ArticleSensors (Basel, Switzerland)2022
A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- WiFi RSS and RTT Indoor Positioning with Graph Temporal Convolution Network.Sensors (Basel, Switzerland) · 2025Article
- From Fingerprinting to Advanced Machine Learning: A Systematic Review of Wi-Fi and BLE-Based Indoor Positioning Systems.Sensors (Basel, Switzerland) · 2025Review
- A Precise and Scalable Indoor Positioning System Using Cross-Modal Knowledge Distillation.Sensors (Basel, Switzerland) · 2024Article
- A Novel Optimized iBeacon Localization Algorithm Modeling.Sensors (Basel, Switzerland) · 2023Article
- Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints.Sensors (Basel, Switzerland) · 2023Article
- Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT Observables.Sensors (Basel, Switzerland) · 2022Article
- Transmission Power Influence on WSN-Based Indoor Localization Efficiency.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.