Complex-valued Adaptive Signal Processing Using Wirtinger Calculus and Its Application to Independent Component Analysis

Front Cover
ProQuest, 2008 - 129 pages
0 Reviews
Then, we study complex independent component analysis (ICA) using our framework. ICA has emerged as a powerful and attractive statistical tool for revealing hidden factors for many types of signals. Two of the most important guiding principles for performing ICA are maximum likelihood and maximization of non-Gaussianity. Following the principle of maximization of non-Gaussianity, we derive a class of effective complex ICA algorithms that provide reliable performance for a wide range of input source distributions. Stability analysis is provided to show its superior convergence rate. We also derive a class of complex ICA algorithms based on maximum likelihood estimation. We perform local stability analysis of maximum likelihood ICA algorithms and show that the complex ICA problem is more difficult to solve with non-circular sources. We also show that the stability conditions are easier to be satisfied when the mixtures are whitened and unitary constraints are imposed. Simulation results further demonstrate these observations with generalized Gaussian distributed sources.
  

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

PRELIMINARIES
8
OPTIMIZATIONINTHE COMPLEX DOMAINUSINGWIRTINGER
30
COMPLEX INDEPENDENT COMPONENT ANALYSIS
49
COMPLEX INDEPENDENT COMPONENT ANALYSIS
70
CONCLUSIONS AND FUTURE WORK
89
Copyright

Common terms and phrases

Bibliographic information