Adaptive Signal Processing: Next Generation Solutions by Tulay Adali, Simon Haykin

April 3, 2017 | Waves Wave Mechanics | By admin | 0 Comments

By Tulay Adali, Simon Haykin

Leading specialists current the most recent learn leads to adaptive sign processing

contemporary advancements in sign processing have made it transparent that major functionality profits could be accomplished past these achieveable utilizing general adaptive filtering techniques. Adaptive sign Processing provides the following iteration of algorithms that might produce those wanted effects, with an emphasis on vital functions and theoretical developments. This hugely targeted source brings jointly prime professionals within the box writing at the key issues of value, every one on the leading edge of its personal quarter of area of expertise. It starts off through addressing the matter of optimization within the advanced area, absolutely constructing a framework that allows taking complete benefit of the facility of complex-valued processing. Then, the demanding situations of multichannel processing of complex-valued indications are explored. This finished quantity is going directly to conceal rapid processing, monitoring within the subspace area, nonlinear sequential nation estimation, and speech-bandwidth extension.

  • Examines the seven most vital issues in adaptive filtering that would outline the next-generation adaptive filtering options

  • Introduces the strong adaptive sign processing tools built in the final ten years to account for the features of real-life information: non-Gaussianity, non-circularity, non-stationarity, and non-linearity

  • beneficial properties self-contained chapters, quite a few examples to elucidate strategies, and end-of-chapter difficulties to enhance realizing of the cloth

  • includes contributions from stated leaders within the box

  • features a recommendations guide for teachers

Adaptive sign Processing is a useful software for graduate scholars, researchers, and practitioners operating within the parts of sign processing, communications, controls, radar, sonar, and biomedical engineering.Content:
Chapter 1 Complex?Valued Adaptive sign Processing (pages 1–85): Tulay Adali and Hualiang Li
Chapter 2 strong Estimation concepts for Complex?Valued Random Vectors (pages 87–141): Esa Ollila and Visa Koivunen
Chapter three faster Equalization (pages 143–210): Philip A. Regalia
Chapter four Subspace monitoring for sign Processing (pages 211–270): Jean Pierre Delmas
Chapter five Particle Filtering (pages 271–331): Petar M. Djuric and Monica F. Bugallo
Chapter 6 Nonlinear Sequential country Estimation for fixing Pattern?Classification difficulties (pages 333–348): Simon Haykin and Ienkaran Arasaratnam
Chapter 7 Bandwidth Extension of Telephony Speech (pages 349–391): Bernd Iser and Gerhard Schmidt

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Extra resources for Adaptive Signal Processing: Next Generation Solutions

Sample text

A widely linear filter augments the data vector with the conjugate of the data, thus providing both the covariance and pseudo-covariance information for a filter designed using a second-order error criterion. The assumption of circularity is a limiting assumption as, in practice, the real and imaginary parts of a signal typically will have correlations and/or different variances. One of the reasons for the prevalence of the circularity assumption in signal processing has been due to the inherent assumption of stationarity of signals.

The covariance function for a complex discrete-time random process X(n) is written as c(n, m) ¼ E{X(n)X Ã (m)} À E{X(n)}E{X Ã (m)} and the correlation function as EfX(n)XÃ (m)g. To completely define the second-order statistics, as in the case of random variables, we also define the pseudo-covariance function [81]—also called the complementary covariance [101] and the relation function [91]—as p(n, m) ¼ E{X(n)X(m)} À E{X(n)}E{X(m)}: In the sequel, to simplify the expressions, we assume zero mean random processes, and hence, the covariance and correlation functions coincide.

2 COMPLEX-VALUED ADAPTIVE SIGNAL PROCESSING Vector Optimization in CN Given a real-differentiable cost function f (w): CN 7! 2. 4, it is the gradient with respect to the conjugate of the variable that results in the maximum change for the complex case. 24) leads to a nonpositive increment, D f ¼ À2mkrw(n) f k2 , while the update that uses Dw ¼ Àmrw(n) f , leads to changes of the form D f ¼ À2mRe{hrwà (n) f , rw(n) f i}, which are not guaranteed to be nonpositive. Here, we consider only first-order corrections since m is typically very small.

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