Volume 36, Issue 2
An Ensemble Kalman Filter Approach Based on Level Set Parameterization for Acoustic Source Identification Using Multiple Frequency Information

Xiao-Mei Yang, Zhi-Liang Deng & Juan-Fang Wang

Commun. Math. Res., 36 (2020), pp. 211-228.

Published online: 2020-05

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  • Abstract

In this paper, a reconstruction problem of the spatial dependent acoustic source from multiple frequency data is discussed. Suppose that the source function is supported on a bounded domain and the piecewise constant intensities of the source are known on the support. We characterize unknown domain by the level set technique. And the level set function can be modeled by a Hamilton-Jacobi system. We use the ensemble Kalman filter approach to analyze the system state. This method can avoid dealing with the nonlinearity directly and reduce the computation complexity. In addition, the algorithm can achieve the stable state quickly with the Hamilton-Jacobi system. From some numerical examples, we show these advantages and verify the feasibility and effectiveness.

  • Keywords

Level set, data assimilation, acoustic source, EnKF.

  • AMS Subject Headings

35R20, 65R20

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CMR-36-211, author = {Yang , Xiao-Mei and Deng , Zhi-Liang and Wang , Juan-Fang}, title = {An Ensemble Kalman Filter Approach Based on Level Set Parameterization for Acoustic Source Identification Using Multiple Frequency Information}, journal = {Communications in Mathematical Research }, year = {2020}, volume = {36}, number = {2}, pages = {211--228}, abstract = {

In this paper, a reconstruction problem of the spatial dependent acoustic source from multiple frequency data is discussed. Suppose that the source function is supported on a bounded domain and the piecewise constant intensities of the source are known on the support. We characterize unknown domain by the level set technique. And the level set function can be modeled by a Hamilton-Jacobi system. We use the ensemble Kalman filter approach to analyze the system state. This method can avoid dealing with the nonlinearity directly and reduce the computation complexity. In addition, the algorithm can achieve the stable state quickly with the Hamilton-Jacobi system. From some numerical examples, we show these advantages and verify the feasibility and effectiveness.

}, issn = {2707-8523}, doi = {https://doi.org/10.4208/cmr.2020-0011}, url = {http://global-sci.org/intro/article_detail/cmr/16929.html} }
TY - JOUR T1 - An Ensemble Kalman Filter Approach Based on Level Set Parameterization for Acoustic Source Identification Using Multiple Frequency Information AU - Yang , Xiao-Mei AU - Deng , Zhi-Liang AU - Wang , Juan-Fang JO - Communications in Mathematical Research VL - 2 SP - 211 EP - 228 PY - 2020 DA - 2020/05 SN - 36 DO - http://doi.org/10.4208/cmr.2020-0011 UR - https://global-sci.org/intro/article_detail/cmr/16929.html KW - Level set, data assimilation, acoustic source, EnKF. AB -

In this paper, a reconstruction problem of the spatial dependent acoustic source from multiple frequency data is discussed. Suppose that the source function is supported on a bounded domain and the piecewise constant intensities of the source are known on the support. We characterize unknown domain by the level set technique. And the level set function can be modeled by a Hamilton-Jacobi system. We use the ensemble Kalman filter approach to analyze the system state. This method can avoid dealing with the nonlinearity directly and reduce the computation complexity. In addition, the algorithm can achieve the stable state quickly with the Hamilton-Jacobi system. From some numerical examples, we show these advantages and verify the feasibility and effectiveness.

Xiao-Mei Yang, Zhi-Liang Deng & Juan-Fang Wang. (2020). An Ensemble Kalman Filter Approach Based on Level Set Parameterization for Acoustic Source Identification Using Multiple Frequency Information. Communications in Mathematical Research . 36 (2). 211-228. doi:10.4208/cmr.2020-0011
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