Multi-satellite scheduling framework and algorithm for very large area observation. (February 2020)

• Record Type: Journal Article
• Authors: Xu, Yingjie; Liu, Xiaolu; He, Renjie; Chen, Yingguo
• Is Part Of: Acta astronautica. Volume 167(2020)
• Page Start: 93
• Page End: 107
• Publication Date: 2020-02
• Subjects: Multi-satellite scheduling -- Very large area observation -- Preprocessing -- Set covering problem -- Genetic algorithm; Astronautics -- Periodicals; Outer space -- Exploration -- Periodicals; Astronautics; Periodicals; 629.405
• DOI: 10.1016/j.actaastro.2019.10.041
• Languages: English
• ISSNs: 0094-5765

Abstract

Abstract: This paper presents a multi-satellite scheduling problem for very large area observation with given specific constraints, derived from satellite capacity and customer requests. It is assumed that the profit was proportional to the coverage of acquired area, and the objective is therefore to maximize the total profits of generated observation schedule. To address the satellite scheduling problem, we first demonstrate a detailed problem description and then transform the problem into set covering problem within several criteria and constraints. Based on that, a mathematical model is established. In order to solve the multi-satellite scheduling problem for large area observation, a new solving framework is proposed. The framework is composed of three phases. In the discretizing phase, an area discretization method is adopted to establish the evaluation system. In the target decomposing phase, area target is decomposed into strips and corresponding visible time windows are calculated. In the scheduling phase, with crossover, mutation and feasibility operators, a genetic algorithm is introduced to generate an optimal observation schedule. Through extensive computational experiments on realistically generated problem with Chinese satellite platform, the effectiveness and reliability of the proposed solving framework are verified. Highlights: Multi-satellite scheduling problem for very large area observation is introduced. The proposed problem is transformed and modeled as a set covering problem with constraints. A new three-phase solving method is developed, including discretizing, decomposing and scheduling.