Hybrid Grid Pattern Star Identification Algorithm Based on Multi-Calibration Star Verification

Abstract

In order to solve the star identification problem in the lost space mode for scientific cameras with small fields of view and higher instruction magnitudes, this paper proposes a star identification algorithm based on a hybrid grid pattern. The application of a hybrid pattern generated by multi-calibration stars in the initial matching enables the position distribution features of neighboring stars around the main star to be more comprehensively described and avoids the interference of position noise and magnitude noise as much as possible. Moreover, calibration star filtering is adopted to eliminate incorrect candidates and pick the true matched navigation star from candidate stars in the initial match. Then, the reference star image is utilized to efficiently verify and determine the final identification results of the algorithm via the nearest principle. The performance of the proposed algorithm in simulation experiments shows that, when the position noise is 2 pixels, the identification rate of the algorithm is 96.43%, which is higher than that of the optimized grid algorithm by 2.21% and the grid algorithm by 4.05%; when the magnitude noise is 0.3 mag, the star identification rate of the algorithm is 96.45%, which is superior to the optimized grid algorithm by 2.03% and to the grid algorithm by 3.82%. In addition, in the actual star image test, star magnitude values of ≤12 mag can be successfully identified using the proposed algorithm.

A Compensation Method for Full-Field-of-View Energy Nonuniformity in Dark-and-Weak-Target Simulators

“…At the same time, there is a demand for ever more accurate information from spacecraft. In spacecraft, the star sensor is the main attitude measurement device [1,2]. The measurement accuracy of the star sensor is crucial to the navigational accuracy of the spacecraft.…”

“…where 1 f is the focal length of the dark-and-weak-target simulator, 2 f is the focal length of the sensor, 1 h is the image height of the dark-and-weak-target simulator, 2 h is the image height of the sensor, 1 p is the image-element size of the dark-and-weak-target simulator, 2 p is the image-element size of the sensor, n is the number of transverse elements, and m is the number of longitudinal elements. Thus, by calculating the field-ofview angles of the simulator and the sensor, the percentage of the simulator's field of view that can be detected by the sensor may be identified.…”