Name
Title | ||
|---|---|---|
Blind signal separation of complex-valued sources based on Gaussian mixture model for time-varying environment |
Abstract | ||
|---|---|---|
In this paper, blind signal separation algorithms based on Gaussian mixture model of complex-valued sources are introduced. Compared with the traditional algorithms for complex-valued signals, they have two advantages: first, since they are adaptive, the changes in the environment can be tracked; second, the probability density function matching mechanism is applied to the algorithms through a Gaussian mixture model, in which way, the information of the complex-valued signals can be taken fully use of. Simulation results show that the stability is well improved by using the Gaussian mixture model while obtaining the same or better tracking ability of changing environment. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/WCSP.2014.6992135 | WCSP |
Keywords | DocType | Citations |
complex-valued source,tracking ability,probability density function matching mechanism,blind signal separation algorithms,mixture models,time-varying environment,blind signal separation,blind source separation,Gaussian processes,complex-valued sources,Gaussian mixture model | Conference | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liu Yang | 1 | 1 | 1.75 |
| hang zhang | 2 | 31 | 16.05 |
| Xinhai Tong | 3 | 0 | 0.68 |