KF-CS: Compressive Sensing on Kalman Filtered Residual

Abstract

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.

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

References in corpus (2)

• LS-CS-residual (LS-CS): Compressive Sensing on Least Squares Residual
• Analyzing Least Squares and Kalman Filtered Compressed Sensing

Cited by in corpus (2)

• LS-CS-residual (LS-CS): Compressive Sensing on Least Squares Residual
• Time Invariant Error Bounds for Modified-CS based Sparse Signal Sequence Recovery