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RSSI-based Indoor Localization Using RSSI-with-Angle-based Localization Estimation Algorithm

机译:使用基于角度的RSSI的本地化估计算法基于RSSI的室内本地化

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For the scenarios of indoors localization and tracking, the solutions generally need complex infrastructure because they would require either a grid of antennas, each having a well-known position (proximity based approach), or a sophisticated algorithm that uses scene fingerprint to estimate the location or the zone of an object by matching the online measurement with the closest offline measurement. Those techniques may not be available in unknown zones, which will make it difficult to locate a lost node. In this paper, with no additional hardware costs, we propose a new RSSIbased approach in order to find a lost node using a known node. By rotating the known node at the same spot we can collect different RSSI for different polar angles. Two pairs of angles with the strongest RSSI will indicate the main lobes of the radiation pattern, namely, zone of the unknown node. Experimental results illustrate a very close estimation of the unknown node zone, reducing up to 84% of the zone uncertainty.
机译:对于室内定位和跟踪的场景,解决方案通常需要复杂的基础架构,因为它们将需要天线网格(每个天线网格具有众所周知的位置)(基于接近度的方法)或使用场景指纹来估计位置的复杂算法。通过将在线测量值与最接近的离线测量值进行匹配来确定对象的区域。这些技术在未知区域中可能不可用,这将使得很难找到丢失的节点。在本文中,在不增加硬件成本的情况下,我们提出了一种基于RSSI的新方法,以便使用已知节点查找丢失的节点。通过在同一点旋转已知节点,我们可以针对不同的极角收集不同的RSSI。 RSSI最强的两对角度将指示辐射方向图的主要波瓣,即未知节点的区域。实验结果说明了对未知节点区域的非常接近的估计,最多减少了84%的区域不确定性。

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