Broadband localization in a dispersive medium through sparse wavenumber analysis

Matched field processing is a powerful tool for accurately localizing targets in dispersive media. However, matched field processing requires a precise model of the medium under test. In underwater acoustics, where matched field processing has been extensively studied, authors often resort to extremely detailed numerical models of the propagation medium, which are computationally expensive and impractical for many applications. As an alternative, this paper uses convex sparse recovery techniques to construct, directly from measured data, an accurate model of a plate medium based on its dispersion characteristics. From this data-driven model, the Green's function between two points can be readily predicted. We demonstrate the effectiveness of this model by localizing a source in a dispersive plate medium. The results visually illustrate our approach to significantly improve localization accuracy and reduce artifacts when compared to a conventional narrowband technique.