Abstract
Satellite remote sensing is developing towards the micro-satellite cluster, which brings new challenges to mission assignment and planning for the cluster. A multi-agent system (MAS) is used, but the time delay caused by communication and computation is rarely considered. To solve the problem, a neural-network-based multi-granularity negotiation method under decentralized architecture is proposed. Firstly, we divided negotiation into three levels of granularity, and they work in different modes. Secondly, a neural network was trained to help the satellite select the best level in real-time. Through experiments, we compared the satellites working in three different levels of granularity, in which a multi-granularity decision was used. As a result of our experiments, a lower cost-effectiveness ratio was obtained, which proved that the multi-granularity negotiation method proposed in this paper is practical.
Highlights
Satellite remote sensing aims to obtain information from the Earth’s surface
Different missions require different kinds of payload, but multiple payloads can hardly be carried by one satellite
We propose a neural-network-based multi-granularity negotiation method under decentralized architecture belonging to distributed mode in [6,7,8]
Summary
Satellite remote sensing aims to obtain information from the Earth’s surface. It has been widely used in geography, Earth science, meteorology, military, etc. A single low Earth orbit satellite is often used to take images of multiple targets multiple times. The increasing number of missions and the higher time resolution requirements call for more satellite members [2]. Different missions require different kinds of payload, but multiple payloads can hardly be carried by one satellite. Multiple payloads can hardly work together at the same time in the narrow imaging time window. Current remote sensing missions rely more and more on large satellite clusters, which brings new challenges to mission assignment and planning [3]
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