Name
Title | ||
|---|---|---|
L1-norm constraint kernel adaptive filtering framework for precise and robust indoor localization under the internet of things |
Abstract | ||
|---|---|---|
•A generalized Student’s t-based kernel function, and a new kernel adaptive filtering (KAF) algorithm named generalized Student’s t kernel adaptive filter (GStKAF) are designed under the KMPE error criterion for indoor positioning under the IoT framework.•The L1-norm is proposed as the penalty to embed into the GStKAF and the resulting sparse GStKAF (SGStKAF) can provide more compact size of the neural networks.•Three experimental results show that the proposed SGStKAF has good robustness against the mixed noise consisting of abrupt noise and Gaussian noise while maintaining the high accuracy performance. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2021.12.026 | Information Sciences |
Keywords | DocType | Volume |
Kernel adaptive filter,Indoor localization,Internet of Things,Abrupt noise,Positioning accuracy | Journal | 587 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Zhao | 1 | 0 | 0.34 |
| Xifeng Li | 2 | 1 | 1.71 |
| Dongjie Bi | 3 | 0 | 0.34 |
| Haojie Wang | 4 | 0 | 0.34 |
| Yongle Xie | 5 | 0 | 0.34 |
| Adi Alhudhaif | 6 | 0 | 0.34 |
| Fayadh Alenezi | 7 | 0 | 0.34 |