Volume 37, Number 3, June 2019
|Page(s)||541 - 546|
|Published online||20 September 2019|
Multi-Stage Star Image Identification Method of Three Field-of-View Star Sensor
School of Automation, Northwestern Polytechnical University, Xi’an, 710129, China
2 National Key Laboratory of Science and Technology on Blind Signal Processing, Chengdu 610041, China
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.
为解决现有三视场星图识别算法效率慢、识别正确率低的问题，提出了一种面向三视场星敏感器的多级星图识别算法：第一阶段利用单一可调参数的广义回归神经网络分别识别三幅单视场星图；第二阶段利用星库中存储的星间角距信息检验导航星识别结果，再以正确识别的导航星信息计算星敏感器的3个视轴指向；第三阶段利用视轴指向辅助未识别与识别错误的导航星完成识别与校正；最终，以三视场内正确识别的导航星精确估计飞行器姿态信息。仿真结果表明，当星点质心定位误差的标准差达到0.07像素时，该星图识别算法对实验样本的识别正确率仍高达98.9%，而识别时间仅为8.464 5 ms。同时，由于提供求解姿态的星点信息较多且分布更广泛，飞行器的三轴姿态估计精度也随之提高。三视场星敏感器估计的飞行器偏航、俯仰、和滚转轴姿态精度分别为1.205 8″，1.086 7″以及1.201 8″。
Key words: three field-of-view / neural network / star image identification / attitude
关键字 : 三视场 / 神经网络 / 星图识别 / 姿态
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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