论文标题
隐式频道图表，并应用于无人机本地化
Implicit Channel Charting with Application to UAV-aided Localization
论文作者
论文摘要
基于诸如到达时间差异之类的特征的传统定位算法受到视力传播的非线损害，这对他们在距离估计中期望的一致性产生了负面影响。取而代之的是，指纹定位对这些传播条件是可靠的，但需要昂贵的大数据集。为了减轻这些局限性，本文利用了最近提供的通道图表的概念，以了解包含节点收集的渠道状态信息（CSI）测量值的空间的几何形状。提出的算法利用了一个深层神经网络，该网络使用其测量的CSI了解了一对节点之间的距离。与标准通道图表方法不同，该算法直接与物理几何形状一起使用，因此仅隐含地了解无线电域的几何形状。仿真结果表明，所提出的算法的表现优于竞争对手，并允许使用无人驾驶飞机在紧急情况下进行准确的定位。
Traditional localization algorithms based on features such as time difference of arrival are impaired by non-line of sight propagation, which negatively affects the consistency that they expect among distance estimates. Instead, fingerprinting localization is robust to these propagation conditions but requires the costly collection of large data sets. To alleviate these limitations, the present paper capitalizes on the recently-proposed notion of channel charting to learn the geometry of the space that contains the channel state information (CSI) measurements collected by the nodes to be localized. The proposed algorithm utilizes a deep neural network that learns distances between pairs of nodes using their measured CSI. Unlike standard channel charting approaches, this algorithm directly works with the physical geometry and therefore only implicitly learns the geometry of the radio domain. Simulation results demonstrate that the proposed algorithm outperforms its competitors and allows accurate localization in emergency scenarios using an unmanned aerial vehicle.
