Abstract
Separation of complex valued signals is a frequently arising problem in signal processing. In this article it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the independent component analysis (ICA) model. ICA is a statistical method for transforming an observed multidimensional random vector into components that are mutually as independent as possible. In this article, a fast fixed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and its computational efficiency is shown by simulations. We also present a theorem on the local consistency of the estimator given by the algorithm.
| Original language | English |
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| Title of host publication | Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium |
| Publisher | IEEE |
| Pages | 357-362 |
| ISBN (Electronic) | 0-7695-0619-4 |
| Publication status | Published - 2000 |
| MoE publication type | A4 Conference publication |
| Event | International Joint Conference on Neural Networks - Como, Italy Duration: 24 Jul 2000 → 27 Jul 2000 |
Conference
| Conference | International Joint Conference on Neural Networks |
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| Abbreviated title | IJCNN |
| Country/Territory | Italy |
| City | Como |
| Period | 24/07/2000 → 27/07/2000 |
Keywords
- complex valued signals
- deflationary separation
- independent component analysis