NDLI: On unbiased estimation of sparse vectors corrupted by Gaussian noise

Content Provider	IEEE Xplore Digital Library
Author	Jung, A. Ben-Haim, Z. Hlawatsch, F. Eldar, Y.C.
Copyright Year	2010
Description	Author affiliation: Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology, Gusshausstrasse 25/389, A-1040, Austria (Jung, A.; Hlawatsch, F.) \|\| Technion-Israel Institute of Technology, Haifa 32000, Israel (Ben-Haim, Z.; Eldar, Y.C.)
Abstract	We consider the estimation of a sparse parameter vector from measurements corrupted by white Gaussian noise. Our focus is on unbiased estimation as a setting under which the difficulty of the problem can be quantified analytically. We show that there are infinitely many unbiased estimators but none of them has uniformly minimum mean-squared error. We then provide lower and upper bounds on the Barankin bound, which describes the performance achievable by unbiased estimators. These bounds are used to predict the threshold region of practical estimators.
Starting Page	3990
Ending Page	3993
File Size	139671
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495781
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gaussian noise Upper bound Performance analysis Radio frequency Noise measurement Noise reduction Parameter estimation Signal to noise ratio Wireless communication Contracts Barankin bound Unbiased estimation sparsity denoising Cramér-Rao bound
Content Type	Text
Resource Type	Article

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