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Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. This book enables readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven parts?each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB solutions.
Wiley-IEEE Press eBook Chapters
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This prelims comprise: Half Title Title Copyright Dedication Contents Preface Notation Symbols Acknowledgments View full abstract»
This chapter contains sections titled: Variance of a Random Variable Dependent Random Variables Complex-Valued Random Variables Vector-Valued Random Variables Gaussian Random Vectors View full abstract»
This chapter contains sections titled: Hermitian and Positive-Definite Matrices Range Spaces and Nullspaces of Matrices Schur Complements Cholesky Factorization QR Decomposition Singular Value Decomposition Kronecker Products View full abstract»
This chapter contains sections titled: Cauchy-Riemann Conditions Scalar Arguments Vector Arguments View full abstract»
This chapter contains sections titled: Estimation Without Observations Estimation Given Dependent Observations Orthogonality Principle Gaussian Random Variables View full abstract»
This chapter contains sections titled: Optimal Estimator in the Vector Case Spherically Invariant Gaussian Variables Equivalent Optimization Criterion View full abstract»
This chapter contains sections titled: Summary of Main Results Bibliographic Notes View full abstract»
This chapter contains sections titled: Problems Computer Project View full abstract»
This chapter contains sections titled: Mean-Square Error Criterion Minimization by Differentiation Minimization by Completion of Squares Minimization of the Error Covariance Matrix Optimal Linear Estimator View full abstract»
This chapter contains sections titled: Design Examples Orthogonality Condition Existence of Solutions Nonzero-Mea Variables View full abstract»
This chapter contains sections titled: Estimation Using Linear Relations Application: Channel Estimation Application: Block Data Estimation Application: Linear Channel Equalization Application: Multiple-Antenna Receivers View full abstract»
This chapter contains sections titled: Minimum-Variance Unbiased Estimation Example: Mean Estimation Application: Channel and Noise Estimation Application: Decision Feedback Equalization Application: Antenna Beamforming View full abstract»
This chapter contains sections titled: Innovations Process State-Space Model Recursion for the State Estimator Computing the Gain Matrix Riccati Recursion Covariance From Measurement and Time-Update Form View full abstract»
This chapter contains sections titled: Summary of Mean Results Bibliographic Notes View full abstract»
This chapter contains sections titled: Problems Computer Projects View full abstract»
This chapter contains sections titled: Linear Estimation Problem Steepest-Descent Method More General Cost Functions View full abstract»
This chapter contains sections titled: Modes of Convergence Optimal Step-Size Weight-Error Vector Convergence Time Constants Learning Curve Contour Curves of the Error Surface Iteration-Dependent Step-Sizes Newton's Method View full abstract»
This chapter contains sections titled: Motivation Instantaneous Approximation Computational Cost Least-Perturbation Property Application: Adaptive Channel Estimation Application: Adaptive Channel Equalization Application: Decision-Feedback Equalization Ensemble-Average Learning Curves View full abstract»
This chapter contains sections titled: Instantaneous Approximation Computational Cost Power Normalization Least-Perturbation Property View full abstract»
This chapter contains sections titled: Non-Blind Algorithms Blind Algorithms Some Properties View full abstract»
This chapter contains sections titled: Instantaneous Approximation Computational Cost Least-Perturbation Property Affine Projection Interpretation View full abstract»
This chapter contains sections titled: Instantaneous Approximation Computational Cost View full abstract»
This chapter contains sections titled: Performance Measure Stationary Data Model Energy Conservation Relation Variance Relation Appendix: Energy Relation Interpretations View full abstract»
This chapter contains sections titled: Variance Relation Small Step-Sizes Separation Principle White Gaussian Input Statement of Results Simulation Results View full abstract»
This chapter contains sections titled: Separation Principle Simulation Results Appendix: Relating NLMS to LMS View full abstract»
This chapter contains sections titled: Real-Valued Data Complex-Valued Data Simulation Results View full abstract»
This chapter contains sections titled: Performance of RLS Performance of Other Filters Performance Table for Small Step-Sizes View full abstract»
This chapter contains sections titled: Motivation Nonstationary Data Model Energy Conservation Relation Variance Relation View full abstract»
This chapter contains sections titled: Performance of LMS Performance of NLMS Performance of Sign-Error LMS Performance of RLS Comparison of Tracking Performance Comparing RLS and LMS Performance of Other Filters Performance Table for Small Step-Sizes View full abstract»
This chapter contains sections titled: Data Model Data-Normalized Adaptive Filters Weighted Energy Conservation Relation Weighted Variance Relation View full abstract»
This chapter contains sections titled: Mean and Variance Relations Mean Behavior Mean-Square Behavior Mean-Square Stability Steady-State Performance Small Step-Size Approximations Appendix: Convergence Time View full abstract»
This chapter contains sections titled: Mean and Variance Relations Mean-Square Stability and Performance Small Step-Size Approximations Appendix: Averaging Analysis View full abstract»
This chapter contains sections titled: NLMS Filter Data-Normalized Filters Appendix: Stability Bound Appendix: Stability of NLMS View full abstract»
This chapter contains sections titled: Transform-Domain Filters DFT-Domain LMS DCT-Domain LMS Appendix: DCT-Transformed Regressors View full abstract»
This chapter contains sections titled: Motivation Block Data Formulation Block Convolution View full abstract»
This chapter contains sections titled: DFT Block Adaptive Filters Subband Adaptive Filters Appendix: Another Constrained Filter Appendix: Overlap-Add Block Adaptive Filters View full abstract»
This chapter contains sections titled: Least-Squares Problem Geometric Argument Algebraic Arguments Properties of Least-Squares Solution Projection Matrices Weighted Least-Squares Regularized Least-Squares Weighted Regularized Least-Squares View full abstract»
This chapter contains sections titled: Motivation RLS Algorithm Regularization Conversion Factor Time-Update of the Minimum Cost Exponentially-Weighted RLS Algorithm View full abstract»
This chapter contains sections titled: Equivalence in Linear Estimation Kalman Filtering and Recursive Least-Squares Appendix: Extended RLS Algorithms View full abstract»
This chapter contains sections titled: Backward Order-Update Relations Forward Order-Update Relations Time-Update Relation View full abstract»
This chapter contains sections titled: Some Difficulties Square-Root Factors Preservation Properties Motivation for Array Methods View full abstract»
This chapter contains sections titled: Givens Rotations Householder Transformations View full abstract»
This chapter contains sections titled: Inverse QR Algorithm QR Algorithm Extended QR Algorithm Appendix: Array Kalman Filters View full abstract»
This chapter contains sections titled: Hyperbolic Givens Rotations Hyperbolic Householder Transformations Hyperbolic Basis Rotations View full abstract»
This chapter contains sections titled: Time-Update of the Gain Vector Time-Update of the Conversion Factor Initial Conditions Array Algorithm Appendix: Chandrasekhar Filter View full abstract»
This chapter contains sections titled: Regularized Backward Prediction Regularized Forward Prediction Low-Rank Factorization View full abstract»
This chapter contains sections titled: Fast Transversal Filter Faest Filter Fast Kalman Filter Stability Issues View full abstract»
This chapter contains sections titled: Motivation For Lattice Filters Joint Process Estimation Backward Estimation Problem Forward Estimation Problem Time and Order-Update Relations View full abstract»
This chapter contains sections titled: Significance of Data Structure A Posteriori-Based Lattice Filter A Priori-Based Lattice Filter View full abstract»
This chapter contains sections titled: A Priori Error-Feedback Lattice Filter A Posteriori Error-Feedback Lattice Filter Normalized Lattice Filter View full abstract»
This chapter contains sections titled: Order-Update of Output Estimation Errors Order-Update of Backward Estimation Errors Order-Update of Forward Estimation Errors Significance of Data Structure View full abstract»
This chapter contains sections titled: Indefinite Least-Squares Formulation Recursive Minimization Algorithm Time-Update of the Minimum Cost Singular Weighting Matrices Appendix: Stationary Points Appendix: Inertia Conditions View full abstract»
This chapter contains sections titled: A Posteriori-Based Robust Filters -NLMS Algorithm A Priori-Based Robust Filters LMS Algorithm Filters View full abstract»
This chapter contains sections titled: Robustness of LMS Robustness of -NLMS Robustness of RLS View full abstract»
No Abstract. View full abstract»
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