Compressive sensing for autoregressive hidden Markov model signal

Authors:
Ji Wu;Qilian Liang;Zheng Zhou
Affiliations:
Department of Electrical Engineering, University of Texas at Arlington;Department of Electrical Engineering, University of Texas at Arlington;School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
WASA'10 Proceedings of the 5th international conference on Wireless algorithms, systems, and applications
Year:
2010

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Abstract

Compressive sensing(CS) is an emerging filed based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. One challenging problem in compressive sensing is that it is difficult to represent signal in sparse basis, which makes this algorithm sometimes impractical. In this paper, we can setup a new standard compressive sensing problem for autoregressive hidden markov signal by utilizing the original observation vector and the autoregressive coefficients.