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Channel state information (CSI)-based fingerprinting via neural networks(NNs) is a promising approach to enable accurate indoor and outdoor positioningof user equipments (UEs), even under challenging propagation conditions. Inthis paper, we propose a positioning pipeline for wireless LAN MIMO-OFDMsystems which uses uplink CSI measurements obtained from one or moreunsynchronized access points (APs). For each AP receiver, novel features arefirst extracted from the CSI that are robust to system impairments arising inreal-world transceivers. These features are the inputs to a NN that extracts aprobability 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 positionestimate. We provide experimental results with real-world indoor measurementsunder line-of-sight (LoS) and non-LoS propagation conditions for an 80MHzbandwidth IEEE 802.11ac system using a two-antenna transmit UE and two APreceivers each with four antennas. Our approach is shown to achievecentimeter-level median distance error, an order of magnitude improvement overa conventional baseline.

CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion