An End-to-End BLE Indoor Location Estimation Method Using LSTM

Urano, Kenta; Hiroi, Kei; Yonezawa, Takeshi; Kawaguchi, Nobuo

doi:10.23919/icmu48249.2019.9006638

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…This approach utilizes a Euclidean distance formulation rather than relying on indoor radio channel modeling. Urano et al [4] utilized an end-to-end Long Short-Term Memory (LSTM) neural network for indoor location estimation of BLE devices. Their approach demonstrates superior accuracy when compared to trilateration or fingerprint-based methods.…”

Section: Related Workmentioning

confidence: 99%

Experimental Study of Bluetooth Indoor Positioning Using RSS and Deep Learning Algorithms

Wu,

Wong,

Wang

et al. 2024

Mathematics

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Indoor wireless positioning has long been a dynamic field of research due to its broad application range. While many commercial products have been developed, they often are not open source or require substantial and costly infrastructure. Academically, research has extensively explored Bluetooth Low Energy (BLE) for positioning, yet there are a noticeable lack of studies that comprehensively compare traditional algorithms under these conditions. This research aims to fill this gap by evaluating classical positioning algorithms such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Naïve Bayes (NB), and a Received Signal Strength-based Neural Network (RSS-NN) using BLE technology. We also introduce a novel method using Convolutional Neural Networks (CNN), specifically tailored to process RSS data structured in an image-like format. This approach helps overcome the limitations of traditional RSS fingerprinting by effectively managing the environmental dynamics within indoor settings. In our tests, all algorithms performed well, consistently achieving an average accuracy of less than two meters. Remarkably, the CNN method outperformed others, achieving an accuracy of 1.22 m. These results establish a solid basis for future research, particularly towards enhancing the precision of indoor positioning systems using deep learning for cost-effective, easy to set up applications.

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Section: Related Workmentioning

confidence: 99%

Experimental Study of Bluetooth Indoor Positioning Using RSS and Deep Learning Algorithms

Wu,

Wong,

Wang

et al. 2024

Mathematics

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“…In contrast to fingerprinting approaches, deep neural networks may generalize better when the measurement environment undergoes changes with the introduction of additional or different multipath propagation components. 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.…”

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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Soft Computing Techniques for Physical Layer Security of IoT Devices

Siyad

Tamilselvan

2021

Studies in Big Data

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An End-to-End BLE Indoor Location Estimation Method Using LSTM

Cited by 6 publications

References 22 publications

Experimental Study of Bluetooth Indoor Positioning Using RSS and Deep Learning Algorithms

Experimental Study of Bluetooth Indoor Positioning Using RSS and Deep Learning Algorithms

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

Soft Computing Techniques for Physical Layer Security of IoT Devices

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