Deep Learning-Based Approach for Low Probability of Intercept Radar Signal Detection and Classification-Reference-Cited by-同舟云学术

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Deep Learning-Based Approach for Low Probability of Intercept Radar Signal Detection and Classification

Published:2020-10 Issue:10 Volume:65 Page:1179-1191
ISSN:1064-2269
Container-title:Journal of Communications Technology and Electronics
language:en
Short-container-title:J. Commun. Technol. Electron.

Author:

Ghadimi G.,Norouzi Y.,Bayderkhani R.,Nayebi M. M.,Karbasi S. M.

Publisher

Pleiades Publishing Ltd

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiation,Electronic, Optical and Magnetic Materials

Link

https://link.springer.com/content/pdf/10.1134/S1064226920100034.pdf

Reference24 articles.

1. P. E. Pace, Detecting and Classifying Low Probability of Intercept Radar (Artech House, Norwood, 2009).

2. Seung-Hyun Kong, Minjun Kim, Linh Manh Hoang, and Eunhui Kim, “Automatic LPI radar waveform recognition using CNN,” IEEE Access 6, 4207−4219 (2018).

3. Chao Wang, Hao Gao, and Xu-Dong Zhang, “Radar signal classification based on auto-correlation function and directed graphical model,” in Proc. 6th Int. Conf. on Signal Process., Commun. & Comput. (ICSPCC), Hong Kong, August, 2016 (ICSPCC, 2016).

4. K. Konopko, Y. P. Grishin, and D. Jańczak, “Radar signal recognition based on time-frequency representations and multidimensional probability density function estimator,” Signal Processing Symp. (SPSympo), 1−6 (2015).

5. R. Mingqiu, C. Jinyan, and Z. Yuanqing, “Classification of radar signals using time-frequency transforms and fuzzy clustering,” in Microwave and Millimeter Wave Technology (ICMMT 2010), (Int. Conf., Chengdu, China, May 8−11,2010) (ICMMT, 2010), pp. 2067−2070.

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