Single and multiple snapshot compressive beamforming

@article{Gerstoft2015SingleAM,
  title={Single and multiple snapshot compressive beamforming},
  author={Peter Gerstoft and Angeliki Xenaki and Christoph F. Mecklenbr{\"a}uker},
  journal={The Journal of the Acoustical Society of America},
  year={2015},
  volume={138 4},
  pages={
          2003-14
        }
}
For a sound field observed on a sensor array, compressive sensing (CS) reconstructs the direction of arrival (DOA) of multiple sources using a sparsity constraint. The DOA estimation is posed as an underdetermined problem by expressing the acoustic pressure at each sensor as a phase-lagged superposition of source amplitudes at all hypothetical DOAs. Regularizing with an ℓ1-norm constraint renders the problem solvable with convex optimization, and promoting sparsity gives high-resolution DOA… 
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48 Citations
Highly Influential Citations
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Background Citations
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48 Citations

Block-sparse beamforming for spatially extended sources in a Bayesian formulation.
TLDR
Simulations and experimental measurements show that a composite prior is introduced, which simultaneously promotes a piecewise constant profile and sparsity in the solution, provides high-resolution DOA estimation in a general framework, i.e., in the presence of spatially extended sources.
Two-dimensional multiple-snapshot grid-free compressive beamforming
Two-dimensional grid-free compressive beamforming.
TLDR
The grid-free compressive beamforming can provide high-resolution and low-contamination imaging, allowing accurate and fast estimation of two-dimensional DOAs and quantification of source strengths, even with non-uniform arrays and noisy measurements.
Passive Source Localization Using Compressive Sensing
TLDR
This paper presents an underwater passive source localization method by forming an underdetermined linear inversion problem that exhibits the respective characteristics of traditionally used Bartlett and white noise gain constraint methods, such as robustness to environmental/system mismatch and high resolution.
Multi-frequency synchronous two-dimensional off-grid compressive beamforming
A panoramic continuous compressive beamformer with cuboid microphone arrays
TLDR
This work develops a panoramic continuous compressive beamformer with cuboid microphone arrays based on an atomic norm minimization (ANM) and a matrix pencil and paring method to solve the positive semidefinite programming equivalent to the ANM efficiently and forms a solving algorithm based on the alternating direction method of multipliers.
Adaptive filtering algorithm for direction-of-arrival (DOA) estimation with small snapshots
Iterative reweighted atomic norm minimization based two-dimensional multiple-snapshot grid-free compressive beamforming with planar microphone array
Compressive beamforming with a planar microphone array can effectively estimate the two-dimensional directions-of-arrival and quantify the strengths of acoustic sources. Due to the superiorities of…
Two-Dimensional Multiple-Snapshot Grid-Free Compressive Beamforming Using Alternating Direction Method of Multipliers
TLDR
Both simulations and experiments demonstrate that whether a standard uniform rectangular array or a non-uniform array constituted by a small number of microphones is employed, the two-dimensional multiple-snapshot grid-free compressive beamforming using the alternating direction method of multipliers (ADMM) based algorithm is distinctly faster.
Signal reconstruction of fast moving sound sources using compressive beamforming
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