An RSSI-based weighting with accelerometers for real-time indoor positioning

Danklang, Panuwat; Pranekunakol, Thanaluk

doi:10.1109/kst51265.2021.9415843

Cited by 5 publications

(1 citation statement)

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“…For this reason, a variety of indoor positioning systems have been developed, most of them utilizing radio signals such as ultra-wide band (UWB) [ 4 ], Wi-Fi [ 5 ] or Bluetooth. Among these technologies, Bluetooth Low Energy (BLE) has emerged as an affordable mass market alternative with reduced energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Indoor Localization Using Multi-View BLE Signal

Koutris

Siozos

Kopsinis

et al. 2022

Sensors

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In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs.

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Section: Introductionmentioning

confidence: 99%