| Citation: |
WANG Mi, CHEN Junbo, PI Yingdong, WU Qianyu. A Fast and Accurate Orbit Prediction Method for Satellite On-Orbit Autonomous Mission Planning[J]. Geomatics and Information Science of Wuhan University, 2024, 49(6): 879-887. DOI: 10.13203/j.whugis20230223
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Satellite on-orbit autonomous mission planning requires high-precision orbit prediction data to calculate the feasible imaging time windows and imaging posture requirements for the intended observation targets. This process involves integrating these factors with other constraint conditions to accomplish the mission planning.
In the context of limited computational resources onboard satellites, this paper proposes a novel method to reduce resource consumption in orbit prediction process and meet the demands of autonomous mission planning. First, based on two-line element (TLE) and a simplified general perturbation model, the proposed method utilizes multiple sets of orbital data as input and introduces a new measurement metric. Then, it iteratively generates TLE by rationally setting data weights and progressively adjusting them to get the results of orbit prediction. Finally, the total error for orbit prediction and the initial value error of imaging parameters for simulating observation targets are calculated for quantitative evaluation.
Experimental results demonstrate that the proposed method requires only a small amount of input data to achieve a 72-hour orbit prediction with the accuracy better than 4 km and the average computation time of 12.76 s. Compared to high precision orbit propagator model, the proposed method provides higher prediction accuracy and faster execution speed in long-period orbit prediction. The calculated start imaging time error for simulated observation targets is less than 0.2 s, and the initial imaging posture errors in the three-axis directions are all better than 0.03°, which is lower than the attitude pointing accuracy requirement for the imaging process of camera onboard Luojia3-01 satellite.
The proposed method offers high prediction accuracy and demands fewer temporal and spatial resources, making it of great significance for satellite on-orbit autonomous mission planning
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