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Statistical Signal Processing Frequency Estimation /

Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important r...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Kundu, Debasis (Autor), Nandi, Swagata (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New Delhi : Springer India : Imprint: Springer, 2012.
Edición:1st ed. 2012.
Colección:SpringerBriefs in Statistics,
Temas:
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Statistical Signal Processing  |h [electronic resource] :  |b Frequency Estimation /  |c by Debasis Kundu, Swagata Nandi. 
250 |a 1st ed. 2012. 
264 1 |a New Delhi :  |b Springer India :  |b Imprint: Springer,  |c 2012. 
300 |a XVII, 132 p. 21 illus.  |b online resource. 
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490 1 |a SpringerBriefs in Statistics,  |x 2191-5458 
505 0 |a 1 Introduction -- 2 Notations and Preliminaries -- 3 Estimation of Frequencies -- 4 Asymptotic Properties -- 5 Estimating the Number of Components -- 6 Real Data Example -- 7 Multidimensional Models -- 8 Related Models -- References -- Index. 
520 |a Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important role in analyzing the signal. Statistics is also used in the formulation of the appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Statistical signal processing basically refers to the analysis of random signals using appropriate statistical techniques. The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues involved in solving them. We discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. We have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research. 
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