Titlebook: Blind Estimation Using Higher-Order Statistics; Asoke Kumar Nandi (David Jardine Chair of Electric Book 1999 Springer Science+Business Med

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https://doi.org/10.1007/3-540-29252-7his is in effect inverse system modelling, an architecture that is well-known in adaptive filtering theory. The cascade of channel and equaliser should constitute an identity operation, with the exception of a time delay and linear phase shift being allowed.

Foreshadow

https://doi.org/10.1007/3-540-29252-7 [35] use only the system output data to identify the system model under the assumption that the system is driven by an independent and identically distributed (i.i.d.) non-Gaussian sequence that is unobservable.

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https://doi.org/10.1007/3-540-29252-7 have witnessed a large number of theoretical developments as well as real applications. The goal in producing this book has been to focus on the blind estimation area and to record some of these developments in this field.

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een years have witnessed a large number of theoreticaldevelopments as well as real applications. .Blind EstimationUsing. .Higher-Order Statistics. focuses on the blind estimationarea and records some of the major developments in this field. ..Blind Estimation Using Higher-Order Statistics. is a welc

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,Lernprodukte und Nützliches im Internet,st two moments, the analysis of linear systems and signals has so far been quite effective in many circumstances. It has nevertheless been limited by the assumptions of Gaussianity, minimum phase systems, linear systems, etc.