Estimation of rank deficient covariance matrices with Kronecker structure

Mario H. Castaneda, Josef A. Nossek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Given a set of observations, the estimation of covariance matrices is required in the analysis of many applications. To this end, any know structure of the covariance matrix can be taken into account. For instance, in case of separable processes, the covariance matrix is given by the Kronecker product of two factor matrices. Assuming the covariance matrix is full rank, the maximum likelihood (ML) estimate in this case leads to an iterative algorithm known as the flip-flop algorithm in the literature. In this work, we first generalize the flip-flop algorithm to the case when the covariance matrix is rank deficient, which happens to be the case in several situations. In addition, we propose a non-iterative estimation approach which incurs in a performance loss compared to the ML estimate, but at the expense of less complexity.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages394-398
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 4 May 20149 May 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Country/TerritoryItaly
CityFlorence
Period4/05/149/05/14

Keywords

  • Kronecker product
  • covariance matrix estimation
  • flip-flop algorithm
  • separable processes

Fingerprint

Dive into the research topics of 'Estimation of rank deficient covariance matrices with Kronecker structure'. Together they form a unique fingerprint.

Cite this