Particle-filter-based multisensor fusion for solving low-frequency electromagnetic NDE inverse problems

Flaw profile characterization from nondestructive evaluation (NDE) measurements is a typical inverse problem. A novel transformation of this inverse problem into a tracking problem and subsequent application of a sequential Monte Carlo method called particle filtering has been proposed by the authors in an earlier publication. In this paper, the problem of flaw characterization from multisensor data is considered. The NDE inverse problem is posed as a statistical inverse problem, and particle filtering is modified to handle data from multiple measurement modes. The measurement modes are assumed to be independent of each other with principal component analysis used to legitimize the assumption of independence. The proposed particle-filter-based data fusion algorithm is applied to experimental low-frequency NDE data to investigate its feasibility.