CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion
CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion
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DOI：
10.1109/twc.2021.3109789
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发表时间：
2020-09
影响因子：
10.4
通讯作者：
Emre Gonultacs;E. Lei;Jack Langerman;Howard Huang;Christoph Studer
中科院分区：
文献类型：
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作者：
Emre Gonultacs;E. Lei;Jack Langerman;Howard Huang;Christoph Studer
通过神经网络(NNS)的基于信道状态信息(CSI)的指纹识别是一种很有前途的方法，即使在具有挑战性的传播条件下也能够实现用户设备(UE)的精确室内和室外定位。在本文中，我们提出了一种用于无线局域网MIMO-OFDM系统的定位管道，它使用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中提取新的特征，这些特征对于真实世界收发机中出现的系统损伤是稳健的。这些特征是对NN的输入，NN提取指示UE处于给定网格点的可能性的概率图。然后，在多个AP上融合NN输出，以提供最终位置估计。我们给出了一个80 MHz带宽的IEEE 802.11ac系统在视距(LOS)和非视距(LOS)传播条件下的真实室内测量结果，该系统使用一个双天线发射UE和两个AP接收器，每个接收器有四个天线。我们的方法被证明可以达到厘米级的中位距离误差，比传统的基线提高了一个数量级。
Channel state information (CSI)-based fingerprinting via neural networks (NNs) is a promising approach to enable accurate indoor and outdoor positioning of user equipment (UE), 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.