# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 4 Constellation Navigation and Orbit Determination Technology  
### Multi-Constellation GNSS Lunar Satellite Orbit Determination Method  
Affected by the obstruction of the Earth-Moon double stars, the amount of visible satellites provided by a single GNSS constellation does not exceed four most of the time, which cannot meet the theoretical positioning requirements. This poses a challenge to multi-constellation integrated navigation technology. In recent years, in order to increase the quantity of visible stars and select satellites with better geometric structures through satellite selection algorithms to indirectly improve navigation and positioning accuracy [60], major aerospace countries have successively developed from single-constellation systems to multi-constellation GNSS combined systems. This field includes research on satellite selection algorithms for multi-constellation integrated navigation and research on positioning solution methods for multi-constellation integrated navigation. Satellite selection algorithms are generally divided into the optimal satellite selection method and the suboptimal satellite selection method. The optimal satellite selection method calculates the geometric accuracy factors of all combined constellations and selects the combination corresponding to the minimum value as the result of satellite selection. The suboptimal algorithm selects satellites according to the actual needs of users by establishing an algorithm model for satellite selection. The accuracy is usually lower than that of the optimal algorithm, but the calculation complexity of the algorithm is low and the real-time performance is high. In terms of the study of the optimal algorithm, [PERSON] proposed the weighted geometric accuracy factor method [61]. This method is based on the optimal algorithm and obtains its approximate optimal estimate by calculating the weighted geometric accuracy factor, which improves positioning accuracy. Most of the research is based on the suboptimal star algorithm and achieves rapid selection by proposing a suitable positioning error estimation model. Based on the cost function model of the suboptimal algorithm, [PERSON] et al. proposed a cost function model based on altitude angle and carrier-to-noise ratio weighting, which improved the efficiency of satellite selection [62]. The fast star selection algorithm [63] is based on the elevation and azimuth angle sorting method and the principle of uniform distribution of satellites for selection, which greatly reduced the selection time. [PERSON] proposed a satellite selection algorithm based on chaotic particle swarm optimization, which can reduce the selection time to 37.5%, while the geometric accuracy factor error is less than 0.6 [64]. The multi-constellation combination positioning solution method is generally divided into centralized combination and distributed combination. The centralized combination method is a simple inheritance of the single-constellation positioning solution method. Commonly used centralized combination methods include solution methods based on least squares and solution methods based on the Kalman filter. For on-ground batch processing of GNSS observations, the solution method based on least squares uses Taylor expansion to linearize the observation equation, which requires a large number of calculations. It only solves for the user's current location and is suitable for static single-point positioning. There are no accuracy problems related to the use of the least squares approach. The accuracy of the dynamic models plays a crucial role in this case. For real-time application, a Kalman filter approach is preferred. The Kalman filter algorithm establishes a user motion trajectory model and smoothies the observation data to obtain position information with higher accuracy. [PERSON] et al. applied the Cubature Kalman filter algorithm to satellite navigation positioning solutions, and recursively estimated the position of the receiver with time updates and measurement updates [65]. [PERSON] et al.