Journal of Civil Aviation University of China ›› 2024, Vol. 42 ›› Issue (5): 45-51.

• Safe Operations in Integrated Airspace • Previous Articles     Next Articles

Identification of interference type of Beidou navigation satellite system based on
learning from time-frequency graph

LIU Ruihuaa , ZHANG Yantinga , MA Zanb
  

  1. a. College of Electronic Information and Automation; b. College of Safety Science and Engineering, CAUC, Tianjin 300300, China
  • Received:2023-09-14 Revised:2023-12-01 Online:2024-12-21 Published:2025-04-08

Abstract:

Aiming at the problem that existing interference identification algorithms rely on feature engineering to extract features, which is cumbersome and the identification accuracy is greatly affected by the value of signal-to-noise ratio,
the interference type identification method of Beidou navigation satellite system (BDS) based on learning from
time-frequency graph under different signal-to-noise ratio is proposed in this paper. Taking the B1I signal of the
airborne BDS as the object, the original B1I signal and the B1I signal containing interference are subjected to
short-time Fourier transform. The time-frequency graph obtained after the transform is used as the input vector of
the support vector machine and convolutional neural network model to complete the detection and identification of
interference types. The simulation results show that the average identification accuracy of both machine learning
identification algorithms has reached over 99%, which is about 30% higher than that of traditional decision tree
identification algorithms, solving the problem of existing interference identification algorithms that heavily rely on
manually designed feature engineering to extract interference signal features and have low accuracy. The research
results can provide prior information for subsequent interference suppression work and improve the safety of BDS
in the aviation field.

Key words:

convolutional neural network

CLC Number: