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Persistent standoff tracking guidance using constrained particle filter for multiple UAVs

机译:使用约束粒子滤波器的持久对峙跟踪指导,用于多个无人机

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This paper presents a new standoff tracking framework of a moving ground target using UAVs with limited sensing capabilities and motion constraints. To maintain persistent track of the target even in case of target loss for a certain period, this study predicts the target existence area using the particle filter and produces control commands that ensure that all predicted particles can stay within the field-of-view of the UAV sensor at all times. To improve target position prediction and estimation accuracy, the road information is incorporated into the constrained particle filter where the road boundaries are modelled as inequality constraints. Both Lyapunov vector field guidance and nonlinear model predictive control-based methods are applied, and the characteristics of them are compared using numerical simulations. (C) 2018 Elsevier Masson SAS. All rights reserved.
机译:本文提出了一种新的对空地面目标的对峙跟踪框架,该框架使用具有有限感知能力和运动约束的无人机进行飞行。为了即使在目标损失一定时间的情况下也能保持对目标的持续跟踪,本研究使用粒子过滤器预测了目标存在区域,并产生了控制命令,以确保所有预测的粒子都可以停留在目标的视场内。随时都有无人机传感器。为了提高目标位置的预测和估计精度,将道路信息合并到约束粒子过滤器中,在其中将道路边界建模为不平等约束。应用了Lyapunov矢量场制导和基于非线性模型预测控制的方法,并通过数值模拟比较了它们的特性。 (C)2018 Elsevier Masson SAS。版权所有。

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