Modified pyramid algorithm for small FOV star image recognition

A modified pyramid algorithm is proposed for autonomous star identification of small field-of-view(FOV) star images in the general lost in space case, by selecting outer product of star vectors as characteristic matching quantity, in order to avoid poor discrimination of inner product. The strategy for modification to characteristic matching quantity is analyzed. And the issues related to the modified pyramid algorithm are discussed, involving preprocessing of the original star catalogue, construction of the database of characteristic matching quantity and the K vector, noise reduction and star centroiding, the star identification procedure, and so on. This algorithm is realized by Visual C++ programming. Its performance is tested by simulation. Finally, it is actually applied to star identification for a small FOV star tracker. Experimental results demonstrate that the star identification success rate of the modified pyramid algorithm is 96.7%, the size of data files loaded in memory is about 26.4 MByte, and the average time consumption of star identification is about 131.8 ms. This algorithm meets well the basic requirements of autonomous star identification in the lost in space case, such as high identification success rate, moderate resource consumption, fast identification speed, and strong robustness.