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1. C. Yardim, P. Gerstoft, and W. S. Hodgkiss, “ Tracking of geoacoustic parameters using Kalman and particle filters,” J. Acoust. Soc. Am. 125, 746760 (2009).
2. O. Carrière, J. P. Hermand, and J. V. Candy, “ Inversion for time-evolving sound-speed field in a shallow ocean by ensemble Kalman filtering,” IEEE. J. Ocean. Eng. 34, 586602 (2009).
3. C. Yardim, P. Gerstoft, and W. S. Hodgkiss, “ Geoacoustic and source tracking using particle filtering: Experimental results,” J. Acoust. Soc. Am. 128, 7587 (2010).
4. F. Le Gland, V. Monbet, and V.-D. Tran, “ Large sample asymptotics for the ensemble Kalman filter,” in The Oxford Handbook of Nonlinear Filtering, edited by D. Crisan and B. Rozovskii ( Oxford University Press, Oxford, UK, 2011), pp. 598631.
5. J. Li and H. Zhou, “ Tracking of time-evolving sound speed profiles in shallow water using an ensemble Kalman-particle filter,” J. Acoust. Soc. Am. 133, 13771386 (2013).
6. N. Papadakis, E. Mémin, A. Cuzol, and N. Gengembre, “ Data assimilation with the weighted ensemble Kalman filter,” Tellus, 62A, 673697 (2010).
7. P. Gerstoft and C. F. Mecklenbräuker, “ Ocean acoustic inversion with estimation of a posteriori probability distributions,” J. Acoust. Soc. Am. 104, 808819 (1998).
8. B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications ( Artech House, Boston, MA, 2004), Chap. 3, pp. 3562.
9. D. Tang, J. N. Moum, J. F. Lynch, P. Abbot, R. Chapman, P. H. Dahl, T. F. Duda, G. Gawarkiewicz, S. M. Glenn, J. A. Goff, H. Graber, J. Kemp, A. Maffei, J. D. Nash, and A. Newhall, “ Shallow Water'06: A joint acoustic propagation/nonlinear internal wave physics experiment,” Oceanography 20, 156167 (2007).
10. Y.-T. Lin, A. E. Newhall, T. F. Duda, P. F. J. Lermusiaux, and P. J. Haley, “ Merging multiple partial-depth data time series using objective empirical orthogonal function fitting,” IEEE J. Ocean. Eng. 35, 710721 (2010).
11. L. B. LeBlanc and F. H. Middleton, “ An underwater acoustic sound velocity data model,” J. Acoust. Soc. Am. 67, 20552062 (1980).
12. M. D. Collins, “ A split-step Padé solution for the parabolic equation method,” J. Acoust. Soc. Am. 93, 17361742 (1993).

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This paper implements a weighted ensemble Kalman filter for tracking time-evolving sound speed profiles. The approach first updates the particles following the procedure of the ensemble Kalman filter and then resamples the updated particles according to their importance weights. The weights are evaluated by the classical geoacoustic inversion likelihood obtained from the Bartlett power objective functions. Example illustrates that the new method outperforms the ensemble with a comparable computational load.


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