Abstract
© 2016 IEEE. During the past years, the development of indoor localization systems has been a hot topic in research, because the global navigation satellite systems suffer from a significant performance degradation due to the fact that the line of sight to the satellites is not available. The proposed system employs the received signal strength indicator from multiple anchor nodes from an operating wireless sensor network (WSN). In addition, we place multiple receivers around the user's body and thanks to machine learning techniques; we are able to estimate the distance and angle between the user and any of the anchor nodes of the WSN. This allows us to estimate the heading of the user without the use of inertial sensors or magnetometers. Finally, the user's position estimation is refined using an extended Kalman filter that considers the constant velocity kinematic model. The system has been validated in multiple real scenarios obtaining a root mean squared error around the meter for the different tests performed.
| Original language | English |
|---|---|
| Article number | 7384674 |
| Pages (from-to) | 2545-2553 |
| Journal | IEEE Sensors Journal |
| Volume | 16 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 15 Apr 2016 |
Keywords
- Indoor localization
- Kalman filters
- Machine learning
- Navigation
- Wireless sensor networks