A new criterion for blind deconvolution of colored input signals

In this paper, a new criterion with memory nonlinearity is introduced for blind deconvolution problems when the input signals are colored. The basic idea is to make use of the autocorrelation of the input sequence as the only statistical knowledge about the data. An adaptive weight algorithm is presented and tested with simulation examples of signals of known autocorrelation function. It is shown that the optimum memory size is directly related to the significant values of the autocorrelation function, and that the new algorithm converges faster than the Godard algorithm.<>