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Abstract: The vast amount of spectrum available for millimeter wave (mmWave) wireless communication systems will support accurate real-time positioning concurrent with communication signaling. This paper demonstrates that accurate estimates of the position of an unknown node can be determined using estimates of time of arrival (ToA), angle of arrival (AoA), as well as data fusion or machine learning. Real-world data at 28 GHz and 73 GHz is used to show that AoA-based localization techniques will need to be augmented with other positioning techniques. The fusion of AoA-based positioning with received power measurements for RXs in an office which has dimensions of 35 m by 65.5 m is shown to provide location accuracies ranging from 16 cm to 3.25 m, indicating promise for accurate positioning capabilities in future networks. Received signal strength intensity (RSSI) based positioning techniques that exploit the ordering of the received power can be used to determine rough estimates of user position. Prediction of received signal characteristics is done using 2-D ray tracing.
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Interpersonal Proximity Detection Using RSSI–Based Techniques
We present an experimental study assessing the ability of two RSSI-based methods at detecting inter- personal distances shorter than 1 meter or 2 meters. The first method uses the power received from the smartphone carried by another person, while the second one measures the disparity in the power received by the two smartphones from one or more fixed BLE beacons. Our results show that use of the RSSI disparity enables discrimination results that are as good or better than using the RSSI received from another smartphone.
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- Award ID(s):
- 1632158
- PAR ID:
- 10300124
- Date Published:
- Journal Name:
- Indoor Positioning and Indoor Navigation (IPIN 21) - Work-In-Progress papers
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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