Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103824
Title: Variational Bayesian algorithm for quantized compressed sensing
Authors: Yang, Zai
Xie, Lihua
Zhang, Cishen
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2013
Source: Yang, Z., Xie, L.,& Zhang, C. (2013). Variational Bayesian Algorithm for Quantized Compressed Sensing. IEEE Transactions on Signal Processing, 61(11), 2815-2824.
Series/Report no.: IEEE transactions on signal processing
Abstract: Compressed sensing (CS) is on recovery of high dimensional signals from their low dimensional linear measurements under a sparsity prior and digital quantization of the measurement data is inevitable in practical implementation of CS algorithms. In the existing literature, the quantization error is modeled typically as additive noise and the multi-bit and 1-bit quantized CS problems are dealt with separately using different treatments and procedures. In this paper, a novel variational Bayesian inference based CS algorithm is presented, which unifies the multi- and 1-bit CS processing and is applicable to various cases of noiseless/noisy environment and unsaturated/saturated quantizer. By decoupling the quantization error from the measurement noise, the quantization error is modeled as a random variable and estimated jointly with the signal being recovered. Such a novel characterization of the quantization error results in superior performance of the algorithm which is demonstrated by extensive simulations in comparison with state-of-the-art methods for both multi-bit and 1-bit CS problems.
URI: https://hdl.handle.net/10356/103824
http://hdl.handle.net/10220/16966
ISSN: 1053-587X
DOI: http://dx.doi.org/10.1109/TSP.2013.2256901
metadata.item.grantfulltext: none
metadata.item.fulltext: No Fulltext
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