Data pre-processing for high-resolution adaptive algorithms

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/86134

dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.contributor.author	Vázquez Grau, Gregorio
dc.contributor.author	Gasull Llampallas, Antoni
dc.date	1988
dc.identifier.citation	Vazquez, G., Gasull, A. Data pre-processing for high-resolution adaptive algorithms. A: IEEE International Symposium on Circuits and Systems. "1988 IEEE International Symposium on Circuits and Systems: Espo, Finland, 7-9 June 1988". Espoo: Institute of Electrical and Electronics Engineers (IEEE), 1988, p. 763-766.
dc.identifier.citation	10.1109/ISCAS.1988.15037
dc.identifier.uri	http://hdl.handle.net/2117/86134
dc.language.iso	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=15037
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject	Algorithms
dc.subject	Filtering and prediction theory
dc.subject	Spectral analysis
dc.subject	Algorismes
dc.title	Data pre-processing for high-resolution adaptive algorithms
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	The inclusion of adaptive methods in high-resolution spectral estimation algorithms is considered. The generation of a complete family of spectral estimators from the normalized maximum-likelihood method (MLM) is discussed. It is shown how the generalized power MLM can be used to generate adaptive schemes for improving resolution. The authors propose the substitution of the conventional mean-square filtering error by quadratic objectives built as inner products of the coefficient error vector of the estimator filter
dc.description.abstract	Award-winning

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