Vol.1, No.3, 2019, pp.107-116, doi:10.32604/jbd.2019.07294
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: Proton198601@hotmail.com.
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
Robust frequency estimation, linear prediction, impulsive noise, weighted l1-norm minimization.
Cite This Article
Chen, Y., Duan, L., Sun, W., Xu, J. (2019). l1-norm Based GWLP for Robust Frequency Estimation. Journal on Big Data, 1(3), 107–116.