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Aggregation of GPS WLAN and BLE Localization Measurements for Mobile Devices in Simulated Environments

机译:模拟环境中移动设备的GPSWLAN和BLE本地化测量的汇总

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摘要

There are multiple available technologies to find the location of a mobile device, such as the Global Positioning System (GPS), Bluetooth Low-Energy beacons (BLE), and Wireless LAN (WLAN) localization. We propose a novel method to estimate the location of a moving device by aggregating information from multiple positioning systems into a single, more precise location estimation. The aggregated location is calculated as the place in which the product of the probability density functions (PDF) of individual methods has the maximum value. The experimental probability density functions of the three analyzed technologies are fitted by gamma distributions based on error histograms found in the literature and measurement data. The location measurements of the individual technologies are provided at different time instants, so the weighted product of the PDFs is used to improve aggregation accuracy. The discrete event-simulation model was used to evaluate the aggregation method with the Gauss–Markov mobility model. Simulations demonstrated that the calculated aggregated location was more accurate than any of the methods taken as the input, and average error was decreased by almost 13% compared to an arithmetic mean of the three considered localization methods, and by more than 36% compared to the single method with the highest accuracy.
机译:有多种可用的技术来查找移动设备的位置,例如全球定位系统(GPS),蓝牙低能耗信标(BLE)和无线LAN(WLAN)本地化。我们提出了一种通过将来自多个定位系统的信息聚合到单个更精确的位置估计中来估计移动设备位置的新颖方法。将汇总位置计算为各个方法的概率密度函数(PDF)的乘积具有最大值的位置。三种分析技术的实验概率密度函数通过基于文献中发现的误差直方图和测量数据的伽马分布进行拟合。各个技术的位置测量值是在不同的时刻提供的,因此使用PDF的加权乘积来提高聚合精度。离散事件模拟模型用于评估具有高斯-马尔可夫流动模型的聚合方法。仿真表明,所计算的聚集位置比任何一种输入方法都更加准确,与三种考虑的定位方法的算术平均值相比,平均误差降低了近13%,与之相比,平均误差降低了36%以上。具有最高准确性的单一方法。

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