A Fingerprint Technique for Indoor Localization Using Autoencoder Based Semi-Supervised Deep Extreme Learning Machine
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In recent years, because of the growing demand for location based services in indoor environment and development of Wi-Fi, fingerprint-based indoor localization has attracted many researchers’ interest. In Wireless Sensor Networks (WSNs), fingerprint based localization methods estimate the target location by using a pattern matching model for the measurements of the Received Signal Strength (RSS) from the available transmitter sensors, which are collected by a smartphone with internal sensors. Due to the dynamic nature of the environment, the fingerprint database needs to be updated, periodically. Hence, it is better to add new fingerprint data to the primary database in order to update them. However, collecting the labeled data is time consuming and labor intensive. In this paper, we propose a novel algorithm, which uses high level extracted features by an autoencoder to improve the localization performance in the classification process. Furthermore, to update the fingerprint data base, we also add crowd-sourced labeled and unlabeled data in order to improve the localization performance, gradually. Simulation results indicate that the proposed method provides a significant improvement in localization performance, using high level extracted features by the autoencoder, and by increasing the number of unlabeled training data.
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Context-aware and semi-supervised calibration methods have been developed to adapt BLE ranging models to changing playground environments and to improve accuracy even when labeled training data are limited.
“Furthermore, context-aware and semi-supervised calibration methods have been developed to adapt BLE ranging models to changing playground environments and to improve accuracy even when labeled training data are limited [22,23].”
From Child Playground Entry/Exit Tracking and Log Visualization System Using BLE Beacons and Smart Devices (2026) · cites this paper as [23]
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Chung, M. (2026) · Signals · doi:10.3390/signals7040067
“Furthermore, context-aware and semi-supervised calibration methods have been developed to adapt BLE ranging models to changing playground environments and to improve accuracy even when labeled training data are limited [22,23].”
- Authors
- Ezzati Khatab, Z., Hajihoseini Gazestani, A., Ghorashi, S. A., & Ghavami, M.
- Affiliations
- University of East London (GB)
- London South Bank University (GB)
- Corresponding author
- Ghorashi, Seyed Ali
- Venue
- Signal Processing
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 181
- Pages
- 107915
- ISSN
- 0165-1684 (ISSN-L)
- OpenAlex
- W3106644101
- Cited by
- 44
- References
- 49
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 7 · 2023: 9 · 2022: 14 · 2021: 5
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Signal Processing (publishedVersion)
- Research Open (London South Bank University) (acceptedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- indoor positioning, location fingerprinting, autoencoder, semi-supervised learning, extreme learning machine, crowdsourced data, feature extraction, location-based services
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