ArticleSensors (Basel, Switzerland)2022
A Low-Cost Foot-Placed UWB and IMU Fusion-Based Indoor Pedestrian Tracking System for IoT Applications.
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.
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Who cites it
8 citing papers in PubMed.
- A Switched Approach for Smartphone-Based Pedestrian Navigation.Sensors (Basel, Switzerland) · 2024Article
- Research on Positioning and Navigation System of Greenhouse Mobile Robot Based on Multi-Sensor Fusion.Sensors (Basel, Switzerland) · 2024Article
- Proximity Sensor for Measuring Social Interaction in a School Environment.Sensors (Basel, Switzerland) · 2024Article
- Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments Using Bluetooth Technology.Sensors (Basel, Switzerland) · 2023Article
- Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review.Sensors (Basel, Switzerland) · 2023Review
- A Secure ZUPT-Aided Indoor Navigation System Using Blockchain in GNSS-Denied Environments.Sensors (Basel, Switzerland) · 2023Article
- Pedestrian Localization with Stride-Wise Error Estimation and Compensation by Fusion of UWB and IMU Data.Sensors (Basel, Switzerland) · 2023Article
- Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning.Cyborg and bionic systems (Washington, D.C.) · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
Among existing wireless and wearable indoor pedestrian tracking solutions, the ultra-wideband (UWB) and inertial measurement unit (IMU) sensors are the popular options due to their accurate and globally referenced positioning, and low-cost and compact size, respectively. However, the UWB position accuracy is compromised by the indoor non-line of sight (NLOS) and the IMU estimation suffers from orientation drift as well as requiring position initialization. To overcome these limitations, this paper proposes a low-cost foot-placed UWB and IMU fusion-based indoor pedestrian tracking system. Our data fusion model is an improved loosely coupled Kalman filter with the inclusion of valid UWB observation detection. In this manner, the proposed system not only adjusts the consumer-grade IMU's accumulated drift but also filters out any NLOS instances in the UWB observation. We validated the performance of the proposed system with two experimental scenarios in a complex indoor environment. The root mean square (RMS) positioning accuracy of our data fusion model is enhanced by 60%, 53%, and 27% compared to that of the IMU-based pedestrian dead reckoning, raw UWB position, and conventional fusion model, respectively, in the single-lap NLOS scenario, and by 70%, 34%, and 12%, respectively, in the multi-lap LOS+NLOS scenario.
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