Theoretical Analysis of Compressive Sensing via Random Filter

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dc.creatorLi, Lianlin
dc.creatorXiang, Yin
dc.creatorLi, Fang
dc.date2008-11-02
dc.date.accessioned2026-07-07T10:14:48Z
dc.date.available2026-07-07T10:14:48Z
dc.descriptionIn this paper, the theoretical analysis of compressive sensing via random filter, firstly outlined by J. Romberg [compressive sensing by random convolution, submitted to SIAM Journal on Imaging Science on July 9, 2008], has been refined or generalized to the design of general random filter used for compressive sensing. This universal CS measurement consists of two parts: one is from the convolution of unknown signal with a random waveform followed by random time-domain subsampling; the other is from the directly time-domain subsampling of the unknown signal. It has been shown that the proposed approach is a universally efficient data acquisition strategy, which means that the n-dimensional signal which is S sparse in any sparse representation can be exactly recovered from Slogn measurements with overwhelming probability.
dc.identifierhttps://arxiv.org/abs/0811.0152
dc.identifierhttp://arxiv.org/abs/0811.0152
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/173007
dc.subjectInformation Theory
dc.titleTheoretical Analysis of Compressive Sensing via Random Filter
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