Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine
Nowadays, the increasing demands of location-based services (LBS) have spurred the rapid development of indoor positioning systems (IPS). However, the performance of IPSs is affected by the fluctuation of the measured signal. In this study, a Gaussian filtering algorithm based on an extreme learning...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-02-01
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| Series: | Symmetry |
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| Online Access: | http://www.mdpi.com/2073-8994/9/3/30 |
| _version_ | 1811307136987168768 |
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| author | Changzhi Wang Zhicai Shi Fei Wu |
| author_facet | Changzhi Wang Zhicai Shi Fei Wu |
| author_sort | Changzhi Wang |
| collection | DOAJ |
| description | Nowadays, the increasing demands of location-based services (LBS) have spurred the rapid development of indoor positioning systems (IPS). However, the performance of IPSs is affected by the fluctuation of the measured signal. In this study, a Gaussian filtering algorithm based on an extreme learning machine (ELM) is proposed to address the problem of inaccurate indoor positioning when significant Received Signal Strength Indication (RSSI) fluctuations happen during the measurement process. The Gaussian filtering method is analyzed and compared, which can effectively filter out the fluctuant signals that were caused by the environment effects in an RFID-based positioning system. Meanwhile, the fast learning ability of the proposed ELM algorithm can reduce the time consumption for the offline and online service, and establishes the network positioning regression model between the signal strengths of the tags and their corresponding positions. The proposed positioning system is tested in a real experimental environment. In addition, system test results demonstrate that the positioning algorithms can not only provide higher positioning accuracy, but also achieve a faster computational efficiency compared with other previous algorithms. |
| first_indexed | 2024-04-13T08:59:16Z |
| format | Article |
| id | doaj.art-c1c3ffa72c6242799f8c62d47ee8bc19 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-13T08:59:16Z |
| publishDate | 2017-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-c1c3ffa72c6242799f8c62d47ee8bc192022-12-22T02:53:12ZengMDPI AGSymmetry2073-89942017-02-01933010.3390/sym9030030sym9030030Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning MachineChangzhi Wang0Zhicai Shi1Fei Wu2School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaNowadays, the increasing demands of location-based services (LBS) have spurred the rapid development of indoor positioning systems (IPS). However, the performance of IPSs is affected by the fluctuation of the measured signal. In this study, a Gaussian filtering algorithm based on an extreme learning machine (ELM) is proposed to address the problem of inaccurate indoor positioning when significant Received Signal Strength Indication (RSSI) fluctuations happen during the measurement process. The Gaussian filtering method is analyzed and compared, which can effectively filter out the fluctuant signals that were caused by the environment effects in an RFID-based positioning system. Meanwhile, the fast learning ability of the proposed ELM algorithm can reduce the time consumption for the offline and online service, and establishes the network positioning regression model between the signal strengths of the tags and their corresponding positions. The proposed positioning system is tested in a real experimental environment. In addition, system test results demonstrate that the positioning algorithms can not only provide higher positioning accuracy, but also achieve a faster computational efficiency compared with other previous algorithms.http://www.mdpi.com/2073-8994/9/3/30indoor positioning systemRFIDextreme learning machineGaussian filtering |
| spellingShingle | Changzhi Wang Zhicai Shi Fei Wu Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine Symmetry indoor positioning system RFID extreme learning machine Gaussian filtering |
| title | Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine |
| title_full | Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine |
| title_fullStr | Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine |
| title_full_unstemmed | Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine |
| title_short | Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine |
| title_sort | intelligent rfid indoor localization system using a gaussian filtering based extreme learning machine |
| topic | indoor positioning system RFID extreme learning machine Gaussian filtering |
| url | http://www.mdpi.com/2073-8994/9/3/30 |
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