A Canonical Genetic Algorithm for Blind Inversion of Linear Channels

It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic Algorithm (GA) to blindly invert linear channels. The use of GA may be more appropriate in the case of small number of samples, where other gradient-like methods fails because of poor estimation of statistics. The experimental results show that the presented method is able to invert unknown filters with good numerical results, even if only 100 samples or less are available.