66 citations · 83 across the 6 of their papers we have counts for
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Mixture Gaussian Signal Estimation with L_infty Error Metric
Jin Tan, Dror Baron, Liyi Dai
We consider the problem of estimating an input signal from noisy measurements in both parallel scalar Gaussian channels and linear mixing systems. The performance of the estimation…
Performance Regions in Compressed Sensing from Noisy Measurements
Junan Zhu, Dror Baron
In this paper, compressed sensing with noisy measurements is addressed. The theoretically optimal reconstruction error is studied by evaluating Tanaka's equation. The main contribu…
Signal reconstruction in linear mixing systems with different error metrics
Jin Tan, Dror Baron
We consider the problem of reconstructing a signal from noisy measurements in linear mixing systems. The reconstruction performance is usually quantified by standard error metrics…
Information Complexity and Estimation
Dror Baron
We consider an input generated by an unknown stationary ergodic source that enters a signal processing system , resulting in . We observe through a noisy cha…
Belief propagation for joint sparse recovery
Jongmin Kim, Woohyuk Chang, Bangchul Jung +2
Compressed sensing (CS) demonstrates that sparse signals can be recovered from underdetermined linear measurements. We focus on the joint sparse recovery problem where multiple sig…
Bayesian Compressive Sensing via Belief Propagation
Dror Baron, Shriram Sarvotham, Richard G. Baraniuk
Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-N…