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A robust sparse regularization technique for source localization that accounts for the joint effects of sensor position errors and noise is presented. Finding a good choice of the regularization parameter is a key component in sparse optimization problems and its automated determination is typically a non-trivial task. Our approach attempts to statistically determine an upper bound of the mean-squared...
In this work, we explore the problem of blind deconvolution in the context of sparse signals. We show that the ℓ0-norm works as a contrast function, if the length of the impulse response of the system is smaller than the shortest distance between two spikes of the input signal. Demonstrating this sufficient condition is our basic theoretical result. However, one of the problems of dealing with the...
Signal recovery from the amplitudes of the Fourier transform, or equivalently from the autocorrelation function is a classical problem. Due to the absence of phase information, signal recovery requires some form of additional prior information. In this paper, the prior information we assume is sparsity. We develop a convex optimization based framework to retrieve the signal support from the support...
We consider the problem of recovering a set of correlated signals (e.g., images from different viewpoints) from a few linear measurements per signal. We assume that each sensor in a network acquires a compressed signal in the form of linear measurements and sends it to a joint decoder for reconstruction. We propose a novel joint reconstruction algorithm that exploits correlation among underlying signals...
Compressed sensing is a technique for efficiently sampling signals which are sparse in some transform domain. Recently, the idea of compressed sensing has been used in the radar system. When the number of targets on the range-Doppler plane is small, the target scene can be reconstructed by employing the compressed sensing techniques. In this paper, we extend this idea to the MIMO radar. In the MIMO...
This paper studies a novel decomposition technique, suitable for blind separation of linear mixtures of signals comprising finite-length symbols. The observed symbols are first modeled as channel responses in a multiple-input-multiple-output (MIMO) model, while the channel inputs are conceptually considered sparse positive pulse trains carrying the information about the symbol arising times. Our decomposition...
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