• DOI:10.1109/TSP.2009.2038959
Corpus ID: 10569233

Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms

@article{Carmi2010MethodsFS,
  title={Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms},
  author={Avishy Carmi and P. Gurfil and D. Kanevsky},
  journal={IEEE Transactions on Signal Processing},
  year={2010},
  volume={58},
  pages={2405-2409}
}

Avishy Carmi, P. Gurfil, D. Kanevsky

Published 2010

Mathematics, Computer Science

IEEE Transactions on Signal Processing

We present two simple methods for recovering sparse signals from a series of noisy observations. The theory of compressed sensing (CS) requires solving a convex constrained minimization problem. We propose solving this optimization problem by two algorithms that rely on a Kalman filter (KF) endowed with a pseudo-measurement (PM) equation. Compared to a recently-introduced KF-CS method, which involves the implementation of an auxiliary CS optimization algorithm (e.g., the Dantzig selector), our… CONTINUE READING

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