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September 10, 2025JASA Express LettersOpen Access

Fast deconvolved beamforming for arbitrary arrays based on off-grid sparse Bayesian learning

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This letter demonstrates improved spatial resolution using off-grid sparse Bayesian learning in beamforming, indicating reduced modeling errors.

The method enhances spatial resolution without increasing the array aperture, improving beamforming accuracy.
Utilizing off-grid sparse Bayesian learning reduces modeling errors for targets not on sampling grids.
Controlling output beams accelerates convergence, providing efficient coverage of spatial areas of interest.
Simulation results indicate significant performance improvements in dCv beamforming methods.

Huang et al. (2025) studied this question.

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This letter demonstrates improved spatial resolution using off-grid sparse Bayesian learning in beamforming, indicating reduced modeling errors.