This paper presents a methodology for identifying defects by multiple sensors under the presence of both sensor and defect uncertainties. This methodology introduces a representation of the beliefs of both the locations of defects and the sensors each by a probability density function and updates them using the extended Kalman filter. Since the beliefs are recursively maintained while the sensor is moving and the associated observation data are updated, the proposed methodology considers not only the current observation data but also the prior knowledge, the past observation data and beliefs, which include both sensor and defect uncertainties. The concept of differential entropy also has been introduced and is utilized as a performance measure to evaluate the result of defect identification and handle the identification of multiple defects. The verification and evaluation of the proposed methodology performance were conducted via parametric numerical studies. The results have shown the successful identification of defects with reduced uncertainty when the number of measurements increases, even under the presence of large sensor uncertainties. Furthermore, the proposed methodology was applied to the more realistic problem of identifying multiple defects located on a specimen and have demonstrated its applicability to practical defect identification problems.

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