Single-snapshot DOA estimation by using Compressed Sensing

Fortunati, Stefano; Grasso, Raffaele; Gini, Fulvio; Greco, Maria; LePage, Kevin D.

doi:10.1186/1687-6180-2014-120

Cited by 128 publications

(81 citation statements)

References 41 publications

(77 reference statements)

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“…VI). Other excellent papers 11 have already performed performance evaluation for single snapshot, consistent with our simulations. We are not aware of performance evaluation for multiple snapshots.…”

Section: Introductionsupporting

confidence: 74%

See 1 more Smart Citation

Multiple and single snapshot compressive beamforming

Gerstoft

Xenaki

Mecklenbräuker

2015

The Journal of the Acoustical Society of America

196

110

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For a sound field observed on a sensor array, compressive sensing (CS) reconstructs the directionof-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 phaselagged superposition of source amplitudes at all hypothetical DOAs. Regularizing with an 1-norm constraint renders the problem solvable with convex optimization, and promoting sparsity gives highresolution DOA maps. Here, the sparse source distribution is derived using maximum a posteriori (MAP) estimates for both single and multiple snapshots. CS does not require inversion of the data covariance matrix and thus works well even for a single snapshot where it gives higher resolution than conventional beamforming. For multiple snapshots, CS outperforms conventional high-resolution methods, even with coherent arrivals and at low signal-to-noise ratio. The superior resolution of CS is demonstrated with vertical array data from the SWellEx96 experiment for coherent multi-paths.

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Section: Introductionsupporting

confidence: 74%

“…Unlike the high-resolution subspace-based DOA estimators 7,8 , DOA estimation via CS is reliable even with a single snapshot [9][10][11] . The least absolute shrinkage and selection operator (LASSO) 12 has been extended to multiple measurement vectors (here multiple snapshots) 3,13 .…”

Section: Introductionmentioning

confidence: 99%

Multiple and single snapshot compressive beamforming

Gerstoft

Xenaki

Mecklenbräuker

2015

The Journal of the Acoustical Society of America

196

110

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“…All four algorithms reach an error floor because the grid is finite. In [35], this phenomenon is referred to as off-grid effect.…”

Section: Cs Spatial Formulationmentioning

confidence: 99%

Target parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensing

Ali

Ahmed

Al-Naffouri

et al. 2017

EURASIP J. Adv. Signal Process.

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Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms which do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer number of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays when CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially for small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian matching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the unknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of snapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is shown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.

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“…CS offers high-resolution DOA estimation due to the sparsity constraint and computational efficiency due to convex relaxation of the`0-norm optimization problem [2], [3], [11].…”

Section: Sparse Doa Estimationmentioning

confidence: 99%

Sparse DOA estimation with polynomial rooting

Xenaki

Gerstoft

Fernández-Grande

2015

2015 3rd International Workshop on Compressed Sensing Theory and Its Applications to Radar, Sonar and Remote Sensing (CoSeRa)

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Abstract-Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve highresolution imaging. Utilizing the dual optimal variables of the CS optimization problem, it is shown with Monte Carlo simulations that the DOAs are accurately reconstructed through polynomial rooting (Root-CS). Polynomial rooting is known to improve the resolution in several other DOA estimation methods. However, traditional methods involve the estimation of the cross-spectral matrix hence they require many snapshots and stationary incoherent sources and are suitable only for uniform linear arrays (ULA). Root-CS does not have these limitations as demonstrated on experimental towed array data from ocean acoustic measurements.

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Single-snapshot DOA estimation by using Compressed Sensing

Cited by 128 publications

References 41 publications

Multiple and single snapshot compressive beamforming

Multiple and single snapshot compressive beamforming

Target parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensing

Sparse DOA estimation with polynomial rooting

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