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Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters

机译:基于分布式高度信息滤波器的目标跟踪多UAV多普勒信息融合

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摘要

Multi-Unmanned Aerial Vehicle (UAV) Doppler-based target tracking has not been widely investigated, specifically when using modern nonlinear information filters. A high-degree Gauss–Hermite information filter, as well as a seventh-degree cubature information filter (CIF), is developed to improve the fifth-degree and third-degree CIFs proposed in the most recent related literature. These algorithms are applied to maneuvering target tracking based on Radar Doppler range/range rate signals. To achieve this purpose, different measurement models such as range-only, range rate, and bearing-only tracking are used in the simulations. In this paper, the mobile sensor target tracking problem is addressed and solved by a higher-degree class of quadrature information filters (HQIFs). A centralized fusion architecture based on distributed information filtering is proposed, and yielded excellent results. Three high dynamic UAVs are simulated with synchronized Doppler measurement broadcasted in parallel channels to the control center for global information fusion. Interesting results are obtained, with the superiority of certain classes of higher-degree quadrature information filters.
机译:基于多人的空中车辆(UAV)基于多普勒的目标跟踪尚未被广泛研究,特别是在使用现代非线性信息过滤器时。开发出高度高斯 - Hermite信息滤波器以及第七度Cuberature Information滤波器(CIF)以改善最近相关文献中提出的第五级和第三度CIFS。这些算法基于雷达多普勒范围/范围信号应用于操纵目标跟踪。为实现此目的,在模拟中使用不同的测量模型,例如仅限范围,范围率和仅轴承跟踪。在本文中,通过高级等级的正交信息过滤器(HQIF)来解决和解决移动传感器目标跟踪问题。提出了一种基于分布式信息滤波的集中式融合架构,并产生了优异的结果。用在并行通道中广播的同步多普勒测量模拟三个高动态无人机对全球信息融合的控制中心。获得有趣的结果,具有某些等级高度正交信息过滤器的优越性。

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