Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter

Yaozong Liu, Fawang Han, Xuesong Xu and Hong Zhang

International Journal of Distributed Sensor Networks, 2015, vol. 11, issue 9, 758391

Abstract: Radio Frequency Identification (RFID) technology is widely used in object tracking and tracing, especially in real-time locating system (RTLS). Due to the external and internal influence of RFID systems, a lot of redundant and uncertain location streams could be generated in RFID-based RTLS applications, which could seriously affect the accuracy of estimation for RFID mobile object position and cause great difficulties in RFID-based RTLS applications. In this paper, we systematically analyzed the characteristics of RFID location streams. We then derived the optimal weight for the attributes of RFID location streams by applying information entropy based methods and used probability matrix to optimize weight attributes in location streams. We also proposed an optimal estimation particle filter algorithm (OEPF) based on traditional particle filter, which greatly reduced the data redundancy and realized online measurement for the uncertainty of RFID location streams. Finally, the experimental results showed that, compared to the existing algorithms, our algorithm effectively improved the accuracy of location estimation in ensuring the premise of real-time.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2015/758391 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:11:y:2015:i:9:p:758391

DOI: 10.1155/2015/758391

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().