Unscented Transform: A Powerful Tool for Measurement Uncertainty Evaluation

Abstract

An original approach for uncertainty evaluation in indirect measurements is presented hereinafter. The approach applies the unscented transform to the measurement model (i.e., the functional relationship between output and input quantities) in order to gain a reliable estimate of output expectation and standard deviation (measurement uncertainty). Thanks to some useful properties of the transform, notable limits of the current GUM recommendations can be overcome. In particular, reliable estimates are also granted in the presence of nonlinear and/or nonanalytical measurement models or complex digital signal processing algorithms. A number of numerical tests are conducted on simulated and actual measurement data. Remarkable concurrence between obtained estimates and those granted by Monte Carlo simulations confirms the efficacy of the proposed approach

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