# 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}
}
• Published 2010
• Computer Science, Mathematics
• 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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