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Sparse signals can be sensed with a reduced number of projections and then reconstructed if compressive sensing is employed. Traditionally, the projection matrix is chosen as a random matrix, but a projection sensing matrix that is optimally designed for a certain class of signals can further improve the reconstruction accuracy. This paper considers the problem of designing the projection matrix Φ...
The compressive sensing (CS) theory has shown that sparse signals can be reconstructed exactly from much fewer measurements than traditionally believed. What's more, using ℓp-norm minimization with p < 1 can do so with much fewer measurements than with p=1. In this paper, a novel algorithm is proposed for computing local minima of the nonconvex problem in the block-sparse system. A series of experiments...
It's well known that sparse signals can be sensed with a reduced number of projections and then reconstructed if compressive sensing (CS) is employed. We consider the problem of designing the projection matrix Φ for a compressive sensing system in which the dictionary Ψ is assumed to be given. Moreover, we consider the more general case in which the measurements are contaminated by white Gaussian...
In this paper, a novel sub-band fusion technique for SAR echo using compressed sensing was proposed. As we all known, high resolution and wide swath are the trend of SAR systems, while the range resolution of SAR is limited by the bandwidth of transmitted signal. Our proposed algorithm aimed to take the compressed sensing and sub-band fusion into the SAR systems, realize high resolution and reduce...
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