[1] SU Po, RONG Yong-jie, XU Jian-sheng, et al. Research on the recognition and position method for linear array laser radar of terminal sensitive projectile［J］. Journal of Projectiles, Rockets, Missiles and Guidance, 2017, 37(1): 161-164.

[2] WU Jun-an, GUO Rui, LIU Rong-zhong, et al. Armored target extraction based on linear array LiDAR of terminal sensitive sub-ammunition［J］. Infrared and Laser Engineering, 2018, 48(3): 0330002.

[3] MA Chao-jie, YANG Hua, LI Xiao-xia, et al. Implementation of automatic target recognition by imagine Ladar in complex scenes［J］. Optics and Precision Engineering, 2009, 17(7): 1714-1720

[4] HUANG Tao, HU Yi-hua, ZHAO Gang. Target extraction and classification base on imaging LADAR range image［J］. Journal of Infrared and Millimeter Waves, 2011, 30(2): 170-183.

[5] HANS C P, TRYM V H, HALVOR A, et al. Extraction and classification of vehicles in Ladar imagery［C］. SPIE, 2013, 8731(4): 507-522.

[6] RUSU R B, BRADSKI G, THIBAUX R, et al. Fast 3D recognition and pose using the viewpoint feature histogram［C］. IEEE International Conference on Intelligent Robots and Systems, 2010: 2155-2162.

[7] ALDOMA A, VINCZE M, BLODOW N, et al. CAD-model recognition and 6DOF pose estimation using 3D cues［C］. IEEE International Conference on Computer Vision Workshops, 2011: 585-592.

[8] RUSU R B, BLODOW N, BEETZ M. Fast point feature histograms (FPFH) for 3D registration［C］. IEEE International Conference on Robotics and Automation, 2009: 1848-1853.

[9] PRAKHYA S M, LIU B, LIN W, et al. B-SHOT：a binary 3D feature descriptor for fast key point matching on 3D point clouds［J］. Autonomous Robots, 2017, 41(7): 1501-1520.

[10] LECUN Y, BENGIO Y, HINTION G. DEEP learning［J］. Nature, 2015, 521(7553): 436-444.

[11] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition［J］. Computer Science, 2015. arXiv:1512.033385v1．

[12] HE X, WANG A L, GHAMISI P, et al. LiDAR data classification using spatial transformation and CNN［J］. IEEE Geoscience and Remote Sensing Letters, 2018, (99):1-5.

[13] YANG W H, YU F. LiDAR image classification based on convolutional neural networks［C］. International Conference on Computer Network, Electronic and Automation, 2017, 37: 221-225.

[14] CHEN Y S, LI C Y, GHAMISI P, et al. Deep fusion of hyperspectral and LiDAR data for thematic classification［C］. IEEE International Geoscience And Remote Sensing Symposium, 2016, 3590-3504.

[15] GAO H B, CHENG B, WANG J Q, et al. Object classification using CNN-based fusion of vision and LiDAR in autonomous vehicle environment［J］. IEEE Transactions on Industrial Informatics, 2018, (99): 4224-4231.

[16] ZHOU Y, TUZEL O. Voxelnet: End-to-end learning for point cloud based 3d object detection［J］. 2017, Arxiv: 1711.06396

[17] STRIGL D, KOFLER K, PODLIPNIG S. Performance and scalability of GPU-based convolutional neural networks［C］. Proceedings of Euromicro International Conference on Parallel, Distributed and Network-Based Processing. Piscataway, NJ: IEEE Press, 2010: 317-324.

[18] LIU Zhi-cheng, ZHU Yong-xin, WANG Hui, et al. Parallel acceleration design of convolutional neural network based on FPGA［J］. Microelectronics & Computer, 2018, 35(10): 80-85.

[19] CAI Rui-chu, YU Yang, ZHONG Chun-rong, et al. Design and scheduling of convolutional neural network accelerator for cloud FPGAs［J］. Application Research of Computers. DOI: 10.19734/j.issn.1001-3695.2018.05.0507

[20] Xilinx. 2017. Xilinx Zynq-7000 All Programmable SoC［EB/OL］. ［2019-04-18］. https://www.xilinx.com/products/silicon-devices/soc/zynq-7000.html.