红外与激光工程, 2019, 48 (3): 0330002, 网络出版: 2019-04-06   

末敏弹线阵激光雷达对地面装甲目标的提取方法

Armored target extraction method based on linear array LiDAR of terminal sensitive sub-ammunition
作者单位
1 南京理工大学 智能弹药技术国防重点学科实验室, 江苏 南京 210094
2 埃因霍温理工大学 机械工程系, 荷兰 埃因霍温 5600MB
3 西南技术物理研究所, 四川 成都 610041
摘要
为了提高末敏弹在复杂战场环境下对地面装甲目标的识别概率, 提出了一种用于弹载线阵激光成像雷达的目标提取方法, 结合末敏弹边旋转边下降的稳态运动特点, 实现了线阵列激光雷达对扫描区域的三维点云成像。首先通过对点云中高度数据分析, 提出了基于高度与梯度的组合阈值分割算法, 实现了地面背景的快速分割; 然后利用坐标变换, 对有坡度的地面进行调整, 并通过典型装甲目标的几何尺寸自动获取种子点进行区域增长分割; 最后利用最小外接矩形特征获取目标的几何信息, 由目标的几何特征实现装甲目标的提取。仿真结果表明: 此方法可以实现线阵列激光雷达在50~120 m高度下对地面装甲目标的准确提取, 从而为新型末敏弹目标探测提供技术支撑。
Abstract
In order to improve identification probability of terminal sensitive sub-ammunition under complex battle circumstances, the method of extracting the armor target based on the linear array LiDAR was proposed. Combined with stable scanning theory, 3D point cloud imaging on the scanning area was realized. Firstly, the height and gradient combination threshold segmentation algorithm was proposed by analyzing the height data in the point cloud; Then the coordinate transformation was used to adjust the slope ground, and the seed were automatically acquired through the geometric size of the typical armored targets for regional growth segmentation; finally, the extraction of armored targets was achieved through the geometric features of the target. The simulation shows that linear array LiDAR can accurately extract the armor target at the height of 50-120 m in battlefield, which provides technical support for the target detection of new terminal sensitive sub-ammunition.
参考文献

[1] 张俊, 刘荣忠, 郭锐, 等. 末敏弹稳态扫描段红外特性的实验研究[J]. 红外与激光工程, 2013, 42(11): 2876-2881.

    Zhang Jun, Liu Rongzhong, Guo Rui, et al. Experimental study on infrared characteristics of terminal-sensitive projectile at steady-state scanning stage[J]. Infrared and Laser Engineering, 2013, 42(11): 2876-2881. (in Chinese)

[2] 于加其, 杨树新, 朱伯立. 弹载激光成像雷达距离像的目标提取技术[J]. 北京理工大学学报, 2016, 36(12): 1279-1282.

    Yu Jiaqi, Yang Shuxin, Zhu Boli. Target extraction base on range image from missile-borne imaging LADAR[J].Transactions of Beijing Institute of Technology, 2016, 36(12): 1279-1282. (in Chinese)

[3] 王帅, 孙华燕, 郭惠超. 适用于激光点云匹配的重叠区域提取方法[J]. 红外与激光工程, 2017, 46(S1): S126002.

    Wang Shuai, Sun Huayan, Guo Huichao. Overlapping region extraction method for laser point clouds registration[J]. Infrared and Laser Engineering, 2017, 46(S1): S126002. (in Chinese)

[4] 田青华, 百瑞林, 李杜. 基于改进欧式聚类的散乱工件点云分割[J]. 激光与光电子学进展, 2017, 54(12): 121503.

    Tian Qinghua, Bai Ruilin, Li Du. Point cloud segmentation of scattered workpiece based on improved euclinean clustering[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121503. (in Chinese)

[5] William R Green, Hans Grobler. Normal distribution transform graph based point cloud segmentation[C]//Pattern Recognition Association of South Africa and Robotics and Mecha-tronics International Conference, 2015: 54-59.

[6] Mossman F. Interlacing self-localization moving object tracking and mapping for 3D range sensors [D]. Germany: KIT Science Publishing, 2013.

[7] 张名芳, 付锐, 郭应石, 等. 基于三维不规则点云的地面分割算法[J]. 吉林大学学报(工学版), 2010, 39(5): 979-984.

    Zhang Mingfang, Fu Rui, Guo Yingshi, et al. Road segmentation method based on irregular three dimensional point cloud[J]. Journal of Jilin University (Engineering and Technology Edition), 2010, 39(5): 979-984. (in Chinese)

[8] 王克勇, 宋承天, 邓甲昊. 用于成像探测目标识别的块特征提取方法[J]. 红外与激光工程, 2010, 39(5): 979-984.

    Wang Keyong, Song Chengtian, Deng Jiahao. Block feature extraction method for target detection in imaging fuze[J]. Infrared and Laser Engineering, 2010, 39(5): 979-984. (in Chinese)

[9] 马超, 杨华, 李小霞, 等. 复杂场景下应用成像Ladar 的自动目标识别[J]. 光学 精密工程, 2009, 17(7): 1714-1720.

    Ma Chaojie, Yang Hua, Li Xiaoxia, et al. Imlementation of automatic target recognition by imagine Ladar in complex scenes[J]. Optics and Precision Engineering, 2009, 17(7): 1714-1720. (in Chinese)

[10] 黄涛, 胡以华, 赵钢. 基于激光成像雷达距离图像的目标提取与分类技术[J]. 红外与毫米波学报, 2011, 30(2): 170-183.

    Huang Tao, Hu Yihua, Zhao Gang. Target extraction and classification base on imaging LADAR range image[J]. J Infrared Millim Waves, 2011, 30(2): 170-183. (in Chinese)

[11] Palm H C, Havardsholm T V, Ajer H, et al. Extraction and classification of vehicles in Ladar imagery[C]//Processing of SPIE Defense, Security, and Sensing Baltimore, USA: International Society for Optics and Photonics, 2009,7382: 738203.

[12] 刘志青, 李鹏程, 郭海涛, 等. 基于相关向量机的机械LiDAR点云数据分类[J]. 红外与激光工程, 2016, 45(S1): S130006.

    Liu Zhiqing, Li Pengcheng, Guo Haitao, et al. Airborne LiDAR point cloud data classification based on relevabce vector machine [J]. Infrared and Laser Engineering, 2016, 45(S1): S130006. (in Chinese)

[13] 刘志青, 李鹏程, 陈小卫, 等. 基于信息向量机的机载激光雷达点云数据分类[J]. 光学 精密工程, 2016, 24(1): 210-219.

    Liu Zhiqing, Li Pengcheng, Chen Xiaowei, et al. Classification of airborne LiDAR point cloud data based on information vector machine[J]. Optics and Precision Engineering, 2016, 24(1): 210-219. (in Chinese)

[14] 惠振阳, 胡友健. 基于LiDAR 数字高程模型构建的数学形态学滤波方法综述[J]. 激光与光电子学进展, 2016, 53:080001.

    Hui Zhenyang, Hu Youjian, Review on morphological filtering algorithms based on LiDAR digital elevation model construction [J]. Laser & Optoelectronics Progress, 2016, 53:080001. (in Chinese)

武军安, 郭锐, 刘荣忠, 刘磊, 柯尊贵. 末敏弹线阵激光雷达对地面装甲目标的提取方法[J]. 红外与激光工程, 2019, 48(3): 0330002. Wu Jun′an, Guo Rui, Liu Rongzhong, Liu Lei, Ke Zungui. Armored target extraction method based on linear array LiDAR of terminal sensitive sub-ammunition[J]. Infrared and Laser Engineering, 2019, 48(3): 0330002.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!