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A generalization of weighted subspace fitting to full-rank models
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0002-3599-5584
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0003-2298-6774
2001 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 49, no 5, 1002-1012 p.Article in journal (Refereed) Published
Abstract [en]

The idea of subspace fitting provides a popular framework for different applications of parameter estimation and system identification. Previously, some algorithms have been suggested based on similar ideas, for a sensor array processing problem where the underlying data model is not low rank. We show that two of these algorithms (DSPE and DISPARE) fail to give consistent estimates and introduce a general class of subspace fitting-like algorithms for consistent estimation of parameters from a possibly full-rank data model. The asymptotic performance is analyzed, and an optimally weighted algorithm is derived. The result gives a lower bound on the estimation performance for any estimator based on a low-rank approximation of the linear space spanned by the sample data. We show that in general, for full-rank data models, no subspace-based method can reach the Cramer-Rao lower bound (CRB)

Place, publisher, year, edition, pages
2001. Vol. 49, no 5, 1002-1012 p.
Keyword [en]
array signal processing, eigenvalues and eigenfunctions, nonlinear estimation, parameter estimation, performance analysis, scattering parameters, statistical analysis, direction-of-arrival, distributed sources, angular spread, array, signals, performance, estimators, amplitude
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-20535DOI: 10.1109/78.917804ISI: 000168093600009OAI: diva2:339231
QC 20100525 QC 20111107Available from: 2010-08-10 Created: 2010-08-10 Last updated: 2012-01-09Bibliographically approved

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