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

TL;DR

This paper presents a novel scheduling algorithm for Agile Earth Observation Satellites that integrates onboard processing and real-time monitoring, significantly improving data resolution and update frequency.

Contribution

It introduces a new heuristic-based approach for satellite scheduling that incorporates onboard data processing and real-time target monitoring, enhancing observation quality.

Findings

Increased resolution of collected frames by up to 10%.

Reduced variance in target monitoring frequency by up to 83%.

Outperforms FIFO scheduling in data freshness and quality.

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.