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首页> 外文期刊>EURASIP journal on advances in signal processing >A POMDP Framework for Coordinated Guidance of Autonomous UAVs for Multitarget Tracking
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A POMDP Framework for Coordinated Guidance of Autonomous UAVs for Multitarget Tracking

机译:一个POMDP框架,用于多目标跟踪的自主无人机的协同制导

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This paper discusses the application of the theory of partially observable Markov decision processes (POMDPs) to the design of guidance algorithms for controlling the motion of unmanned aerial vehicles (UAVs) with onboard sensors to improve tracking of multiple ground targets. While POMDP problems are intractable to solve exactly, principled approximation methods can be devised based on the theory that characterizes optimal solutions. A new approximation method called nominal belief-state optimization (NBO), combined with other application-specific approximations and techniques within the POMDP framework, produces a practical design that coordinates the UAVs to achieve good long-term mean-squared-error tracking performance in the presence of occlusions and dynamic constraints. The flexibility of the design is demonstrated by extending the objective to reduce the probability of a track swap in ambiguous situations.
机译:本文讨论了部分可观察的马尔可夫决策过程(POMDP)理论在设计用于控制带有机载传感器的无人机的运动的制导算法中的应用,以改善对多个地面目标的跟踪。尽管POMDP问题很难准确解决,但可以基于表征最佳解决方案的理论来设计原则上的近似方法。一种新的称为名义置信状态优化(NBO)的近似方法,与POMDP框架内的其他特定于应用的近似方法和技术相结合,产生了一种实用的设计,可以协调无人飞行器以实现良好的长期均方误差跟踪性能。遮挡和动态约束的存在。通过扩展目标以减少歧义情况下轨道交换的可能性,证明了设计的灵活性。

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