ArticleSensors (Basel, Switzerland)2022
Deep Learning-Based Indoor Localization Using Multi-View BLE Signal.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Machine Learning Models for Indoor Positioning Using Bluetooth RSSI and Video Data: A Case Study.Sensors (Basel, Switzerland) · 2025Article
- A Hybrid Approach for IoT Security: Combining Ensemble Learning with Fuzzy Logic.Sensors (Basel, Switzerland) · 2025Article
- A Bluetooth Indoor Positioning System Based on Deep Learning with RSSI and AoA.Sensors (Basel, Switzerland) · 2025Article
- An Experimental Evaluation of Indoor Localization in Autonomous Mobile Robots.Sensors (Basel, Switzerland) · 2025Article
- A Precise and Scalable Indoor Positioning System Using Cross-Modal Knowledge Distillation.Sensors (Basel, Switzerland) · 2024Article
- Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment.Sensors (Basel, Switzerland) · 2023Article
- Multi-Sensor Data Fusion Solutions for Blind and Visually Impaired: Research and Commercial Navigation Applications for Indoor and Outdoor Spaces.Sensors (Basel, Switzerland) · 2023Review
- Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication.Sensors (Basel, Switzerland) · 2023Article
- Improving BLE-Based Passive Human Sensing with Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- An Improved Method Based on Bluetooth Low-Energy Fingerprinting for the Implementation of PEPS System.Sensors (Basel, Switzerland) · 2022Article
- Advances in Indoor Positioning and Indoor Navigation.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors.
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Abstract
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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