In this study, the performance of chirplet signal decomposition (CSD) and empirical mode decomposition (EMD) cou pled with Hilbert spectrum have been evaluated and compared for ultrasonic imaging applications. Numerical and ex perimental results indicate that both the EMD and CSD are able to decompose sparsely distributed chirplets from noise. In case of signals consisting of multiple interfering chirplets, the CSD algorithm, based on successive search for esti mating optimal chirplet parameters, outperforms the EMD algorithm which estimates a series of intrinsic mode func tions (IMFs). In particular, we have utilized the EMD as a signal conditioning method for Hilbert time-frequency rep resentation in order to estimate the arrival time and center frequency of chirplets in order to quantify the ultrasonic sig nals. Experimental results clearly exhibit that the combined EMD and CSD is an effective processing tools to analyze ultrasonic signals for target detection and pattern recognition.
KeywordsUltrasoundHilbert Time-Frequency RepresentationEmpirical Mode DecompositionChirplet Signal DecompositionDetectionEstimation
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