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UAV-aided localization algorithm with relay for train-mounted mobile terminals

机译:具有培训移动终端的继电器的无人机辅助定位算法

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In some complex environment, the train and the base station (BS) cannot connect directly. And the train-mounted mobile terminals cannot receive global positioning system (GPS) signals because of the shadow of the train shells. To address this issue, an unmanned aerial vehicle (UAV) aided localization algorithm with relay is proposed in this paper, which consists of four steps: coordinates dividing, step-by-step positioning, coupling and results comparison. The proposed method transforms the complex in-train environment into a traditional indoor scene to avoid the influence of non-line-of-sight (NLOS). It locates the absolute position of the train in a wireless sensor network (WSN) unit composed of nodes installed on UAVs. In addition, aiming at reducing the distance-measuring errors caused by velocity difference between the train and the UAV, an error compensation factor is carried out in received signal strength indicator (RSSI). Based on the simulation results, we find that the proposed method is more accurate working in confined space where GPS signals cannot be captured. Compared with traditional methods, it can also correct the velocity difference errors. The positioning system achieves a mean positioning accuracy of 2.13m. (C) 2019 Elsevier B.V. All rights reserved.
机译:在一些复杂的环境中,列车和基站(BS)无法直接连接。由于火车壳的阴影,火车安装的移动终端无法接收全球定位系统(GPS)信号。为了解决这个问题,本文提出了一种无人驾驶空中车辆(UAV)辅助带继电器的辅助定位算法,其中包括四个步骤:坐标分割,逐步定位,耦合和结果比较。该方法将复杂的列车内环境转变为传统的室内场景,以避免视力(NLO)的影响。它位于由安装在UAV上安装的节点组成的无线传感器网络(WSN)单元中列车的绝对位置。另外,旨在减少由列车和UAV之间的速度差异引起的距离测量误差,在接收的信号强度指示符(RSSI)中执行误差补偿因子。基于仿真结果,我们发现所提出的方法在无法捕获GPS信号的密闭空间中更准确。与传统方法相比,它还可以校正速度差错。定位系统实现平均定位精度为2.13米。 (c)2019 Elsevier B.v.保留所有权利。

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