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

The deconvolved beamforming (dCv) improves spatial resolution without expanding the array aperture but fails for the shift-variant beam pattern and the real targets, which are not located on the sampling grids. To solve them, this Letter extends the off-grid sparse Bayesian learning (OGSBL) to dCv because the generalized convolutional model considers the beam pattern at each angle in beam domain. OGSBL reduces modeling errors by parameterizing sampled locations in coarse grids. Controlling the number of output beams from conventional beamforming to cover the spatial area of interest could accelerate convergence without sacrificing accuracy. The simulation results confirm the good performance.