ArticleSensors (Basel, Switzerland)2023
Neural-Network-Based Localization Method for Wi-Fi Fingerprint Indoor Localization.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- A Wi-Fi/PDR Fusion Localization Method Based on Genetic Algorithm Global Optimization.Sensors (Basel, Switzerland) · 2025Article
- Comparative study of indoor positioning datasets focusing on localization accuracy success rate and floor classification.Scientific reports · 2025Article
- A Dual-Modality CNN Approach for RSS-Based Indoor Positioning Using Spatial and Frequency Fingerprints.Sensors (Basel, Switzerland) · 2025Article
- Hyperbolic Spatial Covariance Modeling with Adaptive Signal Filtering for Robust Wi-Fi Indoor Positioning.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Despite the high demand for Internet location service applications, Wi-Fi indoor localization often suffers from time- and labor-intensive data collection processes. This study proposes a novel indoor localization model that utilizes fingerprinting technology based on a convolutional neural network to address this issue. The aim is to enhance Wi-Fi indoor localization by streamlining the data collection process. The proposed indoor localization model leverages a 3D ray-tracing technique to simulate the wireless received signal strength intensity (RSSI) across the field. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of Wi-Fi indoor localization. In addition, an RSSI heatmap fingerprint dataset generated from the ray-tracing simulation is trained on the proposed indoor localization model. To optimize and evaluate the model's performance in real-world scenarios, experiments were conducted using simulated datasets obtained from the publicly available databases of UJIIndoorLoc and Wireless InSite. The results show that the new approach solves the problem of resource limitation while achieving a verification accuracy of up to 99.09%.
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Registered trials
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