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Flaw profile estimation from measurements is a typical inverse problem in electromagnetic nondestructive evaluation (NDE). The application of recursive Bayesian nonlinear filters based on sequential Monte Carlo methods, in conjunction with measurement process models and a Markovian crack growth model, is a new approach for solving such inverse problems. The approach resembles the classical discrete-time tracking problem and is robust to the noisy measurement data. This paper reports a comparative study of the results of employing different measurement models in this Bayesian inversion framework. The results are evaluated on the basis of accuracy and computational cost.