An Improved UKF Algorithm Based on RBF Neural Network for PEC Response Signal Extraction

To suppress noise and extract weak features in pulsed eddy current response signals, in this study, we employ the unscented Kalman filter (UKF) algorithm in conjunction with the radial basis function (RBF) neural network to establish the predictive and observational equations for the pulsed eddy current response signal. The RBF neural network's strong nonlinear capabilities are leveraged, while the UKF algorithm is integrated to mitigate noise and extract the signal. To assess the efficacy of this approach, we utilize COMSOL simulation data for validation purposes. Our findings demonstrate that the proposed algorithm effectively reduces noise, particularly for signals with a signal-to-noise ratio of 30 dB.