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A Recursive Restricted Total Least-squares Algorithm

Rhode, S. 1; Usevich, K.; Markovsky, I.; Gauterin, F. 1
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)


We show that the generalized total least squares (GTLS) problem with a singular noise covariance matrix is equivalent to the restricted total least squares (RTLS) problem and propose a recursive method for its numerical solution. The method is based on the generalized inverse iteration. The estimation error covariance matrix and the estimated augmented correction are also characterized and computed recursively. The algorithm is cheap to compute and is suitable for online implementation. Simulation results in least squares (LS), data least squares (DLS), total least squares (TLS), and RTLS noise scenarios show fast convergence of the parameter estimates to their optimal values obtained by corresponding batch algorithms.

Volltext §
DOI: 10.5445/IR/1000043463
DOI: 10.1109/TSP.2014.2350959
Zitationen: 27
Web of Science
Zitationen: 26
Zitationen: 26
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2014
Sprache Englisch
Identifikator ISSN: 0018-9278, 0096-1620, 0096-3518, 1053-587X, 1558-2582, 1558-2663, 1941-0476
KITopen-ID: 1000043463
Erschienen in IEEE transactions on signal processing
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Band 62
Heft 21
Seiten 5652-5662
Bemerkung zur Veröffentlichung This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication. The final version of record is available at,
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