Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

TL;DR

This paper introduces a novel scheduling method for Earth observation satellites targeting large regions, combining a rapid coverage calculation with a greedy resampling particle swarm optimization to improve scheduling efficiency and effectiveness.

Contribution

It develops a new coverage calculation technique and a greedy resampling PSO algorithm specifically for large region satellite observation scheduling, outperforming traditional methods.

Findings

GI-RPSO improves scheduling results by 5.42% over traditional PSO.

The method enhances scheduling efficiency and reliability.

Extensive experiments validate the effectiveness of the proposed approach.

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 paper 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 particle swarm optimization and the widely used greedy algorithm, the proposed GI-RPSO can improve the scheduling result by 5.42% and 15.86%, respectively.