2009/12/08 by Namrata Vaswani, Vaswani, Namrata
Computer Science · Mathematics · #FOS: Computer and information sciences #Information Theory (cs.IT) #Methodology (stat.ME) #cs.IT #math.IT #stat.ME
paper · pdf · doi:10.48550/arxiv.0912.1628
7 pages, 2 figures, submitted to the IEEE for possible publication
arxiv created 2010/03/24 · arxiv updated 2010/03/25
We consider the problem of recursively reconstructing time sequences of sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear incoherent measurements with additive noise. The idea of our proposed solution, KF CS-residual (KF-CS) is to replace compressed sensing (CS) on the observation by CS on the Kalman filtered (KF) observation residual computed using the previous estimate of the support. KF-CS error stability over time is studied. Simulation comparisons with CS and LS-CS are shown.