Abstract
Using Singular Value Decomposition (SVD), we develop an algorithm for signal recovery in compressive sensing. If the signal or sparse basis is properly chosen, an accurate estimate of the signal could be obtained by a simple and efficient signal recovery method even in the presence of additive noise. The theoretical and simulation results show that our approach is scalable both in terms of number of measurements required for stable recovery and computational complexity.
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Xu, L., Liang, Q. (2010). Compressive Sensing Using Singular Value Decomposition. In: Pandurangan, G., Anil Kumar, V.S., Ming, G., Liu, Y., Li, Y. (eds) Wireless Algorithms, Systems, and Applications. WASA 2010. Lecture Notes in Computer Science, vol 6221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14654-1_44
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DOI: https://doi.org/10.1007/978-3-642-14654-1_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14653-4
Online ISBN: 978-3-642-14654-1
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