[1] Wu Wenda, Zhang Bao, Hong Yongfeng, et al. Design of co-aperture antenna for airborne infrared and synthetic aperture radar[J]. Chinese Optics, 13, 595-604(2020).

[2] Amoon M, Rez-radai G A. Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moment features[J]. IET Computer Vision, 8, 77-85(2014).

[3] Fu Fancheng. SAR target recognition based on target region matching[J]. Electronics Optics & Control, 25, 37-40(2018).

[4] Xie Qing, Zhang Hong. Multi-level SAR image enhancement based on regularization with application to target recognition[J]. Journal of Electronic Measurement and Instrumentation, 32, 157-162(2018).

[5] Anagnostopoulos G C. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors[J]. Nonlinear Analysis, 71, 2934-2939(2009).

[6] Papson S, Narayanan R M. Classification via the shadow region in SAR Imagery[J]. IEEE Transactions on Aerospace and Electronic System, 40, 969-980(2012).

[7] [7] Mishra A K, Motaung T. Application of linear nonlinear PCA to SAR ATR[C]Radioelektronika, 2015: 16.

[8] Han Ping, Wang Huan. Research on the synthetic aperture radar target recognition based on KPCA and sparse representation[J]. Journal of Signal Processing, 29, 1696-1701(2013).

[9] 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, 1376-1385(2015).

[10] Li Shuai, Xu Yuelei, Ma Shiping, et al. SAR target recognition using wavelet transform and deep sparse autoencoders[J]. Video Engineering, 38, 31-35(2014).

[11] 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, 8, 3316-3328(2015).

[12] Chang M, You X, Cao Z. Bidimensional empirical mode decomposition for SAR image feature extraction with application to target recognition[J]. IEEE Access, 6, 7, 135720-135731(2019).

[13] Ding Baiyuan, 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, 6, 157-166(2017).

[14] 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, 219, 130-143(2017).

[15] Liu Yang. Target recognition of SAR images based on multi-level matching of attributed scattering centers[J]. Journal of Electronic Measurement and Instrumentation, 33, 192-198(2019).

[16] Hao Yan, Bai Yanping, Zhang Xiaofei. Synthetic aperture radar target recognition based on KNN[J]. Fire Control & Command Control, 43, 113-115，120(2018).

[17] Liu Changqing, Chen Bo, Pan Zhouhao, et al. Research on target recognition technique via simulation SAR and SVM classifier[J]. Journal of CAEIT, 11, 257-262(2016).

[18] Liu H C, Li S T. Decision fusion of sparse representation and support vector machine for SAR image target recognition[J]. Neurocomputing, 113, 97-104(2013).

[19] Xing X W, Ji K F, Zou H X, et al. Sparse representation based SAR vehicle recognition along with aspect angle[J]. The Scientific World Journal, 834140, 1-10(2014).

[20] [20] Zhang L, Tao Z W, Wang B J. SAR image target recognition using kernel sparse representation based on reconstruction coefficient energy maximization rule[C]IEEE ICASSP, 2016: 23692373.

[21] Tan Cuimei, Xu Tingfa, Ma Xu, et al. Graph-spectral hyperspectral video restoration based on compressive sensing[J]. Chinese Optics, 11, 949-957(2018).

[22] 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, 54, 4806-4817(2016).

[23] 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, 49, 20201010(2020).

[24] 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, 28, 727-735(2020).