[1] |
TAIT P. Introduction to Radar Target Recognition[M]. London: IET Digital Library, 2005: 3–14.
|
[2] |
RATCHES J A. Review of current aided/automatic target acquisition technology for military target acquisition tasks[J]. Optical Engineering, 2011, 50(7): 072001. doi: 10.1117/1.3601879
|
[3] |
BHANU B. Automatic target recognition: State of the art survey[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, AES-22(4): 364–379. doi: 10.1109/TAES.1986.310772
|
[4] |
VERLY J G, DELANOY R L, and DUDGEON D E. Machine intelligence technology for automatic target recognition[J]. The Lincoln Laboratory Journal, 1989, 2(2): 277–311.
|
[5] |
SCHACHTER B J. Automatic Target Recognition[M]. 3rd ed. John Wiley & Sons, Ltd, 2018: 1–33.
|
[6] |
BLACKNELL D and GRIFFITHS H. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR)[M]. London: IET Digital Library, 2013: 157–174.
|
[7] |
崔皓. 分布式架构原理与实践[M]. 北京: 人民邮电出版社, 2021: 117–169.CUI Hao. Principles and Practices of Distributed Architecture[M]. Beijing: Posts & Telecommunications Press, 2021: 117–169.
|
[8] |
LIU Bing. Learning on the Job: Online lifelong and continual learning[C]. The 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020: 13544–13549.
|
[9] |
郁文贤, 郭桂蓉. ATR的研究现状和发展趋势[J]. 系统工程与电子技术, 1994, 16(6): 25–32. doi: 10.3321/j.issn:1001-506X.1994.06.005YU Wenxian and GUO Guirong. The state of the arts of automatic target recognition[J]. Systems Engineering and Electronics, 1994, 16(6): 25–32. doi: 10.3321/j.issn:1001-506X.1994.06.005
|
[10] |
AFRL and DARPA. Sensor data management system website, MSTAR database[EB/OL]. https://www.sdms.afrl.af.mil/index.php?collection=mstar, 2022.
|
[11] |
BRYANT A. Target recognition and adaption in contested environments (TRACE)[EB/OL]. https://www.darpa.mil/program/trace, 2022.
|
[12] |
郭炜炜, 杜小勇, 胡卫东, 等. 基于稀疏先验的SAR图像目标方位角稳健估计方法[J]. 信号处理, 2008, 24(6): 889–893. doi: 10.3969/j.issn.1003-0530.2008.06.001GUO Weiwei, DU Xiaoyong, HU Weidong, et al. A robust target aspect estimation method from SAR images based on sparse prior[J]. Journal of Signal Processing, 2008, 24(6): 889–893. doi: 10.3969/j.issn.1003-0530.2008.06.001
|
[13] |
郁文贤, 李明国. 军事电子信息处理中的人工神经网络技术[J]. 国防科技大学学报, 1998, 20(3): 3–5.YU Wenxian and LI Mingguo. Applications of artificial neural networks technology to military electronic information processing[J]. Journal of National University of Defense Technology, 1998, 20(3): 3–5.
|
[14] |
黎湘, 郁文贤, 庄钊文, 等. 决策层信息融合的神经网络模型与算法研究[J]. 电子学报, 1997, 25(9): 117–120. doi: 10.3321/j.issn:0372-2112.1997.09.030LI Xiang, YU Wenxian, ZHUANG Zhaowen, et al. Decision fusion for target recognition based on neural network model[J]. Acta Electronica Sinica, 1997, 25(9): 117–120. doi: 10.3321/j.issn:0372-2112.1997.09.030
|
[15] |
KOVAR J J, KNECHT J, and CHENOWETH D. Automatic classification of infrared ship imagery[C]. SPIE 0292, Images and Data from Optical Sensors, San Diego, USA, 1981: 234–240.
|
[16] |
AVCI E. A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)[J]. Expert Systems with Applications, 2013, 40(10): 3984–3993. doi: 10.1016/j.eswa.2013.01.011
|
[17] |
郭桂蓉. 模糊模式识别[M]. 长沙: 国防科技大学出版社, 1993: 134–168.GUO Guirong. FUZZY Pattern Recognition[M]. Changsha: National University of Defense Technology Press, 1993: 134–168.
|
[18] |
HU M K. Visual pattern recognition by moment invariant[J]. IRE Transactions on Information Theory, 1962, 8(2): 179–187. doi: 10.1109/TIT.1962.1057692
|
[19] |
何友, 黄勇, 关键, 等. 海杂波中的雷达目标检测技术综述[J]. 现代雷达, 2014, 36(12): 1–9. doi: 10.16592/j.cnki.1004-7859.2014.12.004HE You, HUANG Yong, GUAN Jian, et al. An overview on radar target detection in sea clutter[J]. Modern Radar, 2014, 36(12): 1–9. doi: 10.16592/j.cnki.1004-7859.2014.12.004
|
[20] |
艾小锋, 赵锋, 刘晓斌, 等. 双/多基地雷达目标探测与识别[M]. 北京: 电子工业出版社, 2020: 54–95.AI Xiaofeng, ZHAO Feng, LIU Xiaobin, et al. Bi-Station/Multi-Station Radar Target Detection and Recognition[M]. Beijing: Publishing House of Electronics Industry, 2020: 54–95.
|
[21] |
BARTON D K. Radar System Analysis and Modeling[M]. Boston: Artech House, 2005: 85–125.
|
[22] |
CHUANG C W and MOFFATT D L. Natural resonances of radar targets via Prony’s method and target discrimination[J]. IEEE Transactions on Aerospace and Electronic Systems, 1976, AES-12(5): 583–589. doi: 10.1109/TAES.1976.308260
|
[23] |
CHEN J S and WALTON E K. Comparison of two target classification techniques[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, AES-22(1): 15–22. doi: 10.1109/TAES.1986.310688
|
[24] |
郭桂蓉, 郁文贤, 胡步法. 一种有效的舰船目标识别新方法[J]. 系统工程与电子技术, 1990(6): 1–7, 15. doi: 10.3321/j.issn:1001-506X.1990.06.001GUO Guirong, YU Wenxian, and HU Bufa. A new effective method for ship target recognition[J]. Systems Engineering and Electronics, 1990(6): 1–7, 15. doi: 10.3321/j.issn:1001-506X.1990.06.001
|
[25] |
袁莉, 刘宏伟, 保铮. 基于中心矩特征的雷达HRRP自动目标识别[J]. 电子学报, 2004, 32(12): 2078–2081. doi: 10.3321/j.issn:0372-2112.2004.12.036YUAN Li, LIU Hongwei, and BAO Zheng. Automatic target recognition of radar HRRP based on central moments features[J]. Acta Electronica Sinica, 2004, 32(12): 2078–2081. doi: 10.3321/j.issn:0372-2112.2004.12.036
|
[26] |
ZHANG Xuefeng, CHEN Bo, LIU Hongwei, et al. Infinite max-margin factor analysis via data augmentation[J]. Pattern Recognition, 2016, 52: 17–32. doi: 10.1016/j.patcog.2015.10.020
|
[27] |
CHEN Jian, DU Lan, and LIAO Leiyao. Discriminative mixture variational autoencoder for semisupervised classification[J]. IEEE Transactions on Cybernetics, 2022, 52(5): 3032–3046. doi: 10.1109/TCYB.2020.3023019
|
[28] |
DU Chuan, CHEN Bo, XU Bin, et al. Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition[J]. Signal Processing, 2019, 158: 176–189. doi: 10.1016/j.sigpro.2019.01.006
|
[29] |
COPSEY K and WEBB A. Bayesian gamma mixture model approach to radar target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1201–1217. doi: 10.1109/TAES.2003.1261122
|
[30] |
DU Lan, CHEN Jian, HU Jing, et al. Statistical modeling with label constraint for radar target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(2): 1026–1044. doi: 10.1109/TAES.2019.2925472
|
[31] |
吴一戎, 朱敏慧. 合成孔径雷达技术的发展现状与趋势[J]. 遥感技术与应用, 2000, 15(2): 121–123. doi: 10.3969/j.issn.1004-0323.2000.02.012WU Yirong and ZHU Minhui. The developing status and trends of synthetic aperture radar[J]. Remote Sensing Technology and Application, 2000, 15(2): 121–123. doi: 10.3969/j.issn.1004-0323.2000.02.012
|
[32] |
HOLMES Q A, NUESCH D R, and SHUCHMAN R A. Textural analysis and real-time classification of sea-ice types using digital SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1984, GE-22(2): 113–120. doi: 10.1109/TGRS.1984.350602
|
[33] |
IKEUCHI K, WHEELER M D, YAMAZAKI T, et al. Model-based SAR ATR system[C]. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, Orlando, USA, 1996: 376–387.
|
[34] |
ANAGNOSTOPOULOS G C. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors[J]. Nonlinear Analysis: Theory, Methods & Applications, 2009, 71(12): e2934–e2939. doi: 10.1016/j.na.2009.07.030
|
[35] |
SAIDI M N, DAOUDI K, KHENCHAF A, et al. Automatic target recognition of aircraft models based on ISAR images[C]. 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 2009: IV-685–IV-688.
|
[36] |
ZHENG Qinfen, DER S Z, and MAHMOUD H I. Model-based target recognition in pulsed ladar imagery[J]. IEEE Transactions on Image Processing, 2001, 10(4): 565–572. doi: 10.1109/83.913591
|
[37] |
RUEL S, ENGLISH C E, MELO L, et al. Field testing of a 3D automatic target recognition and pose estimation algorithm[C]. SPIE 5426, Automatic Target Recognition XIV, Orlando, USA, 2004: 102–111.
|
[38] |
GRONWALL C, GUSTAFSSON F, and MILLNERT M. Ground target recognition using rectangle estimation[J]. IEEE Transactions on Image Processing, 2006, 15(11): 3400–3408. doi: 10.1109/TIP.2006.881965
|
[39] |
VASILE A N. Pose independent target recognition system using pulsed ladar imagery[D]. [Master dissertation], Massachusetts Institute of Technology, 2004.
|
[40] |
ERNISSE B E, ROGERS S K, DESIMIO M P, et al. Complete automatic target cuer/recognition system for tactical forward-looking infrared images[J]. Optical Engineering, 1997, 36(9): 2593–2603. doi: 10.1117/1.601484
|
[41] |
INGGS M R and ROBINSON A D. Ship target recognition using low resolution radar and neural networks[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2): 386–393. doi: 10.1109/7.766923
|
[42] |
NING Wu, CHEN Wugun, and ZHANG Xinggan. Automatic target recognition of ISAR object images based on neural network[C]. IEEE International Conference on Neural Networks and Signal Processing, Nanjing, China, 2003: 373–376.
|
[43] |
黄金, 梁彦, 程咏梅, 等. 基于序列图像的自动目标识别算法[J]. 航空学报, 2006, 27(1): 87–93. doi: 10.3321/j.issn:1000-6893.2006.01.017HUANG Jin, LIANG Yan, CHEN Yongmei, et al. Automatic target recognition method based on sequential images[J]. Acta Aeronautica et Astronautica Sinica, 2006, 27(1): 87–93. doi: 10.3321/j.issn:1000-6893.2006.01.017
|
[44] |
宋锐, 张静, 夏胜平, 等. 一种基于BP神经网络群的自适应分类方法及其应用[J]. 电子学报, 2001, 29(12A): 1950–1953. doi: 10.3321/j.issn:0372-2112.2001.z1.055SONG Rui, ZHANG Jing, XIA Shengping, et al. An adaptive classification method of BP-NN group based classification system and its application[J]. Acta Electronica Sinica, 2001, 29(12A): 1950–1953. doi: 10.3321/j.issn:0372-2112.2001.z1.055
|
[45] |
AVCI E and COTELI R. A new automatic target recognition system based on wavelet extreme learning machine[J]. Expert Systems with Applications, 2012, 39(16): 12340–12348. doi: 10.1016/j.eswa.2012.04.012
|
[46] |
李明国, 郁文贤. 神经网络的函数逼近理论[J]. 国防科技大学学报, 1998, 20(4): 70–76.LI Mingguo and YU Wenxian. On neural networks founction approximation[J]. Journal of National University of Defense Technology, 1998, 20(4): 70–76.
|
[47] |
胡卫东, 郁文贤, 郭桂蓉. 一种有效的神经网络检测器[J]. 国防科技大学学报, 1997, 19(3): 18–22.HU Weidong, YU Wenxian, and GUO Guirong. An effective neural network detector[J]. Journal of National University of Defense Technology, 1997, 19(3): 18–22.
|
[48] |
路军, 郁文贤, 郭桂蓉, 等. 子波神经网络及其在自动目标识别中的应用[J]. 系统工程与电子技术, 1995, 17(11): 11–18. doi: 10.3321/j.issn:1001-506X.1995.11.003LU Jun, YU Wenxian, GUO Guirong, et al. Application of WNN in automatic target recognition[J]. Systems Engineering and Electronics, 1995, 17(11): 11–18. doi: 10.3321/j.issn:1001-506X.1995.11.003
|
[49] |
夏胜平, 张乐锋, 虞华, 等. 基于RSOM树模型的机器学习原理与算法研究[J]. 电子学报, 2005, 33(5): 939–944. doi: 10.3321/j.issn:0372-2112.2005.05.037XIA Shengping, ZHANG Lefeng, YU Hua, et al. Theory and algorithm of machine learning based on RSOM tree model[J]. Acta Electronica Sinica, 2005, 33(5): 939–944. doi: 10.3321/j.issn:0372-2112.2005.05.037
|
[50] |
李予蜀, 余农, 吴常泳, 等. 红外航空图像自动目标识别的形态滤波神经网络算法[J]. 航空学报, 2002, 23(4): 368–372. doi: 10.3321/j.issn:1000-6893.2002.04.018LI Yushu, YU Nong, WU Changyong, et al. Morphological neural networks with applications to automatic target recognition in aeronautics infrared image[J]. Acta Aeronautica et Astronautica Sinica, 2002, 23(4): 368–372. doi: 10.3321/j.issn:1000-6893.2002.04.018
|
[51] |
AVCI E, TÜRKOĞLU I, and POYRAZ M. A new approach based on wavelet nero genetic network for automatic target recognition with X-band Doppler radar[J]. Istanbul University Journal of Electrical and Electronics Engineering, 2006, 6(2): 157–168.
|
[52] |
KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet Classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84–90. doi: 10.1145/3065386
|
[53] |
GAO Fei, HUANG Teng, SUN Jinping, et al. A new algorithm for SAR image target recognition based on an improved deep convolutional neural network[J]. Cognitive Computation, 2019, 11(6): 809–824. doi: 10.1007/s12559-018-9563-z
|
[54] |
ZHU Pingping, ISAACS J, FU Bo, et al. Deep learning feature extraction for target recognition and classification in underwater sonar images[C]. IEEE 56th Annual Conference on Decision and Control, Melbourne, Australia, 2017: 2724–2731.
|
[55] |
田壮壮, 占荣辉, 胡杰民, 等. 基于卷积神经网络的SAR图像目标识别研究[J]. 雷达学报, 2016, 5(3): 320–325. doi: 10.12000/JR16037TIAN Zhuangzhuang, ZHAN Ronghui, HU Jiemin, et al. SAR ATR based on convolutional neural network[J]. Journal of Radars, 2016, 5(3): 320–325. doi: 10.12000/JR16037
|
[56] |
CHEN Sizhe and WANG Haipeng. SAR target recognition based on deep learning[C]. 2014 IEEE International Conference on Data Science and Advanced Analytics, Shanghai, China, 2014: 541–547.
|
[57] |
贺丰收, 何友, 刘准钆, 等. 卷积神经网络在雷达自动目标识别中的研究进展[J]. 电子与信息学报, 2020, 42(1): 119–131. doi: 10.11999/JEIT180899HE Fengshou, HE You, LIU Zhunga, et al. Research and development on applications of convolutional neural networks of radar automatic target recognition[J]. Journal of Electronics &Information Technology, 2020, 42(1): 119–131. doi: 10.11999/JEIT180899
|
[58] |
DING Baiyuan, WEN Gongjian, MA Conghui, et al. An efficient and robust framework for SAR target recognition by hierarchically fusing global and local features[J]. IEEE Transactions on Image Processing, 2018, 27(12): 5983–5995. doi: 10.1109/TIP.2018.2863046
|
[59] |
ZHANG Jinsong, XING Mengdao, and XIE Yiyuan. FEC: A feature fusion framework for SAR target recognition based on electromagnetic scattering features and deep CNN features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3): 2174–2187. doi: 10.1109/TGRS.2020.3003264
|
[60] |
LI Yi, DU Lan, and WEI Di. Multiscale CNN based on component analysis for SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5211212. doi: 10.1109/TGRS.2021.3100137
|
[61] |
FENG Sijia, JI Kefeng, ZHANG Linbin, et al. SAR target classification based on integration of ASC parts model and deep learning algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 10213–10225. doi: 10.1109/JSTARS.2021.3116979
|
[62] |
王容川, 庄志洪, 王宏波, 等. 基于卷积神经网络的雷达目标HRRP分类识别方法[J]. 现代雷达, 2019, 41(5): 33–38. doi: 10.16592/j.cnki.1004-7859.2019.05.007WANG Rongchuan, ZHUANG Zhihong, WANG Hongbo, et al. HRRP classification and recognition method of Radar target based on convolutional neural network[J]. Modern Radar, 2019, 41(5): 33–38. doi: 10.16592/j.cnki.1004-7859.2019.05.007
|
[63] |
CHEVALIER M, THOME N, CORD M, et al. Low resolution convolutional neural network for automatic target recognition[C]. 7th International Symposium on Optronics in Defence and Security, Paris, France, 2016: 1–9.
|
[64] |
喻玲娟, 王亚东, 谢晓春, 等. 基于FCNN和ICAE的SAR图像目标识别方法[J]. 雷达学报, 2018, 7(5): 622–631. doi: 10.12000/JR18066YU Lingjuan, WANG Yadong, XIE Xiaochun, et al. SAR ATR based on FCNN and ICAE[J]. Journal of Radars, 2018, 7(5): 622–631. doi: 10.12000/JR18066
|
[65] |
ZHANG Zenghui, GUO Weiwei, ZHU Shengnan, et al. Toward arbitrary-oriented ship detection with rotated region proposal and discrimination networks[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(11): 1745–1749. doi: 10.1109/LGRS.2018.2856921
|
[66] |
ZHAO Juanping, ZHANG Zenghui, YU Wenxian, et al. A cascade coupled convolutional neural network guided visual attention method for ship detection from SAR images[J]. IEEE Access, 2018, 6: 50693–50708. doi: 10.1109/ACCESS.2018.2869289
|
[67] |
KHELLAL A, MA Hongbin, and FEI Qing. Convolutional neural network based on extreme learning machine for maritime ships recognition in infrared images[J]. Sensors, 2018, 18(5): 1490. doi: 10.3390/s18051490
|
[68] |
史国军. 深度特征联合表征的红外图像目标识别方法[J]. 红外与激光工程, 2021, 50(3): 20200399. doi: 10.3788/irla20200399SHI Guojun. Target recognition method of infrared imagery via joint representation of deep features[J]. Infrared and Laser Engineering, 2021, 50(3): 20200399. doi: 10.3788/irla20200399
|
[69] |
PAN Mian, LIU Ailin, YU Yanzhen, et al. Radar HRRP target recognition model based on a stacked CNN–Bi-RNN with attention mechanism[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5100814. doi: 10.1109/TGRS.2021.3055061
|
[70] |
LI Rui, WANG Xiaodan, WANG Jian, et al. SAR target recognition based on efficient fully convolutional attention block CNN[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 19: 4005905. doi: 10.1109/LGRS.2020.3037256
|
[71] |
OSAHOR U M and NASRABADI N M. Design of adversarial targets: Fooling deep ATR systems[C]. SPIE 10988, Automatic Target Recognition XXIX, Baltimore, USA, 2019: 82–91.
|
[72] |
HUANG Teng, ZHANG Qixiang, LIU Jiabao, et al. Adversarial attacks on deep-learning-based SAR image target recognition[J]. Journal of Network and Computer Applications, 2020, 162: 102632. doi: 10.1016/j.jnca.2020.102632
|
[73] |
GOEL A, AGARWAL A, VATSA M, et al. DNDNet: Reconfiguring CNN for adversarial robustness[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, USA, 2020: 22–23.
|
[74] |
DING Jun, CHEN Bo, LIU Hongwei, et al. Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 364–368. doi: 10.1109/LGRS.2015.2513754
|
[75] |
BAI Xueru, ZHOU Xuening, ZHANG Feng, et al. Robust pol-ISAR target recognition based on ST-MC-DCNN[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 9912–9927. doi: 10.1109/TGRS.2019.2930112
|
[76] |
ZHAI Yikui, DENG Wenbo, XU Ying, et al. Robust SAR automatic target recognition based on transferred MS-CNN with L2-regularization[J]. Computational Intelligence and Neuroscience, 2019, 2019: 9140167. doi: 10.1155/2019/9140167
|
[77] |
郭炜炜, 张增辉, 郁文贤, 等. SAR图像目标识别的可解释性问题探讨[J]. 雷达学报, 2020, 9(3): 462–476. doi: 10.12000/JR20059GUO Weiwei, ZHANG Zenghui, YU Wenxian, et al. Perspective on explainable SAR target recognition[J]. Journal of Radars, 2020, 9(3): 462–476. doi: 10.12000/JR20059
|
[78] |
HUANG Zhongling, PAN Zongxu, and LEI Bin. Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data[J]. Remote Sensing, 2017, 9(9): 907. doi: 10.3390/rs9090907
|
[79] |
MALMGREN-HANSEN D, KUSK A, DALL J, et al. Improving SAR automatic target recognition models with transfer learning from simulated data[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1484–1488. doi: 10.1109/LGRS.2017.2717486
|
[80] |
ZHAO Siyuan, ZHANG Zenghui, ZHANG Tao, et al. Transferable SAR image classification crossing different satellites under open set condition[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4506005. doi: 10.1109/LGRS.2022.3159179
|
[81] |
ZHAO Siyuan, ZHANG Zenghui, GUO Weiwei, et al. An automatic ship detection method adapting to different satellites SAR images with feature alignment and compensation loss[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5225217. doi: 10.1109/TGRS.2022.3160727
|
[82] |
KARJALAINEN A I, MITCHELL R, and VAZQUEZ J. Training and validation of automatic target recognition systems using generative adversarial networks[C]. 2019 IEEE Sensor Signal Processing for Defence Conference (SSPD), Brighton, UK, 2019: 1–5.
|
[83] |
YANG Shuang, SHI Xiaoran, and ZHOU Feng. Automatic target recognition for low-resolution SAR images based on super-resolution network[C]. IEEE 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Xiamen, China, 2019: 1–6.
|
[84] |
AHMADIBENI A, JONES B, BOROOSHAK L, et al. Automatic target recognition of aerial vehicles based on synthetic SAR imagery using hybrid stacked denoising auto-encoders[C]. SPIE 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII, 2020: 71–82.
|
[85] |
ZELNIO E G. Advanced decision-making systems in future avionics: Automatic target recognition example[C]. 1998 IEEE Aerospace Conference Proceedings, Snowmass, USA, 1998: 309–313.
|
[86] |
HUANG Lanqing, LIU Bin, LI Boying, et al. OpenSARShip: A dataset dedicated to Sentinel-1 ship interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 195–208. doi: 10.1109/JSTARS.2017.2755672
|
[87] |
孙显, 王智睿, 孙元睿, 等. AIR-SARShip-1.0: 高分辨率SAR舰船检测数据集[J]. 雷达学报, 2019, 8(6): 852–862. doi: 10.12000/JR19097SUN Xian, WANG Zhirui, SUN Yuanrui, et al. AIR-SARShip-1.0: High-resolution SAR ship detection dataset[J]. Journal of Radars, 2019, 8(6): 852–862. doi: 10.12000/JR19097
|
[88] |
刘宁波, 董云龙, 王国庆, 等. X波段雷达对海探测试验与数据获取[J]. 雷达学报, 2019, 8(5): 656–667. doi: 10.12000/JR19089LIU Ningbo, DONG Yunlong, WANG Guoqing, et al. Sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2019, 8(5): 656–667. doi: 10.12000/JR19089
|
[89] |
回丙伟, 宋志勇, 王琦, 等. 空中弱小目标检测跟踪测试基准[J]. 航空兵器, 2019, 26(6): 56–59. doi: 10.12132/ISSN.1673-5048.2019.0234HUI Bingwei, SONG Zhiyong, WANG Qi, et al. A benchmark for dim or small aircraft targets detection and tracking[J]. Aero Weaponry, 2019, 26(6): 56–59. doi: 10.12132/ISSN.1673-5048.2019.0234
|
[90] |
张静, 宋锐, 郁文贤. 雷达目标识别中的BP神经网络算法改进及应用[J]. 系统工程与电子技术, 2005, 27(4): 582–585. doi: 10.3321/j.issn:1001-506X.2005.04.003ZHANG Jing, SONG Rui, and YU Wenxian. Improvements and applications of BP neural network algorithm in radar target recognition[J]. Systems Engineering and Electronics, 2005, 27(4): 582–585. doi: 10.3321/j.issn:1001-506X.2005.04.003
|
[91] |
BLASCH E, SEETHARAMAN G, and DAREMA F. Dynamic data driven applications systems (DDDAS) modeling for automatic target recognition[C]. SPIE 8744, Automatic Target Recognition XXIII, Baltimore, USA, 2013: 165–174.
|
[92] |
WILLIAMS D P, COUILLARD M, and DUGELAY S. On human perception and automatic target recognition: Strategies for human-computer cooperation[C]. 22nd IEEE International Conference on Pattern Recognition, Stockholm, Sweden, USA, 2014: 4690–4695.
|
[93] |
TELLEZ O L. Human-in-the-loop for autonomous underwater threat recognition[C]. OCEANS 2018 MTS/IEEE Charleston, Charleston, USA, 2018: 1–5.
|
[94] |
TELLEZ O L. Underwater threat recognition: Are automatic target classification algorithms going to replace expert human operators in the near future?[C]. OCEANS 2019-Marseille, Marseille, France, 2019: 1–4.
|
[95] |
IRVINE J M. Evaluating assisted target recognition performance: An assessment of DARPA’s SAIP system[C]. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, Orlando, USA, 1999: 693–704.
|
[96] |
EVERSDEN A. DARPA issues solicitation for moving-target recognition project[EB/OL]. https://www.c4isrnet.com/home/2020/07/21/darpa-issues-solicitation-for-moving-target-recognition-project/, 2020.
|
[97] |
BUSCHMANN F, MEUNIER R, ROHNERT H, et al. Pattern-Oriented Software Architecture, Volume 1, A System of Patterns[M]. New York: John Wiley & Sons, 1996: 3–7.
|
[98] |
FORD N, PARSONS R, and KUA P. Building Evolutionary Architectures: Support Constant Change[M]. Beijing: O’Reilly Media, Inc. , 2017: 95–164.
|
[99] |
宋锐. 雷达舰船目标识别系统实现技术研究[D]. [博士论文], 国防科技大学, 2003.SONG Rui. Technology research on realization of radar ship target recognition system[D]. [Ph. D. dissertation], University of Defense Science and Technology, 2003.
|
[100] |
郁文贤, 计科峰, 柳彬. 星载SAR与AIS综合的海洋目标信息处理技术[M]. 北京: 科学出版社, 2017: 164–200.YU Wenxian, JI Kefeng, and LIU Bin. Satellite Based SAR and AIS Synthetic Oceanic Target Information Processing Technology[M]. Beijing: Science Press, 2017: 164–200.
|
[101] |
郁文贤. 智能化识别方法及其在舰船雷达目标识别系统中的应用[D]. [博士论文], 国防科技大学, 1992.YU Wenxian. Intelligent recognition method and its application in ship radar target recognition system[D]. [Ph. D. dissertation], University of Defense Science and Technology, 1992.
|
[102] |
郁文贤, 郭桂蓉. 通用型对海监视雷达目标识别与综合显控系统[R]. 长沙: 国防科技大学, 2004.YU Wenxian and GUO Guirong. Universal sea-surveillance radar target recognition and comprehensive display and control system[R]. Changsha: University of Defense Science and Technology, 2004.
|
[103] |
CALVO-FULLANA M, MOX D, PYATTAEV A, et al. ROS-NetSim: A framework for the integration of robotic and network simulators[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1120–1127. doi: 10.1109/LRA.2021.3056347
|
[104] |
张静. 岸对海雷达智能数据分析与目标判性系统研制报告[R]. 国防科技大学ATR重点实验室, 2003.ZHANG Jing. Shore-to-the-sea radar intelligent data analysis and target classification system report[R]. ATR Laboratory, University of Defense Science and Technology, 2003.
|
[105] |
郭桂蓉, 庄钊文. ATR柔性技术研究报告[R]. 国防科技大学, 1995.GUO Guirong and ZHUANG Zhaowen. ATR adaptive technology research report[R]. University of Defense Science and Technology, 1995.
|