Jiang Ping, Liu Chengzhi, Yang Wenbo, Kang Zhe, Fan Cunbo, Li Zhenwei
Changchun Observatory of National Astronomical Observators, Chinese Academy of Sciences, Changchun, 130117, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
NPJ Microgravity. 2022 May 5;8(1):14. doi: 10.1038/s41526-022-00200-z.
In the past few years, the increasing amount of space debris has triggered the demand for distributed surveillance systems. Long exposure time can effectively improve the target detection capability of the wide-area surveillance system. Problems that also cause difficulties in space-target detection include large amounts of data, countless star points, and discontinuous or nonlinear targets. In response to these problems, this paper proposes a high-precision space-target detection and tracking pipeline that aims to automatically detect debris data in space. First, a guided filter is used to effectively remove the stars and noise, then Hough transform is used to detect space debris, and finally Kalman filter is applied to track the space debris target. All experimental images are from Jilin Observatory, and the telescope is in star-tracking mode. Our method is practical and effective. The results show that the proposed automatic extraction channel of space debris can accurately detect and track space targets in a complex background.
在过去几年中，日益增多的空间碎片引发了对分布式监视系统的需求。长曝光时间能够有效提高广域监视系统的目标检测能力。在空间目标检测中造成困难的问题还包括大量数据、无数星点以及不连续或非线性目标。针对这些问题，本文提出了一种高精度空间目标检测与跟踪流程，旨在自动检测太空中的碎片数据。首先，使用引导滤波器有效去除恒星和噪声，然后利用霍夫变换检测空间碎片，最后应用卡尔曼滤波器跟踪空间碎片目标。所有实验图像均来自吉林天文台，且望远镜处于恒星跟踪模式。我们的方法实用且有效。结果表明，所提出的空间碎片自动提取通道能够在复杂背景下准确检测和跟踪空间目标。
