Neural Networks for Radar Waveform Recognition
AbstractFor 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Zhang, M.; Diao, M.; Gao, L.; Liu, L. Neural Networks for Radar Waveform Recognition. Symmetry 2017, 9, 75.
Zhang M, Diao M, Gao L, Liu L. Neural Networks for Radar Waveform Recognition. Symmetry. 2017; 9(5):75.Chicago/Turabian Style
Zhang, Ming; Diao, Ming; Gao, Lipeng; Liu, Lutao. 2017. "Neural Networks for Radar Waveform Recognition." Symmetry 9, no. 5: 75.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.