A new regularization approach for the unconstrained linear least square problem is proposed and assessed. This approach improves the classical methods based on the singular value decomposition approach by employing the Gaussian filter, a kind of filter which has been proved to be effective in several applications related to noise suppression. In particular, as a benchmark, it is hereby considered the estimation of the heat flux density at the internal wall surface in a forced convection problem in ducts, by solving the inverse heat conduction problem in the solid wall only using temperature distribution available at the exterior boundary. Results on both synthetic and experimental data are reported with the aim of discussing the effectiveness of the proposed method in comparison to other similar solution approaches.
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