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Comparison of Floor Detection Approaches for Suburban Area

机译:郊区地板地板检测方法比较

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As a part of smart-buildings, indoor localisation systems -alternative to Global Positioning System localisation - bring constantly improving results. Several localisation methods works with a horizontal localisation error less than few meters. However, for small suburban houses, horizontal localisation is not as important as detection of the current floor, which in is still a challenge in multi-storey buildings. This paper compares several approaches that can be used in fingerprinting-based floor detection systems. The tests include the following fingerprints: pressure measures, Wi-Fi signals, and two generations of cellular networks signals. The tests have been done in the suburban 3-storey building with underdeveloped Wi-Fi and cellular infrastructure. Notwithstanding, the floor detection based on Received Signal Strength from both infrastructures reached from 98 to 100%. Additionally, we showed that differences in the number of measures and differences in the number of received signals were not a major factor that influenced on accuracy.
机译:作为智能建筑的一部分,室内定位系统 - 替代全球定位系统本地化 - 带来不断提高的结果。若干本地化方法适用于不到少数米的水平定位误差。然而,对于小郊区的房屋,水平本地化并不像对当前楼层的检测一样重要,这在多层建筑物中仍然是一个挑战。本文比较了若干方法,可用于指纹识别的地板检测系统。该测试包括以下指纹:压力测量,Wi-Fi信号和两代蜂窝网络信号。该测试已经在郊区3层楼的建筑中进行,欠发达的Wi-Fi和蜂窝基础设施。尽管如此,基于来自两个基础设施的接收信号强度的地板检测从98到100%达到。此外,我们表明,接收信号数量的措施数量和差异的差异不是对准确性影响的主要因素。

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