Affiliations: [a] Management and Safety Engineering College, Air Force Engineering University, Xi’an, China
| [b] School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
| [c] Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, China
Correspondence:
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Corresponding author. Mingfa Zheng, School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China. Tel.: +86 15289351366; Fax: +86 029 84297881; E-mail: mingfazheng@126.com.
Abstract: Unmanned Aerial Vehicle (UAV) is very useful for information gathering in Intelligence, Surveillance, and Reconnaissance (ISR) mission. On the basis of uncertainty theory, the main purpose of this paper is to study a new kind of UAV ISR mission planning problem involving multiple objectives under uncertain environment. In particular, the mission planning objectives are influenced by the same uncertain factors simultaneously, that is to say, they are correlated or dependent with each other. In this case, the traditional multiobjective approach cannot guarantee the uncertain nature and take into account the dependence among uncertain objectives in the UAV ISR mission planning problem. In order to overcome the disadvantage in traditional approach, a new solution approach is introduced for obtaining the PE Pareto efficient routes for it, which involves transforming the original ISR mission planning problem into a single objective uncertain programming problem. Two specific compromise models are proposed using this new solution approach respectively, whose validity is proved theoretically. Finally, an application case study with 13 ISR targets is provided and solved. The results show that the proposed model and solution approach have excellent consistency and efficiency in solving the uncertain UAV ISR mission planning problem presented in this paper.
