Navigation jamming signal recognition based on long short-term memory neural networks

作     者：FU Dong LI Xiangjun MOU Weihua MA Ming OU Gang FU Dong;LI Xiangjun;MOU Weihua;MA Ming;OU Gang

作者机构：College of Electronic Science and TechnologyNational University of Defense TechnologyChangsha 410005China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2022年第33卷第4期

页      面：835-844页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 0811[工学-控制科学与工程] 

基　　金：supported by the National Natural Science Foundation of China (62003354)。 

主　　题：satellite navigation jamming recognition time-frequency(TF)analysis long short-term memory(LSTM) 

摘      要：This paper introduces the time-frequency analyzed long short-term memory(TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System(GNSS) receiver. The method introduces the long shortterm memory(LSTM) neural network into the recognition algorithm and combines the time-frequency(TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise(WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network(CNN).