Spectral analysis for ultrasonic nondestructive evaluation applications using autoregressive, Prony, and multiple signal classification methods

In the ultrasonic nondestructive evaluation (NDE) of materials, spectral analysis of backscattered echoes is a useful tool for flaw detection, frequency-shift estimation, and dispersive echo characterization. In order to evaluate the local information, spectral analysis must be applied to short data segments and must offer high-frequency resolution. In this paper three high-resolution model-based spectral estimation techniques, i.e., the autoregressive (AR) method using the Burg algorithm, Prony’s method for exponential signal representation, and the multiple signal classification (MUSIC) method, have been studied for ultrasonic NDE applications. These algorithms have been applied to both simulated data and experimental measurements for frequency estimation and flaw detection. The maximum energy frequency estimates using these methods show significant sensitivity to changes in the frequency of ultrasonic echoes. The AR method shows a more robust performance for frequency estimation than the Prony or MUSIC methods.