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Indoor Positioning System With Cellular Network Assistance Based on Received Signal Strength Indication of Beacon

机译:基于接收信号强度指示的孔网辅助室内定位系统

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With the development of the mobile Internet, the demand for indoor location based services(ILBS) is increasing. Applications and devices in indoor environments can determine their position based on Received Signal Strength Indication(RSSI) of bluetooth Beacons. However, because of multipath effects, shadow fading and the blockage of obstacles, RSSI values are unstable for positioning. The propagation model are various in different environments, which have caused great difficulty in high-precision indoor positioning. In this paper, a novel positioning algorithm based on cellular network and bluetooth Beacon & x2019;s RSSI is proposed for ILBS. The whole algorithm is divided into two parts: Offline Preparation and Online Positioning. During Offline Preparation, Beacon node is cellular networked in regular quadrilateral; a universal ranging model is derived from traditional ranging model for high-precision positioning. During Online Positioning, missing RSSI values are replaced firstly; next, Outliers Removed Median-Kalman Filter is used to process RSSI, which makes the signal smoother to range; then, the cell in which the mobile terminal is located is determined according to the strongest four RSSI values; after that, using filtered RSSI, distances without height influence could be calculated; finally, Weighted Multi-Point Positioning Algorithm With Cellular Network Assistance is used to calculate the real-time location of the mobile terminal. Experiment results show that our algorithm achieves average accuracy in 0.3m & x2212;0.5m and limits the maximum error within 0.8 m.
机译:随着移动互联网的发展,基于室内位置的服务(ILB)的需求正在增加。室内环境中的应用和设备可以基于蓝牙信标的接收信号强度指示(RSSI)来确定其位置。然而,由于多径效应,阴影衰落和障碍物的堵塞,RSSI值对于定位不稳定。传播模型在不同的环境中是各种各样的,这引起了在高精度室内定位的难度。本文提出了一种基于蜂窝网络和蓝牙信标的新型定位算法和X2019; S RSSI的ILBS。整个算法分为两部分:离线准备和在线定位。在离线准备期间,信标节点是常规四边形的蜂窝网络;普遍测距模型来自传统的高精度定位测距模型。在线定位期间,首先替换缺少的RSSI值;接下来,删除了异常值的中位数-Kalman滤波器来处理RSSI,这使得信号变得更平滑到范围;然后,根据最强的四个RSSI值确定其中所定位的移动终端的小区;之后,使用过滤的RSSI,可以计算没有高度影响的距离;最后,使用具有蜂窝网络辅助的加权多点定位算法来计算移动终端的实时位置。实验结果表明,我们的算法在0.3M&X2212中实现了平均精度; 0.5米,并限制0.8米内的最大误差。

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