Particle filter based multisensor fusion for solving electromagnetic NDE inverse problems

Flaw profile estimation from measurements is a typical inverse problem in electromagnetic nondestructive evaluation (NDE). The increasing availability of multiple measurement modes in NDE requires the development of multisensor data fusion algorithms to solve the NDE inverse problem. This paper proposes a multisensor data fusion algorithm for flaw profiling, based on a recursive state space approach. The problem of flaw profile estimation from given multisensor data is formulated using multiple measurement process models and a state transition model. This formulation enables the application of Bayesian non-linear filters based on sequential Monte Carlo methods. The new approach is computationally efficient if computationally simple measurement models are employed. Moreover, the technique is robust to noisy measurement data. The initial results indicate significant improvement in the accuracy of inversion results when more than one type of measurement data is used for flaw profile estimation.