复杂环境下多无人机协同地面目标跟踪问题研究

发布时间：2018-07-06 13:28

本文选题：UAV + 目标状态估计　；参考：《北京理工大学》2015年硕士论文

【摘要】：随着无人机应用范围的拓宽和技术的发展，无人机（Unmanned Aerial Vehicle, UAV）协同目标跟踪问题作为其他任务过程的子任务，得到了诸多专家学者的关注和发展。本文针对复杂环境下的多UAV协同地面机动目标跟踪问题，重点围绕UAV对地面机动目标的状态融合估计跟踪和UAV观测航迹规划两个问题进行了研究。首先，针对目标跟踪问题，分析了复杂环境下多UAV协同目标跟踪问题求解框架，给出了分层递阶分布式主动求解结构框图，并做了简单说明。UAV目标跟踪过程中存在诸多约束，包括无人机运动学约束、传感器测量约束、通信拓扑变化以及UAV飞行空域约束等。本文针对这些约束问题进行了分析和建模，为后续UAV目标状态融合估计和UAV观测轨迹生成求解过程提供了数学基础。其次，针对“智能”反跟踪地面机动目标的状态融合估计方法进行了研究。UAV通过对目标的观测信息估计目标的运动状态是执行跟踪任务的基本条件，尤其对于机动性强的目标而言，对目标状态的有效估计是UAV实时跟踪目标的关键因素。本文在分析扩展卡尔曼滤波器（Extended Kalman Filter, EKF）和无迹信息滤波器（UnscentedInformation Filter, UIF）算法的基础上，对最小最大化滤波器(Minimax Filter, MF)进行扩展，应用于非线性目标跟踪过程中，并改进提出了分布式一致性MF滤波器形式，，应用于解决以下问题：1）被跟踪目标具有一定“智能”，即能够进行反跟踪反监视机动情况下，仍然可以对目标状态进行持续估计；2）在通信拓扑变化，测量受限条件下的多UAV协同目标状态融合估计；3）多UAV分布式一致性目标状态融合估计。最后，针对存在静态障碍和动态威胁源的情况下，UAV对地面机动目标的跟踪航迹生成算法进行了研究。UAV在飞行过程中受到空域限制，本文应用李亚普诺夫导航向量场（Lyapunov Guidance Vector Field,LGVF）引导UAV以指定对峙距离盘旋跟踪地面目标的基础上，结合避碰势场函数和相对速度空间的动态规划方法，解决了UAV目标跟踪过程中飞行空域内存在静态障碍和动态威胁源时的避碰航迹规划，持续对目标运动轨迹的跟踪问题。
[Abstract]:With the development of unmanned aerial vehicle (UAV) application and technology, the cooperative target tracking problem of Unmanned Aerial vehicle (UAV), as a sub-task of other tasks, has been concerned and developed by many experts and scholars. In this paper, the tracking problem of multi-UAV cooperative ground maneuvering targets in complex environments is studied, focusing on the state fusion estimation and tracking of UAV maneuvering targets and track planning of UAV observations. First of all, the framework of multi-UAV cooperative target tracking problem in complex environment is analyzed, and the hierarchical distributed active solution structure block diagram is given, and some constraints in the process of target tracking are explained simply. Including UAV kinematics constraints, sensor measurement constraints, communication topology changes and UAV airspace constraints. In this paper, the constraint problems are analyzed and modeled, which provides a mathematical basis for the subsequent UAV target state fusion estimation and UAV observation trajectory generation process. Secondly, the state fusion estimation method of "intelligent" anti-tracking ground maneuvering target is studied. UAV estimates the moving state of the target through the observation information of the target is the basic condition of carrying out the tracking task. Especially for targets with strong maneuverability, the effective estimation of target state is the key factor for UAV to track targets in real time. Based on the analysis of extended Kalman filter (EKF) and Unscented Information filter (UIF) algorithm, this paper extends the minimum maximum filter (MF) and applies it to nonlinear target tracking. The distributed consistency MF filter is improved to solve the following problem: 1) the target is intelligent, that is, the target state can be continuously estimated under the condition of anti-tracking and anti-surveillance maneuver. 2) under the change of communication topology, the state fusion estimation of multi-UAV cooperative target under the condition of measurement constraints 3) multi-UAV distributed consistent target state fusion estimation. Finally, the tracking track generation algorithm of ground maneuvering target with UAV under the condition of static obstacle and dynamic threat source is studied. UAV is restricted by airspace during flight. In this paper, based on the Lyapunov guidance Vector LGVF (Lyapunov guidance Vector LGVF) to guide the UAV to determine the stand-off range hovering tracking ground target, the paper combines the collision avoidance potential field function and the dynamic programming method of relative velocity space. The collision avoidance trajectory planning is solved when there are static obstacles and dynamic threat sources in the flight airspace during UAV target tracking, and the problem of tracking the moving track of the target continuously is solved.
【学位授予单位】：北京理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：V279;TN713

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