ArticleSensors (Basel, Switzerland)2021
Fingerprint Feature Extraction for Indoor Localization.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- From Fingerprinting to Advanced Machine Learning: A Systematic Review of Wi-Fi and BLE-Based Indoor Positioning Systems.Sensors (Basel, Switzerland) · 2025Review
- Deterministic Localization for Fully Automatic Operation: A Survey and Experiments.Sensors (Basel, Switzerland) · 2024Article
- An Improved Method Based on Bluetooth Low-Energy Fingerprinting for the Implementation of PEPS System.Sensors (Basel, Switzerland) · 2022Article
- Human-to-Human Position Estimation System Using RSSI in Outdoor Environment.Sensors (Basel, Switzerland) · 2022Article
- WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs' fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards,
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Registered trials
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