Neural Networks for Radar Waveform Recognition

被引:70
作者
Zhang, Ming [1 ]
Diao, Ming [1 ]
Gao, Lipeng [1 ]
Liu, Lutao [1 ]
机构
[1] Harbin Engn Univ, Coll Informat & Telecommun, Harbin 150001, Peoples R China
来源
SYMMETRY-BASEL | 2017年 / 9卷 / 05期
基金
中国国家自然科学基金;
关键词
radar countermeasure; waveform recognition; T-F distribution; convolutional neural network; TIME-FREQUENCY ANALYSIS; MODULATION RECOGNITION; ZERNIKE MOMENTS; REPRESENTATION; ALGORITHM; FAULT;
D O I
10.3390/sym9050075
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
For passive radar detection system, radar waveform recognition is an important research area. In this paper, we explore an automatic radar waveform recognition system to detect, track and locate the low probability of intercept (LPI) radars. The system can classify (but not identify) 12 kinds of signals, including binary phase shift keying (BPSK) (barker codes modulated), linear frequency modulation (LFM), Costas codes, Frank code, P1-P4 codesand T1-T4 codeswith a low signal-to-noise ratio (SNR). It is one of the most extensive classification systems in the open articles. A hybrid classifier is proposed, which includes two relatively independent subsidiary networks, convolutional neural network (CNN) and Elman neural network (ENN). We determine the parameters of the architecture to make networks more effectively. Specifically, we focus on how the networks are designed, what the best set of features for classification is and what the best classified strategy is. Especially, we propose several key features for the classifier based on Choi-Williams time-frequency distribution (CWD). Finally, the recognition system is simulated by experimental data. The experiments show the overall successful recognition ratio of 94.5% at an SNR of -2 dB.
引用
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页数:20
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