# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 1 Introduction
His framework supported allocation targets for satellites and collective sensing of a target for multi-satellites. He [18] considered three important sections of planning (store sequence of tasks, action sequence, and the satellite status) to deal with scientific event discovery, satellite faults, cloud obscuration, and emergencies. The main problem of the remote sensing mission lies in the conflict between resource consumption and mission accomplishment. The researchers considered many factors. [PERSON] [19] proposed a modular framework that combines orbital mechanics, attitude control, and scheduling optimization working on Cubesats. [PERSON] [20] discussed the impact of clouds in the case of joint probabilities, and established a sample average approximation (SAA) model accordingly. [PERSON] [21] also proposed a branch and cut algorithm based on lazy constraint generation. Although these kinds of limited resources onboard have been considered, the influence of time delay on negotiation is less related to that. The time delay is mainly caused by limited communication ability and computational resources. When a satellite keeps tracking a target, it has to act out a continuous attitude maneuver which affects the pointing accuracy of its antenna, resulting in a decrease of the communication quality. Besides, tracking a target produces a large number of images, the process of which is heavy work for a single micro-satellite. A common way is to transmit those images to other micro-satellites. However, image transmission occupies a large amount of communication bandwidth, so that less communication bandwidth can be left for negotiation. Lastly, the image process itself is computationally expensive, so that a micro-satellite can hardly negotiate with others when processing images. In conclusion, a large time delay may cause the delay of negotiation and may finally lead to negotiation failure. In this paper, we propose a neural-network-based multi-granularity negotiation method under decentralized architecture belonging to distributed mode in [6; 7; 8]. Firstly, we propose a multi-granularity negotiation model. The negotiation process is divided into three levels of granularity: low-level granularity, middle-level granularity, and high-level granularity. In each granularity, satellites transmit different kinds of information and use different negotiation processes and assignment algorithms. Secondly, we establish a neural network to intelligently select the appropriate level of granularity. The neural network could output the success probabilities of negotiation in each level of granularity. Then a simple decision mode based on the probabilities is used to select the level in real-time. The remainder of the paper is organized as follows. In Section 2, the modeling process of the multi-granularity negotiation method is introduced considering three aspects: mission preprocesses for all levels of granularity, multi-granularity partitioning of the multi-agent negotiation model, and intelligent real-time level selection of negotiation granularity. In Section 3, experiments to verify this method in different conditions are described. The proposed method is discussed in Section 4. Finally, the conclusions of the study are given in Section 5.