When the electro-optic tracking system is used for space target tracking, it is difficult to extract the target from the field of view occasionally due to the impact of electromagnetic interference, cloud cover or earth shadow etc., and the closed-loop tracking system can barely work in severe cases. At this point the predicted orbit can be used to guide the system to ensure smooth scanning and tracking. In this paper, random sample consensus (RANSAC) algorithm is introduced, which has been widely used in feature extraction in computer vision, to achieve higher prediction accuracy. The loss function of RANSAC algorithm is improved and the WRANSAC algorithm is proposed according to the distribution of observed data, which is used to deal with the limited observation data in real time to track the space target. After the algorithm is adopted, the fault tolerance of observation data is improved and the sensitivity of the model is reduced. The accuracy and robustness of the prediction results are much better than that of the least squares method. The validity of the WRANSAC algorithm is proved by the comparison between the predicted trajectory and the actual trajectory.
Home > Journal Home > Opto-Electronic Engineering
Opto-Electronic Engineering
ISSN: 1003-501X
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
Research on the application of RANSAC algorithm in electro-optical tracking of space targets
Author Affiliations
Correspondence: huangym@ioe.ac.cn
First published at:Nov 15, 2019
Abstract
References
[1] Ma J G. The basic technologies of the acquisition, tracking and pointing systems[J]. Opto-Electronic Engineering, 1989(3): 1–42.
马佳光. 捕获跟踪与瞄准系统的基本技术问题[J]. 光学工程, 1989(3): 1–42.
[2] Cai H Y, Ding L, Huang Z H, et al. An accurate calibration method of the ball screen projection point targets tracking system[J]. Opto-Electronic Engineering, 2018, 45(8): 170656.
蔡怀宇, 丁蕾, 黄战华, 等. 球幕点目标投影跟踪系统的精确标定方法[J]. 光电工程, 2018, 45(8): 170656.
[3] Zhang P L, Wang J J. Research of LEO satellite orbit predic-tion for vehicle-borne optical measuring equipment[J]. Mathematics in Practice and Theory, 2015, 45(6): 128–132.
张沛露, 王建军. 车载跟瞄设备低轨卫星预测方法研究[J]. 数学的实践与认识, 2015, 45(6): 128–132.
[4] Li T, Zhang J C. The tracking accurancy analysis of single maneuvering targets[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2004, 24(2): 94–96.
李涛, 张金成. 单机动目标跟踪精度分析[J]. 弹箭与制导学报, 2004, 24(2): 94–96.
[5] Wei G, Jiang C F, Yang K T. Precision azimuth prediction method for electro-optical tracking[J]. Opto-Electronic Engi-neering, 2006, 33(5): 6–11.
魏刚, 江传富, 杨坤涛. 方位角准确预测法在光电跟踪中的应用研究[J]. 光电工程, 2006, 33(5): 6–11.
[6] Pan X G, Zhou H Y, Wang J Q, et al. Orbit prediction algorithm of LEO satellite based on optical measurement in short arc with single station[J]. Acta Astronomica Sinica, 2009, 50(4): 445–458.
潘晓刚, 周海银, 王炯琦, 等. 基于单站短弧段光学观测的低轨卫星轨道预报算法[J]. 天文学报, 2009, 50(4): 445–458.
[7] Choi S, Kim T, Yu W. Robust video stabilization to outlier motion using adaptive RANSAC[C]//2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA, 2009: 1897–1902.
[8] Chum O, Matas J. Optimal randomized RANSAC[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(8): 1472–1482.
[9] Huang Z F, Wang J Z, Chen Z P. Motion characteristics analysis of space target and stellar target in opto-electronic observa-tion[J]. Opto-Electronic Engineering, 2012, 39(4): 67–72.
黄宗福, 汪金真, 陈曾平. 光电探测中空间目标和恒星目标运动特性分析[J]. 光电工程, 2012, 39(4): 67–72.
[10] Luo H, Mao Y D, Yu Y, et al. A method of GEO targets recognition in wide-field opto-electronic telescope observation[J]. Opto-Electronic Engineering, 2017, 44(4): 418–426.
罗浩, 毛银盾, 于涌, 等. 利用超大视场光电望远镜观测GEO中的目标识别方法[J]. 光电工程, 2017, 44(4): 418–426.
[11] Cen M, Fu C Y, Liu X F, et al. Position prediction method for satellite tracking based on error-space estimate[J]. Opto-Electronic Engineering, 2007, 34(6): 15–19.
岑明, 傅承毓, 刘兴法, 等. 误差空间估计的卫星跟踪位置预测[J]. 光电工程, 2007, 34(6): 15–19.
[12] Cheng W L, Wang X J, Wan Z J, et al. Research and imple-mentation of target tracking algorithm in compression domain on miniaturized DSP platform[J]. Opto-Electronic Engineering, 2017, 44(10): 972–982.
程卫亮, 王向军, 万子敬, 等. 压缩域目标跟踪算法在小型化DSP平台上的研究与实现[J]. 光电工程, 2017, 44(10): 972–982.
[13] Li Z W, Zhang T, Sun M G. Fast recognition and precise orientation of space objects in star background[J]. Optics and Precision Engineering, 2015, 23(2): 589–599.
李振伟, 张涛, 孙明国. 星空背景下空间目标的快速识别与精密定位[J]. 光学 精密工程, 2015, 23(2): 589–599.
[14] Xu M, Lu J. Distributed RANSAC for the robust estimation of three-dimensional reconstruction[J]. IET Computer Vision, 2012, 6(4): 324–333.
[15] Chen F X, Wang R S. Fast RANSAC with preview model parameters evaluation[J]. Journal of Software, 2005, 16(8): 1431–1437.
陈付幸, 王润生. 基于预检验的快速随机抽样一致性算法[J]. 软件学报, 2005, 16(8): 1431–1437.
[16] Hast A, Nysj? J, Marchetti A. Optimal RANSAC—towards a repeatable algorithm for finding the optimal set[J]. Journal of WSCG, 2013, 21(1): 21–30.
[17] Chum O, Matas J, Kittler J. Locally optimized RANSAC[C]//Proceedings of 25th DAGM Symposiumon Pattern Recognition, Magdeburg, Germany, 2003, 2781: 236–243.
[18] Subbarao R, Meer P. Beyond RANSAC: user independent robust regression[C]//2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), New York, NY, USA, 2006.
[19] Xiao C B, Feng D Z, Feng X W. Fast RANSAC algorithm with resample optimization[J]. Journal of Computer-Aided Design & Computer Graphics, 2016, 28(4): 606–613.
肖春宝, 冯大政, 冯祥卫. 重抽样优化的快速随机抽样一致性算法[J]. 计算机辅助设计与图形学学报, 2016, 28(4): 606–613.
Keywords:
Funds:
Project Fund for Background Model in Space Science, Chinese Academy of Sciences (XDA15020400)
Export Citations as:
For
Get Citation:
Yan Lingjie, Huang Yongmei, Zhang Yahui, et al. Research on the application of RANSAC algorithm in electro-optical tracking of space targets[J]. Opto-Electronic Engineering, 2019, 46(11): 180540.
