Bluetooth Low Energy and CNN-Based Angle of Arrival Localization in Presence of Rayleigh Fading

Abstract

Bluetooth Low Energy (BLE) is one of the key technologies empowering the Internet of Things (IoT) for indoor positioning. In this regard, Angle of Arrival (AoA) localization is one of the most reliable techniques because of its low estimation error. BLE-based AoA localization, however, is in its infancy as only recently directionfinding feature is introduced to the BLE specification. Furthermore, AoA-based approaches are prone to noise, multi-path, and path-loss effects. The paper proposes an efficient Convolutional Neural Network (CNN)-based indoor localization framework to tackle these issues specific to BLE-based settings. We consider indoor environments without presence of Line of Sight (LoS) links affected by Additive White Gaussian Noise (AWGN) with different Signal to Noise Ratios (SNRs) and Rayleigh fading channel. Moreover, by assuming a 3-D indoor environment, the destructive effect of the elevation angle of the incident signal is considered on the position estimation. The effectiveness of the proposed CNN-AoA framework is evaluated via an experimental testbed, where In-phase/Quadrature (I/Q) samples, modulated by Gaussian Frequency Shift Keying (GFSK), are collected by four BLE beacons. Simulation results corroborate effectiveness of the proposed CNN-based AoA technique to track mobile agents with high accuracy in the presence of noise and Rayleigh fading channel.

THIP: A trajectory history‐based indoor positioning algorithm using stress‐free floor plan for patient monitoring

“…In our architecture, a wearable sensor node (Beacon) is a wristband with BLE technology that works on IEEE802.15.1 standard. Robustness and small size make them a perfect choice to use in every environment and infrastructure 55 . Also, BLE beacon power consumption is lower than expected, 56 so among various types of wearable wristbands, we consider passive motion‐triggered because, on the one hand, passive wristband power consumption is lower than active wristband in the long term and, on the other hand, these wristbands are sensitive to movement, and as the user moves, they are activated and send messages to access points.…”

“…Robustness and small size make them a perfect choice to use in every environment and infrastructure. 55 Also, BLE beacon power consumption is lower than expected, 56 so among various types of wearable wristbands, we consider passive motion-triggered because, on the one hand, passive wristband power consumption is lower than active wristband in the long term and, on the other hand, these wristbands are sensitive to movement, and as the user moves, they are activated and send messages to access points. Transmission power would be another important point that we should take into account, since as the transmission range increases, the power consumption increases as well.…”

A self-learning mean optimization filter to improve bluetooth 5.1 AoA indoor positioning accuracy for ship environments

“…Despite their low power consumption and long range, they are highly resistant to frequency selective fading and provide a sufficient indoor range. The usage of BLE tags on factory floors and in homes is increasing, so they are becoming very popular for tracking operations ( HajiAkhondi-Meybodi et al, 2021 , Mohaghegh et al, 2021 , Hajiakhondi-Meybodi et al, 2020 , Hajiakhondi-Meybodi et al, 2020 , Alexandrov and Monov, 2017 , Yen et al, 2021 , Huang et al, 2020 , Ye et al, 2022 ). So the Bluetooth 5.1 AoA indoor positioning technology has been proposed.…”

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

“…Most of the existing approaches that employ neural networks (NNs) for indoor localization, either estimate the position directly [ 20 , 21 ] or via a combination of distance estimation and trilateration [ 22 ]. It has to be noted that AoA-based neural network localization approaches exist [ 23 ] but have so far been scarce. There are, however, successful applications of NN-based AoA estimation of single APs, using as features IQ values [ 24 ], MUSIC generated spatial power spectrum features and exploitation of the temporal domain [ 25 , 26 ] or PDDA generated power spectrum features [ 27 , 28 ].…”