This book highlights the practical models and algorithms of earth observation satellite (EOS) task scheduling. EOS task scheduling is a typical complex combinatorial optimization problem with NP-Hard computational complexity. It is a key technology in aerospace scheduling and has attracted global attention. Based on the actual needs of the EOS operation control center, the book summarizes and reviews the state of the art in this research and engineering field. In both deterministic scenarios and dynamic scenarios, the book elaborates on the typical models, algorithms, and systems in centralized, distributed, and onboard autonomous task scheduling. The book also makes an outlook on the promising technologies for EOS task planning and scheduling in the future. It is a valuable reference for professionals, researchers, and students in satellite-related technology.
This book is a translation of an original Chinese edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation.
Hao Chen
Dr. Hao Chen is currently a professor at the National University of Defense Technology, China. His research interests include data mining, machine learning, and evolutionary computation.
Shuang Peng
Dr. Shuang Peng is currently an assistant professor at the National University of Defense Technology, China. His research interests include satellite intelligent scheduling, machine learning, and evolutionary computation.
Chun Du
Dr. Chun Du is currently an associate professor at the National University of Defense Technology, China. His research interests include machine learning, machine vision, and remote sensing. Jun Li
Dr. Jun Li is currently a professor at the National University of Defense Technology, China. His research interests include management and analysis of big data, and spatial information system.
