Computer Science – Information Theory
Scientific paper
2009-03-29
Proc. IEEE Intl. Conf. Acous. Speech Sig. Proc. (ICASSP), 2009
Computer Science
Information Theory
Proc. IEEE Intl. Conf. Acous. Speech Sig. Proc. (ICASSP), 2009
Scientific paper
In recent work, we studied the problem of causally reconstructing time sequences of spatially sparse signals, with unknown and slow time-varying sparsity patterns, from a limited number of linear "incoherent" measurements. We proposed a solution called Kalman Filtered Compressed Sensing (KF-CS). The key idea is to run a reduced order KF only for the current signal's estimated nonzero coefficients' set, while performing CS on the Kalman filtering error to estimate new additions, if any, to the set. KF may be replaced by Least Squares (LS) estimation and we call the resulting algorithm LS-CS. In this work, (a) we bound the error in performing CS on the LS error and (b) we obtain the conditions under which the KF-CS (or LS-CS) estimate converges to that of a genie-aided KF (or LS), i.e. the KF (or LS) which knows the true nonzero sets.
No associations
LandOfFree
Analyzing Least Squares and Kalman Filtered Compressed Sensing does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Analyzing Least Squares and Kalman Filtered Compressed Sensing, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Analyzing Least Squares and Kalman Filtered Compressed Sensing will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-202526