Bayesian Compressive Sensing as Applied to Directions-of-Arrival Estimation in Planar Arrays

Carlin, Matteo; Rocca, Paolo; Oliveri, Giacomo; Massa, Andrea

doi:10.1155/2013/245867

Cited by 29 publications

(18 citation statements)

References 31 publications

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“…The most successful one is L1-SVD [5], which exploits the L 1 norm to reconstruct sparse signals and applies singular value decomposition (SVD) to reduce computational complexity. In [6][7][8], the Bayesian compressive sensing (BCS) framework is successfully applied in DOA estimation. In [9], a algorithm called L1-SRACV is presented for DOA estimation, based on sparse representation of array covariance vectors.…”

Section: Introductionmentioning

confidence: 99%

Off-Grid Doa Estimation Based on Sparse Representation and Rife Algorithm

Xu¹,

Chen²,

Gao³

2017

PIER M

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Abstract-In this paper, off-grid DOA estimation based on sparse representation and Rife algorithm is presented to improve performance when the sparse signal directions are not on the predefined angular grids. The algorithm is divided into two steps. Firstly, the real-valued sparse representation of array covariance vector (RV-SRACV) algorithm is used to do off-grid DOA estimation, and it does not need to estimate the noise power. Secondly, Rife algorithm is used to correct the DOA estimation, and after that the DOA can be accurately estimated. The effectiveness and superior performance of the proposed algorithm are demonstrated in the simulation results.

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Section: Introductionmentioning

confidence: 99%

Off-Grid Doa Estimation Based on Sparse Representation and Rife Algorithm

Xu¹,

Chen²,

Gao³

2017

PIER M

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“…In [15], an approach based on a Bayesian version of the CS has been proposed in order to deal with the real-time DoAs estimation for linear antenna arrays. In addition, this technique has been successively extended in order to deal with planar arrays [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-resolution BCS-based approach for DoA estimation

Carlin¹,

Rocca²,

Viani³

et al. 2015

2015 IEEE International Symposium on Antennas and Propagation &Amp; USNC/URSI National Radio Science Meeting

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International audienceThe problem of the estimation of the number and angles of arrival of electromagnetic signals impinging on a linear array of sensors is addressed by means of a multi-resolution approach based on the Bayesian Compressive Sensing (BCS). The BCS is used to retrieve at each step of the multi-zooming strategy the most probable directions of arrival (DoAs) as well as to provide the confidence level of the obtained estimations. The DoAs estimation is then refined by zooming on the spatial regions where the signals have been detected and the zoom velocity is defined according to the confidence on the reliability of the estimations in the previous step. A set of preliminary numerical results is provided in order to assess the performance of the proposed techniqu

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“…In addition, the same same technique have been recently extended to the DoAs estimation in planar arrays [25]. The objective of this work is to present a resume of different DoAs estimation based on BCS framework.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic direction of arrival estimation through Bayesian compressive sensing

Carlin

Rocca

Oliveri

et al. 2014

The 8th European Conference on Antennas and Propagation (EuCAP 2014)

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In this paper a class of recently introduced Bayesian approaches for the solution of directions-of-arrival (DoAs) estimation problems is illustrated and discussed. More in detail, the problem of the estimation of the DoAs of electromagnetic narrow-band signals impinging on an antenna array is addressed in this paper by means of a strategy based on the Bayesian Compressive Sensing (BCS). Firstly, the relation between the data (i.e. the measured voltages) and the unknown DoAs is formulated as a linear relationship with sparse solution. Successively, the problem is solved by applying the BCS in order to estimate the most probable sparse solution fitting the measured input data. Preliminary numerical examples aimed at assessing the effectiveness of the considered techniques are presented.

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Bayesian Compressive Sensing as Applied to Directions-of-Arrival Estimation in Planar Arrays

Cited by 29 publications

References 31 publications

Off-Grid Doa Estimation Based on Sparse Representation and Rife Algorithm

Off-Grid Doa Estimation Based on Sparse Representation and Rife Algorithm

Multi-resolution BCS-based approach for DoA estimation

Probabilistic direction of arrival estimation through Bayesian compressive sensing

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