Expectation-maximization Gaussian-mixture approximate message passing

Vila, Jeremy; Schniter, Philip

doi:10.1109/ciss.2012.6310932

Cited by 130 publications

(304 citation statements)

References 15 publications

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“…2. Using simple message passing rules accelerated by generalized approximate message passing (GAMP) [10], [9], we can perform fast approximate inference to obtain a minimum mean squared error (MMSE) estimate of the complex reflectivity of the scene. Movers then can be directly inferred from the posterior probabilities on x i and c i produced by the iterative message passing.…”

Section: Knowledge-aided Gmti In a Bayesian Frameworkmentioning

confidence: 99%

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Knowledge-aided GMTI in a Bayesian framework

Riedl¹,

Potter²

2015

2015 IEEE Radar Conference (RadarCon)

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Limited or heterogeneous training data restrict the ability to estimate clutter covariance for radar detection of moving targets. In addition, traditional range-cell by range-cell processing neglects the effects of target sidelobe leakage across range, increasing false alarms and limiting the dynamic range of detectable targets. We propose a knowledge-aided Bayesian formulation for moving target detection that admits fast computation. The approach jointly images clutter and targets, learns clutter from a single range, and deconvolves target sidelobes across range. The proposed processing is illustrated on KASSPER I data.

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Section: Knowledge-aided Gmti In a Bayesian Frameworkmentioning

confidence: 99%

“…The p(c i ) incorporates the prior information derived from the DEM. Finally, the model parameters are automatically tuned in the algorithm using an expectation-maximization procedure [9].…”

Section: Knowledge-aided Gmti In a Bayesian Frameworkmentioning

confidence: 99%

Knowledge-aided GMTI in a Bayesian framework

Riedl¹,

Potter²

2015

2015 IEEE Radar Conference (RadarCon)

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“…While being highly effective in general, both [20] and [21] have limitations. For example, the mixture using normal components in [20] is known to be sensitive to outliers, and the performance degrades with smaller sample size [23].…”

Section: Introductionmentioning

confidence: 97%

“…For example, the mixture using normal components in [20] is known to be sensitive to outliers, and the performance degrades with smaller sample size [23]. Meanwhile, the work [21] is designed exclusively for nonnegative signals, and is not capable in handling signals with both positive and negative significant elements.…”

Section: Introductionmentioning

confidence: 98%

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A Framework for Compressive Sensing of Asymmetric Signals Using Normal and Skew-Normal Mixture Prior

Wang

Rahnavard

2015

IEEE Trans. Commun.

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In this work, we are interested in the compressive sensing of sparse signals whose significant coefficients are distributed asymmetrically with respect to zero. To properly address this problem, we develop a framework utilizing a two-state normal and skew normal mixture density as the prior distribution of the signal. The significant and insignificant coefficients of the signal are represented by skew normal and normal distributions, respectively. A novel approximate message passing based algorithm is developed to estimate the signal from its compressed measurements. A fast gradient-based estimator is designed to infer the density of each state. Experiment results on simulated data and two real-world tests, i.e., multi-input multi-output (MIMO) communication system and weather sensor network, confirm that our proposed technique is powerful in exploiting asymmetrical feature, and outperforms many sophisticated methods.

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A Bayesian model for highly accelerated phase-contrast MRI

Rich

Potter

Jin³

et al. 2015

Magn. Reson. Med.

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Purpose Phase-contrast magnetic resonance imaging (PC-MRI) is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial and temporal resolutions, real-time application, and extensions to 4D flow imaging in clinical settings. We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique to PC-MRI. Theory and Methods ReVEAL models physical correlations across space, time, and velocity encodings. The proposed Bayesian approach exploits the relationships in both magnitude and phase among velocity encodings. A fast iterative recovery algorithm is introduced based on message passing. For validation, prospectively undersampled data are processed from a pulsatile flow phantom and five healthy volunteers. Results ReVEAL is in good agreement, quantified by peak velocity and stroke volume (SV), with reference data for acceleration rates R ≤ 10. For SV, Pearson r ≥ 0.996 for phantom imaging (n = 24) and r ≥ 0.956 for prospectively accelerated in vivo imaging (n = 10) for R ≤ 10. Conclusion ReVEAL enables accurate quantification of blood flow from highly undersampled data. The technique is extensible to 4D flow imaging, where higher acceleration may be possible due to additional redundancy.

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Expectation-maximization Gaussian-mixture approximate message passing

Cited by 130 publications

References 15 publications

Knowledge-aided GMTI in a Bayesian framework

Knowledge-aided GMTI in a Bayesian framework

A Framework for Compressive Sensing of Asymmetric Signals Using Normal and Skew-Normal Mixture Prior

A Bayesian model for highly accelerated phase-contrast MRI

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