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

doi:10.23919/JSEE.2022.000083

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (4): 835-844.doi: 10.23919/JSEE.2022.000083

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

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

Dong FU(), Xiangjun LI(), Weihua MOU*(), Ming MA(), Gang OU()

¹ College of Electronic Science and Technology, National University of Defense Technology, Changsha 410005, China

Received:2021-03-05 Accepted:2022-02-10 Online:2022-08-30 Published:2022-08-30
Contact: Weihua MOU E-mail:fudong@nudt.edu.cn;809706022@qq.com;drmou@163.com;maming@nudt.edu.cn;Ougangcs@139.com
About author:|FU Dong was born in 1997. He received his B.S. degree at the College of Intelligence Science and Technology, National University of Defense Technology (NUDT), Changsha, China, in 2019. He is currently pursuing his M.S. degree at the College of Electronic Science and Technology, NUDT. His research interests include GNSS jamming and anti-jamming, satellite navigation time, and frequency technology. E-mail: fudong@nudt.edu.cn||LI Xiangjun was born in 1997. He received his B.S. degree at the College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China, in 2019. He is pursuing his M.S. degree at the College of Electronic Science and Technology, NUDT. His research interest is satellite navigation and positioning. E-mail: 809706022@qq.com||MOU Weihua was born in 1979. He received his Ph.D. degree from National University of Defense Technology (NUDT), Changsha, China, in 2017. He is a professor and master’s supervisor at the College of Electronic Science and Technology, NUDT. His main research interests include GNSS signal processing, GNSS signal simulation, and GPU parallel computing. E-mail: drmou@163.com||MA Ming was born in 1989. He received his Ph.D. degree from College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China, in 2018. He is a lecturer in NUDT. His research interests include GNSS anti-jamming and time and frequency technology. E-mail: maming@nudt.edu.cn||OU Gang was born in 1969. He is a professor in the College of Electronic Science and Technology, National University of Defense Technology. He has over 20 years of experience in satellite navigation system research areas including high performance GNSS baseband chip and receiver design and GNSS test and evaluation technology. E-mail: Ougangcs@139.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62003354)

Abstract

Abstract:

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 short-term 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).

Key words: satellite navigation, jamming recognition, time-frequency (TF) analysis, long short-term memory (LSTM)

Dong FU, Xiangjun LI, Weihua MOU, Ming MA, Gang OU. Navigation jamming signal recognition based on long short-term memory neural networks[J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 835-844.

Figures/Tables 13

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Parameter	Value
JNR/dB	10
IF/MHz	24
Pulse jamming period/μs	10
Duty cycle/%	10
Sweep jamming bandwidth/MHz	40
Audio jamming frequency/MHz	1575.42
Spread spectrum jamming carrier frequency/MHz	1575.42
Code rate/Mcps	2.046

Layer	Type	Parameter
1	BiLSTM	200
2	Fully connected	5
3	Softmax	5

Jamming type	Parameter	Case 1	Case 2	Case 3	Case 4
Pulse	Period/μs	1	10	100	1000
Sweep	Sweep rate/(MHz/s)	0.4	4	40	400
DSSS	Code rate/ Mcps	0.2046	1.023	2.046	10.23

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

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