For the purpose of improving the recognition rate and the anti-noise ability, a new star pattern recognition algorithm is proposed, which is based on optimal singular value decomposition matching. The algorithm can effectively solve the recognition failure caused by redundant matching results, which the triangle recognition algorithm and the pyramid algorithm can not. Compared with traditional star pattern recognition algorithms, the recognition rate and the anti-noise ability of the new algorithm proposed in this paper are improved. With the star position noise added (Gauss noise, mean=0, σ=3pixel), the recognition rate is 98.91%, and the average recognition time assumption is 20.21ms.
The division of the celestial sphere is the premise of star tracking algorithm for a star sensor, and the uniformity of the division is an important performance index of dividing methods. Based on analyzing existing celestial sphere dividing methods, a uniform celestial sphere dividing method based on spherical rectangle is proposed. With solid angle as the measuring basis, this method divides the celestial sphere into spherical rectangles, the boundaries of which are calculated by the solid angle integral. The celestial sphere is divided into 648 celestial sub-blocks, the solid angles of which are equal to each other. On the basis of the uniform dividing method, the navigation star sub-block table is established. Among the existing celestial sphere dividing methods, the best result is that the biggest partition is 22.86% larger than the smallest one. Compared with the existing methods, the dividing method proposed in this paper completes the uniform dividing, which has a better effect. Finally, the simulation experiment result verifies the effectiveness of the navigation star sub-block table for the star tracking algorithm.
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