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A diagonal growth curve model and some signal processing applications
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2006 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 9, 3363-3371 p.Article in journal (Refereed) Published
Abstract [en]

We consider a variation of the growth-curve (GC) model, referred to as the diagonal growth-curve (DGC) model, where the steering vectors and waveforms are both known and the complex amplitude matrix is constrained to be diagonal. A closed-form approximate maximum likelihood (AML) estimator for this model is derived based on the maximum likelihood principle. We analyze the statistical properties of this method theoretically and show that the AML estimate is unbiased and asymptotically statistically efficient for a large snapshot number. Via several numerical examples in array signal processing and spectral analysis, we also show that the proposed AML estimator can achieve better estimation accuracy and exhibit greater robustness than the best existing methods.

Place, publisher, year, edition, pages
2006. Vol. 54, no 9, 3363-3371 p.
Keyword [en]
array signal processing, complex amplitude estimation, Cramer-Rao bound, generalized least squares, growth-curve model, least squares, maximum likelihood estimation, mean squared error, spectral analysis
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-20426DOI: 10.1109/TSP.2006.879296ISI: 000239977000010OAI: oai:DiVA.org:uu-20426DiVA: diva2:48199
Available from: 2006-12-08 Created: 2006-12-08 Last updated: 2011-06-21Bibliographically approved

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