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Subspace-based parameter estimation of symmetric noncausal autoregressive signals from noisy measurements
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
1999 (English)In: IEEE TRANSACTIONS ON SIGNAL PROCESSING, ISSN 1053-587X, Vol. 47, no 2, 321-331 p.Article in journal (Other scientific) Published
Abstract [en]

Symmetric noncausal auto-regressive signals (SNARS) arise in several, mostly spatial, signal processing applications. In this paper, we introduce a subspace fitting approach for parameter estimation of SNARS from noise-corrupted measurements. We show that

Place, publisher, year, edition, pages
1999. Vol. 47, no 2, 321-331 p.
Keyword [en]
autoregressive models; deconvolution; noncausal modeling; subspace-based parameter estimation; 2-SIDED LINEAR PREDICTION; IMPULSE RESPONSES; MUSIC
Identifiers
URN: urn:nbn:se:uu:diva-27357OAI: oai:DiVA.org:uu-27357DiVA: diva2:55252
Note
Addresses: Stoica P, Uppsala Univ, Syst & Control Grp, Uppsala, Sweden. Uppsala Univ, Syst & Control Grp, Uppsala, Sweden.Available from: 2005-05-05 Created: 2005-05-05 Last updated: 2011-01-14

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