@Article{CMR-36-2,
author = {Yang, Xiao-Mei and Zhi-Liang, Deng 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 = {<p style="text-align: justify;">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.</p>},
issn = {2707-8523},
doi = {https://doi.org/10.4208/cmr.2020-0011},
url = {https://global-sci.com/article/81530/an-ensemble-kalman-filter-approach-based-on-level-set-parameterization-for-acoustic-source-identification-using-multiple-frequency-information}
}