Weighted structured sparse reconstruction-based lamb wave imaging exploiting multipath edge reflections in an isotropic plate

Lamb wave-based structural health monitoring techniques have the ability to scan a large area with relatively few sensors. Lamb wave imaging is a signal processing strategy that generates an image for locating scatterers according to the received Lamb waves. This paper presents a Lamb wave imaging method, which is formulated as a weighted structured sparse reconstruction problem. A dictionary is constructed by an analytical Lamb wave scattering model and an edge reflection prediction technique, which is used to decompose the experimental scattering signals under the constraint of weighted structured sparsity. The weights are generated from the correlation coefficients between the scattering signals and the predicted ones. Simulation and experimental results from an aluminum plate verify the effectiveness of the present method, which can generate images with sparse pixel values even with very limited number of sensors.