Indoor Mobile Localization in Mixed Environment with RSS Measurements
Mobile localization is a significant issue for wireless sensor networks (WSNs). However, it is a problem for the indoor localization using received signal strength (RSS) measurements that the signal is contaminated by the anisotropy fading and interference due to walls and furniture. Standard scheme...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/106475 |
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| _version_ | 1849699902753341440 |
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| author | Zhengguo Cai Lin Shang Dan Gao Kang Zhao Yingguan Wang |
| author_facet | Zhengguo Cai Lin Shang Dan Gao Kang Zhao Yingguan Wang |
| author_sort | Zhengguo Cai |
| collection | DOAJ |
| description | Mobile localization is a significant issue for wireless sensor networks (WSNs). However, it is a problem for the indoor localization using received signal strength (RSS) measurements that the signal is contaminated by the anisotropy fading and interference due to walls and furniture. Standard schemes such as Kalman filter are inadequate as the random transition of line-of-sight (LOS)/non-line-of-sight (NLOS) conditions occurs frequently. This paper proposes an indoor mobile localization scheme with RSS measurements in a mixed LOS and NLOS environment. First, a new efficient composite measurement model is induced and validated, which approximates the complex effects of LOS and NLOS channels. Second, a greedy anchor sensor selection strategy is adopted to break through the constraints of hardware consistency and the multipath interference. Third, for the Markov transition between LOS and NLOS conditions, an effective unscented Kalman filter (UKF) based interactive multiple model (IMM) is proposed to estimate not only the posterior model probabilities but also a weighted-sum position estimation with the aid of likelihood function. To evaluate the proposed algorithm, a complete hardware and software platform for mobile localization has been constructed. The numerical study, relying on the actual experiments, illustrates that the proposed UKF based IMM achieves a substantial gain in precision and robustness, compared with other works. |
| format | Article |
| id | doaj-art-353506a224694fbf84ffceb09ab07c8e |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-353506a224694fbf84ffceb09ab07c8e2025-08-20T03:18:26ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-05-011110.1155/2015/106475106475Indoor Mobile Localization in Mixed Environment with RSS MeasurementsZhengguo CaiLin ShangDan GaoKang ZhaoYingguan WangMobile localization is a significant issue for wireless sensor networks (WSNs). However, it is a problem for the indoor localization using received signal strength (RSS) measurements that the signal is contaminated by the anisotropy fading and interference due to walls and furniture. Standard schemes such as Kalman filter are inadequate as the random transition of line-of-sight (LOS)/non-line-of-sight (NLOS) conditions occurs frequently. This paper proposes an indoor mobile localization scheme with RSS measurements in a mixed LOS and NLOS environment. First, a new efficient composite measurement model is induced and validated, which approximates the complex effects of LOS and NLOS channels. Second, a greedy anchor sensor selection strategy is adopted to break through the constraints of hardware consistency and the multipath interference. Third, for the Markov transition between LOS and NLOS conditions, an effective unscented Kalman filter (UKF) based interactive multiple model (IMM) is proposed to estimate not only the posterior model probabilities but also a weighted-sum position estimation with the aid of likelihood function. To evaluate the proposed algorithm, a complete hardware and software platform for mobile localization has been constructed. The numerical study, relying on the actual experiments, illustrates that the proposed UKF based IMM achieves a substantial gain in precision and robustness, compared with other works.https://doi.org/10.1155/2015/106475 |
| spellingShingle | Zhengguo Cai Lin Shang Dan Gao Kang Zhao Yingguan Wang Indoor Mobile Localization in Mixed Environment with RSS Measurements International Journal of Distributed Sensor Networks |
| title | Indoor Mobile Localization in Mixed Environment with RSS Measurements |
| title_full | Indoor Mobile Localization in Mixed Environment with RSS Measurements |
| title_fullStr | Indoor Mobile Localization in Mixed Environment with RSS Measurements |
| title_full_unstemmed | Indoor Mobile Localization in Mixed Environment with RSS Measurements |
| title_short | Indoor Mobile Localization in Mixed Environment with RSS Measurements |
| title_sort | indoor mobile localization in mixed environment with rss measurements |
| url | https://doi.org/10.1155/2015/106475 |
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