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Location Tracking in Mobile Ad Hoc Networks Using Particle Filters

机译:使用粒子过滤器的移动临时网络中的位置跟踪

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Mobile ad hoc networks (MANET) are dynamic networks formed on-the-fly as mobile nodes move in and out of each others’ transmission ranges. In general, the mobile ad hoc networking model makes no assumption that nodes know their own locations. However, recent research shows that location-awareness can be beneficial to fundamental tasks such as routing and energy-conservation. On the other hand, the cost and limited energy resources associated with common, low-cost mobile nodes prohibits them from carrying relatively expensive and power-hungry location-sensing devices such as GPS. This paper proposes a mechanism that allows non-GPS-equipped nodes in the network to derive their approximated locations from a limited number of GPS-equipped nodes. In our method, all nodes periodically broadcast their estimated location, in term of a compressed particle filter distribution. Non-GPS nodes estimate the distance to their neighbors by measuring the received signal strength of incoming messages. A particle filter is then used to estimate the approximated location from the sequence of distance estimates. Simulation studies show that our solution is capable of producing good estimates equal or better than the existing localization methods such as APS-Euclidean.
机译:移动ad hoc网络(MANET)是随着移动节点在彼此移动的传输范围内移动的动态网络。通常,移动ad hoc网络模型不会假设节点知道自己的位置。然而,最近的研究表明,位置意识可能有利于路由和节约等基本任务。另一方面,与普通低成本的移动节点相关的成本和有限的能量资源禁止它们携带相对昂贵和且耗电的位置感测设备,例如GPS。本文提出了一种机制,允许网络中的非GPS的节点从有限数量的配备GPS的节点导出它们的近似位置。在我们的方法中,所有节点在压缩粒子滤波器分布的术语中定期广播其估计位置。非GPS节点通过测量传入消息的接收信号强度来估计与其邻居的距离。然后使用粒子滤波器来估计距离估计序列的近似位置。仿真研究表明,我们的解决方案能够产生比APS-Euclidean等现有定位方法等于或更好的良好估计。

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