| Title |
Multi-targets device-free localization based on sparse coding in smart city |
| ID_Doc |
36621 |
| Authors |
Zhao, M; Qin, DY; Guo, RL; Xu, GC |
| Title |
Multi-targets device-free localization based on sparse coding in smart city |
| Year |
2019 |
| Published |
International Journal Of Distributed Sensor Networks, 15, 6 |
| DOI |
10.1177/1550147719858229 |
| Abstract |
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. |
| Author Keywords |
Device-free localization; multi-targets; sparse coding; subspace; iterative shrinkage threshold algorithm; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000472461000001 |
| WoS Category |
Computer Science, Information Systems; Telecommunications |
| Research Area |
Computer Science; Telecommunications |
| PDF |
https://journals.sagepub.com/doi/pdf/10.1177/1550147719858229
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