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Calibration Procedures for Indoor Location Using Fingerprinting

机译：使用指纹进行室内位置的校准程序

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摘要

Fingerprinting is a location technique, based on the use of wireless networks, where data stored during the offline phase is compared with data collected by the mobile node during the online phase. When this location technique is used in a real-life scenario there is a high probability that the mobile node used throughout the offline phase is different from the mobile nodes that will be used during the online phase. This means that there might be very significant differences between the Received Signal Strength values acquired by the mobile node being located and the ones previously stored in the Fingerprinting Map. As a consequence, this difference between RSS values might contribute to increase the location estimation error. One possible solution to minimize these differences is to adapt the RSS values, acquired during the online phase, before sending them to the Location Estimation Algorithm. Also the internal parameters of the Location Estimation Algorithms, for example the weights of the Weighted k-Nearest Neighbour, might need to be tuned for every type of terminal. This paper focuses both approaches, using Direct Search optimization methods to adapt the Received Signal Strength and to tune the Location Estimation Algorithm parameters. As a result it was possible to decrease the location estimation error originally obtained without any calibration procedure.
机译：指纹识别是一种基于无线网络使用的定位技术，其中将脱机阶段存储的数据与移动节点在联机阶段收集的数据进行比较。在现实生活中使用此定位技术时，很有可能整个脱机阶段使用的移动节点与将在联机阶段使用的移动节点不同。这意味着，所定位的移动节点所获取的接收信号强度值与先前存储在指纹图谱中的值之间可能会有非常大的差异。结果，RSS值之间的这种差异可能会增加位置估计误差。最小化这些差异的一种可能解决方案是在将在线阶段获取的RSS值发送到位置估计算法之前，对其进行调整。此外，可能需要针对每种类型的终端调整位置估计算法的内部参数，例如加权k最近邻居的权重。本文将重点介绍这两种方法，使用直接搜索优化方法来调整接收信号强度并调整位置估计算法参数。结果，有可能减少最初获得的位置估计误差，而无需任何校准程序。

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