Scalable and Efficient Clustering for Fingerprint-Based Positioning
Author:
Affiliation:
1. ALGORITMI Research Centre, University of Minho, Guimarães, Portugal
2. Institute of New Imaging Technologies, Universitat Jaume I, Castellón, Spain
3. Electrical Engineering Unit, Tampere University, Tampere, Finland
Funder
European Union’s H2020 Research and Innovation Programme under the Marie Skłodowska-Curie
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/10038283/09993735.pdf?arnumber=9993735
Reference68 articles.
1. TILoc: Improving the Robustness and Accuracy for Fingerprint-Based Indoor Localization
2. An Online Radio Map Update Scheme for WiFi Fingerprint-Based Localization
3. Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning
4. A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting
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1. 2-D Localization System for Mobile IoT Devices Using a Single Wi-Fi Access Point With a Passive Frequency-Scanned Antenna;IEEE Internet of Things Journal;2023-09-01
2. Accuracy of a Single Position Estimate for kNN-Based Fingerprinting Indoor Positioning Applying Error Propagation Theory;IEEE Sensors Journal;2023-08-15
3. LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning;Sensors;2023-08-01
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