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首页> 外文会议>Unmanned Systems Technology IX; Proceedings of SPIE-The International Society for Optical Engineering; vol.6561 >A UAV Routing and Sensor Control Optimization Algorithm for Target Search
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A UAV Routing and Sensor Control Optimization Algorithm for Target Search

机译：一种用于目标搜索的无人机路由和传感器控制优化算法

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摘要

An important problem in unmanned air vehicle (UAV) and UAV-mounted sensor control is the target search problem: locating target(s) in minimum time. Current methods solve the optimization of UAV routing control and sensor management independently. While this decoupled approach makes the target search problem computationally tractable, it is suboptimal. In this paper, we explore the target search and classification problems by formulating and solving a joint UAV routing and sensor control optimization problem. The routing problem is solved on a graph using receding horizon optimal control. The graph is dynamically adjusted based on the target probability distribution function (PDF). The objective function for the routing optimization is the solution of a sensor control optimization problem. An optimal sensor schedule (in the sense of maximizing the viewed target probability mass) is constructed for each candidate flight path in the routing control problem. The PDF of the target state is represented with a particle filter and an "occupancy map" for any undiscovered targets. The tradeoff between searching for undiscovered targets and locating tracks is handled automatically and dynamically by the use of an appropriate objective function. In particular, the objective function is based on the expected amount of target probability mass to be viewed.
机译：目标搜索问题是在无人飞行器（UAV）和安装在无人机上的传感器控制中的一个重要问题：在最短时间内找到目标。当前的方法独立地解决了无人机航路控制和传感器管理的优化问题。尽管这种分离的方法使目标搜索问题在计算上易于处理，但它不是最佳的。在本文中，我们通过制定和解决联合无人机航路和传感器控制优化问题来探索目标搜索和分类问题。使用后退水平最优控制在图上解决路由问题。根据目标概率分布函数（PDF）动态调整图表。路由优化的目标函数是传感器控制优化问题的解决方案。针对航线控制问题中的每个候选飞行路径，都构造了一个最佳传感器计划（在最大化所查看目标概率质量的意义上）。目标状态的PDF用粒子过滤器和任何未发现目标的“占用图”表示。通过使用适当的目标函数，可以自动动态地处理搜索未发现的目标和定位轨迹之间的折衷。特别地，目标函数基于要观察的目标概率质量的预期量。

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