Multi-Stage Star Image Identification Method of Three Field-of-View Star Sensor

To improve the low efficiency and low navigation star identification rate of existing star image identification methods for three field-of-view (FOV) star sensor, a multi-stage star image identification method is proposed. Firstly, the generalized regression neural network which has only one adjustable parameter, is used to identify the star images in each field-of-view. Secondly, the star angular distance saved in the navigation star database is used to verify the identification results, and then the optical directions of the three FOVs are calculated by using the correctly identified navigation stars. Thirdly, the optical directions are utilized to auxiliary correct the unidentified and erroneous identified navigation stars. Finally, the high-accuracy probe attitude is estimated by using the correctly identified navigation stars in the three FOVs. The simulation results show that the identification rates of the experimental samples is of 98.9% when the standard deviation of star centroid positioning error increases to 0.07 pixels, but the identification time is only of 8.464 5 ms. Meanwhile, since the three field-of-view star sensor captures the more dispersed navigation stars, the probe attitude accuracy of yaw, pitch and roll angles by using the present method is improved evidently, which is of 1.205 8″, 1.086 7″, and 1.201 8″, respectively.

This article proposes a multi-level star map recognition algorithm for three field of view star sensors. In the first stage, a neural network is used for star map recognition within the field of view. In the second stage, the navigation star recognition results are verified using the inter star angular distance information stored in the star database. When the correctly recognized field of view is less than 3, the three line of sight directions of the star sensor are calculated using the correctly recognized navigation star information; In the third stage, visual axis pointing is used to assist in identifying and correcting unidentified and incorrectly identified navigation stars. Compared with the literature “Three field of view fast star map recognition method based on rough measurement of position and orientation”, this algorithm has significant advantages in the robustness and recognition time of star map recognition in the field of view. When the standard deviation of the center of mass positioning error of the star point is 0.07 pixels, the recognition accuracy of the algorithm in this paper for the experimental samples still remains 98.9%, and the recognition time is only 8.464 5 ms. The high recognition rate of the three field star map and the reasonable distribution of star points also improve the accuracy of the aircraft’s yaw, pitch, and roll attitudes to 1.205 8 “, 1.086 7”, and 1.201 8 “, respectively.