Large Region Targets Observation Scheduling by Multiple Satellites Using Resampling Particle Swarm Optimization

Journal article (2022)

Authors

Yi Gu Beihang University
Chao Han Beihang University
Yuhan Chen China Satellite Network Innovation Company, Ltd.
Shenggang Liu Beihang University
X. Wang Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering , Queen Mary University of London
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Yi Gu, Chao Han, Yuhan Chen, Shenggang Liu, X. Wang
DOI: https://doi.org/10.1109/TAES.2022.3205565
Large region targets Multiple satellites Observation scheduling Resampling particle swarm optimization (PSO)
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

The last decades have witnessed a rapid increase of Earth observation satellites (EOSs), leading to the increasing complexity of EOSs scheduling. On account of the widespread applications of large region observation, this article aims to address the EOSs observation scheduling problem for large region targets. A rapid coverage calculation method employing a projection reference plane and a polygon clipping technique is first developed. We then formulate a nonlinear integer programming model for the scheduling problem, where the objective function is calculated based on the developed coverage calculation method. A greedy initialization-based resampling particle swarm optimization (GI-RPSO) algorithm is proposed to solve the model. The adopted greedy initialization strategy and particle resampling method contribute to generating efficient and effective solutions during the evolution process. In the end, extensive experiments are conducted to illustrate the effectiveness and reliability of the proposed method. Compared to the traditional PSO and the widely used greedy algorithm, the proposed GI-RPSO can improve the scheduling result by 5.42% and 15.86%, respectively.