Adaptive Complex-Valued Independent Component Analysis Based on Second-Order Statistics

Abstract EN

This paper proposes a two-stage fast convergence adaptive complex-valued independent component analysis based on second-order statistics of complex-valued source signals.

The first stage constructs a cost function by extending the real-valued whiten cost function to a complex-valued domain and optimizes the cost function using a complex-valued gradient.

The second stage uses the restriction that the pseudocovariance matrix of the separated signal is a diagonal matrix to construct the cost function and the geodesic method is used to optimize the cost function.

Compared with other adaptive complex-valued independent component analysis, the proposed method shows a faster convergence rate and smaller error.

Computer simulations were performed on synthesized signals and communications signals.

The simulation results demonstrate the validity of the proposed algorithm.