title: Coordinated Target Tracking via a Hybrid Optimization Approach.
abstract: Recent advances in computer science and electronics have greatly expanded the capabilities of unmanned aerial vehicles (UAV) in both defense and civil applications, such as moving ground object tracking. Due to the uncertainties of the application environments and objects' motion, it is difficult to maintain the tracked object always within the sensor coverage area by using a single UAV. Hence, it is necessary to deploy a group of UAVs to improve the robustness of the tracking. This paper investigates the problem of tracking ground moving objects with a group of UAVs using gimbaled sensors under flight dynamic and collision-free constraints. The optimal cooperative tracking path planning problem is solved using an evolutionary optimization technique based on the framework of chemical reaction optimization (CRO). The efficiency of the proposed method was demonstrated through a series of comparative simulations. The results show that the cooperative tracking paths determined by the newly developed method allows for longer sensor coverage time under flight dynamic restrictions and safety conditions.
authors.affiliation: State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China. yinwangee@nuaa.edu.cn.|College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China. yinwangee@nuaa.edu.cn.
authors.forename: Yin
authors.initials: Y
authors.lastname: Wang
authors.affiliation: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China. caoyan626@126.com.
authors.forename: Yan
authors.initials: Y
authors.lastname: Cao
keywords: UAV cooperation; evolutionary algorithm; persistent tracking; unmanned aerial vehicles