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Unscented compression sensing

Carmi, Avishy and Mihaylova, Lyudmila and Kanevsky, Dimitri (2012) Unscented compression sensing. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. IEEE, Kyoto, Japan, pp. 5249-5252. ISBN 978-1-4673-0045-2

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Abstract

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 pseudo-measurement 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
Additional Information: @inproceedings{DBLP:conf/icassp/CarmiMK12, author = {Avishy Carmi and Lyudmila Mihaylova and Dimitri Kanevsky}, title = {Unscented compressed sensing}, booktitle = {ICASSP}, year = {2012}, pages = {5249-5252}, ee = {http://dx.doi.org/10.1109/ICASSP.2012.6289104}, crossref = {DBLP:conf/icassp/2012}, bibsource = {DBLP, http://dblp.uni-trier.de} }
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
ID Code: 56273
Deposited By: ep_importer_pure
Deposited On: 27 Jul 2012 14:11
Refereed?: No
Published?: Published
Last Modified: 23 Sep 2013 15:49
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/56273

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