A variational technique for source localization based on a sparse signal reconstruction perspective

Çetin, Müjdat; Malioutov,; Willsky,

doi:10.1109/icassp.2002.1005309

Cited by 21 publications

(22 citation statements)

References 5 publications

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“…2. There are two same strength sources with a separation angle of 8 and we consider three cases with different SNR values of 0, 3, and 6 dB. We repeated 100 trials for each condition and averaged over these RMSEs.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Various works employed the CS technique, using an l 1 -norm minimization for regularization, in the DOA estimation problems. [6][7][8][9][10][11][12][13][14] CS based DOA estimation techniques, compared to conventional DOA estimation techniques, showed high resolution and robustness to coherent arrivals, which is fatal for classical super-resolution algorithms, e.g., the minimum variance distortion-less response (MVDR) 15 and the multiple signal classification (MUSIC). 16 The conventional CS based estimation techniques employ finite discrete grids to estimate the parameters of interest, which actually exist in a continuous parameter space [10][11][12][13] and one of the major drawbacks of the conventional CS based estimation techniques is basis mismatch, which occurs when the exact K support (the position of non-zero components of solution vector) does not locate on the discretized grid due to inadequate N a priori bases of the sensing matrix.…”

Section: Introductionmentioning

confidence: 99%

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Multiple snapshot grid free compressive beamforming

Park

Choo

Seong

2018

The Journal of the Acoustical Society of America

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Compressive sensing (CS) based estimation technique utilizes a sparsity promoting constraint and solves the direction-of-arrival (DOA) estimation problem efficiently with high resolution. In this paper a grid free CS based DOA estimation technique is proposed, which uses sequential multiple snapshot data. Conventional CS technique suffers from a basis mismatch issue, while grid free CS technique is relieved of basis mismatch problem. Moreover, when the DOAs are stationary, multiple snapshot processing provides stable estimates over fluctuating single snapshot processing results. For multiple snapshot processing, the generalized version of total variation norm (group total variation norm) is implemented to impose a common sparsity pattern of multiple snapshot solution vectors in a continuous angular domain. Furthermore, an extended version is proposed using the singular value decomposition technique to mitigate computational complexity resulting from a large number of multiple snapshots. Data from SWellEx-96 are used to examine the proposed method. From the experimental data, it was observed that the present method not only offers high resolution even when the sources are coherent, but also the basis mismatch in the conventional CS method can be avoided.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple snapshot grid free compressive beamforming

Park

Choo

Seong

2018

The Journal of the Acoustical Society of America

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“…Regularization with the ' 2 -norm is also typical in near-field acoustical holography (NAH) methods for sound field reconstruction. 2,3 Since there are usually only a few sources, regularization with the ' 1 -norm 4,5 attracts increasing attention [6][7][8][9][10][11][12][13][14][15][16] as it provides high-resolution DOA reconstruction due to its sparsity promoting characteristics. In underwater acoustics, using multiple constraints to account for simultaneous characteristics of the solution as sparsity and smoothness (i.e., ' 1 -norm and ' 2 -norm) is shown to improve the resolution of shallow-water source localization with matched-field processing.…”

Section: Introductionmentioning

confidence: 99%

Block-sparse beamforming for spatially extended sources in a Bayesian formulation

Xenaki

Fernández-Grande

Gerstoft

2016

The Journal of the Acoustical Society of America

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Direction-of-arrival (DOA) estimation refers to the localization of sound sources on an angular grid from noisy measurements of the associated wavefield with an array of sensors. For accurate localization, the number of angular look-directions is much larger than the number of sensors, hence, the problem is underdetermined and requires regularization. Traditional methods use an ℓ2-norm regularizer, which promotes minimum-power (smooth) solutions, while regularizing with ℓ1-norm promotes sparsity. Sparse signal reconstruction improves the resolution in DOA estimation in the presence of a few point sources, but cannot capture spatially extended sources. The DOA estimation problem is formulated in a Bayesian framework where regularization is imposed through prior information on the source spatial distribution which is then reconstructed as the maximum a posteriori estimate. A composite prior is introduced, which simultaneously promotes a piecewise constant profile and sparsity in the solution. Simulations and experimental measurements show that this choice of regularization provides high-resolution DOA estimation in a general framework, i.e., in the presence of spatially extended sources.

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“…The EM [6], [7] and SAGE [8] algorithms based on a maximum likelihood method also have been used for DOA estimation as a super resolution technique, although the computational load is larger than for MUSIC and ESPRIT. Recently, much attention has been paid to DOA estimation using compressed sensing (CS) techniques [9]- [11], which have been used mainly in the areas of signal processing, image compression, and wireless communication networks. A technique to estimate DOAs of ultrawideband (UWB) signals is also effective at improving the estimation accuracy [12].…”

Section: Introductionmentioning

confidence: 99%

DOA Estimation for Multi-Band Signal Sources Using Compressed Sensing Techniques with Khatri-Rao Processing

Terada

Nishimura

Ogawa

et al. 2014

IEICE Trans. Commun.

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SUMMARYMuch attention has recently been paid to direction of arrival (DOA) estimation using compressed sensing (CS) techniques, which are sparse signal reconstruction methods. In our previous study, we developed a method for estimating the DOAs of multi-band signals that uses CS processing and that is based on the assumption that incident signals have the same complex amplitudes in all the bands. That method has a higher probability of correct estimation than a single-band DOA estimation method using CS. In this paper, we propose novel DOA estimation methods for multi-band signals with frequency characteristics using the Khatri-Rao product. First, we formulate a method that can estimate DOAs of multiband signals whose phases alone have frequency dependence. Second, we extend the scheme in such a way that we can estimate DOAs of multi-band signals whose amplitudes and phases both depend on frequency. Finally, we evaluate the performance of the proposed methods through computer simulations and reveal the improvement in estimation performance.

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A variational technique for source localization based on a sparse signal reconstruction perspective

Cited by 21 publications

References 5 publications

Multiple snapshot grid free compressive beamforming

Multiple snapshot grid free compressive beamforming

Block-sparse beamforming for spatially extended sources in a Bayesian formulation

DOA Estimation for Multi-Band Signal Sources Using Compressed Sensing Techniques with Khatri-Rao Processing

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