Efficient Indoor Localization via Switched-Beam Antennas

Abstract

Location-based services have become extremely popular. As a result, indoor localization has received a lot of attention from both industry and academia. Despite the plethora of existing approaches, no method can achieve high accuracy without major, expensive changes to existing systems. For example, to achieve good accuracy, fingerprinting methods require many APs within range of a client, time-of-arrival methods require tight synchronization, client hardware changes and line-of-sight, and direction-of-arrival methods require expensive antenna front ends and line-of-sight. In this paper we take advantage of inexpensive, off-the-shelf switched-beam antennas (SBAs) to increase the diversity of measurements used for fingerprint-based localization. We show using experiments that a single AP equipped with n SBAs may infer equally rich localization information as n APs equipped with n omnidirectional antennas each, as long as the SBAs are properly configured. We then establish via extensive experiments that a single packet reception from a commodity client at a single AP equipped with a handful of SBAs (e.g. 8 SBAs costing a couple of dollars more than omni antennas) achieves localization accuracy in the order of half a meter with or without line-of-sight, in any indoor environment, with zero airtime overhead and zero client support. !

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

“…[335], [336] Mapping [337], [338] Indoor [331], [332], [335], [336], [338] Outdoor [328]- [330], [333], [334] Sensors [331], [335], [336], [338] Radars [328], [332], [337] mmWave/THz [329], [330], [333], [334] ToA, RSS, RMSE Neural networks Sustainable develop. [339]- [344] Local trust zones [345]- [353] Massive twinning [298], [354] Indoor [339], [341]- [344], [348], [349], [354] Outdoor [345]- [347], [350]- [353] Sensors [339]- [344], [347]- [349], [354] mmWave/THz [345], [346], [350]-...…”

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

“…For example, [205] noticed that the phase difference between antenna pairs is correlated with the true AoA, Ref. [206] use the recorded directional RSSI value, with simplified angle calculation method in [207], to estimate the AoA. Note that with a large number of antennas, finer estimates of AoA become easier to obtain.…”

“…[214]- [217], [226], [228]- [239] MIMO [240] [61], [190], [205], [206], [211], [217], [226], [228], [230]- [239], [241], [242] Massive MIMO [113], [174], [208], [209], [212]- [216], [218],…”

“…Using directional antennas in a WiFi system, [206] builds fingerprints using RSSI (or CSI amplitude) and uses an NN with two hidden layers as a classifier to predict the closest fingerprint to the observed values. Using a rough estimate of the AoA provides an improved localization accuracy.…”

“…For instance, the authors in [362] test the Pseudolite system , where they have to deploy the system indoor. Switchedbeam antennas with a USRP board can acquire directional RSSI in WiFi systems [206]. A testbed with a CC2530 chipset for device-free localizationbased Zigbee has been used in [295].…”