BLIND DECONVOLUTION: TECHNIQUES AND APPLICATIONS
Fu‐Chun Zheng · ERA · 1992
This thesis is primarily concerned with developing new parameter based blind deconvolution algorithms and studying their applications.The blind deconvolution problem for minimum phase (MP) systems is well understood, and in this case the well known predictive schemes can be employed.When systems are nonminimum phase (NMP), however, the predictive deconvolution methods can only generate the spectrally equivalent MP solution.This is because the predictive schemes are based only on autocorrelations, which are completely blind to the phase properties of systems.In order to solve the blind deconvolution problem of NMP systems, higher order cumulant (HOC) analysis is adopted in this thesis.The reason for this is that HOC carry the phase information of systems only to a linear phase shift.the parametric approach is adopted due to its advantages in terms of variance and resolution over nonparametric methods.Both MA and AR based models are studied in this work.A new robust blind deconvolution algorithm for MP systems: variance approximation and series decoupling (VASD), is presented first.It is shown that this algorithm possesses some advantages over the existing ones with the same purpose.