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Compressive sensing (CS) enables reconstructing a sparse signal from fewer samples than those required by the classic Nyquist sampling theorem. In general, CS signal recovery algorithms have high computational complexity. However, several signal processing problems such as signal detection and classification can be tackled directly in the compressive measurement domain. This makes recovering the original...
We consider in this paper detecting stochastic signals with known probability density function (PDF) from their compressive measurements. We refer to it as the compressive detection problem. The Neyman-Pearson (NP) theorem is applied to derive the NP detectors for Gaussian signals. Our work is more general over the existing literature in the sense that we do not require that the measurement matrix...
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