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

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 propose a framework for addressing the Agile Earth Observation Satellite Scheduling Problem (AEOSSP) in a real-time remote monitoring scenario integrating onboard data processing. The AEOSSP consists of determining the optimal sequence of target observations to maximize overall observation profit, and, 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 spatial resolution of the collected frames by up to 18.6% on average, while reducing the variance in the monitoring frequency of the targets within the instance by up to 84%, ensuring more up-to-date information across the entire set compared to First-In First-Out (FIFO) and genetic algorithm (GA)-based methods.