Vol.1, No.3, 2019, pp.107-116, doi:10.32604/jbd.2019.07294
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: .
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.
Robust frequency estimation, linear prediction, impulsive noise, weighted l1-norm minimization.
Cite This Article
. , "l1-norm based gwlp for robust frequency estimation," Journal on Big Data, vol. 1, no.3, pp. 107–116, 2019.
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