Open Access
ARTICLE
l1-norm Based GWLP for Robust Frequency Estimation
Yuan Chen1, Liangtao Duan1, Weize Sun2, *, Jingxin Xu3
1 University of Science & Technology Beijing, Beijing, 100083, China.
2 ShenZhen University, Shenzhen, 518060, China.
3 Department of Housing and Public Works, Queensland Government, Brisbane, Australia.
* Corresponding Author: Weize Sun. Email: .
Journal on Big Data 2019, 1(3), 107-116. https://doi.org/10.32604/jbd.2019.07294
Abstract
In this work, we address the frequency estimation problem of a complex singletone embedded in the heavy-tailed noise. With the use of the linear prediction (LP) property
and
l1-norm minimization, a robust frequency estimator is developed. Since the proposed
method employs the weighted
l1-norm on the LP errors, it can be regarded as an extension
of the
lp-generalized weighted linear predictor. Computer simulations are conducted in the
environment of α-stable noise, indicating the superiority of the proposed algorithm, in
terms of its robust to outliers and nearly optimal estimation performance.
Keywords
Cite This Article
Y. Chen, L. Duan, W. Sun and J. Xu, "
l1-norm based gwlp for robust frequency estimation,"
Journal on Big Data, vol. 1, no.3, pp. 107–116, 2019. https://doi.org/10.32604/jbd.2019.07294