Sparse spectral-line estimation for nonuniformly sampled multivariate time series: SPICE, LIKES and MSBL
2012 (English)In: 2012 Proceedings Of The 20th European Signal Processing Conference (EUSIPCO), 2012, 445-449 p.Conference paper (Refereed)
In this paper we deal with the problem of spectral-line analysis ofnonuniformly sampled multivariate time series for which we introduce two methods: the ﬁrst method named SPICE (sparse iterativecovariance based estimation) is based on a covariance ﬁtting framework whereas the second method named LIKES (likelihood-basedestimation of sparse parameters) is a maximum likelihood technique. Both methods yield sparse spectral estimates and they donot require the choice of any hyperparameters. We numericallycompare the performance of SPICE and LIKES with that of the recently introduced method of multivariate sparse Bayesian learning(MSBL).
Place, publisher, year, edition, pages
2012. 445-449 p.
, European Signal Processing Conference, ISSN 2219-5491
IdentifiersURN: urn:nbn:se:uu:diva-184854ISBN: 978-146731068-0OAI: oai:DiVA.org:uu-184854DiVA: diva2:568071
20th European Signal Processing Conference (EUSIPCO 2012), 27-31 Aug, 2012, Bucharest