ArticleSensors (Basel, Switzerland)2017
Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems.Sensors (Basel, Switzerland) · 2022Article
- Analysis of Received Signal Strength Quantization in Fingerprinting Localization.Sensors (Basel, Switzerland) · 2020Article
- Crowdsensing Influences and Error Sources in Urban Outdoor Wi-Fi Fingerprinting Positioning.Sensors (Basel, Switzerland) · 2020Article
- A Meta-Review of Indoor Positioning Systems.Sensors (Basel, Switzerland) · 2019Review
- Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon.Sensors (Basel, Switzerland) · 2019Article
- WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest.Sensors (Basel, Switzerland) · 2018Article
- GSOS-ELM: An RFID-Based Indoor Localization System Using GSO Method and Semi-Supervised Online Sequential ELM.Sensors (Basel, Switzerland) · 2018Article
- Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone.Sensors (Basel, Switzerland) · 2018Article
- Secure Indoor Localization Based on Extracting Trusted Fingerprint.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal strength intensity from multiple wireless access points at a given point. The positioning accuracy, within a few meters, enables the use of Wi-Fi fingerprinting in many different applications. However, it has been detected that the positioning error might be very large in a few cases, which might prevent its use in applications with high accuracy positioning requirements. Hybrid methods are the new trend in indoor positioning since they benefit from multiple diverse technologies (Wi-Fi, Bluetooth, and Inertial Sensors, among many others) and, therefore, they can provide a more robust positioning accuracy. In order to have an optimal combination of technologies, it is crucial to identify when large errors occur and prevent the use of extremely bad positioning estimations in hybrid algorithms. This paper investigates why large positioning errors occur in Wi-Fi fingerprinting and how to detect them by using the received signal strength intensities.
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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.