• 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 Pini Gurfil and Dimitri Kanevsky},
  journal={IEEE Transactions on Signal Processing},
  year={2010},
  volume={58},
  pages={2405-2409}
}

• A. Carmi, P. Gurfil, D. Kanevsky
• Published 1 April 2010
• 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 method can be straightforwardly implemented in a stand-alone manner, as it is exclusively based on the well-known KF formulation. In our first algorithm, the PM equation constrains the l 1 norm of the estimated state. In this case, the augmented measurement equation becomes linear, so a regular KF can be used. In our second algorithm, we replace the l 1 norm by a quasi-norm lp , 0 ¿ p < 1. This modification considerably improves the accuracy of the resulting KF algorithm; however, these improved results require an extended KF (EKF) for properly computing the state statistics. A numerical study demonstrates the viability of the new methods.