Data-driven and calibration-free Lamb wave source localization with sparse sensor arrays.

Most Lamb wave localization techniques require that we know the wave's velocity characteristics; yet, in many practical scenarios, velocity estimates can be challenging to acquire, are unavailable, or are unreliable because of the complexity of Lamb waves. As a result, there is a significant need for new methods that can reduce a system's reliance on a priori velocity information. This paper addresses this challenge through two novel source localization methods designed for sparse sensor arrays in isotropic media. Both methods exploit the fundamental sparse structure of a Lamb wave's frequency-wavenumber representation. The first method uses sparse recovery techniques to extract velocities from calibration data. The second method uses kurtosis and the support earth mover's distance to measure the sparseness of a Lamb wave's approximate frequency-wavenumber representation. These measures are then used to locate acoustic sources with no prior calibration data. We experimentally study each method with a collection of acoustic emission data measured from a 1.22 m by 1.22 m isotropic aluminum plate. We show that both methods can achieve less than 1 cm localization error and have less systematic error than traditional time-of-arrival localization methods.