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On MMSE estimation: A linear model under Gaussian mixture statistics
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2638-6047
NTNU - Norwegian University of Science and Technology.
NTNU - Norwegian University of Science and Technology.
NTNU - Norwegian University of Science and Technology.
2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 7, 3840-3845 p.Article in journal (Refereed) Published
Abstract [en]

In a Bayesian linear model, suppose observation y = Hx + n stems from independent inputs x and n which are Gaussian mixture (GM) distributed. With known matrix H, the minimum mean square error (MMSE) estimator for x , has analytical form. However, its performance measure, the MMSE itself, has no such closed form. Because existing Bayesian MMSE bounds prove to have limited practical value under these settings, we instead seek analytical bounds for the MMSE, both upper and lower. This paper provides such bounds, and relates them to the signal-to-noise-ratio (SNR).

Place, publisher, year, edition, pages
2012. Vol. 60, no 7, 3840-3845 p.
Keyword [en]
estimation theory
National Category
Signal Processing
Research subject
SRA - ICT
Identifiers
URN: urn:nbn:se:kth:diva-98492DOI: 10.1109/TSP.2012.2192112ISI: 000305578800041Scopus ID: 2-s2.0-84862616919OAI: oai:DiVA.org:kth-98492DiVA: diva2:537412
Funder
ICT - The Next Generation
Note

QC 20120730

Available from: 2012-07-30 Created: 2012-06-26 Last updated: 2017-12-07Bibliographically approved

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