基于光电跟踪设备对空间目标进行跟踪测量时，由于电磁干扰、云层遮挡或者地影等因素的影响，造成空间目标成像在设备视场中无法提取，严重时甚至导致系统闭环跟踪不能平稳进行。此时可以采用理论引导的方式，利用预测轨迹继续进行跟踪搜索。本文将广泛用于计算机视觉领域特征提取的随机抽样一致性(RANSAC)算法引入轨迹预测，并根据观测数据分布的特点进行改进提出WRANSAC算法，用于实时处理有限的历史观测数据，进行轨迹预测。引入该算法后，在对空间目标轨迹预测时，对历史观测数据的容错能力提高，对模型的敏感性降低，预测结果的准确性和鲁棒性远远优于最小二乘法。通过对比预测轨迹和实际轨迹，证明了该算法的有效性。
RANSAC算法在空间目标光电跟踪中的应用研究
作者单位信息
通信作者： huangym@ioe.ac.cn
出版日期：2019年11月15日
摘要
参考文献
[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.
基金项目：
中国科学院空间科学背景型号项目(XDA15020400)
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严灵杰, 黄永梅, 张涯辉, 等. RANSAC算法在空间目标光电跟踪中的应用研究[J]. 光电工程, 2019, 46(11): 180540.
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