论文部分内容阅读
随着小卫星应用技术的日趋成熟,测量精度高的CCD星敏感器备受青睐,快速而可靠的星图识别算法成为卫星姿态确定系统中最为关键的部分。要进行恒星的识别,就需要从星敏感器获得原始的图像,并从中提取要识别的目标及其特征。由于航天实验费用昂贵,星敏感器的地面调试、软件算法的最初模拟,不可能都进行实时星空拍摄,因此为了调试和评价星图识别算法,有必要利用计算机在地面上模拟生成星敏感器实时拍摄到的星空图片。本文的研究工作主要是星图模拟与星图去噪。首先学习了解CCD星敏感器原理,在此基础上模拟得到原始导航星图,然后模拟得到加了运动噪声的退化星图,最后学习维纳滤波和Matlab算法,利用Matlab算法中的维纳滤波方法对退化图像进行图像去噪复原技术研究,通过星图模拟和图像复原得到的仿真星图可以做成一个星图模拟器为星图识别工作提供仿真基础。
With the application of small satellite technology matures, high precision CCD star sensor is favored, fast and reliable algorithm for the recognition of satellite image become the most crucial part of the satellite attitude determination system. To do star identification, you need to get the original image from the star sensor and extract the target to be identified and its characteristics. Due to the high cost of space experiment, the ground commissioning of the star sensor and the initial simulation of the software algorithm, it is impossible to conduct real-time sky photography. Therefore, in order to debug and evaluate the star image recognition algorithm, it is necessary to use a computer to simulate real- Shot of the sky picture. The main work of this paper is star map simulation and star map de-noising. First learn to understand the CCD star sensor principle, on this basis, the original navigation star map is simulated, and then get the degenerated star map with motion noise. Finally, the Wiener filter and Matlab algorithm are studied. The Wiener filter method Degenerate image de-noising restoration technology research, through the star map simulation and image restoration simulation star map can be made into a star map simulator to provide the basis for the star map recognition.