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- W2211589247 endingPage "93" @default.
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- W2211589247 abstract "Compressed sensing deals with the reconstruction of signals from sub-Nyquist samples by exploiting the sparsity of their projections onto known subspaces. In contrast, this article is concerned with the reconstruction of second-order statistics, such as covariance and power spectrum, even in the absence of sparsity priors. The framework described here leverages the statistical structure of random processes to enable signal compression and offers an alternative perspective at sparsity-agnostic inference. Capitalizing on parsimonious representations, we illustrate how compression and reconstruction tasks can be addressed in popular applications such as power-spectrum estimation, incoherent imaging, direction-of-arrival estimation, frequency estimation, and wideband spectrum sensing." @default.
- W2211589247 created "2016-06-24" @default.
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- W2211589247 date "2016-01-01" @default.
- W2211589247 modified "2023-09-30" @default.
- W2211589247 title "Compressive Covariance Sensing: Structure-based compressive sensing beyond sparsity" @default.
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- W2211589247 doi "https://doi.org/10.1109/msp.2015.2486805" @default.
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