RANSAC算法在空间目标光电跟踪中的应用研究

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. doi: 10.12086/oee.2019.180540
引用本文: 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. doi: 10.12086/oee.2019.180540
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. doi: 10.12086/oee.2019.180540
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. doi: 10.12086/oee.2019.180540

RANSAC算法在空间目标光电跟踪中的应用研究

  • 基金项目:
    中国科学院空间科学背景型号项目(XDA15020400)
详细信息
    作者简介:
    通讯作者: 黄永梅(1968-),女,研究员,博士生导师,主要从事光电跟踪控制、空间量子激光通信、地基大口径天文望远镜等方面的研究。E-mail: huangym@ioe.ac.cn
  • 中图分类号: TB872

Research on the application of RANSAC algorithm in electro-optical tracking of space targets

  • Fund Project: Supported by Project Fund for Background Model in Space Science, Chinese Academy of Sciences (XDA15020400)
More Information
  • 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。

  • Overview: Monitoring and tracking the space debris (referred to as space target) is an important work of electro-optic tracking system. When measuring and tracking space targets in medium and high orbits, due to the long distance between the targets and observation station, the equivalent magnitude is relatively high. In order to enhance the detection capacity of tracking system, the aperture of electro-optic detectors is generally designed to be large. Meanwhile, the field of view of electro-optic detectors is usually small, namely angular component level, for the sake of suppressing the stray light in atmospheric channel. When the space target is shielded by clouds or enters the shadows or penumbra of the earth, the target image cannot be extracted from the field of view of CCD (charge coupled device) camera, the closed-loop tracking of the system cannot be barely work in severe cases. In this case, theoretical orbital data can be used to guide the mount of the system, which keeps track of the target in a short time. However, a large number of high-precision observation data in previous tracking process is discarded. In the absence of multi-station intersection measurement and orbit determination for space targets, guiding and tracking by predicting trajectory is an important way to solve this problem. In this paper, a real-time prediction and tracking algorithm based on short-arc observation data is studied in the background of single station which cannot be intersected.

    Random sample consensus (RANSAC) algorithm, which has been widely used in feature extraction in computer vision, is introduced in this paper to achieve higher prediction accuracy. The loss function of RANSAC algorithm is improved according to the distribution of observed data. Considering the fact that smaller errors are usually caused by noise from the interior point and larger errors may be affected by external points, and the boundary between the inner points and the outer points is usually imprecise in practice, the error within the threshold range is taken into account to increase the penalty for larger errors and decrease the penalty for smaller errors. What's more, the improved loss function has a continuous first derivative and varies more gently near the threshold, the sensitivity of the algorithm to threshold is further reduced. The improved algorithm is called WRANSAC algorithm.

    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.

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  • 图 1  最小抽样次数随数据错误率变化曲线图

    Figure 1.  Minimum sampling frequency changing with data error rate

    图 2  损失函数曲线图

    Figure 2.  Loss function curve

    图 3  WRANSAC算法流程图

    Figure 3.  Flow chart of the WRANSAC algorithm

    图 4  内点代价函数柱状图

    Figure 4.  Histogram of interior point cost function

    图 5  内点权值柱状图

    Figure 5.  Histogram of internal point weights

    图 6  预测72 min的轨迹图

    Figure 6.  Prediction of 72 minutes

    图 7  预测72 min的误差分布图

    Figure 7.  The error distribution for the prediction of 72 minutes

    表 1  15 min、30 min和72 min预测误差特性

    Table 1.  Prediction error statistics for 15/30/72 minutes

    预测时长/min 算法 sin LSQ RANSAC WRANSAC
    15 最大值/(″) 34.9151 29.1957 19.5649 17.0257
    最小值/(″) -6.9270 -10.8216 -17.7444 -16.8968
    均值/(″) 15.1429 0.1934 2.4577 0.9701
    方差 124.3353 90.9505 59.9375 57.9286
    30 最大值/(″) 36.3312 32.0761 11.1779 10.8932
    最小值/(″) -13.1593 -18.8263 -23.3834 -21.5939
    均值/(″) 13.0059 -0.6765 -2.0592 -4.6598
    方差 218.0527 163.9206 84.8496 82.1343
    72 最大值/(″) 6.6790 286.3128 26.8620 17.7788
    最小值/(″) -120.0175 -207.9139 -19.1023 -18.0916
    均值/(″) -32.2389 6.0681 0.7308 -0.7330
    方差 602.6599 15953.6643 118.8533 89.7496
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出版历程
收稿日期:  2018-10-23
修回日期:  2019-01-11
刊出日期:  2019-11-01

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