Deconvolution of linear systems by constrained regression and its relationship to the Wiener theory

In this paper we discuss the problem of deconvolution of the output of a linear system in the presence of noise, and a previously known technique for solving integral equations is applied. It is shown how this solution is equivalent to constrained linear regression and that this may be computed in the frequency domain. Finally, the relationship between deconvolution by constrained linear regression and by Wiener theory is derived, and it is shown that the constrained regression technique requires far less a priori knowledge than does the Wiener theory.