FREQUENCY RESPONSE FUNCTION ESTIMATION VIA A ROBUST WAVELET DE-NOISING METHOD

The FRF (frequency response function) estimation can be performed by the vibration analysis of a linear time-invariant dynamic system. Since a single FRF estimate is highly sensitive to measurement errors of input/output signals, the mean averaging of repeatedly observed FRF estimates is employed in most of the practical applications. The main result of this work is in reducing the number of averaging operations and enhancing estimation accuracy by using a robust wavelet de-noising method. This approach removes outliers and zero-mean Gaussian noise simultaneously and effectively while preserving most of the important signal features of a true FRF with a dramatically smaller number of operations as compared with the traditional mean-averaging procedure. The robust wavelet de-noising method is based on a wavelet-related median filtering and a wavelet shrinkage to reduce the effect of outliers and zero-mean Gaussian noise respectively. The effectiveness of the present FRF estimation technique is demonstrated using both simulated and experimental data.