ArticleSensors (Basel, Switzerland)2016
Continuous Indoor Positioning Fusing WiFi, Smartphone Sensors and Landmarks.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Axes Mapping and Sensor Fusion for Attitude-Unconstrained Pedestrian Dead Reckoning.Sensors (Basel, Switzerland) · 2026Article
- A Review of Indoor Localization Methods Leveraging Smartphone Sensors and Spatial Context.Sensors (Basel, Switzerland) · 2024Review
- A Performance Improvement for Indoor Positioning Systems Using Earth's Magnetic Field.Sensors (Basel, Switzerland) · 2023Article
- Article
- A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting.Sensors (Basel, Switzerland) · 2022Article
- Collaborative Smartphone-Based User Positioning in a Multiple-User Context Using Wireless Technologies.Sensors (Basel, Switzerland) · 2020Article
- An Approach to Robust INS/UWB Integrated Positioning for Autonomous Indoor Mobile Robots.Sensors (Basel, Switzerland) · 2019Article
- Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting.Sensors (Basel, Switzerland) · 2017Article
- Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization.Sensors (Basel, Switzerland) · 2017Article
- An Indoor Positioning Method for Smartphones Using Landmarks and PDR.Sensors (Basel, Switzerland) · 2016Article
- Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting.Sensors (Basel, Switzerland) · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To exploit the complementary strengths of WiFi positioning, pedestrian dead reckoning (PDR), and landmarks, we propose a novel fusion approach based on an extended Kalman filter (EKF). For WiFi positioning, unlike previous fusion approaches setting measurement noise parameters empirically, we deploy a kernel density estimation-based model to adaptively measure the related measurement noise statistics. Furthermore, a trusted area of WiFi positioning defined by fusion results of previous step and WiFi signal outlier detection are exploited to reduce computational cost and improve WiFi positioning accuracy. For PDR, we integrate a gyroscope, an accelerometer, and a magnetometer to determine the user heading based on another EKF model. To reduce accumulation error of PDR and enable continuous indoor positioning, not only the positioning results but also the heading estimations are recalibrated by indoor landmarks. Experimental results in a realistic indoor environment show that the proposed fusion approach achieves substantial positioning accuracy improvement than individual positioning approaches including PDR and WiFi positioning.
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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.