Model-based estimation pursuit for sparse decomposition of ultrasonic echoes

Sparse signal decomposition techniques used for ultrasonic signal analysis are mainly based on the matching pursuit (MP) method using generic time-frequency dictionaries or more specific dictionaries designed for ultrasound measurements. These dictionary-based MP (DBMP) methods often perform inadequately in extracting meaningful echo components because of the rigid structure of pre-defined dictionaries. More recently emerged model-based signal decomposition methods decompose an ultrasonic signal in terms of parametric model echoes using successive echo partitioning, parameter estimation and echo subtraction. Although these techniques offer more flexibility in signal decomposition, they still use correlation in matching parametric model echoes to partitioned signal components. This study presents a model-based estimation pursuit (MBEP) method that utilises statistical estimation principles in echo matching, as a result provides a greater flexibility and control in signal decomposition. In particular MBEP algorithm utilises maximum a posteriori estimation and incorporates prior knowledge into signal decomposition. Unlike DBMP methods, MBEP obtains physically meaningful decompositions that have direct interpretations for ultrasonic testing. The superior performance of MBEP has been demonstrated using simulated and experimental ultrasonic signals.