论文标题
使用概率融合
CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion
论文作者
论文摘要
基于神经网络（NNS）的基于渠道状态信息（CSI）的指纹是一种有前途的方法，即使在挑战性的传播条件下，也可以准确地室内和室外定位（UES）。在本文中，我们为无线LAN MIMO-OFDM系统提出了定位管道，该系统消耗从一个或多个非同步接入点（AP）获得的上行链路CSI测量。对于每个AP接收器，首先从CSI中提取新的功能，这些功能可用于现实世界中引起的系统障碍。这些功能是提取概率图的NN的输入，表明UE处于给定网格点的可能性。然后将NN输出融合在多个AP上，以提供最终位置估计。我们为80MHz带宽IEEEE 802.11ac系统在室内（LOS）和非LOS传播条件下进行现实世界的室内测量结果提供实验结果，并使用两种Antenna Transmit UE和两个AP接收器，每个接收器各有四个天线。我们的方法表明可以实现厘米级的中值距离误差，这是比传统基线的数量级改进。
Channel state information (CSI)-based fingerprinting via neural networks (NNs) is a promising approach to enable accurate indoor and outdoor positioning of user equipments (UEs), even under challenging propagation conditions. In this paper, we propose a positioning pipeline for wireless LAN MIMO-OFDM systems which uses uplink CSI measurements obtained from one or more unsynchronized access points (APs). For each AP receiver, novel features are first extracted from the CSI that are robust to system impairments arising in real-world transceivers. These features are the inputs to a NN that extracts a probability map indicating the likelihood of a UE being at a given grid point. The NN output is then fused across multiple APs to provide a final position estimate. We provide experimental results with real-world indoor measurements under line-of-sight (LoS) and non-LoS propagation conditions for an 80MHz bandwidth IEEE 802.11ac system using a two-antenna transmit UE and two AP receivers each with four antennas. Our approach is shown to achieve centimeter-level median distance error, an order of magnitude improvement over a conventional baseline.
