Super-exponential methods for blind deconvolution

The authors present a class of iterative methods for solving the problem of blind deconvolution of an unknown possibly nonminimum phase linear system driven by an unobserved input process. The methods converge monotonically at a very fast super-exponential rate to the desired solution in which the inverse of the unknown system is identified, and the input process is recovered up to a delay and possibly a constant phase shift. The proposed methods are universal in the sense that they do not impose any restrictions on the probability distribution of the input process, provided that it is non-Gaussian