Fast and Robust Star Identification Using Color Ratio Information

Abstract

Star identification is a critical technology in the research of star sensors. Conventional star identification algorithms identify stars based only on the geometry position information. As a result, these algorithms are sensitive to positional noise or require a high number of imaged stars in the field of view (FOV). In this article, a star identification algorithm using color ratio information is proposed to solve this problem. An analytical model is developed to predict the performance of the proposed algorithm. In addition, an optimized strategy is proposed to reduce the identification time by selecting stars with the same color ratio information to construct the observation triangles. Experimental results on simulation and real sky images show that the proposed algorithm is robust to high-level position noise, and it requires three stars to find a unique match. Compared with the pyramid algorithm, the proposed algorithm uses fewer stars and achieves a higher star identification rate over the entire sky in a shorter time even under harsh conditions.