1 research outputs found
CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion
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 CSI-based positioning pipeline for wireless LAN
MIMO-OFDM systems operating indoors, which relies on NNs that extract a
probability map indicating the likelihood of a UE being at a given grid point.
We propose methods to fuse these probability maps at a centralized processor,
which enables improved positioning accuracy if CSI is acquired at different
access points (APs) and extracted from different transmit antennas. To improve
positioning accuracy, we propose the design of CSI features that are robust to
hardware and system impairments arising in real-world MIMO-OFDM transceivers.
We provide experimental results with real-world indoor measurements under
line-of-sight (LoS) and non-LoS propagation conditions, and for multi-antenna
and multi-AP measurements. Our results demonstrate that probability fusion
significantly improves positioning accuracy without requiring exact
synchronization between APs and that centimeter-level median distance error is
achievable.Comment: Submitted to a journa