Download Array and Statistical Signal Processing by Abdelhak M. Zoubir, Mats Viberg, Rama Chellappa and Sergios PDF

By Abdelhak M. Zoubir, Mats Viberg, Rama Chellappa and Sergios Theodoridis (Eds.)

This 3rd quantity of a 5 quantity set, edited and authored by means of international top specialists, provides a assessment of the rules, tools and strategies of significant and rising learn subject matters and applied sciences in array and statistical sign processing.

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    • Quick instructional stories of significant and rising issues of study in array and statistical sign processing
    • Presents middle ideas and indicates their application
    • Reference content material on middle ideas, applied sciences, algorithms and purposes
    • Comprehensive references to magazine articles and different literature on which to construct extra, extra particular and special wisdom
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    14 (3) (1986) 1080–1100. [11] J. Rissanen, Modeling by the shortest data description, Automatica 14 (1978) 465–471. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, Prentice Hall, 1998. [13] S. Kay, Exponentially embedded families—new approaches to model order estimation, IEEE Trans. Aerosp. Electron. Syst. 41 (1) (2005) 333–344. [14] H. Bozdogan, Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions, Psychometrika 52 (3) (1987) 345–370.

    Since the underfitting risk does not seem to be unduly large in many practical experiments, many researchers recommend the use of AICc instead of AIC. 4 Information and Coding Theory Based Methods 19 a sound theoretical justification only in the regression model with normal errors, where it is an unbiased estimate of the relative K-L information; note that AIC is by its construction only an asymptotically unbiased estimate of the relative K-L information. Besides the regression model, the reasonings for C N , lay merely on its property of reducing the risk of overfitting.

    5) where w∗ (t) denotes the conjugated window function. , STFTII (t, ) = e− j t STFT(t, ) for real valued windows w(τ ). In the sequel we will mainly use the first definition of the STFT [23]. Example 1. 6) x(t) = δ(t − t1 ) + δ(t − t2 ) + e j 1 t + e j 2 t . 7) where W ( ) is the FT of the used window. 3 for various window lengths, along with the ideal representation. 8) where ∗ denotes convolution in . It may be interpreted as an inverse FT of the frequency localized version of X ( ), with localization window W ( ) = FT{w(τ )}.

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