Block sparse compressed sensing in ultrasonic NDE echo analysis and parameter estimation

In ultrasonic NDE applications, the received signal carries valuable physical information along the wave propagation path such as locations, orientation and sizes of discontinuities. Various signal processing algorithms have been utilized to interrogate ultrasonic echoes, highly overlapped and sometimes noise-contaminated. As of assessing and monitoring the in-situ outsized structures, it becomes challenging to collect and analyze a large volume of data due to the workload time and computational expense. Recently, compressed sensing (CS) has been introduced to exploit signal sparsity, where most coefficients are null or close to zero in the sparse domain. It significantly reduces the system sampling rate. This study aims to explore the CS application in ultrasound echo estimation and analysis.