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3E-3 A Comparative Study of Echo Estimation Techniques for Ultrasonic NDE Applications

In this study two different echo estimation techniques with a chirplet model are evaluated: chirplet signal decomposition based on the chirplet transform (CTSD), and the matching pursuit signal decomposition framework that employs Maximum Likelihood Estimation (MPSD). Both techniques are used to decompose backscattered signals into a linear expansion of chirplet echoes and estimate the chirplet parameters. The chirplet parameter estimation is unbiased with minimum variance, i.e., it attains analytically derived Cramer-Rao lower bounds. When applied to simulated and experimental ultrasonic signals, both algorithms perform robustly, yield accurate echo estimations and result in considerable SNR enhancements. Moreover, the MPSD algorithm outperforms the CTSD in moderate noise levels whereas the CTSD performs better than MPSD in severe noise levels. Numerical and analytical results indicate that the two presented signal decomposition algorithms are effective tools for ultrasonic signal analysis accounting for narrow-band, broadband, and symmetric, skewed, dispersive or nondispersive echoes. The present study provides guidelines which can be useful when dealing with signal analysis, pattern recognition, target sizing and material characterization