Name
Abstract | ||
|---|---|---|
AbstractPassive indoor localisation acts as a key enabler for various emerging applications such as secured region monitoring, smart homes, intelligent nursing, etc. Despite of years of research, their accuracy of localisation still remains unsatisfactory for practical uses. The main hurdle lies in the coarse measurement of wireless channels, e.g., received signal strength indicator RSSI, employed in most existing schemes. In this work, we explore the potential of using channel state information CSI for fine-grained passive indoor localisation on a single communication link. To achieve high accuracy, we propose a solution based CSI fingerprint and devise two novel localisation estimator approaches suited to different conditions: weighted Bayesian WBayes and the maximum similarity metric MSM. Compared with RSSI, CSI has demonstrated itself with a high accuracy of location distinction. Experimental results show that our schemes can achieve a higher accuracy. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1504/IJAHUC.2016.078475 | Periodicals |
Keywords | Field | DocType |
passive indoor localisation, CSI, channel state information, physical layer, localisation estimator | Communication link,Telecommunications,Wireless,Computer science,Communication channel,Fingerprint,Real-time computing,Physical layer,Estimator,Bayesian probability,Channel state information,Distributed computing | Journal |
Volume | Issue | ISSN |
23 | 1/2 | 1743-8225 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liangyi Gong | 1 | 38 | 14.57 |
| Yang Wu | 2 | 69 | 22.62 |
| Dapeng Man | 3 | 29 | 10.54 |
| Jiguang Lv | 4 | 11 | 4.32 |