Enhanced NMF Separation of Mixed Signals in Strong Noise Environment
| Autor: | Nae Zheng, Dong Peng, Yinghua Tian, Liuyang Gao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Kullback–Leibler divergence
General Computer Science Kullback-Leibler divergence Computer science Noise (signal processing) General Engineering Signal Least squares Blind signal separation Matrix decomposition Non-negative matrix factorization non-negative matrix factorization least squares General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering Divergence (statistics) Algorithm lcsh:TK1-9971 Signal separation |
| Zdroj: | IEEE Access, Vol 7, Pp 84649-84657 (2019) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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