Bluetooth Direction Finding using Recurrent Neural Network

Babakhani, Pedram; Merk, Timon; Mahlig, Matthias; Sarris, Ioannis; Kalogiros, Dimitris I.; Karlsson, Peter

doi:10.1109/ipin51156.2021.9662611

Cited by 5 publications

(11 citation statements)

References 18 publications

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“…Our data structure is different from the traditional multiple anchors/frequency data and multiple general instances learning data. The multiple anchors/frequency data adopted in the previous research [10,11] can be considered as one kind of multivariate time series data, [27] in which each sensor has exactly one data feature. Based on the anchor information, our model input data can have multiple features from one anchor (sensor).…”

Section: Remarkmentioning

confidence: 99%

“…Besides, previous BLE 5.1 IPS research assumes that the data are collected simultaneously from all anchors/broadcasting channels. [ 9,10 ] It is indeed not the case in practice. Anchor data present themselves randomly within a time period.…”

Section: Introductionmentioning

confidence: 99%

“…While deep neural networks are successfully applied to different application domains, it is no surprise that they are also used in IPS design. [9][10][11] Recently, some efforts have been made to develop deep learning (DL)-based models for BLE 5.1 IPS such as using the convolutional neural network (CNN) [11] and recurrent neural network. [10] These approaches often use the fingerprint-based method such that object positioning is converted into a classification problem of the I/Q sample fingerprints.…”

mentioning

confidence: 99%

“…[9][10][11] Recently, some efforts have been made to develop deep learning (DL)-based models for BLE 5.1 IPS such as using the convolutional neural network (CNN) [11] and recurrent neural network. [10] These approaches often use the fingerprint-based method such that object positioning is converted into a classification problem of the I/Q sample fingerprints. Despite the merits of previous research, there are still some critical challenges in the integration of DL models with the positioning process with multianchor BLE 5.1 I/Q sample data.…”

mentioning

confidence: 99%

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Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

Lyu,

Chan,

Hung

et al. 2023

Advanced Intelligent Systems

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Indoor positioning system (IPS) technologies have widespread applications in logistics, intelligent manufacturing, healthcare monitoring, etc. The recently released Bluetooth low‐energy (BLE) 5.1 specification enables in‐phase and quadrature‐phase (I/Q) data measurements. It allows angle of arrival estimation and becomes a natural choice for IPS implementation. Conventional BLE 5.1 IPSs use multiple anchors to provide massive redundancy to improve system robustness. It however demands effective approaches to leverage redundancy. Besides, interference due to various environmental factors can introduce severe errors to I/Q data and affect positioning accuracy. Facing these challenges, herein, a novel deep learning‐based multianchor BLE 5.1 IPS is proposed. The system aggregates measurements from multiple anchors and makes them available at regular time steps. Then, a novel attentional filtering network tailored to infer high‐quality I/Q sample data is developed and a spatial regularization loss incorporating spatial location relationships to strengthen the feature embedding discrimination is proposed. Two multianchor BLE 5.1 I/Q sample datasets are developed and released for public download. Numerical experiments are carried out to compare the proposed method with previous BLE 5.1 IPS methods and methods utilizing other radio frequency data. Results indicate that the proposed method consistently achieves submeter accuracy and significantly outperforms the state‐of‐the‐art approaches.

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Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

Lyu,

Chan,

Hung

et al. 2023

Advanced Intelligent Systems

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“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Koutris

Siozos

Kopsinis

et al. 2022

Sensors

Self Cite

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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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