Indoor Pedestrian Positioning Tracking Algorithm with Sparse Anchor Nodes
Joint Authors
Pengpeng, Chen
Zehui, Cai
Yong, Zhou
Source
International Journal of Distributed Sensor Networks
Issue
Vol. 2013, Issue - (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-08-19
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Telecommunications Engineering
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Yong, Zhou& Zehui, Cai& Pengpeng, Chen. 2013. Indoor Pedestrian Positioning Tracking Algorithm with Sparse Anchor Nodes. International Journal of Distributed Sensor Networks،Vol. 2013, no. -, pp.1-7.
https://search.emarefa.net/detail/BIM-457082
Modern Language Association (MLA)
Yong, Zhou…[et al.]. Indoor Pedestrian Positioning Tracking Algorithm with Sparse Anchor Nodes. International Journal of Distributed Sensor Networks Vol. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-457082
American Medical Association (AMA)
Yong, Zhou& Zehui, Cai& Pengpeng, Chen. Indoor Pedestrian Positioning Tracking Algorithm with Sparse Anchor Nodes. International Journal of Distributed Sensor Networks. 2013. Vol. 2013, no. -, pp.1-7.
https://search.emarefa.net/detail/BIM-457082
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-457082