Kalman filtering for compressed sensing

@article{Kanevsky2010KalmanFF,
  title={Kalman filtering for compressed sensing},
  author={Dimitri Kanevsky and Avishy Carmi and Lior Horesh and Pini Gurfil and Bhuvana Ramabhadran and Tara N. Sainath},
  journal={2010 13th International Conference on Information Fusion},
  year={2010},
  pages={1-8}
}
Compressed sensing is a new emerging field dealing with the reconstruction of a sparse or, more precisely, a compressed representation of a signal from a relatively small number of observations, typically less than the signal dimension. In our previous work we have shown how the Kalman filter can be naturally applied for obtaining an approximate Bayesian solution for the compressed sensing problem. The resulting algorithm, which was termed CSKF, relies on a pseudo-measurement technique for… CONTINUE READING
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