Scheduling Agile Earth Observation Satellites with Onboard Processing and Real-Time Monitoring

2025-06-13

AI Summary

This paper addresses the Agile Earth Observation Satellite Scheduling Problem (AEOSSP), which seeks to optimize observation task sequences under resource constraints to maximize overall mission utility. Method: For the first time, it integrates onboard real-time data processing capability into a multi-satellite collaborative scheduling model, proposing a solution framework that combines a priority-driven constructive heuristic with an efficient local search strategy to jointly optimize task planning and in-orbit processing. Contribution/Results: Compared to conventional FIFO-based scheduling, the proposed approach improves average frame resolution by 10% and reduces variance in target revisit frequency by 83%, thereby significantly enhancing both information timeliness and spatiotemporal balance of Earth surface monitoring. The method establishes a scalable scheduling paradigm for near-real-time dynamic Earth observation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Agentic search
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Optimize AEOS scheduling for maximum observation profit
• Integrate onboard processing for real-time remote monitoring
• Enhance data resolution and reduce monitoring frequency variance

Innovation

• Onboard data processing for real-time monitoring
• Constructive heuristic with Local Search strategy
• Priority indicators for optimal observation sequence