Abstract:
Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for bandlimited signals. Recovery g...Show MoreMetadata
Abstract:
Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for bandlimited signals. Recovery guarantees for CS are generally derived based on the assumption that measurement projections are selected independently at random. However, for many practical signal acquisition applications, this assumption is violated as the projections must be taken in groups. In this paper, we consider such applications and derive requirements on the number of measurements needed for successful recovery of signals when groups of dependent projections are taken at random. We find a penalty factor on the number of required measurements with respect to the standard CS scheme that employs conventional independent measurement selection and verify the predicted penalty through simulations.
Published in: 2012 IEEE Statistical Signal Processing Workshop (SSP)
Date of Conference: 05-08 August 2012
Date Added to IEEE Xplore: 04 October 2012
ISBN Information:
Print ISSN: 2373-0803