Development of an Algorithm for Positioning an Object According to Data from an Active Sensor Network Bluetooth Low Energy Beacons

A. Astafiev, A. Demidov, A. Zhiznyakov, I. Kondrushin, I. V. Voronin
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Abstract

The paper considers the process of developing an algorithm for positioning an object according to data from an active sensor network Bluetooth Low Energy beacons. To develop the algorithm, a laboratory bench was developed that includes Bluetooth Low Energy beacons, a debug board, a smartphone and a personal computer. The stand uses software developed by the authors. At the first stage, we consider the construction of a triangulation for a controlled room by points, which are presented in the form of Bluetooth Low Energy beacons. In fact, this is an interpretation of the plan of the controlled area into a description of the propagation environment of radio beacon signals, taking into account physical obstacles. In addition to triangulation, a cache is created, which increases the speed of work with a digital map of the controlled room. The following describes the process of selecting beacons to apply multilateration. A modified multilateration algorithm has been developed taking into account the specifics of the choice of beacons. A mathematical description of the developed stages of the positioning algorithm is given. Experimental studies of the proposed algorithm have been carried out in a closed room measuring 5 by 10 meters. Based on the conducted experiment, it can be concluded that the developed algorithm allows positioning a moving mobile device in real time with an accuracy of 5–54 cm. Using additional tools to refine the positioning results can improve this result.
基于有源传感器网络蓝牙低能量信标数据的目标定位算法研究
本文研究了一种基于蓝牙低能量信标的有源传感器网络数据定位算法的开发过程。为了开发该算法,开发了一个实验室工作台,其中包括低功耗蓝牙信标,调试板,智能手机和个人电脑。该展台使用作者开发的软件。在第一阶段,我们考虑按点构建一个受控房间的三角测量,这些点以蓝牙低能量信标的形式呈现。实际上,这是在考虑到物理障碍的情况下,将控制区域的平面图解释为无线电信标信号传播环境的描述。除了三角测量之外,还创建了一个缓存,这增加了控制房间的数字地图的工作速度。下面描述了选择信标以应用多倍体的过程。考虑到信标选择的特殊性,提出了一种改进的多倍体算法。给出了定位算法发展阶段的数学描述。该算法在5 × 10米的密闭室内进行了实验研究。通过实验可以得出结论,所开发的算法可以实现对移动设备的实时定位,精度为5-54 cm。使用其他工具来细化定位结果可以改善这一结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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