Carmi, Avishy and Mihaylova, Lyudmila and Kanevsky, Dimitri (2012) Unscented compressed sensing. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. IEEE, Japan, pp. 5249-5252. ISBN 978-1-4673-0045-2Full text not available from this repository.
In this paper we present a novel compressed sensing (CS) algorithm for the recovery of compressible, possibly time-varying, signal from a sequence of noisy observations. The newly derived scheme is based on the acclaimed unscented Kalman filter (UKF), and is essentially self reliant in the sense that no peripheral optimization or CS algorithm is required for identifying the underlying signal support. Relying exclusively on the UKF formulation, our method facilitates sequential processing of measurements by employing the familiar Kalman filter predictor corrector form. As distinct from other CS methods, and by virtue of its pseudomeasurement mechanism, the CS-UKF, as we termed it, is non iterative, thereby maintaining a computational overhead which is nearly equal to that of the conventional UKF.
|Item Type:||Contribution in Book/Report/Proceedings|
|Uncontrolled Keywords:||Compressed sensing ; Kalman filter ; Sigma point filter ; Sparse signal recovery ; Unscented Kalman Filter|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||25 Sep 2012 09:21|
|Last Modified:||25 Sep 2012 09:21|
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