Neural Networks for Radar Waveform Recognition
Neural Networks for Radar Waveform Recognition
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用于雷达波形识别的神经网络
DOI：
10.3390/sym9050075
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发表时间：
2017-05
期刊：
影响因子：
2.7
通讯作者：
Liu LT （Liu Lutao）
中科院分区：
文献类型：
--
作者：
Zhang M （Zhang Ming）;Diao M （Diao Ming）;Gao LP （Gao Lipeng）;Liu LT （Liu Lutao）
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.
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影响因子：
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作者：
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DOI：
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发表时间：
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期刊：
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发表时间：
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期刊：
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DOI：
10.1109/taes.2005.1541437
发表时间：
2005-07-01
影响因子：
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DOI：
10.1109/jproc.2015.2501978
发表时间：
2016-02
期刊：
Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
影响因子：
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作者：
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通讯作者：
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