Data fusion algorithm for pulsed eddy current detection

A weighted data fusion algorithm based on matching pursuit (MP)-wavelet packet (WP) atomic decomposition and its applications in pulsed eddy current (PEC) non-destructive testing systems for estimation of feature parameters is presented. MP-WP atomic decomposition is used to estimate each noise-free pulse response from its noisy observation of a single-sensor PEC probe and obtain the peak value parameter from each estimated response. A weighted data fusion algorithm, on the basis of minimum mean square error (MMSE), is applied to fuse each obtained peak value together to get final optimum parameter estimation. Based on the difference of each noisy pulse response and its estimation, the variance of noise in each pulse response can be computed, respectively. Accordingly, the weight of each pulse response for data fusion is calculated by the variance of its noise. Finally, the peak value parameter is estimated by the utilised data fusion algorithm. In terms of MMSE, this weighted fusion presents an optimum estimation of the feature parameter of multi-pulse responses of PEC sensor, compared with normal averaging process.