Constrained Regularization Inversion of PCS Based on Interior Point Algorithm

Photon Correlation Spectroscopy (PCS) is a powerful technique for the sizing of particle size distribution (PSD). It is an ill-posed inversion problem. According to actual situation, PSD is non-negative. In order to inverse desired PSD, during the inversion process, non-negative condition should is imposed for inversion problem. This paper proposes a regularization inversion method based on interior-point algorithm for PSD constraint. This method changes constrained optimization problems into unconstrained optimization problem. The simulation data of unimodal distribution particles were inversed by this method. The results demonstrate this method is feasible for PCS inversion.