Distributed filtering over sensor networks for autonomous navigation of UAVs

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• 作者：Gerasimos G. Rigatos (1) grigat@ieee.org
• 关键词：UAV &#8211 ; Extended information filter &#8211 ; Unscented information filter &#8211 ; Distributed particle filter &#8211 ; Derivative ; free extended information filter &#8211 ; Nonlinear control &#8211 ; Multi ; source multi ; target tracking &#8211 ; Sensor fusion &#8211 ; Autonomous navigation &#8211 ; Sensor networks
• 刊名：Intelligent Service Robotics
• 出版年：2012
• 出版时间：July 2012
• 年：2012
• 卷：5
• 期：3
• 页码：179-198
• 全文大小：1.7 MB
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• 作者单位：1. Department of Engineering, Harper Adams University College, Shropshire, TF10 8NB UK
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Control Engineering
  Artificial Intelligence and Robotics
  User Interfaces and Human Computer Interaction
  Vibration, Dynamical Systems and Control
  Complexity
  
• 出版者：Springer Berlin Heidelberg
• ISSN：1861-2784

文摘

The paper studies the problem of localization and autonomous navigation of a multi-UAV system with the use of Distributed Filtering methods (DF). It is considered that m UAV (helicopter) models are monitored by n different ground stations. The overall concept is that at each monitoring station a filter is used to track each UAV by fusing measurements which are provided by various UAV sensors, while by fusing the state estimates from the distributed local filters an aggregate state estimate for each UAV is obtained. In particular, the paper proposes first the extended information filter (EIF) and the unscented information filter (UIF) as possible approaches for fusing the state estimates provided by the local monitoring stations, under the assumption of Gaussian noises. The EIF and UIF estimated state vector is in turn used by a flatness-based controller that makes the UAV follow the desirable trajectory. Moreover, the distributed particle filter (DPF) is proposed for fusing the state estimates provided by the local monitoring stations (local filters). The motivation for using DPF is that it is well-suited to accommodate non-Gaussian measurements. The DPF estimated state vector is again used by the flatness-based controller to make each UAV follow a desirable flight path. Finally, a derivative-free implementation of the extended information filter (DEIF) is introduced aiming at obtaining more accurate estimates of the UAV state vector in real-time. The performance of the EIF, of the UIF, of the DPF and of the DEIF is evaluated through simulation experiments in the case of a 2-UAV model monitored and remotely navigated by two local stations.