基于目标特征尺寸的可视化被动测距系统

杨金宝; 杨晨; 刘建国; 祝宁华; 于丽娟; 刘亚超

doi:10.3788/OPE.20182601.0245

您当前的位置：

首页 >

文章列表页 >

基于目标特征尺寸的可视化被动测距系统

信息科学 | 更新时间：2020-08-13

- 基于目标特征尺寸的可视化被动测距系统
- Visual passive ranging system based on target feature size
- 光学精密工程 2018年26卷第1期页码：245-252
- 作者机构：
  
  1.中国科学院半导体研究所固态光电信息技术实验室, 北京 100083
  2.中国科学院大学电子电气与通信工程学院, 北京 100049
  3.中国科学院大学材料科学与光电技术学院, 北京 100049
  4.北京环境特性研究所光学辐射重点实验室, 北京 100854
- 作者简介：
  
  [ "杨金宝(1987-), 男, 安徽安庆人, 博士研究生, 工程师, 主要从事光电信号与信息处理方面的研究。E-mail:yangjinbao20054723@126.com" ]
- 基金信息：
  
  国家重点基础研究发展计划资助项目(2014CB340102)
- DOI：10.3788/OPE.20182601.0245
  中图分类号： TN29;TP391.4;TN971.+5
- 收稿日期：2017-07-06，
  
  录用日期：2017-8-20，
  
  纸质出版日期：2018-01-25
- 稿件说明：
移动端阅览
杨金宝, 杨晨, 刘建国, 等. 基于目标特征尺寸的可视化被动测距系统[J]. 光学精密工程, 2018,26(1):245-252.

Jin-bao YANG, Chen YANG, Jian-guo LIU, et al. Visual passive ranging system based on target feature size[J]. Optics and precision engineering, 2018, 26(1): 245-252.
杨金宝, 杨晨, 刘建国, 等. 基于目标特征尺寸的可视化被动测距系统[J]. 光学精密工程, 2018,26(1):245-252. DOI： 10.3788/OPE.20182601.0245.

Jin-bao YANG, Chen YANG, Jian-guo LIU, et al. Visual passive ranging system based on target feature size[J]. Optics and precision engineering, 2018, 26(1): 245-252. DOI： 10.3788/OPE.20182601.0245.

摘要

为克服传统光电被动测距系统稳定性差、远距离条件下测距精度收敛性差、系统难以小型化等不足，本文提出一种基于目标特征尺寸的可视化光电被动测距系统。建立了被动测距模型并分析测距原理，从理论上分析获得距离反演公式和测距误差精度；设计了被动测距算法并分析相关成像参数获取途径和方法；通过高清成像器、半导体激光器和信号处理器等软硬件设计，实现了系统的可视化被动测距功能和全天候图像信息获取；最后通过目标特征尺寸及其成像系统参数反演目标距离，实现了在成像过程中实时测距并进行被动测距实验验证。实验结果表明，被动测距精度优于10%，当前软硬件参数配置下，目标的测距距离大于1 km，测距鲁棒性好，性能稳定，可广泛应用于全天候目标图像信息获取和光电被动测距的实际工程实践中。

Abstract

In order to overcome the shortage of traditional optical passive ranging system such as low stability

difficult to guarantee insufficient ranging accuracy in long-distance condition and difficult to reduce the system volume and weight

a method for visualization of optical passive ranging system based on target feature size was presented in this paper. Passive ranging model was established and ranging principle was analyzed. Theoretical analysis was carried out to obtain the distance inversion formula and the ranging error accuracy. Through hardware and software design

the visual passive ranging function of the system was achieved and the all-weather image information acquisition was realized. Finally

target distance was retrieved by the target feature size and the imaging system parameters

real-time ranging was realized during the imaging process

and the passive ranging experiment was carried out. Experimental results show that the accuracy of passive ranging is better than 10%. Under the current hardware and software configuration

the target ranging distance is greater than 1km. The method has good robustness and stable performance. It can be widely used in the practical engineering of all-weather target imaging information acquisition and photoelectric passive ranging.

关键词

Keywords

references

MCKAY D L, WOHLERS M R, CHUANG C K, et al .. Airborne validation of an IR passive TBM ranging sensor[J]. SPIE , 1999, 3698:491-500.

路远, 时家明, 凌永顺, 等.红外被动定位研究[J].红外与激光工程, 2001, 30(6):405-409.

LU Y, SHI J M, LING Y SH, et al .. Research on infrared passitive location[J]. Infrared and Laser Engineering , 2001, 30(6):405-409. (in Chinese)

JEFFREY W, DRAPER J S, GOBEL R W. Monocular passive ranging[C]. Proceedings of IRIS Meeting of specialty Group on Targets, Back Grounds and Discrimination, SPIE 1994: 113-130.

BALDACCI A, CORSINI G, DIANI D, et al .. Ranging by means of monocular passive systems[J]. SPIE , 1999, 3720:473-482.

赵勋杰, 高稚允.光电被动测距技术[J].光学技术, 2003, 29(6):652-656.

ZHAO X J, GAO ZH Y. Review of passive range sensing techniques[J]. Optical Technique , 2003, 29(6):652-656. (in Chinese)

JARVIS R A. A perspective on range finding techniques for computer vision[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 1983, PAMI-5(2):122-139.

OKADA K, INABA M, INOUE H. Integration of real-time binocular stereo vision and whole body information for dynamic walking navigation of humanoid robot[C]. IEEE, Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems , 2003: 131-136.

ELLIS S R, BUCHER U J, MENGES B M. The relationship of binocular convergence and errors in judged distance to virtual objects[J]. IFAC Proceedings Volumes , 1995, 28(15):253-257.

闫宗群, 刘秉琦, 华文深, 等.利用氧气吸收被动测距的近程实验[J].光学精密工程, 2013, 21(11):2744-2750.

YAN Z Q, LIU B Q, HUA W SH, et al .. Short-range experiment of passive ranging by oxygen absorption[J]. Opt. Precision Eng ., 2013, 21(11):2744-2750. (in Chinese)

路远, 冯云松, 凌永顺, 等.红外三色被动测距[J].光学精密工程, 2012, 20(12):2680-2685.

LU Y, FENG Y S, LING Y SH, et al .. Infrared three-color passive ranging by colorimetric method[J]. Opt. Precision Eng ., 2012, 20(12):2680-2685. (in Chinese)

赵勋杰, 李成金.双目立体实时测距系统的关键技术研究[J].激光与红外, 2006, 36(9):874-877.

ZHAO X J, LI CH J. Research of key-tech in a binocular real-time ranging system[J]. Laser & Infrared , 2006, 36(9):874-877. (in Chinese)

刘红波, 赵勋杰, 彭翔.基于双焦成像的测距技术研究[J].激光与红外, 2010, 40(10):1136-1140.

LIU H B, ZHAO X J, PENG X. Research on range techniques based on bifocal imaging[J]. Laser & Infrared , 2010, 40(10):1136-1140. (in Chinese)

黄士科, 夏涛, 张天序.基于红外图像的被动测距方法[J].红外与激光工程, 2007, 36(1):109-112, 126.

HUANG SH K, XIA T, ZHANG T X. Passive ranging method based on infrared images[J]. Infrared and Laser Engineering , 2007, 36(1):109-112, 126. (in Chinese)

辛云宏, 杨万海, 王保平.一种基于双波段的红外搜索与跟踪系统的单站测距方法[J].红外技术, 2004, 26(1):5-8.

XIN Y H, YANG W H, WANG B P. A method of single observer ranging based on double bands of IRSTS[J]. Infrared Technology , 2004, 26(1):5-8. (in Chinese)

钱铮铁.一种用于红外警戒系统的被动测距方法[J].红外与毫米波学报, 2001, 20(4):311-314.

QIAN ZH T. Passive ranging method for IRST systems[J]. Journal of Infrared and Millimeter Waves , 2001, 20(4):311-314. (in Chinese)

尧燕玲, 陈永强, 陈君.基于人体切片的人体特征尺寸提取[J].山东纺织经济, 2010(5):80-82.

YAO Y L, CHEN Y Q, CHEN J. The extraction of feature size based on the Human body slice[J]. Shandong Textile Economy , 2010(5):80-82. (in Chinese)

王新伟, 周燕, 范松涛, 等. 基于深度指纹获取远距离视频监控目标特征尺寸的方法: 中国, CN201010564580. 9[P]. 2011-05-25.

WANG X W, ZHOU Y, FAN S T, et al . . Method for acquiring characteristic size of remote video monitored target on basis of depth fingerprint: CN, CN201010564580. 9[P]. 2011-05-25. (in Chinese)

李玉, 徐艳, 赵雪梅, 等.利用高斯混合模型的多光谱图像模糊聚类分割[J].光学精密工程, 2017, 25(2):509-518.

LI Y, XU Y, ZHAO X M, et al .. Multispectral image segmentation by fuzzy clustering algorithm used Gaussian mixture model[J]. Opt. Precision Eng ., 2017, 25(2):509-518. (in Chinese)

王永忠, 梁彦, 潘泉, 等.基于自适应混合高斯模型的时空背景建模[J].自动化学报, 2009, 35(4):371-378.

WANG Y ZH, LIANG Y, PAN Q, et al .. Spatiotemporal background modeling based on adaptive mixture of Gaussians[J]. Acta Automatica Sinica , 2009, 35(4):371-378. (in Chinese)

方帅, 薛方正, 徐心和.基于背景建模的动态目标检测算法的研究与仿真[J].系统仿真学报, 2005, 17(1):159-161, 165.

FANG SH, XUE F ZH, XU X H. Moving object detection based on self-adaptive background modeling[J]. Journal of System Simulation , 2005, 17(1):159-161, 165. (in Chinese)

HENRIQUES J F, CASEIRO R, MARTINS P, et al .. High-speed tracking with Kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2015, 37(3):583-596.

浏览量

550

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

混合像元分解在氧气吸收被动测距中的应用

氧分子A吸收带的随机Malkmus被动测距

相关K分布法在氧气吸收被动测距中的应用

基于氧气吸收被动测距技术的近程实验

红外三色被动测距