基于双胞循环神经网络的雷达捷变频行为识别

doi:10.12305/j.issn.1001-506X.2024.03.15

Abstract

Abstract:

Radar program-controlled frequency agility behavior has a certain ability to resist narrow-band aiming jamming, and can realize the functions of measurement and moving target indication, which brings some difficulties to jamming guidance. For this, a random frequency template method is proposed to model the program-controlled frequency agility behavior of radar, and a bi-cell recurrent neural network (BRNN) is designed to identify the program-controlled frequency agility behavior. The simulation results show that the BRNN can effectively identify the frequency agility behavior of radar program-controlled, and predict the future frequency sequence with a certain probability, which can effectively provide guidance for narrow-band aiming jamming. The simulation results also show that the proposed network can effectively remember and identify a group of nonlinear time sequence.

Key words: frequency agility, behavior recognition, recurrent neural network (RNN), memory cell

CLC Number:

TN972

Xianpeng MENG, Limin LIU, Jian DONG, Li WANG, Wenhua HU. Radar frequency agility behavior recognition based on bi-cell recurrent neural network[J]. Systems Engineering and Electronics, 2024, 46(3): 898-905.

Figures/Tables 9

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References 41

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正确率	N_step
正确率	16	14	12	10	8
p₁^t	79.13	78.88	79.49	79.92	79.34
p₈^t	54.18	53.87	54.92	55.16	54.93

K	BP	RBF	Volterra	LSTM	BRNN
p₁^t	5.23	8.38	9.01	80.83	79.34

正确率	N_train
正确率	10 512	8 409	6 307	4 204	2 102
p₁^t	79.34	79.32	78.21	75.81	66.88
p₈^t	54.93	54.58	53.07	50.61	41.79

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Radar frequency agility behavior recognition based on bi-cell recurrent neural network

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