Matched field processing is a model-based framework for localizing targets in complex propagation environments. In underwater acoustics, it has been extensively studied for improving localization performance in multimodal and multipath media. For guided wave structural health monitoring problems, matched field processing has not been widely applied but is an attractive option for damage localization due to equally complex propagation environments. Although effective, matched field processing is often challenging to implement because it requires accurate models of the propagation environment, and the optimization methods used to generate these models are often unreliable and computationally expensive. To address these obstacles, this paper introduces data-driven matched field processing, a framework to build models of multimodal propagation environments directly from measured data, and then use these models for localization. This paper presents the data-driven framework, analyzes its behavior under unmodeled multipath interference, and demonstrates its localization performance by distinguishing two nearby scatterers from experimental measurements of an aluminum plate. Compared with delay-based models that are commonly used in structural health monitoring, the data-driven matched field processing framework is shown to successfully localize two nearby scatterers with significantly smaller localization errors and finer resolutions.
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