Abstract
Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements. In this paper, we propose to use a mid-grained intermediate-level channel measurement - spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the IEEE 802.11ad/ay standards - to construct the fingerprinting database. These intermediate channel measurements are further utilized by a deep learning approach for multiple purposes: 1) location-only classification; 2) simultaneous location-and-orientation classification; and 3) direct coordinate estimation. Furthermore, the effectiveness of the framework is thoroughly validated by an in-house experimental platform consisting of 3 access points using commercial-off-the-shelf millimeter-wave WiFi routers. The results show a 100% accuracy if the location is only interested, about 99% for simultaneous location-and-orientations classification, and an averaged root mean-square error (RMSE) of 11.1 cm and an average median error of 9.5 cm for direct coordinate estimate, greater than 2-fold improvements over the RMSE of 28.7 cm and median error of 23.6 cm for RSSI-like single SNR-based localization.
Highlights
Localization of people, objects and devices in indoor environments has received tremendous attention over the past few decades
We propose to fingerprint beam SNR measurements for location and orientation for indoor localization as they provide relatively rich information on spatial propagation paths of mmWave signals used during beam training phase in IEEE 802.11ad standards, and are accessible from COTS 802.11ad chipsets
The results show that the deep learning (DL) approach with the beam SNRs can achieve an accuracy of 100%
Summary
Localization of people, objects and devices in indoor environments has received tremendous attention over the past few decades. Most WiFi-based indoor localization frameworks use either fine-grained channel state information (CSI) from the physical layer [3]–[12] or coarse-grained RSSI measurements from the MAC layer [13]–[29] for fingerprinting or direct localization; see more detailed literature review . T. Koike-Akino et al.: Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi. The CSI measurement is more fine-grained but requires access to physical-layer interfaces and high computational power to process a large amount of sub-carrier data. We propose to fingerprint beam SNR measurements for location and orientation for indoor localization as they provide relatively rich information on spatial propagation paths of mmWave signals used during beam training phase in IEEE 802.11ad standards, and are accessible from COTS 802.11ad chipsets.
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