Analyzing least squares and kalman filtered compressed sensing,” in long version, available at http://www.ece.iastate.edu/ ∼namrata/ AnalyzeKFCS.pdf

Analyzing least squares and kalman filtered compressed sensing,” in long version, available at http://www.ece.iastate.edu/ ∼namrata/ AnalyzeKFCS.pdf (2008)

by Namrata Vaswani

Citations:

4 - 2 self

Datum	Value	Source
TITLE	Analyzing least squares and kalman filtered compressed sensing,” in long version, available at http://www.ece.iastate.edu/ ∼namrata/ AnalyzeKFCS.pdf	INFERENCE
AUTHOR NAME	Namrata Vaswani	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of ECE, Iowa State University, Ames	SVM HeaderParse 0.2
AUTHOR ADDR	IA, namrata@iastate.edu	SVM HeaderParse 0.2
ABSTRACT	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.	SVM HeaderParse 0.2
YEAR	2008	INFERENCE
CITATIONS	8 found	ParsCit 1.0