Fault-Tolerant Fusion Algorithm of Trajectory and Attitude of Spacecraft Based on Multi-Station Measurement Data

Abstract

Aiming at the practical situation that the navigation processes of spacecrafts usually rely on several different kinds of tracking equipments which track the spacecraft by turns, a series of new outlier-tolerant fusion algorithms are build to determine the whole flight path as well as attitude parameters. In these new algorithms, the famous gradient descent methods are used to find out the outliers-tolerant flight paths from an integrated data-fusion function designed delicately. In this paper, these new algorithms are used to determine reliably the flight paths and attitude parameters in the situation that a spacecraft is tracked by a series of equipments working by turns and there are some outliers arising in the data series. Advantages of these new algorithms are not only plenary fusion of all of the data series from different kinds of equipments but also discriminatory usage: on the one hand, if the data are dependable, the useable information contained in these data are sufficiently used; on the other hand, if the data are outliers, the bad information from these data are efficiently eliminated from these algorithms. In this way, all of the computational flight paths and attitude parameters are insured to be consistent and reliable.