Volume 25 Issue 3
Jun.  2025
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CHEN Min, ZHU Tong-hui, HE Wei-kun, LU Jian-bo, ZHOU Hong. GNSS interference detection method based on combined processing of civil aviation ADS-B multi-quality indicator and multi-features[J]. Journal of Traffic and Transportation Engineering, 2025, 25(3): 317-329. doi: 10.19818/j.cnki.1671-1637.2025.03.021
Citation: CHEN Min, ZHU Tong-hui, HE Wei-kun, LU Jian-bo, ZHOU Hong. GNSS interference detection method based on combined processing of civil aviation ADS-B multi-quality indicator and multi-features[J]. Journal of Traffic and Transportation Engineering, 2025, 25(3): 317-329. doi: 10.19818/j.cnki.1671-1637.2025.03.021
shu

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

doi: 10.19818/j.cnki.1671-1637.2025.03.021
  • 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
  • Received Date: 2024-06-27
  • Accepted Date: 2025-03-12
  • Rev Recd Date: 2025-01-06
  • Publish Date: 2025-06-28
    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.

     

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