| dc.description.abstract | This paper describes how to organize the algorithm of the star tracker , and proposes a new star identification algorithm. Different cameras have different parameters. Even with the same type of the camera, there are still some differences in the images with the different cameras. If the algorithms do not use the adjusting library and parameters according to the camera parameters, they may be affected or even impossible to complete. The new method closely follows the impact of changes in interest to stars. It is possible to use the same library and parameters to complete work.
The new star identification method, “Group Average Value Method”, extends from two methods, ＂one-dimensional vector method＂ [1], and ＂Pyramid method＂ [2]. One-dimensional vector method has a simple search method and is easy to build a database, but it is susceptible to the position error, false stars, and lost stars. Its stability is lower than that of the pyramid method. It is stable and fast when it needs to accurately correspond to the camera parameters. When the number of stars in the database of the pyramid method is increases, the size of the database will increase rapidly. It is necessary to simplify the database and increase the speed of the search method. Once the camera parameters are changed, it takes time to study the adjustment of the database. In order to achieve a new method with high stability, the advantages of the two methods are taken out and integrated into a new star identification method, namely the “Group Average Value Method”.
The new method solves the position error. The lost stars or false stars within a certain number, it can work well. It also has a simple method to establish database and search the stars in the database. It can achieve stable work with different camera and dynamic magnitude.
Finally, through the PC simulation test, Laboratory platform test, and real sky test, the stability of the new algorithm was confirmed. Additional conditions can be added in the algorithm which of the simulation failure or error regions. They can reduce or avoid the occurrence of identification failures or errors. In addition, it is necessary to study the impact of dynamic conditions of the images to confirm the changes in recognition rate and attitude accuracy caused by smeared star spots. | en_US |