References
- R.G. Wiley, ELINT: The interception and analysis of radar signals, Artech House Publishers, London, USA, 2006.
- Q. Guo et al., Recognition of radar emitter signals based on SVD and AF main ridge slice, J. Commun. Netw. 17 (2015), no. 5, 491-498. https://doi.org/10.1109/JCN.2015.000087
- D. Zeng et al., Automatic modulation classification of radar signals using the generalized time-frequency fepresentation of Zhao, Atlas and Marks, IET Radar, Sonar Navig. 5 (2010), no. 4, 507-516. https://doi.org/10.1049/iet-rsn.2010.0174
- J. Ma et al., Robust radar waveform recognition algorithm based on random projections and sparse classification, IET Radar Sonar Navig. 8 (2014), no. 4, 290-296. https://doi.org/10.1049/iet-rsn.2013.0088
- C. Andre, S.L. Hegarat-Mascle, and R. Reynaud, Evidential framework for data fusion in a multi-sensor surveillance system, Eng. Appl. Artif. Intell. 43 (2015), 166-180. https://doi.org/10.1016/j.engappai.2015.04.012
- Z.W. Zhou, G.M. Huang, and J. Gao, An emitter fusion recognition algorithm based on multi-collaborative representation, in Proc. Int. Cong. Image Signal Process, Liaoning, China, 2015, pp. 1231-1235.
- C.X. Chen, M.H. He, and H.F. Li, An improved radar emitter recognition method based on Dezert-Smarandache theory, Chinese J. Electron. 24 (2015), no. 3, 611-615. https://doi.org/10.1049/cje.2015.07.029
- Y. Bengio, Learning Deep Architectures for AI: Foundations and Trends in Machine Learning, Now Publishers, USA, 2009.
- J. Schmidhuber, Deep learning in neural networks: an overview, Neural Netw. 61 (2015), 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
- X.B. Wang et al., Radar emitter recognition based on the short time fourier transform and convolutional neural networks, in Proc. Int. Cong. Image Signal Process., BioMed. Eng. Inform., Shanghai, China, 2017, pp. 1-5.
- C. Wang, J. Wang, and X.D. Zhang, Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network, in Proc. Int. Conf. Acoust., Speech, Signal Process., New Orleans, LA, USA, 2017, pp. 2437-2441.
- S.H. Kong et al., Automatic LPI radar waveform recognition using CNN, IEEE Access 6 (2018), 4207-4219. https://doi.org/10.1109/ACCESS.2017.2788942
- M. Skurichina and R.P.W. Duin, Bagging, boosting and the random subspace method for linear classifiers, Pattern Anal. Appl. 5 (2002), no. 2, 121-135. https://doi.org/10.1007/s100440200011
- Y. Freund, Boosting a weak learning algorithm by majority, Inf. Comput. 121 (1995), no. 2, 256-285. https://doi.org/10.1006/inco.1995.1136
- T. Thayaparan et al., Time-frequency approach to radar detection, imaging, and classification, IET Signal Process. 4 (2010), no. 4, 325-328. https://doi.org/10.1049/iet-spr.2010.9095
- R. Moradi and R. Yousefzadeh, Recognizing objectionable images using convolutional neural nets, in Proc. Signal Process. Intell. Sys. Conf., Tehran, Iran, 2015, pp. 133-137.
- M. Anthimopoulos et al., Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE Trans. Med. Imaging 35 (2016), no. 5, 1207-1216. https://doi.org/10.1109/TMI.2016.2535865
- Y. Freund and R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Lecture Notes Comput. Sci. 55 (1999), 23-37.
- L. Breiman, Bagging predictors, Mach. Learn. 24 (1996), no. 2, 123-140. https://doi.org/10.1007/BF00058655
- D.C. Tao et al., Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval, IEEE Trans. Pattern Anal. Mach. Intell. 28 (2006), no. 7, 1088-1099. https://doi.org/10.1109/TPAMI.2006.134
- J. Lunden and V. Koivunen, Automatic radar waveform recognition, IEEE J. Sel. Top Signal Process. 1 (2007), no. 1, 124-136. https://doi.org/10.1109/JSTSP.2007.897055