Name
Abstract | ||
|---|---|---|
This paper presents a new frequency domain algorithm for blind deconvolution. Our frequency domain algorithm can overcome the difficulty appearing at the frequency bin and avoids the ambiguity of frequency coupling. Like other frequency domain algorithms, our algorithm needs not the identically distributed (iid) assumption of source signals. Our result shows that as long as observed signals are more one than source signals, the source signals can be separated from the observed mixtures. Therefore, the frequency domain algorithm proposed in this paper can be applied in wider field than previous algorithms. Finally, experiments demonstrate the performance of our algorithm. © 2008 IEEE. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CIS.2008.136 | Proceedings - 2008 International Conference on Computational Intelligence and Security, CIS 2008 |
Keywords | Field | DocType |
blind signal separation,mimo,frequency domain,prediction algorithms,frequency domain analysis,blind source separation,matrices,blind deconvolution,deconvolution | Frequency domain,Mathematical optimization,Coupling,Bin,Blind deconvolution,Computer science,MIMO,Algorithm,Deconvolution,Independent and identically distributed random variables,Blind signal separation | Conference |
Volume | Issue | ISSN |
1 | null | null |
ISBN | Citations | PageRank |
978-0-7695-3508-1 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Zhao | 1 | 21 | 5.73 |
| Zhaoshui He | 2 | 354 | 24.10 |
| Shengli Xie | 3 | 2530 | 161.51 |