MuDLoc combines amplitude and phase Wi-Fi channel features from multiple access points with a discriminative multi-view subspace projection, and reports mean localization errors of 0.24 m in a lab and 0.15 m in a corridor.
The case for efficient and robust rf-based device-free localization,
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A Multi-View Discriminant Learning Approach for Indoor Localization Using Bimodal Features of CSI
MuDLoc combines amplitude and phase Wi-Fi channel features from multiple access points with a discriminative multi-view subspace projection, and reports mean localization errors of 0.24 m in a lab and 0.15 m in a corridor.