Improved Triangular Algorithm for Small Field of View Star Sensor

In the fields of scientific exploration and satellite high-precision positioning, star sensors need to obtain attitude information in a short time.In order to improve accuracy,it is necessary to increase the angular resolution,reduce the field of view and use a higher magnitude as a reference.The star pattern recognition algorithm is the key to quickly obtain the attitude information for star sensors.When the traditional triangle algorithm faces a situation where the number of stars is small and the proportion of dark stars is larger,the recognition accuracy will quickly drop to 70%~80%,which needs to be improved.Motivated by this,an efficient star pattern recognition algorithm based on triangle angular distance matching is proposed.The proposed method combines the feature of magnitude interval difference and introduces the fourth star verification,so as to reduce redundant matching. Simulation results show that the proposed method improves the recognition rate to 98.4% while the recognition speed is not lower than that of the classical improved triangle algorithm.In the case of introducing position noise and magnitude noise,it still maintains a recognition rate of more than 93%,which has strong robustness.

This article proposes a star pattern recognition algorithm suitable for small field of view star sensors. This algorithm uses triangle angular distance as the basic feature and star magnitude interval difference as an additional feature. Based on the information of angular distance and star magnitude interval, the selection of triangles is optimized, and a fourth star is introduced for verification to further remove redundant matching to improve the recognition accuracy and robustness to noise of the algorithm. According to the simulation experiment results, the recognition rate of this algorithm is 98.4%, and the average recognition time is 36.13ms, which greatly improves the recognition rate compared to the traditional triangle method. Compared to other algorithms, it has strong robustness against image position noise and maintains a recognition rate of over 93%. When introducing magnitude noise, the recognition rate of the algorithm did not significantly decrease, reaching over 95%. The next step will be to optimize the dual star and edge star problems to improve the recognition efficiency and robustness of the algorithm.