Abstract
Compressive Sensing (CS), also known as compressive sampling, is a new digital signal processing technique that aims at recovering the original signal from a very few number of measurements. Recently, several algorithms have been proposed to reconstruct the signal by exploiting its sparsity property. This signal reconstruction depends strongly on the sensing matrix, which key to CS. In this paper, we propose an improved Bernoulli sensing matrix based on full-orthogonal Hadamard codes. Simulations show that the use of the proposed sensing matrix in CS improves significantly the performance of signal reconstruction. In fact, it outperforms the Bernoulli and the Partial Hadamard matrices.
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Nouasria, H., Et-tolba, M. (2017). An Improved Bernoulli Sensing Matrix for Compressive Sensing. In: Sabir, E., García Armada, A., Ghogho, M., Debbah, M. (eds) Ubiquitous Networking. UNet 2017. Lecture Notes in Computer Science(), vol 10542. Springer, Cham. https://doi.org/10.1007/978-3-319-68179-5_49
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DOI: https://doi.org/10.1007/978-3-319-68179-5_49
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