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...
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| Published in: | International journal of distributed sensor networks Vol. 15; no. 6; p. 155014771985822 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London, England
SAGE Publications
01.06.2019
Wiley |
| Subjects: | |
| ISSN: | 1550-1329, 1550-1477, 1550-1477 |
| Online Access: | Get full text |
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| Summary: | 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. |
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| ISSN: | 1550-1329 1550-1477 1550-1477 |
| DOI: | 10.1177/1550147719858229 |