Regularized Tapered Sample Covariance Matrix

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorOllila, Esaen_US
dc.contributor.authorBreloy, Arnauden_US
dc.contributor.departmentDept Signal Process and Acousten
dc.contributor.groupauthorEsa Ollila Groupen
dc.date.accessioned2022-06-22T09:02:14Z
dc.date.available2022-06-22T09:02:14Z
dc.date.issued2022en_US
dc.descriptionPublisher Copyright: © 1991-2012 IEEE.
dc.description.abstractCovariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting a banded structure) or beamforming (widening the spectral null width associated with an interferer). In this paper, the focus is on high-dimensional setting where the dimension p is high, while the data aspect ratio n/p is low. We propose an estimator called Tabasco (TApered or BAnded Shrinkage COvariance matrix) that shrinks the tapered sample covariance matrix towards a scaled identity matrix. We derive optimal and estimated (data adaptive) regularization parameters that are designed to minimize the mean squared error (MSE) between the proposed shrinkage estimator and the true covariance matrix. These parameters are derived under the general assumption that the data is sampled from an unspecified elliptically symmetric distribution with finite 4th order moments (both real- and complex-valued cases are addressed). Simulation studies show that the proposed Tabasco outperforms all competing tapering covariance matrix estimators in diverse setups. An application to space-time adaptive processing (STAP) also illustrates the benefit of the proposed estimator in a practical signal processing setup.en
dc.description.versionPeer revieweden
dc.format.extent15
dc.format.extent2306-2320
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationOllila, E & Breloy, A 2022, ' Regularized Tapered Sample Covariance Matrix ', IEEE Transactions on Signal Processing, vol. 70, pp. 2306-2320 . https://doi.org/10.1109/TSP.2022.3169269en
dc.identifier.doi10.1109/TSP.2022.3169269en_US
dc.identifier.issn1053-587X
dc.identifier.otherPURE UUID: 8f75b23c-31c2-486a-bcee-4402255081d2en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/8f75b23c-31c2-486a-bcee-4402255081d2en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85129368877&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/84645901/Regularized_Tapered_Sample_Covariance_Matrix.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/115328
dc.identifier.urnURN:NBN:fi:aalto-202206224168
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofseriesIEEE Transactions on Signal Processingen
dc.relation.ispartofseriesVolume 70en
dc.rightsopenAccessen
dc.subject.keywordbandingen_US
dc.subject.keywordelliptically symmetric distributionsen_US
dc.subject.keywordregu- larizationen_US
dc.subject.keywordSample covariance matrixen_US
dc.subject.keywordshrinkageen_US
dc.subject.keywordsphericityen_US
dc.subject.keywordtaperingen_US
dc.titleRegularized Tapered Sample Covariance Matrixen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
Files