Adaptive Non-Uniform Compressive Sampling For Time-Varying Signals

Keywords

Adaptive sensing; Bayesian inference; Compressive sensing; Non-uniform sampling; Sequential measurements; Time-varying sparse signals

Abstract

In this paper, adaptive non-uniform compressive sampling (ANCS) of time-varying signals, which are sparse in a proper basis, is introduced. ANCS employs the measurements of previous time steps to distribute the sensing energy among coefficients more intelligently. To this aim, a Bayesian inference method is proposed that does not require any prior knowledge of importance levels of coefficients or sparsity of the signal. Our numerical simulations show that ANCS is able to achieve the desired non-uniform recovery of the signal. Moreover, if the signal is sparse in canonical basis, ANCS can reduce the number of required measurements significantly.

Publication Date

5-10-2017

Publication Title

2017 51st Annual Conference on Information Sciences and Systems, CISS 2017

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CISS.2017.7926148

Socpus ID

85020180376 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85020180376

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