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This paper addresses the robust reconstruction problem of a sparse signal from compressed measurements. We propose a robust formulation for sparse reconstruction that employs the $\ell _1$ -norm as the loss function for the residual error and utilizes a generalized nonconvex penalty for sparsity inducing. The $\ell _1$ -loss is less sensitive to outliers in the measurements than the popular $\ell _2$...
This work addresses the issue of sparse reconstruction in compressive sensing (CS) for speech signals. We propose a novel sparse reconstruction algorithm based on the approximate message passing (AMP) framework, via exploiting the intrinsic structures of real-life speech signals in the modified discrete cosine transform (MDCT) domain. We use a Gaussian mixture model to characterize the marginal distribution...
This work considers the robust sparse recovery problem in compressive sensing (CS) in the presence of impulsive measurement noise. We propose a robust formulation for sparse recovery using the generalized ℓp-norm with 0 < p < 2 as the metric for the residual error under ℓ1-norm regularization. An alternative direction method (ADM) has been proposed to solve this formulation efficiently. Moreover,...
In a distributed sensor network for sound source localization, it is important to develop a method which can accurately localize the sound source with a small amount of data collected by the sensors. Traditional methods for combining the Compressive Sensing (CS) with the sound source localization are not always accurate and effective in real applications due to the fact that time delay between different...
The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach...
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