Non-cooperative targets, such as space debris, defunct spacecrafts and LEO constellation satellites, have brought serious risks to the space station. The rapid detection and orbital parameters’ determination of the fast-approaching non-cooperative target can greatly improve the protection ability of the space station. In this paper, a novel rapid detection and orbital parameters’ determination method based on the collaborative observation of the space station, and a fly-around nano-satellite is developed. The early-warning region of the space station is established, and considering the observation constraints of the nano-satellite, the non-cooperative target detection strategy is provided, which includes a collaborative observation configuration as well as the attitude variation of the cameras, and the detection efficiency is analyzed. Then, the orbital parameters’ filtering model of the non-cooperative target based on the collaborative observation is constructed, and the Unscented Kalman filter method is utilized to determinate the orbital parameters of the non-cooperative target. Considered the observability of the initial collaborative observation configuration, this paper analyzes the observation configuration with low observability in different scenarios, and proposes an optimal orbital maneuver algorithm for the nano-satellite. This algorithm can realize a fuel-optimal orbital maneuver that satisfies the minimum line-of-sight angle constraint of the collaborative observation.

To optimize the geomagnetic reference vector and the solar reference vector calculation method, this paper proposes a geomagnetic fitting algorithm and simplifies the DE430 ephemeris structure to speed up the determination rate of the geomagnetic and solar reference vectors. The two optimized reference vector determination methods are simulated on the Attitude Measurement Unit (AMU). The simulation results show that the single-step elapsed time of the geomagnetic fitting algorithm is reduced by 99.5% compared with the IGRF-13 geomagnetic model algorithm, and the data capacity is reduced by 97.3% compared with the look-up table method. The data capacity of the simplified ephemeris algorithm is 66.9% lower than that of the DE430 ephemeris algorithm, and the calculation accuracy is nearly 3 orders of magnitude higher than that of the approximate calculation method. This method can significantly shorten the attitude determination time and increase the attitude determination frequency, dramatically improving attitude determination accuracy and attitude control accuracy. The AMU supported by these two algorithms has sound engineering practical value.