Name
Abstract | ||
|---|---|---|
With the rapid development of WLAN infrastructure, fingerprint-based positioning using signal strength has become a promising localization solution in indoor space. Commonly fingerprint-based positioning methods face two challenges in large indoor space, one is floor recognition in large building with multifloor, and the other is signal strength variance due to heterogeneous devices and environmental factors. In this paper, we propose a novel two-stage positioning approach to address these challenges of fingerprint-based positioning methods in large indoor space. Firstly, we design a floor-level recognition feature based on WiFi access points and the RSS values to recognize floor. For solving the signal strength variance problem, we propose a new metric to capture the similarity of location fingerprints probability distribution using KL Divergence. To demonstrate the utility of our approach, we have performed comprehensive experiments in a large indoor building. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1155/2016/1289013 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Field | DocType | Volume |
Hybrid positioning system,Computer science,Simulation,Fingerprint,Real-time computing,Probability distribution,Signal strength,Feature based,RSS,Kullback–Leibler divergence,Distributed computing | Journal | 12 |
Issue | ISSN | Citations |
6 | 1550-1477 | 1 |
PageRank | References | Authors |
0.35 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zengwei Zheng | 1 | 27 | 7.99 |
| Yuanyi Chen | 2 | 1 | 0.35 |
| Sinong Chen | 3 | 1 | 0.69 |
| Lin Sun | 4 | 145 | 9.46 |
| dan chen | 5 | 25 | 4.24 |