过检测导航质量指标的波动持续时间特征、质量指标变化同时航迹断裂特征以及多种质量指标同时下降特征等异常行为筛选出潜在干扰航班；对潜在航班进行干扰特征点提取，考虑到干扰航班在空间上具有聚集性，再利用MeanShift聚类方法实现了干扰航班的检测。试验结果表明：所提多质量指标多特征联合处理的GNSS干扰检测方法与单质量指标干扰检测方法相比精确率提高21.3%，能有效降低误检率，与熵权法相比查全率提高7%，在保证误检率不增加的前提下能有效降低漏检率, 同时所提方法无需大量数据进行预训练，检测所需时间相比机器学习方法降低了98.4%，具有较好的实时性和工程应用价值，可为民用航空无线电干扰检测中的受干扰航班、受干扰时间和位置的确定提供解决方案。
- 空中交通 /
- ADS-B /
- GNSS干扰 /
- 质量指标 /
- 航迹断裂 /
- 多特征联合处理
Abstract: To solve the issue of weak navigation satellite signals, which were highly susceptible to various intentional or unintentional interferences that pose aviation safety risks, cause flight delays, and reduce operational efficiency, the abnormal trajectory caused by global navigation satellite system (GNSS) radio frequency interference and the variations in navigation quality indicators in automatic dependent surveillance-broadcast (ADS-B) data were utilized, a GNSS interference detection method based on the joint processing of multiple quality indicators and features of ADS-B data was proposed, which was compatible with various quality indicators from DO-260, and DO-260A/B. In a GNSS interference environment, variation features from ADS-B data were extracted. Potential interfering flights were identified by detecting abnormal behaviors such as the fluctuation duration of navigation quality indicators, simultaneous quality indicator changes with trajectory breakage, and simultaneous decreases in multiple quality indicators. The potential interfering flights were further analyzed by extracting interference feature points. Considering the spatial aggregation of interfering flights, the MeanShift clustering method is then used to detect the interfering flights. Experimental results show that the proposed GNSS interference detection method based on joint processing of multiple quality indicators and features improves precision by 21.3% compared to single quality indicator methods, effectively reducing the false detection rate. Compared to the entropy weighting method, the recall rate improves by 7%, and the method can effectively reduce the miss detection rate without increasing the false detection rate. Furthermore, this method does not require large datasets for pre-training and reduces detection time by 98.4% compared to machine learning methods, offering better real-time performance and engineering application value. It can provide solutions for determining the interfering flights, interference times, and locations in civil aviation radio interference detection.
- air traffic /
- ADS-B /
- GNSS interference /
- quality indicator /
- trajectory breakdown /
- combined processing of multi-feature

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图 1 NACp、SIL随NIC波动的相关性分析

Figure 1. Correlation of NACp and SIL variance with NIC

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图 2 GNSS干扰影响下的异常航迹

Figure 2. Abnormal trajectories under influence of GNSS interference

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图 3 干扰特征1

Figure 3. Interference feature 1

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图 4 干扰特征2

Figure 4. Interference feature 2

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图 5 干扰特征3

Figure 5. Interference feature 3

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图 6 DBSCAN算法流程

Figure 6. DBSCAN algorithm process

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图 7 ADS-B数据多特征联合处理的GNSS干扰检测方法流程

Figure 7. Flow of GNSS interference detection method using combined processing of ADS-B multi-features

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图 8 提取干扰特征1

Figure 8. Extraction of interference feature 1

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图 9 符合干扰特征1航班

Figure 9. Flights matching interference feature 1

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图 10 航迹断裂检测

Figure 10. Trajectory breakdown detection

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图 11 航迹偏移检测

Figure 11. Trajectory shifting detection

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图 12 某干扰航班多指标波动

Figure 12. Multi-index fluctuation of jammed flight

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图 13 聚类确定干扰航班

Figure 13. Confirmation of jammed flights using clustering

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表 1 不同机载ADS-B硬件版本支持的导航质量指标

Table 1. Navigation quality indicators supported by different ADS-B on-board hardware

导航质量指标	缩写	英文全称	分类级别	对应的机载硬件版本
导航不确定性级别	NUCp	Navigation Uncertain Category for Position	0~9	DO-260
导航位置准确度级别	NACp	Navigation Accuracy Category for Position	0~11	DO-260A/B
导航速度准确度级别	NACv	Navigation Accuracy Category for Velocity	0~4	DO-260A/B
导航完好性级别	NIC	Navigation Integrity Category	0~11	DO-260A/B
源完好性级别	SIL	Source Integrity Level	0~3	DO-260A/B
系统设计确信度	SDA	System Design Assurance	0~3	DO-260B

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表 2 ADS-B导航质量指标特征分类

Table 2. Features classification of ADS-B navigation quality indicators

序号	全称	备注
1	质量指标正常	指标位于正常值范围之内；依据中国民航局下发的《广播式自动相关监视(ADS-B)管制运行规程》规定，即NIC不小于6，NUCp不小于5，SIL不小于2，且指标值保持恒定
2	质量指标轻微波动	指标位于正常值范围之内，并在正常范围内波动
3	质量指标异常	指标超出正常值范围

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表 3 ADS-B数据异常航迹分类

Table 3. Classification of ADS-B abnormal trajectory

序号	全称	现象	可能的产生原因
1	航迹中断	航迹断裂，并持续一定时间	航班机载ADS-B系统受地面障碍物的遮蔽、或地表曲率遮蔽影响，导致空地数据传输中断，出现航迹中断现象；机载GNSS系统受到干扰，无法获取GNSS位置信息，ADS-B无法通过其他机载导航系统获取位置信息，进而停止发送位置报告^[24]
2	航迹偏移	航迹产生一定幅度、一定角度的偏移	机载GNSS系统受到干扰，无法获取GNSS位置信息，ADS-B通过其他机载导航系统(通常为机载惯性导航系统)获取位置信息并发送。由于机载惯性导航装置其位置信息与GNSS位置信息在存在着系统性误差，从而导致航迹出现整体偏移

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表 4 ADS-B机载硬件版本受GNSS干扰影响特征分类

Table 4. GNSS interference features classification of ADS-B on board hardware versions

干扰特征	适用版本	具体现象
特征1	DO260、DO260A/B	受干扰时，NUCp或NIC指标发生波动，但是航迹保持正常
特征2	DO260、DO260A/B	受干扰时，NUCp或NIC指标发生波动，航迹发生中断或者航迹发生偏移以及航迹位置精度下降
特征3	DO260A/B	受干扰时，多个指标发生波动，且波动时间相近

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表 5 基于多特征联合处理的混淆矩阵

Table 5. Confusion matrix based on combined processing for multi-features

预测样本	真实样本
预测样本	干扰航班	正常航班
预测干扰航班	T_p=0.839	F_p=0.002
预测正常航班	F_N=0.161	T_N=0.998

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表 6 基于多特征联合处理的试验结果

Table 6. Experimental results of combined processing for multi-features

方法	精确率	准确率	查全率	F₁分数	检测时间/s
单指标检测法	0.750	0.982	0.967	0.845	1.2
熵权法	0.909	0.985	0.769	0.833	1.5
基于LSTM的干扰检测方法	0.940	0.981	0.987	0.963	158.2
本文方法	0.963	0.990	0.839	0.896	2.6

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