Signal Separation Based on Extended Least Squares
Authors
Liuyang Gao, Song Chen, Yinghua Tian, Jiaying Yue
Corresponding Author
Liuyang Gao
Available Online December 2018.
- DOI
- 10.2991/jimec-18.2018.51How to use a DOI?
- Keywords
- signal separation; least squares; Kullback-Leibler divergence.
- Abstract
In this paper, a new signal separation method is proposed in this paper to solve the problem of poor separation effect of mixed signals in strong noise environment. Based on least squares (LS), the improved optimization model is extended by Kullback-Leibler dispersion to remove the random noise. Theoretical analysis and simulation experiments show that the algorithm proposed in this paper is superior to the existing algorithm in estimating the source signal, especially when the mixed signal is completely immersed in noise, the recovery effect of the source signal is more obvious than the existing algorithm.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Liuyang Gao AU - Song Chen AU - Yinghua Tian AU - Jiaying Yue PY - 2018/12 DA - 2018/12 TI - Signal Separation Based on Extended Least Squares BT - Proceedings of the 2018 3rd Joint International Information Technology,Mechanical and Electronic Engineering Conference (JIMEC 2018) PB - Atlantis Press SP - 238 EP - 241 SN - 2589-4943 UR - https://doi.org/10.2991/jimec-18.2018.51 DO - 10.2991/jimec-18.2018.51 ID - Gao2018/12 ER -