KF-CS: Compressive Sensing on Kalman Filtered Residual

Details KF-CS: Compressive Sensing on Kalman Filtered Residual KF-CS: Compressive Sensing on Kalman Filtered Residual

2009-12-08

arxiv.org/abs/0912.1628v3

Computer Science

Information Theory

7 pages, 2 figures, submitted to the IEEE for possible publication

Scientific paper

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.

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