Dropout Autoencoder Fingerprint Augmentation for Enhanced Wi-Fi FTM-RSS Indoor Localization
Author:
Affiliation:
1. Department of Electronic Engineering, Wireless Systems Laboratory, Hanyang University, Seoul, South Korea
2. KIOS Research and Innovation COE, University of Cyprus, Nicosia, Cyprus
3. VTT Technical Research Centre, Nokia Campus, Espoo, Finland
Funder
National Research Foundation of Korea
Korea government
LuxTurrim5G+ project
Business Finland and the KIOS CoE
European Union’s Horizon 2020 research and innovation Programme
Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy and the Huawei Technologies research
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation
Link
http://xplorestaging.ieee.org/ielx7/4234/10178697/10115527.pdf?arnumber=10115527
Reference17 articles.
1. Noise Learning-Based Denoising Autoencoder
2. Adversarial Learning-Enabled Automatic WiFi Indoor Radio Map Construction and Adaptation With Mobile Robot
3. Regularizing Deep Neural Networks by Enhancing Diversity in Feature Extraction
4. Federated Learning for Indoor Localization via Model Reliability With Dropout
5. A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron
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2. Neural network-based DOA estimation for distributed sources in massive MIMO systems;AEU - International Journal of Electronics and Communications;2024-03
3. Fingerprinting-Based Indoor Localization With Hybrid Quantum-Deep Neural Network;IEEE Access;2023
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