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1. A. B. Baggeroer, “Matched field processing: Source localization in correlated noise as an optimum parameter estimation problem,” J. Acoust. Soc. Am. 83, 571587 (1988).
2. A. B. Baggeroer, W. A. Kuperman, and P. N. Mikhalevsky, “An overview of matched field methods in ocean acoustics,” IEEE J. Ocean. Eng. 18, 401424 (1993).
3. J. L. Krolik, “Matched-field minimum variance beamforming in a random ocean channel,” J. Acoust. Soc. Am. 92, 14081419 (1992).
4. Z.-H. Michalopoulou, “Robust multi-tonal matched-field inversion: A coherent approach,” J. Acoust. Soc. Am. 104, 163170 (1998).
5. C. Debever and W. A. Kuperman, “Robust matched-field processing using a coherent broadband white noise constraint processor,” J. Acoust. Soc. Am. 122, 19791986 (2007).
6. W. Mantzel, J. Romberg, and K. Sabra, “Compressive matched-field processing,” J. Acoust. Soc. Am. 132, 90102 (2012).
7. S. E. Dosso and M. J. Wilmut, “Maximum-likelihood and other processors for incoherent and coherent matched-field localization,” J. Acoust. Soc. Am. 132, 22732285 (2012).
8. D. B. Harris and T. Kvaerna, “Super-resolution with seismic arrays using empirical matched field processing,” Geophys. J. Int. 182, 14551477 (2010).
9. M. Papazoglou and J. Krolik, “Matched-field estimation of aircraft altitude from multiple over-the-horizon radar revisits,” IEEE Trans. Signal Process. 47, 966976 (1999).
10. P. Gerstoft, D. F. Gingras, L. T. Rogers, and W. S. Hodgkiss, “Estimation of radio refractivity structure using matched-field array processing,” IEEE Trans. Antennas Propag. 48, 345356 (2000).
11. G. Turek and W. A. Kuperman, “Applications of matched-field processing to structural vibration problems,” J. Acoust. Soc. Am. 101, 14301440 (1997).
12. R. K. Ing and M. Fink, “Ultrasonic imaging using spatio-temporal matched field (STMF) processing-applications to liquid and solid waveguides,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control 48, 374386 (2001).
13. W. A. Kuperman, W. S. Hodgkiss, H. C. Song, T. Akal, C. Ferla, and D. R. Jackson, “Phase conjugation in the ocean: Experimental demonstration of an acoustic time-reversal mirror,” J. Acoust. Soc. Am. 103, 2540 (1998).
14. J. M. F. Moura and Y. Jin, “Time reversal imaging by adaptive interference canceling,” IEEE Trans. Signal Process. 56, 233247 (2008).
15. J. E. Michaels, “Detection, localization and characterization of damage in plates with an in situ array of spatially distributed ultrasonic sensors,” Smart Mater. Struct. 17, 035035 (2008).
16. T. Clarke and P. Cawley, “Enhancing the defect localization capability of a guided wave SHM system applied to a complex structure,” Struct. Health Monit. 10, 247259 (2011).
17. A. Tolstoy, “Applications of matched-field processing to inverse problems in underwater acoustics,” Inverse Prob. 16, 16551666 (2000).
18. P. Gerstoft, “Inversion of seismoacoustic data using genetic algorithms and a posteriori probability distributions,” J. Acoust. Soc. Am. 95, 770782 (1994).
19. S. E. Dosso, P. L. Nielsen, and M. J. Wilmut, “Data error covariance in matched-field geoacoustic inversion,” J. Acoust. Soc. Am. 119, 208219 (2006).
20. N. M. Shapiro and M. H. Ritzwoller, “Monte-Carlo inversion for a global shear-velocity model of the crust and upper mantle,” Geophys. J. Int. 151, 88105 (2002).
21. J. B. Harley and J. M. F. Moura, “Sparse recovery of the multimodal and dispersive characteristics of Lamb waves,” J. Acoust. Soc. Am. 133, 27322745 (2013).
22. D. L. Donoho, M. Elad, and V. N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory 52, 618 (2006).
23. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. 43, 129159 (1998).
24. M. Lowe, “Matrix techniques for modeling ultrasonic waves in multilayered media,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control 42, 525542 (1995).
25. K. F. Graff, Wave Motion in Elastic Solids, 1st ed. (Dover Publications, New York, 1991), Chap. 8.2.3, pp. 458480.
26. J. D. Achenbach, Wave Propagation in Elastic Solids (Elsevier Science Publishers B.V., Amsterdam, 1975), Chap. 5.11, 425 pp.
27. F. B. Jensen, W. A. Kuperman, M. B. Porter, and H. Schmidt, Computational Ocean Acoustics (Springer, New York, NY, 2011), Chap. 2.4.5, pp. 118133.
28. J. B. Harley and J. M. F. Moura, “Broadband localization in a dispersive medium through sparse wavenumber analysis,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, Vancouver, BC, 2013, pp. 40714075.
29.Note that, due to a typo, Eq. (17) in Ref. 21 incorrectly defines the normalization constant μ(d).
30. R. M. Levine and J. E. Michaels, “Model-based imaging of damage with Lamb waves via sparse reconstruction,” J. Acoust. Soc. Am. 133, 15251534 (2013).
31. S. Aeron, S. Bose, H.-P. Valero, and V. Saligrama, “Broadband dispersion extraction using simultaneous sparse penalization,” IEEE Trans. Signal Process. 59, 48214837 (2011).
32. G. Chardon, A. Leblanc, and L. Daudet, “Plate impulse response spatial interpolation with sub-Nyquist sampling,” J. Sound Vib. 330, 56785689 (2011).
33. I. Zorych and Z.-H. Michalopoulou, “Particle filtering for dispersion curve tracking in ocean acoustics,” J. Acoust. Soc. Am. 124, EL45EL50 (2008).
34. J. Hall and J. E. Michaels, “Minimum variance ultrasonic imaging applied to an in situ sparse guided wave array,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control 57, 23112323 (2010).
35. S. Kunis and H. Rauhut, “Random sampling of sparse trigonometric polynomials. II. Orthogonal matching pursuit versus basis pursuit,” Found. Comput. Math. 8, 737763 (2008).
36. R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, “A simple proof of the restricted isometry property for random matrices,” Constr. Approx. 28, 253263 (2008).
37. H. Rauhut, “Stability results for random sampling of sparse trigonometric polynomials,” IEEE Trans. Inf. Theory 54, 56615670 (2008).
38. M. E. Pfetsch and A. M. Tillmann, “The computational complexity of the restricted isometry property, the null space property, and related concepts in compressed sensing,” IEEE Trans. Inf. Theory 60(2), 12481259 (2013).
39. J. D. Blanchard, C. Cartis, and J. Tanner, “Decay properties of restricted isometry constants,” IEEE Signal Process. Lett. 16, 572575 (2009).
40. J. D. Blanchard, C. Cartis, and J. Tanner, “Compressed sensing: How sharp is the restricted isometry property?SIAM Rev. 53, 105125 (2011).
41. R. Vershynin, Introduction to the Non-Asymptotic Analysis of Random Matrices, edited by Y. Eldar and G. Kutyniok (Cambridge University Press, Cambridge, 2012), Chap. 5, pp. 210268.
42. D. Gabor, “Theory of communication. Part 1: The analysis of information,” J. Inst. Electr. Eng., Part 3 93, 429441 (1946).
43. H. L. Van Trees, Radar-Sonar Signal Processing and Gaussian Signals in Noise, Detection, Estimation, and Modulation Theory, Part III (John Wiley and Sons, New York, 2001), Chap. 10, pp. 292294.
44. H. C. Song, J. de Rosny, and W. A. Kuperman, “Improvement in matched field processing using the CLEAN algorithm,” J. Acoust. Soc. Am. 113, 13791386 (2003).

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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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