A novel blind deconvolution method via maximum entropy

We propose a new closed form (approximated) expression for the conditional expectation that is based on Maximum Entropy. This expression does not rely on the knowledge of the convolutional noise power nor imposes any restrictions on the probability distribution of the unobserved input sequence and is suitable for the general case of real and complex source signals. In addition, we propose a set of algebraic linear equations for the Lagrange multipliers related to the blind deconvolution problem that can be easily computed in a non iterative approach. Our new derivation leads to a new blind deconvolution algorithm with improved equalization performance compared with Godard's equalizer.