Online adaptive blind deconvolution based on third-order moments

Abstract

Traditional methods for online adaptive blind deconvolution using higher order statistics are often based on even-order moments, due to the fact that the systems considered commonly feature symmetric source signals (i.e., signals having a symmetric probability density function). However, asymmetric source signals facilitate blind deconvolution based on odd-order moments. In this letter, we show that third-order moments give the benefits of faster convergence of algorithms and increased robustness to additive Gaussian noise. The convergence rates for two algorithms based on third- and fourth-order moments, respectively, are compared for a simulated ultra-wideband communication channel.