Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter
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 applicat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-09-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/758391 |
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| _version_ | 1849702163875364864 |
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| author | Yaozong Liu Fawang Han Xuesong Xu Hong Zhang |
| author_facet | Yaozong Liu Fawang Han Xuesong Xu Hong Zhang |
| author_sort | Yaozong Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1aa10ff405fa4fffaea54d8739aed536 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-1aa10ff405fa4fffaea54d8739aed5362025-08-20T03:17:44ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-09-011110.1155/2015/758391758391Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle FilterYaozong Liu0Fawang Han1Xuesong Xu2Hong Zhang3 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China Department of Information Technology, Nanjing Forest Police College, Nanjing 210023, China College of Information and Technology, Nanjing University of Chinese Medicine, Nanjing 210046, China School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaRadio 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.https://doi.org/10.1155/2015/758391 |
| spellingShingle | Yaozong Liu Fawang Han Xuesong Xu Hong Zhang Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter International Journal of Distributed Sensor Networks |
| title | Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter |
| title_full | Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter |
| title_fullStr | Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter |
| title_full_unstemmed | Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter |
| title_short | Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter |
| title_sort | measuring the uncertainty of rfid location streams based on optimal estimation particle filter |
| url | https://doi.org/10.1155/2015/758391 |
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