• 算法设计与分析 •

1. (中国民用航空飞行学院航空工程学院,四川广汉618300)
• 出版日期:2022-03-31 发布日期:2022-03-31
• 作者简介:胡焱(1973—),男,四川大英人,教授,硕士生导师,硕士，研究方向:航空电子设备维修,E-mail: huyan@cafuc.edu.cn; 卓书龙(1995—),男,硕士研究生,研究方向:神经网络,信号处理,E-mail: 2074537396@qq.com； 司成可(1988—),男,重庆大足人,讲师,硕士,研究方向:机器学习,信号处理,E-mail： sichengke@cafuc.edu.cn。
• 基金资助:
中国民用航空飞行学院科研基金资助项目(J2020-033); 大学生创新创业训练计划项目(S201910624014)

### Identification of Data-driven ADS-B Interference Source Signal Type

1. （Aviation Engineering Institute, Civil Aviation Flight University of China, Guanghan 618300, China）
• Online:2022-03-31 Published:2022-03-31

Abstract: When extracting the subtle features of interference signals, the traditional identification methods of interference source signal types have some shortcomings, such as low accuracy and poor recognition effect. In this paper, a deep neural network based ADS-B interference signal modulation type recognition algorithm is proposed. Firstly, ADS-B signal and interference waveform are superimposed and mixed. Simulation signals are transmitted by controlling vector signal generator (VSG) and collected at the receiving end. Then, random noise is artificially added to the received baseband I and Q data, and based on this, tensor training sample datasets are constructed under various SNR scenarios. Finally, the training sample data are used to train the neural network designed in this paper, and the recognition performance of the traditional classification algorithm and that of the neural network algorithm proposed in this paper are compared and analyzed in the sample data set. Experimental results show that the neural network algorithm proposed in this paper has better recognition performance compared with the existing traditional recognition algorithms.