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2015 | Buch

Discrete-Time Signals and Circuits Fundamentals Kapitel lesen Erstes Kapitel lesen

Fundamentals of Adaptive Signal Processing

verfasst von: Aurelio Uncini

Verlag: Springer International Publishing

Buchreihe : Signals and Communication Technology

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

This book is an accessible guide to adaptive signal processing methods that equips the reader with advanced theoretical and practical tools for the study and development of circuit structures and provides robust algorithms relevant to a wide variety of application scenarios. Examples include multimodal and multimedia communications, the biological and biomedical fields, economic models, environmental sciences, acoustics, telecommunications, remote sensing, monitoring and in general, the modeling and prediction of complex physical phenomena. The reader will learn not only how to design and implement the algorithms but also how to evaluate their performance for specific applications utilizing the tools provided. While using a simple mathematical language, the employed approach is very rigorous. The text will be of value both for research purposes and for courses of study.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Discrete-Time Signals and Circuits Fundamentals
Abstract
In all real physical situations, in the communication processes, and in the wider meaning terms, it is usual to think the signals as variable physical quantity or symbols, to which is associated a certain information. A signal that carries information is variable and, in general, we are interested in the time (or other)-domain variation: signal ⟺ function of time or, more generally, signal ⟺ function of several variables.
Aurelio Uncini
Chapter 2. Introduction to Adaptive Signal and Array Processing
Abstract
In the study of signal processing techniques, the term adaptive is used when a system (analogue or digital) is able to adjust their own parameters in response to external stimulations. In other words, an adaptive system autonomously changes its internal parameters for achieving a certain processing goal such as, for example, the minimization of the effect of noise overlying the signal of interest (SOI).
Aurelio Uncini
Chapter 3. Optimal Linear Filter Theory
Abstract
This chapter introduces the Wiener statistical theory of linear filtering that is a reference for the study and understanding of adaptive methods shown below in the text.
Aurelio Uncini
Chapter 4. Least Squares Method
Abstract
This chapter introduces the deterministic counterpart of the statistical Wiener filter theory. The problems of adaptation are addressed in the case where the filter input signals are sequences generated by linear deterministic models without any assumption on their statistics.
Aurelio Uncini
Chapter 5. First-Order Adaptive Algorithms
Abstract
In the two previous chapters, attention was paid on the algorithms for the determination or estimation of filters parameters with a methodology that provides knowledge of the processes statistics or their a priori calculated estimation on an appropriate window signal length. In particular, with regard to the choice of the cost function (CF) to be minimized J(w), the attention has been paid both to the solution methods of the Wiener–Hopf normal equations, which provide a stochastic optimization MMSE solution, and to the form of Yule–Walker that assumed a deterministic (or stochastic approximated) approach, by a least squares error (LSE) solution.
Aurelio Uncini
Chapter 6. Second-Order Adaptive Algorithms
Abstract
This chapter introduces the second-order algorithms for the solution of the Yule–Walker normal equations with online recursive methods, such as error sequential regression (ESR) algorithm [1, 2].
Aurelio Uncini
Chapter 7. Block and Transform Domain Algorithms
Abstract
In this chapter structures and algorithms for the implementation of adaptive filters (AF) with the purpose of improving the convergence speed and reducing the computational cost are presented. In particular, they are classified as block and online methods, operating in the time domain, in the transformed domain (typically the frequency domain), and in frequency subbands mode.
Aurelio Uncini
Chapter 8. Linear Prediction and Recursive Order Algorithms
Abstract
The problem of optimal filtering consists in determining the filter coefficients w opt through the normal equations solution in the Wiener stochastic or the Yule–Walker deterministic form. In practice this is achieved by inverting the correlation matrix R or its estimate R xx . Formally, the problem is simple. Basically, however, this inversion is most often of ill-posed nature. The classical matrix inversion approaches are not robust and in certain applications cannot be implemented. In fact, most of the adaptive signal processing problems concern the computational cost and robustness of the estimation algorithms.
Aurelio Uncini
Chapter 9. Discrete Space-Time Filtering
Abstract
In many scientific and technological areas, acquiring signals relating to the same stochastic process, with a multiplicity of homogeneous sensors and arranged in different spatial positions, is sometimes necessary or simply useful. For example, this is the case of the acquisition of biomedical signals, such as electroencephalogram (EEG), electrocardiogram (ECG), and tomography or of telecommunications signals such as those deriving from the antenna arrays and radars, the detection of seismic signals, the sonar, and the microphone arrays for the acquisition of acoustic signals. The phenomena measured in these applications may have different physical nature but, in any case, the array of sensors, or receivers, is made to acquire processes concerning the propagation of electromagnetic or mechanical waves coming from one or more radiation sources.
Aurelio Uncini
Backmatter
Metadaten
Titel
Fundamentals of Adaptive Signal Processing
verfasst von
Aurelio Uncini
Copyright-Jahr
2015
Electronic ISBN
978-3-319-02807-1
Print ISBN
978-3-319-02806-4
DOI
https://doi.org/10.1007/978-3-319-02807-1

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