Consistent growth in the number of high-resolution Earth observation satellites (EOSs) poses a significant challenge to the downlink scheduling problem for observation data. Satellite-to-ground optical communication is considered promising for downloading massive observation data due to its large communication capacity. To improve the downlink transmission capacity of the EOS observation system, this article focuses on the EOS downlink scheduling problem with satellite-ground optical communication links (EDSPO). First, we establish a mathematical model considering many practical constraints for EDSPO to maximize the profit of downloaded observation data. To tackle this complex scheduling problem, a downlink scheduling algorithm combining with the adaptive large neighborhood search algorithm and the tabu search algorithm (ALNS_TS) is then proposed. In ALNS_TS, a heuristic solution initialization is developed, five removal operators and five repair operators are designed based on the heuristic characteristics of EDSPO, and a task insert algorithm is proposed for repair operators. Moreover, ALNS_TS introduces the tabu value for each downlink transmission task to avoid repeated searches in the neighborhood of the same solution. Finally, extensive experiments are carried out to verify the effectiveness of the ALNS_TS and the superiority of the satellite-ground optical communication link. Simulation results show that the ALNS_TS performs better in profits and the number of completed tasks compared to the existing downlink scheduling algorithms. Besides, the satellite-ground optical communication link has been proven effective in improving the downlink transmission capacity of the EOS observation system.


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    Title :

    Earth Observation Satellite Downlink Scheduling With Satellite-Ground Optical Communication Links


    Contributors:
    Li, Hai (author) / Li, Yongjun (author) / Liu, Yuanhao (author) / Deng, Boyu (author) / Li, Yu (author) / Li, Xin (author) / Zhao, Shanghong (author)


    Publication date :

    2025-04-01


    Size :

    4817044 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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