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Indoor positioning system based on BLE location fingerprinting with classification approach

机译：基于分类方法的BLE位置指纹室内定位系统

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摘要

Position estimation is an important technique for location-based services. Many services and applications, such as navigation assistance, surveillance of patients and social networking, have been developed based on users’ position. Although the GPS plays an important role in positioning systems, its signal strength is extremely weak inside buildings. Thus, other sensing devices are necessary to improve the accuracy of indoor localisation. In the past decade, researchers have developed a series of indoor positioning technologies based on the received signal strength (RSS) of WiFi, ZigBee or Bluetooth devices under the infrastructure of wireless sensor network for location estimation. We can compute the distance of the devices by measuring their RSS, but the correctness of the result is unsatisfactory because the radio signal interference is a considerable issue and the indoor radio propagation is too complicated to model. Using the location fingerprint to estimate a target position is a feasible strategy because the location fingerprint records the characteristics of the signals and the signal strength is related to the space relation. This type of algorithm estimates the location of a target by matching online measurements with the closest a-priori location fingerprints. The matching or classification algorithm is a key issue in the correctness of location fingerprinting. In this paper, we propose an effective location fingerprinting algorithm based on the general and weighted k-nearest neighbour algorithms to estimate the position of the target node. The grid points are trained with an interval of 2 m, and the estimated position error is about 1.8 m. Thus, the proposed method is low computation consumption, and with an acceptable accuracy.
机译：位置估计是基于位置的服务的一项重要技术。根据用户的位置开发了许多服务和应用程序，例如导航辅助，患者监视和社交网络。尽管GPS在定位系统中起着重要作用，但其信号强度在建筑物内部极其微弱。因此，需要其他传感装置来提高室内定位的精度。在过去的十年中，研究人员基于无线传感器网络基础设施中WiFi，ZigBee或蓝牙设备的接收信号强度（RSS），开发了一系列室内定位技术，用于位置估计。我们可以通过测量设备的RSS来计算它们之间的距离，但是结果的正确性不能令人满意，因为无线电信号干扰是一个相当大的问题，并且室内无线电传播太复杂而无法建模。使用位置指纹来估计目标位置是一种可行的策略，因为位置指纹记录了信号的特征，并且信号强度与空间关系有关。这种类型的算法通过将在线测量结果与最接近的先验位置指纹进行匹配来估计目标的位置。匹配或分类算法是位置指纹正确性的关键问题。在本文中，我们提出了一种基于通用和加权k最近邻算法的有效位置指纹识别算法，以估计目标节点的位置。网格点以2μm的间隔训练，估计的位置误差约为1.8μm。因此，所提出的方法计算量低，并且具有可接受的精度。

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