Sensitivity analysis applied to an improved Fourier-transform profilometry

Every estimation concerning model parameters has to deal with uncertainty, but its quantification is a complex task to solve, especially for the identification of the different uncertainty sources that affect the system. In this paper we provide an extensive uncertainty analysis on the calibration model of a Fourier-transform profilometry, studying the effect of the uncertainty parameters estimation on the final measuring result. The methods used for the classification are discrete derivatives and the global sensitivity analysis based on Monte Carlo simulations. The experimental results show that the uncertainty propagation of the system parameters to the output strongly depends on different system setups that may be chosen. This dependency is analysed and interpreted.