2012 46th Annual Conference on Information Sciences and Systems (CISS) 2012
DOI: 10.1109/ciss.2012.6310932
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Expectation-maximization Gaussian-mixture approximate message passing

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Cited by 130 publications
(306 citation statements)
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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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“…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%
“…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%
“…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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