KF-CS: Compressive Sensing on Kalman Filtered Residual
Information Theory
2010-03-25 v3 math.IT
Methodology
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.
Keywords
Cite
@article{arxiv.0912.1628,
title = {KF-CS: Compressive Sensing on Kalman Filtered Residual},
author = {Namrata Vaswani},
journal= {arXiv preprint arXiv:0912.1628},
year = {2010}
}
Comments
7 pages, 2 figures, submitted to the IEEE for possible publication