ArticleMicromachines2019
Novel WiFi/MEMS Integrated Indoor Navigation System Based on Two-Stage EKF.
Article in Micromachines, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Research on Pedestrian Indoor Positioning Based on Two-Step Robust Adaptive Cubature Kalman Filter with Smartphone MEMS Sensors.Micromachines · 2023Article
- A Constrained Kalman Filter for Wi-Fi-Based Indoor Localization with Flexible Space Organization.Sensors (Basel, Switzerland) · 2022Article
- Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning.Sensors (Basel, Switzerland) · 2021Article
- Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter.Micromachines · 2021Article
- A New Dataset of People Flow in an Industrial Site with UWB and Motion Capture Systems.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
Indoor navigation has been developing rapidly over the last few years. However, it still faces a number of challenges and practical issues. This paper proposes a novel WiFi/MEMS integration structure for indoor navigation. The two-stage structure uses the extended Kalman filter (EKF) to fuse the information from WiFi/MEMS sensors and contains attitude-determination EKF and position-tracking EKF. In the WiFi part, a partition solution called "moving partition" is originally proposed in this paper. This solution significantly reduces the computation time and enhances the performance of the traditional Weighted K-Nearest Neighbors (WKNN) method. Furthermore, the direction measurement is generated utilizing WiFi positioning results, and a "turn detection" is implemented to guarantee the effectiveness. The navigation performance of the presented integration structure has been verified through indoor experiments. The test results indicate that the proposed WiFi/MEMS solution works well. The root mean square (RMS) position error of WiFi/MEMS is 0.7926 m, which is an improvement of 20.59% and 36.60% when compared to MEMS and WiFi alone. Besides, the proposed algorithm still performs well with very few access points (AP) available and its stability has been proven.
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Registered trials
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