Name
Title | ||
|---|---|---|
Improving passive radio-frequency identification localisation precision with moving direction estimation-based feature improvement |
Abstract | ||
|---|---|---|
To achieve high localisation accuracy and precision is a fundamental requirement of passive radio-frequency identification (RFID) location systems. Current passive RFID location systems mostly deploy a dense RFID tag distribution to give initial localisation accuracy and use advanced localisation algorithms to extract the reliable features for enhancing localisation precision. High quality features are capable of significantly improving precision of passive RFID localisation systems, but it is hardly achieved in practice because of the environmental noise interference and the variety of tag behaviours. A localisation approach by using moving direction estimation to improve the quality of extracted features is proposed for enhancing localisation precision of passive RFID location systems. This approach relies on a general distribution of false-reading occurring probability function derived from experimental measurements. A feature adjustment scheme is introduced to correct these features regarding the estimation of moving direction of object. The experimental results demonstrate that the proposed localisation approach with feature improvement achieves higher precision for the state-of-the art deterministic localisation algorithm (Han et al. 2007) in passive RFID location systems. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1049/iet-wss.2013.0046 | IET Wireless Sensor Systems |
Keywords | Field | DocType |
false-reading occurring probability function,passive radio-frequency identification localisation precision,environmental noise interference,object localisation,initial localisation accuracy,rfid location systems,radiofrequency identification,feature extraction,object tracking,moving direction estimation-based feature improvement,advanced localisation algorithms,deterministic algorithms,dense rfid tag distribution,deterministic localisation algorithm,probability | Computer vision,Pattern recognition,Computer science,Feature extraction,General distribution,Video tracking,Artificial intelligence,Interference (wave propagation),Accuracy and precision,Radio-frequency identification,Probability density function,Environmental noise | Journal |
Volume | Issue | ISSN |
4 | 1 | 2043-6386 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
1 | ||