We present a simple method for recovering sparse signals from a series of noisy observations. Our algorithm is a Kalman filter (KF) that utilize a so-called pseudo-measurement technique for optimizing the convex minimization problem following from the theory of compressed sensing (CS). Compared to the recently introduced CS-KF in [1] which involves the implementation of an additional CS optimization algorithm (e.g., the Dantzig selector), our method is remarkebly easy to implement as it is exclusively based on the KF formulation. The results of an extensive numerical study are provided demonstrating the performance and viability of the new method.
By: Avishy Carmi; Pini Gurfil; Dimitri Kanevsky
Published in: RC24709 in 2008
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