优化遥感卫星调度，实现更高分辨率与更稳定监测频次
Scheduling Agile Earth Observation Satellites with Onboard Processing and Real-Time Monitoring
- 结合星上处理能力，设计优先级指标与局部搜索算法
- 平均分辨率提升10%，目标监测频次方差降低83%
- 适合需要实时遥感数据的应急响应与环境监测场景
敏捷地球观测卫星（AEOS）的出现显著提升了地球观测的灵活性。随着星上计算与通信技术进步，数据压缩效率提高，网络延迟与拥塞降低，支持近实时信息传输。本文研究多星敏捷地球观测调度问题（AEOSSP），旨在通过优化观测序列最大化整体观测收益。提出融合星上实时数据处理与远程监控的调度方法，定义一组优先级指标，并采用构造性启发式算法结合局部搜索策略。实验表明，该算法平均提升采集帧分辨率10%，目标监测频次方差降低83%，相比先进先出（FIFO）方法，能提供更及时、更均衡的全区域信息。
原文摘要 · Abstract (English)
The emergence of Agile Earth Observation Satellites (AEOSs) has marked a significant turning point in the field of Earth Observation (EO), offering enhanced flexibility in data acquisition. Concurrently, advancements in onboard satellite computing and communication technologies have greatly enhanced data compression efficiency, reducing network latency and congestion while supporting near real-time information delivery. In this paper, we address the Agile Earth Observation Satellite Scheduling Problem (AEOSSP), which involves determining the optimal sequence of target observations to maximize overall observation profit. Our approach integrates onboard data processing for real-time remote monitoring into the multi-satellite optimization problem. To this end, we define a set of priority indicators and develop a constructive heuristic method, further enhanced with a Local Search (LS) strategy. The results show that the proposed algorithm provides high-quality information by increasing the resolution of the collected frames by up to 10% on average, while reducing the variance in the monitoring frequency of the targets within the instance by up to 83%, ensuring more up-to-date information across the entire set compared to a First-In First-Out (FIFO) method.
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