Name
Abstract | ||
|---|---|---|
In order to solve the indoor pedestrian positioning and tracking problems under the condition of sparse anchor nodes, this paper presents a new tracking scheme which predicts the staff position under the condition of indoor location fingerprints based on particle filter. In the proposed algorithm, the indoor topology is adopted to constrain and correct the results. Simulation results show that the proposed algorithm can significantly improve the accuracy of indoor pedestrian positioning and tracking more than the Kalman filter and k-nearest neighbor (KNN) algorithms. The simulation results also show that under the condition of sparse nodes deployment good tracking results can still be achieved through the adoption of indoor topology and the average positioning error is about 1.9 m. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1155/2013/247306 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Field | DocType | Volume |
Computer vision,Pedestrian,Software deployment,Computer science,Particle filter,Algorithm,Kalman filter,Artificial intelligence | Journal | 2013 |
Issue | ISSN | Citations |
null | 1550-1477 | 1 |
PageRank | References | Authors |
0.35 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Zhou | 1 | 61 | 12.72 |
| Zehui Cai | 2 | 1 | 0.35 |
| Pengpeng Chen | 3 | 123 | 17.75 |