National Key Laboratory of Science and Technology on ATR, National University of Defense Technology, Changsha 410073, China.
China Xi'an Satellite Control Center, Xi'an 710000, China.
Sensors (Basel). 2019 Sep 18;19(18):4026. doi: 10.3390/s19184026.
Geosynchronous orbit (GSO) is the ideal orbit for communication, navigation, meteorology and other satellites, but the space of GSO is limited, and there are still a large number of space debris threatening the safety of spacecraft. Therefore, real-time detection of GSO debris is necessary to avoid collision accidents. Because radar is limited by transmitting power and operating distance, it is difficult to detect GSO debris, so photoelectric detection becomes the mainstream way to detect GSO debris. This paper presents an adaptive real-time detection algorithm for GSO debris in the charge coupled device (CCD) images. The main work is as follows: An image adaptive fast registration algorithm and an enhanced dilation difference algorithm are proposed. Combining with mathematical morphology, threshold segmentation and global nearest neighbor (GNN) multi-target tracking algorithm, the functions of image background suppression, registration, suspected target extraction and multi-target tracking are realized. The processing results of a large number of measured data show that the algorithm can detect dim geostationary earth orbit (GEO) and non-GEO debris in GSO belt stably and efficiently, and the processing speed meets the real-time requirements, with strong adaptive ability, and has high practical application value.
地球同步轨道（GSO）是通信、导航、气象等卫星的理想轨道，但 GSO 的空间有限，仍然存在大量的空间碎片威胁航天器的安全。因此，实时检测 GSO 碎片是避免碰撞事故的必要条件。由于雷达受到发射功率和工作距离的限制，很难检测到 GSO 碎片，因此光电检测成为检测 GSO 碎片的主流方法。本文提出了一种基于电荷耦合器件（CCD）图像的 GSO 碎片自适应实时检测算法。主要工作如下：提出了一种图像自适应快速配准算法和一种增强的膨胀差分算法。结合数学形态学、阈值分割和全局最近邻（GNN）多目标跟踪算法，实现了图像背景抑制、配准、疑似目标提取和多目标跟踪功能。大量实测数据的处理结果表明，该算法能够稳定、高效地检测 GSO 带内暗的地球同步轨道（GEO）和非 GEO 碎片，处理速度满足实时要求，具有较强的自适应能力，具有很高的实际应用价值。
