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Informationen zum Autor Behrouz Farhang-Boroujeny, University of Utah, USA Klappentext This second edition of Adaptive Filters: Theory and Applications has been updated throughout to reflect the latest developments in this field; notably an increased coverage given to the practical applications of the theory to illustrate the much broader range of adaptive filters applications developed in recent years. The book offers an easy to understand approach to the theory and application of adaptive filters by clearly illustrating how the theory explained in earlier chapters of the book is modified for the various applications discussed in detail in later chapters. This integrated approach makes the book a valuable resource for graduate students; and the inclusion of more advanced applications including antenna arrays and wireless communications makes it a suitable technical reference for engineers, practitioners and researchers.Key features:* Offers a thorough treatment of the theory of adaptive signal processing; incorporating new material on transform domain, frequency domain, subband adaptive filters, acoustic echo cancellation and active noise control.* Provides an in-depth study of applications which now includes extensive coverage of OFDM, MIMO and smart antennas.* Contains exercises and computer simulation problems at the end of each chapter.* Includes a new companion website hosting MATLAB(r) simulation programs which complement the theoretical analyses, enabling the reader to gain an in-depth understanding of the behaviours and properties of the various adaptive algorithms. Zusammenfassung This second edition of Adaptive Filters: Theory and Applications has been updated throughout to reflect the latest developments in this field; notably an increased coverage given to the practical applications of the theory to illustrate the much broader range of adaptive filters applications developed in recent years. Inhaltsverzeichnis 1 Introduction 11.1 Linear Filters 11.2 Adaptive Filters 21.3 Adaptive Filter Structures 31.4 Adaptation Approaches 71.4.1 Approach Based on Wiener Filter Theory 71.4.2 Method of Least-Squares 81.5 Real and Complex Forms of Adaptive Filters 91.6 Applications 91.6.1 Modeling 101.6.2 InverseModeling 111.6.3 Linear Prediction 151.6.4 Interference Cancellation 202 Discrete-Time Signals and Systems 292.1 Sequences and z-Transform 292.2 Parseval's Relation 332.3 System Function 342.4 Stochastic Processes 362.4.1 Stochastic Averages 362.4.2 z-Transform Representations 382.4.3 The power spectral density 392.4.4 Response of Linear Systems to Stochastic Processes 412.4.5 Ergodicity and Time Averages 443 Wiener Filters 493.1 Mean-Squared Error Criterion 493.2 Wiener Filter - Transversal, Real-valued Case 513.3 Principle of Orthogonality 573.4 Normalized Performance Function 593.5 Extension to Complex-Valued Case 593.6 UnconstrainedWiener Filters 623.6.1 Performance Function 623.6.2 Optimum Transfer Function 653.6.3 Modeling 673.6.4 InverseModeling 703.6.5 Noise Cancellation 743.7 Summary and Discussion 804 Eigenanalysis and Performance Surface 914.1 Eigenvalues and Eigenvectors 914.2 Properties of Eigenvalues and Eigenvectors 924.3 Performance Surface 1045 Search Methods 1215.1 Method of Steepest-Descent 1225.2 Learning Curve 1285.3 Effect of Eigenvalue-Spread 1325.4 Newton's Method 1335.5 An Alternative Interpretation of Newton's Algorithm 1366 LMS Algorithm 1416.1 Derivation of LMS Algorithm 1416.2 Average Tap-Weight Behavior of the LMS Algorithm 1436.3 MSE Behavior of the LMS Algorithm 1466.3.1 Learning Curve 1486.3.2 Weight-Error Correlation Matrix 1506.3.3 Excess MSE and Misadjustment 1536.3.4 Stability 1556.3.5 The Effect of Initial Values of TapWeights on the Transient Behavior of the LMS Algorithm 1566.4 Computer Simulations 1576.4.1 Syst...
