Soft Information for Localization-of-Things
Name
Soft-Information-V106.pdf
Description
Accepted version
Size
3.66 MB
Format
Adobe PDF
Checksum (MD5)
9567d0732ca502a734b8fe2693272282
Author(s)
Conti, Andrea
Mazuelas, Santiago
Bartoletti, Stefania
Lindsey, William C
Win, Moe Z
Date Issued
2019
Journal
Proceedings of the IEEE
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
© 2019 IEEE. Location awareness is vital for emerging Internet-of-Things applications and opens a new era for Localization-of-Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/JPROC.2019.2905854
