Cross-spectral matrix denoising for beamforming based on Schatten-p norm

Deconvolution beamforming has gotten increased attention as a way to improve the spatial resolution of delay-and-sum beamforming. It has the ability to decrease sidelobes and increase resolution. However, compared to conventional beamforming, the extra computation of the deconvolution method is a drawback. A more efficient approach is developed to improve the computing speed of the deconvolution method. Specifically, when tackling deconvolution problems, this method improves computational performance by combining Fourier operation with a fast gradient algorithm called the double momentum gradient algorithm. We compare the proposed method with two known effective deconvolution methods, namely the fast Fourier transform non-negative least squares algorithm and the fast iterative shrinkage threshold algorithm. The results of simulation and experiment reveal that the proposed method tends to give a better spatial resolution within a short computational time and is more suitable for engineering applications.

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“…and variable substitution, 2 , this problem is transformed into the following finite-dimensional relaxation problem…”

Section: Optimization Of Algorithm Efficiencymentioning

confidence: 99%

“…Beamforming [1][2][3] is a signal processing technology based on the microphone array and can locate the sound source like a spatial filter. It was mostly employed in radar, communication, and sonar.…”

Section: Introductionmentioning

confidence: 99%