Abstract
Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data andWi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.
| Original language | English |
|---|---|
| Article number | 4379 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 9 |
| Issue number | 20 |
| DOIs | |
| State | Published - 1 Oct 2019 |
Keywords
- Indoor localization
- Kalman filter
- Location fingerprinting
- Pedestrian dead reckoning (PDR)
- Received signal strength indication (RSSI)
- Sensor fusion frameworks
- Smartphone sensors
- Trilateration
- Wi-Fi indoor positioning