Gradient and Fixed-Point Complex ICA Algorithms Based on Kurtosis Maximization

We present two algorithms for independent component analysis of complex-valued signals based on the maximization of absolute value of kurtosis and establish their properties. Both the algorithm derivation and the analysis are carried out directly in the complex domain, without the use of complex-to-real mappings as the cost function satisfies Brandwood's analyticity condition. Simulation results are presented that show the advantages of the new algorithms, especially when the number of sources in the mixture increases.