Name
Title | ||
|---|---|---|
UWB-based indoor high precision localization system with robust unscented Kalman filter |
Abstract | ||
|---|---|---|
In this paper, an indoor localization system is implemented, in which the distances are measured via by the Ultra-Wideband (UWB) and are utilized to determine positions of one blind node through trilateration localization algorithm. Additionally, to overcome the measurement errors caused by complex indoor environment, an Unscented Kalman Filter (UKF) algorithm is proposed to improve the accuracy of the localization results. Finally, the developed localization system is tested and the proposed algorithm is analyzed. It shows that the indoor localization system can achieve the positioning accuracy less than 10cm, providing a promising approach for high precision localization applications. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICCS.2016.7833646 | 2016 IEEE International Conference on Communication Systems (ICCS) |
Keywords | Field | DocType |
Indoor localization system,Ultra-Wideband,Unscented Kalman Filter algorithm | Computer vision,Mathematical optimization,Computer science,Control theory,Kalman filter,Ultra-wideband,Artificial intelligence,Localization system,Simultaneous localization and mapping,Observational error,Trilateration | Conference |
ISBN | Citations | PageRank |
978-1-5090-3424-6 | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haohao Yin | 1 | 0 | 0.34 |
| Weiwei Xia | 2 | 28 | 14.30 |
| Yueyue Zhang | 3 | 20 | 7.77 |
| Lianfeng Shen | 4 | 517 | 65.25 |