ArticleSensors (Basel, Switzerland)2019
Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Blockchain-Enabled Uncertainty-Aware Passive Wi-Fi Localization for Secure Critical Infrastructure Sensor Networks.Sensors (Basel, Switzerland) · 2026Article
- An Indoor Location-Based Augmented Reality Framework.Sensors (Basel, Switzerland) · 2023Article
- DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization.Sensors (Basel, Switzerland) · 2021Article
- Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation.Sensors (Basel, Switzerland) · 2020Article
- Deep CNN for Indoor Localization in IoT-Sensor Systems.Sensors (Basel, Switzerland) · 2019Article
- Novel WiFi/MEMS Integrated Indoor Navigation System Based on Two-Stage EKF.Micromachines · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although wireless fingerprinting has been well researched and widely used for indoor localization, its performance is difficult to quantify. Therefore, when wireless fingerprinting solutions are used as location updates in multi-sensor integration, it is challenging to set their weight accurately. To alleviate this issue, this paper focuses on predicting wireless fingerprinting location uncertainty by given received signal strength (RSS) measurements through the use of machine learning (ML). Two ML methods are used, including an artificial neural network (ANN)-based approach and a Gaussian distribution (GD)-based method. The predicted location uncertainty is evaluated and further used to set the measurement noises in the dead-reckoning/wireless fingerprinting integrated localization extended Kalman filter (EKF). Indoor walking test results indicated the possibility of predicting the wireless fingerprinting uncertainty through ANN the effectiveness of setting measurement noises adaptively in the integrated localization EKF.
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Registered trials
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