Variation of RMSE with the number of snapshots.

<div><p>This letter presents a novel approach to sparse Bayesian underwater acoustic signal direction estimation. The proposed method incorporates interpolation of the coprime array and signal subspace fitting. It addresses the limitations of the hydrophone coprime array in utilizing all...

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Bibliographic Details
Main Author:	Chuanxi Xing (20141665) (author)
Other Authors:	Guangzhi Tan (20141668) (author), Qiang Meng (2853221) (author), Yanling Ran (20141671) (author), Mao Lu (17975339) (author)
Published:	2024
Subjects:	Biophysics Cell Biology Genetics Molecular Biology Neuroscience Biotechnology Infectious Diseases Computational Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified subarrays comprising 5 grid sparse model exhibiting better performance 3 array elements noise ratio conditions method effectively utilizes hydrophone coprime array use bayesian learning virtual array elements reducing noise interference signal subspace fitting target direction estimation coprime array subspace fitting virtual array ocean noise effectively reduces background noise virtual domain source signal received signal low signal xlink "> shallow waters received using posteriori probability performed using numerical simulations null domain novel approach letter presents hydroacoustic signals freedom provided exploiting sparsity covariance matrix atomic norm accurately carried
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Variation of RMSE with the number of snapshots.

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