[1] EL-darymli K, Gill E W, Mcguire P, et al. Automatic target recognition in synthetic aperture radar imagery: a state-of-the-art review [J]. IEEE Access, 2016, 4: 6014-6058. doi:  10.1109/ACCESS.2016.2611492
[2] Wen Gongjian, Zhu Guoqiang, Yin Hongcheng, et al. SAR ATR based on 3D parametric electromagnetic scattering model [J]. Journal of Radar, 2017, 6(2): 115-135. (in Chinese)
[3] Mishra K. Validation of PCA and LDA for SAR ATR[C]//IEEE Tencon, 2008: 1–6.
[4] Han Ping, Wang Huan. Research on the synthetic aperture radar target recognition based on KPCA and sparse representation [J]. Journal of Signal Processing, 2013, 29(13): 1696-1701. (in Chinese)
[5] Cui Z Y, Cao Z J, Yang J Y, et al. Target recognition in synthetic aperture radar via non-negative matrix factorization [J]. IET Radar, Sonar and Navigation, 2015, 9(9): 1376-1385. doi:  10.1049/iet-rsn.2014.0407
[6] Dong G G, Kuang G Y, Wang N, et al. SAR target recognition via joint sparse representation of monogenic signal [J]. IEEE Journal of Selected Topics Applied Earth Observation and Remote Sensing, 2015, 8(7): 3316-3328. doi:  10.1109/JSTARS.2015.2436694
[7] Anagnostopoulos G C. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors [J]. Nonlinear Analysis, 2009, 71(2): 2934-2939.
[8] Zhao Pengju, Gan Kai. SAR target recognition based on hierarchical decision fusion of complementary features [J]. Electronics Optics & Control, 2018, 25(10): 28-32. (in Chinese) doi:  10.3969/j.issn.1671-637X.2018.10.006
[9] Xie Qing, Zhang Hong. Multi-level SAR image enhancement based on regularization with application to target recognition [J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(9): 157-162. (in Chinese)
[10] Ding Boyuan, Wen Gongjian, Yu Liansheng, et al. Matching of attributed scattering center and its application to synthetic aperture radar automatic target recognition [J]. Journal of Radar, 2017, 6(2): 157-166. (in Chinese)
[11] Ding B Y, Wen G J, Zhong J R, et al. A robust similarity measure for attributed scattering center sets with application to SAR ATR [J]. Neurocomputing, 2017, 219: 130-143. doi:  10.1016/j.neucom.2016.09.007
[12] Liu H C, Li S T. Decision fusion of sparse representation and support vector machine for SAR image target recognition [J]. Neurocomputing, 2013, 113: 97-104. doi:  10.1016/j.neucom.2013.01.033
[13] Thiagaraianm J, Ramamurthy K, KneeP P, et al. Sparse representations for automatic target classification in SAR images[C]//4th Communications, Control and Signal Processing, 2010: 1–4.
[14] Chen S Z, Wang H P, Xu F, et al. Target classification using the deep convolutional networks for SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806-4817. doi:  10.1109/TGRS.2016.2551720
[15] Zhang Panpan, Luo Haibo, Ju Moran, et al. An improved capsule and its application in target recognition of SAR images [J]. Infrared and Laser Engineering, 2020, 49(5): 20201010. (in Chinese) doi:  10.3788/irla.26_invited-zhangpanpan
[16] Xu Ying, Gu Yu, Peng Dongliang, et al. SAR ATR based on disentangled representation learning generative adversarial networks and support vector machine [J]. Optics and Precision Engineering, 2020, 28(3): 727-735. (in Chinese) doi:  10.3788/OPE.20202803.0727
[17] Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227. doi:  10.1109/TPAMI.2008.79
[18] Ding B Y, Wen G J. Exploiting multi-view SAR images for robust target recognition [J]. Remote Sensing, 2017, 9(11): 1150. doi:  10.3390/rs9111150
[19] Cai Derao, Song Yuzhen. Joint decision of multi-view SAR images with discrimination analysis with application to SAR ATR [J]. Journal of CAEIT, 2019, 14(1): 37-41. (in Chinese) doi:  10.3969/j.issn.1673-5692.2019.01.007