Enhanced NMF Separation of Mixed Signals in Strong Noise Environment

IEEE Access

Nae Zheng¹  Peng Dong¹  Liuyang Gao¹  Yinghua Tian¹ 

[1] National Digital Switching System Engineering and Technological Research and Development Center (NDSC), Information Engineering University (IEU), Zhengzhou, China;

关键词: Signal separation;    non-negative matrix factorization;    Kullback-Leibler divergence;    least squares;   

DOI  :  10.1109/ACCESS.2019.2921992

来源: DOAJ

【 摘 要 】

Separation of mixed signals from a noisy environment without prior conditions is one of the difficulties in blind signal separation. To solve the problem of poor separation effect of mixed signals in a strong noise environment, we propose an enhanced non-negative matrix factorization method in this paper. By extending the Kullback-Leibler divergence form, this method adopts a new target signal and noise estimation algorithm to overcome the shortcomings of existing methods in noise estimation. Furthermore, combining with the least squares algorithm, the computational complexity is effectively reduced, and the computational efficiency of the algorithm is improved while the source signals are well estimated. The theoretical analysis and simulation results show that the proposed algorithm is better than the existing algorithms in terms of the source signal separation from mixed signals with noise, especially when the signal and noise energy are equivalent and the mixed signals are completely obliterated in the noise, the proposed algorithm has more obvious advantages than the existing algorithms, while the operation efficiency has been improved.

【 授权许可】

Unknown