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…
Figures and Tables from this paper
48 Citations
Block-sparse beamforming for spatially extended sources in a Bayesian formulation.
- EngineeringThe Journal of the Acoustical Society of America
- 2016
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
- EngineeringMechanical Systems and Signal Processing
- 2019
Two-dimensional grid-free compressive beamforming.
- EngineeringThe Journal of the Acoustical Society of America
- 2017
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
- EngineeringSensors
- 2019
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
- EngineeringJournal of Sound and Vibration
- 2021
A panoramic continuous compressive beamformer with cuboid microphone arrays
- PhysicsScientific Reports
- 2019
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
- Engineering, Computer ScienceDigit. Signal Process.
- 2019
Iterative reweighted atomic norm minimization based two-dimensional multiple-snapshot grid-free compressive beamforming with planar microphone array
- EngineeringJournal of Low Frequency Noise, Vibration and Active Control
- 2022
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
- Engineering
- 2020
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
- Computer Science, PhysicsApplied Acoustics
- 2019
References
SHOWING 1-10 OF 51 REFERENCES
Compressive beamforming.
- Environmental ScienceThe Journal of the Acoustical Society of America
- 2014
The high-resolution capabilities and the robustness of CS are demonstrated on experimental array data from ocean acoustic measurements for source tracking with single-snapshot data and the limitations are related to the beampattern.
Grid-free compressive beamforming
- EngineeringThe Journal of the Acoustical Society of America
- 2015
The grid-free CS reconstruction provides high-resolution imaging even with non-uniform arrays, single-snapshot data and under noisy conditions as demonstrated on experimental towed array data.
Single-snapshot DOA estimation by using Compressed Sensing
- Computer Science2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2014
Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit and the theoretical findings are validated by processing a real sonar dataset.
Sequential Bayesian Sparse Signal Reconstruction Using Array Data
- Computer ScienceIEEE Transactions on Signal Processing
- 2013
A sequence of weighted LASSO problems is solved for estimating the temporal evolution of a sparse source field and is evaluated numerically using a uniform linear array in simulations and applied to data which were acquired from a towed horizontal array during the long range acoustic communications experiment.
A sparse signal reconstruction perspective for source localization with sensor arrays
- Computer ScienceIEEE Transactions on Signal Processing
- 2005
This work presents a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold that has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources.
Sensitivity to Basis Mismatch in Compressed Sensing
- Computer ScienceIEEE Trans. Signal Process.
- 2011
This paper establishes achievable bounds for the l1 error of the best k -term approximation and derives bounds, with similar growth behavior, for the basis pursuit l1 recovery error, indicating that the sparse recovery may suffer large errors in the presence of basis mismatch.
Compressive Matched-Field Processing
- Computer ScienceThe Journal of the Acoustical Society of America
- 2012
This paper shows how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors, and how the source can be located by performing a number of "short" correlations between this proxy and the projection of the recorded acoustic data in the compressed space.
Shallow-water sparsity-cognizant source-location mapping.
- PhysicsThe Journal of the Acoustical Society of America
- 2014
This work introduces a robust scheme for shallow-water source localization that exploits the inherent sparse structure of the localization problem and the use of a model characterizing the acoustic propagation environment and the resulting source-location map (SLM) yields reduced ambiguities and improved resolution.
Beamforming using compressive sensing.
- BusinessThe Journal of the Acoustical Society of America
- 2011
Compressive sensing is compared with conventional beamforming using horizontal beamforming of at-sea, towed-array data and quantitatively using signal-to-interference ratio, showing lower levels of background interference than conventionalbeamforming.