Fig. 2

Iterative algorithm to compute uncertainty-based weights. The first iteration: parameter estimation is performed based on the equal-weight cost function using 100 random initial parameter settings obtained by Latin hypercube sampling. We pick the one with the smallest cost to calculate weights. The second and subsequent iterations: the algorithm starts with the optimized parameter from the previous iteration, and performs parameter estimation with respect to the weighted cost function using the weights from the previous iteration. The process iterates until the weights converge