Multi-targets device-free localization based on sparse coding in smart city
With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the po...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-06-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719858229 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849766209431535616 |
|---|---|
| author | Min Zhao Danyang Qin Ruolin Guo Guangchao Xu |
| author_facet | Min Zhao Danyang Qin Ruolin Guo Guangchao Xu |
| author_sort | Min Zhao |
| collection | DOAJ |
| description | With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K -nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city. |
| format | Article |
| id | doaj-art-c47c8bf794854f3e9d78ef1add228579 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c47c8bf794854f3e9d78ef1add2285792025-08-20T03:04:39ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-06-011510.1177/1550147719858229Multi-targets device-free localization based on sparse coding in smart cityMin ZhaoDanyang QinRuolin GuoGuangchao XuWith the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K -nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city.https://doi.org/10.1177/1550147719858229 |
| spellingShingle | Min Zhao Danyang Qin Ruolin Guo Guangchao Xu Multi-targets device-free localization based on sparse coding in smart city International Journal of Distributed Sensor Networks |
| title | Multi-targets device-free localization based on sparse coding in smart city |
| title_full | Multi-targets device-free localization based on sparse coding in smart city |
| title_fullStr | Multi-targets device-free localization based on sparse coding in smart city |
| title_full_unstemmed | Multi-targets device-free localization based on sparse coding in smart city |
| title_short | Multi-targets device-free localization based on sparse coding in smart city |
| title_sort | multi targets device free localization based on sparse coding in smart city |
| url | https://doi.org/10.1177/1550147719858229 |
| work_keys_str_mv | AT minzhao multitargetsdevicefreelocalizationbasedonsparsecodinginsmartcity AT danyangqin multitargetsdevicefreelocalizationbasedonsparsecodinginsmartcity AT ruolinguo multitargetsdevicefreelocalizationbasedonsparsecodinginsmartcity AT guangchaoxu multitargetsdevicefreelocalizationbasedonsparsecodinginsmartcity |