Blind deconvolution of sources of opportunity in ocean waveguides using bilinear channel models.


Journal

The Journal of the Acoustical Society of America
ISSN: 1520-8524
Titre abrégé: J Acoust Soc Am
Pays: United States
ID NLM: 7503051

Informations de publication

Date de publication:
10 2020
Historique:
entrez: 3 11 2020
pubmed: 4 11 2020
medline: 4 11 2020
Statut: ppublish

Résumé

A general blind deconvolution algorithmic framework is developed for sources of opportunity (e.g., ships at known locations) in an ocean waveguide. Here, both channel impulse responses (CIRs) and unknown source signals need to be simultaneously estimated from only the recorded signals on a receiver array using blind deconvolution, which is generally an ill-posed problem without any a priori information or additional assumptions about the underlying structure of the CIRs. By exploiting the typical ray-like arrival-time structure of the CIRs between a surface source and the elements of a vertical line array (VLA) in ocean waveguides, a principle component analysis technique is applied to build a bilinear parametric model linking the amplitudes and arrival-times of the CIRs across all channels for a variety of admissible ocean environments. The bilinear channel representation further reduces the dimension of the channel parametric model compared to linear models. A truncated power interaction deconvolution algorithm is then developed by applying the bilinear channel model to the traditional subspace deconvolution method. Numerical and experimental results demonstrate the robustness of this blind deconvolution methodology.

Identifiants

pubmed: 33138520
doi: 10.1121/10.0001975
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

2267

Auteurs

Ning Tian (N)

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.

Kiryung Lee (K)

Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio 43210, USA.

Justin Romberg (J)

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.

Nicholas Durofchalk (N)

George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.

Karim Sabra (K)

George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.

Classifications MeSH