Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments
Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across differen...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/3/1015 |
| _version_ | 1797416303462449152 |
|---|---|
| author | Yuqing Yin Xu Yang Peihao Li Kaiwen Zhang Pengpeng Chen Qiang Niu |
| author_facet | Yuqing Yin Xu Yang Peihao Li Kaiwen Zhang Pengpeng Chen Qiang Niu |
| author_sort | Yuqing Yin |
| collection | DOAJ |
| description | Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy. |
| first_indexed | 2024-03-09T06:01:27Z |
| format | Article |
| id | doaj.art-8c7cabe30ce94605b6fd762a376c50b6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T06:01:27Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8c7cabe30ce94605b6fd762a376c50b62023-12-03T12:08:22ZengMDPI AGSensors1424-82202021-02-01213101510.3390/s21031015Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor EnvironmentsYuqing Yin0Xu Yang1Peihao Li2Kaiwen Zhang3Pengpeng Chen4Qiang Niu5China Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou 221116, ChinaChina Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaChina Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou 221116, ChinaChina Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou 221116, ChinaIndoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.https://www.mdpi.com/1424-8220/21/3/1015indoor localizationchannel state informationtransfer learningmulti-domain representations |
| spellingShingle | Yuqing Yin Xu Yang Peihao Li Kaiwen Zhang Pengpeng Chen Qiang Niu Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments Sensors indoor localization channel state information transfer learning multi-domain representations |
| title | Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments |
| title_full | Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments |
| title_fullStr | Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments |
| title_full_unstemmed | Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments |
| title_short | Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments |
| title_sort | localization with transfer learning based on fine grained subcarrier information for dynamic indoor environments |
| topic | indoor localization channel state information transfer learning multi-domain representations |
| url | https://www.mdpi.com/1424-8220/21/3/1015 |
| work_keys_str_mv | AT yuqingyin localizationwithtransferlearningbasedonfinegrainedsubcarrierinformationfordynamicindoorenvironments AT xuyang localizationwithtransferlearningbasedonfinegrainedsubcarrierinformationfordynamicindoorenvironments AT peihaoli localizationwithtransferlearningbasedonfinegrainedsubcarrierinformationfordynamicindoorenvironments AT kaiwenzhang localizationwithtransferlearningbasedonfinegrainedsubcarrierinformationfordynamicindoorenvironments AT pengpengchen localizationwithtransferlearningbasedonfinegrainedsubcarrierinformationfordynamicindoorenvironments AT qiangniu localizationwithtransferlearningbasedonfinegrainedsubcarrierinformationfordynamicindoorenvironments |