Multi-Resolution Fusion Convolutional Neural Networks for Intrapulse Modulation LPI Radar Waveforms Recognition-Reference-Cited by-同舟云学术

Multi-Resolution Fusion Convolutional Neural Networks for Intrapulse Modulation LPI Radar Waveforms Recognition

Published:2020-12-01 Issue:12 Volume:E103.B Page:1470-1476
ISSN:0916-8516
Container-title:IEICE Transactions on Communications
language:en
Short-container-title:IEICE Trans. Commun.

Author:

NI Xue¹,WANG Huali¹,ZHU Ying¹,MENG Fan²

Affiliation:

1. Institute of Communications Engineering, Army Engineering University of PLA

2. Nanjing Marine Radar Institute

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Software

Link

https://www.jstage.jst.go.jp/article/transcom/E103.B/12/E103.B_2019EBP3262/_pdf

Reference15 articles.

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2. [2] G. Vanhoy, T. Schucker, and T. Bose, “Classification of LPI radar signals using spectral correlation and support vector machines,” Analog Integr. Circ. Sig. Process., vol.91, no.2, pp.305-313, 2017. 10.1007/s10470-017-0944-0

3. [3] M. Zhang, M. Diao, L. Gao, and L. Liu, “LPI radar waveform recognition based on time-frequency distribution,” Sensors (Basel), vol.16, no.10, p.1682, 2016. 10.3390/s16101682

4. [4] X. Wang, Y. Zhou, D. Zhou, Z. Chen, and Y. Tian, “Research on low probability of intercept radar signal recognition using deep belief network and bispectra diagonal slice,” J. Electron. Inform. Technol., vol.38, no.11, pp.2972-2976, 2016. 10.11999/JEIT160031

5. [5] M. Zhang, M. Diao, L. Gao, and L. Liu, “Neural networks for radar waveform recognition,” Symmetry, vol.9, no.5, p.75, 2017. 10.3390/sym9050075

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