{"product_id":"adaptive-filters-hardback-9780470253885","title":"Adaptive Filters (Hardback) 9780470253885","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAdaptive Filters\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAli H. Sayed (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470253885, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 20 May 2008\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e832 pages\u003cbr\u003e25.7 x 20.1 x 3.1 cm, 1.678 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eAdaptive 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface and Acknowledgments.  \u003cp\u003eNotation and Symbols.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBACKGROUND MATERIAL.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA. Random Variables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Variance of a Random Variable.\u003c\/p\u003e \u003cp\u003eA.2 Dependent Random Variables.\u003c\/p\u003e \u003cp\u003eA.3 Complex-Valued Random Variables.\u003c\/p\u003e \u003cp\u003eA.4 Vector-Valued Random Variables.\u003c\/p\u003e \u003cp\u003eA.5 Gaussian Random Vectors.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB. Linear Algebra.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Hermitian and Positive-Definite Matrices.\u003c\/p\u003e \u003cp\u003eB.2 Range Spaces and Nullspaces of Matrices.\u003c\/p\u003e \u003cp\u003eB.3 Schur Complements.\u003c\/p\u003e \u003cp\u003eB.4 Cholesky Factorization.\u003c\/p\u003e \u003cp\u003eB.5 QR Decomposition.\u003c\/p\u003e \u003cp\u003eB.6 Singular Value Decomposition.\u003c\/p\u003e \u003cp\u003eB.7 Kronecker Products.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC. Complex Gradients.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 Cauchy-Riemann Conditions.\u003c\/p\u003e \u003cp\u003eC.2 Scalar Arguments.\u003c\/p\u003e \u003cp\u003eC.3 Vector Arguments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I: OPTIMAL ESTIMATION.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Scalar-Valued Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Estimation Without Observations.\u003c\/p\u003e \u003cp\u003e1.2 Estimation Given Dependent Observations.\u003c\/p\u003e \u003cp\u003e1.3 Orthogonality Principle.\u003c\/p\u003e \u003cp\u003e1.4 Gaussian Random Variables.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Vector-Valued Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Optimal Estimator in the Vector Case.\u003c\/p\u003e \u003cp\u003e2.2 Spherically Invariant Gaussian Variables.\u003c\/p\u003e \u003cp\u003e2.3 Equivalent Optimization Criterion.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II: LINEAR ESTIMATION.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Normal Equations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Mean-Square Error Criterion.\u003c\/p\u003e \u003cp\u003e3.2 Minimization by Differentiation.\u003c\/p\u003e \u003cp\u003e3.3 Minimization by Completion-of-Squares.\u003c\/p\u003e \u003cp\u003e3.4 Minimization of the Error Covariance Matrix.\u003c\/p\u003e \u003cp\u003e3.5 Optimal Linear Estimator.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Orthogonality Principle.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Design Examples.\u003c\/p\u003e \u003cp\u003e4.2 Orthogonality Condition.\u003c\/p\u003e \u003cp\u003e4.3 Existence of Solutions.\u003c\/p\u003e \u003cp\u003e4.4 Nonzero-Mean Variables.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Linear Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Estimation using Linear Relations.\u003c\/p\u003e \u003cp\u003e5.2 Application: Channel Estimation.\u003c\/p\u003e \u003cp\u003e5.3 Application: Block Data Estimation.\u003c\/p\u003e \u003cp\u003e5.4 Application: Linear Channel Equalization.\u003c\/p\u003e \u003cp\u003e5.5 Application: Multiple-Antenna Receivers.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Constrained Estimation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Minimum-Variance Unbiased Estimation.\u003c\/p\u003e \u003cp\u003e6.2 Example: Mean Estimation.\u003c\/p\u003e \u003cp\u003e6.3 Application: Channel and Noise Estimation.\u003c\/p\u003e \u003cp\u003e6.4 Application: Decision Feedback Equalization.\u003c\/p\u003e \u003cp\u003e6.5 Application: Antenna Beamforming.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Kalman Filter.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Innovations Process.\u003c\/p\u003e \u003cp\u003e7.2 State-Space Model.\u003c\/p\u003e \u003cp\u003e7.3 Recursion for the State Estimator.\u003c\/p\u003e \u003cp\u003e7.4 Computing the Gain Matrix.\u003c\/p\u003e \u003cp\u003e7.5 Riccati Recursion.\u003c\/p\u003e \u003cp\u003e7.6 Covariance Form.\u003c\/p\u003e \u003cp\u003e7.7 Measurement and Time-Update Form.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III: STOCHASTIC GRADIENT ALGORITHMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Steepest-Descent Technique.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Linear Estimation Problem.\u003c\/p\u003e \u003cp\u003e8.2 Steepest-Descent Method.\u003c\/p\u003e \u003cp\u003e8.3 More General Cost Functions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Transient Behavior.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Modes of Convergence.\u003c\/p\u003e \u003cp\u003e9.2 Optimal Step-Size.\u003c\/p\u003e \u003cp\u003e9.3 Weight-Error Vector Convergence.\u003c\/p\u003e \u003cp\u003e9.4 Time Constants.\u003c\/p\u003e \u003cp\u003e9.5 Learning Curve.\u003c\/p\u003e \u003cp\u003e9.6 Contour Curves of the Error Surface.\u003c\/p\u003e \u003cp\u003e9.7 Iteration-Dependent Step-Sizes.\u003c\/p\u003e \u003cp\u003e9.8 Newton?s Method.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. LMS Algorithm.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Motivation.\u003c\/p\u003e \u003cp\u003e10.2 Instantaneous Approximation.\u003c\/p\u003e \u003cp\u003e10.3 Computational Cost.\u003c\/p\u003e \u003cp\u003e10.4 Least-Perturbation Property.\u003c\/p\u003e \u003cp\u003e10.5 Application: Adaptive Channel Estimation.\u003c\/p\u003e \u003cp\u003e10.6 Application: Adaptive Channel Equalization.\u003c\/p\u003e \u003cp\u003e10.7 Application: Decision-Feedback Equalization.\u003c\/p\u003e \u003cp\u003e10.8 Ensemble-Average Learning Curves.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Normalized LMS Algorithm.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Instantaneous Approximation.\u003c\/p\u003e \u003cp\u003e11.2 Computational Cost.\u003c\/p\u003e \u003cp\u003e11.3 Power Normalization.\u003c\/p\u003e \u003cp\u003e11.4 Least-Perturbation Property.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Other LMS-Type Algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Non-Blind Algorithms.\u003c\/p\u003e \u003cp\u003e12.2 Blind Algorithms.\u003c\/p\u003e \u003cp\u003e12.3 Some Properties.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Affine Projection Algorithm.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Instantaneous Approximation.\u003c\/p\u003e \u003cp\u003e13.2 Computational Cost.\u003c\/p\u003e \u003cp\u003e13.3 Least-Perturbation Property.\u003c\/p\u003e \u003cp\u003e13.4 Affine Projection Interpretation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. RLS Algorithm.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Instantaneous Approximation.\u003c\/p\u003e \u003cp\u003e14.2 Computational Cost.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV: MEAN-SQUARE PERFORMANCE.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Energy Conservation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Performance Measure.\u003c\/p\u003e \u003cp\u003e15.2 Stationary Data Model.\u003c\/p\u003e \u003cp\u003e15.3 Energy Conservation Relation.\u003c\/p\u003e \u003cp\u003e15.4 Variance Relation.\u003c\/p\u003e \u003cp\u003e15.A Interpretations of the Energy Relation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16. Performance of LMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Variance Relation.\u003c\/p\u003e \u003cp\u003e16.2 Small Step-Sizes.\u003c\/p\u003e \u003cp\u003e16.3 Separation Principle.\u003c\/p\u003e \u003cp\u003e16.4 White Gaussian Input.\u003c\/p\u003e \u003cp\u003e16.5 Statement of Results.\u003c\/p\u003e \u003cp\u003e16.6 Simulation Results.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17. Performance of NLMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Separation Principle.\u003c\/p\u003e \u003cp\u003e17.2 Simulation Results.\u003c\/p\u003e \u003cp\u003e17.A Relating NLMS to LMS.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18. Performance of Sign-Error LMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Real-Valued Data.\u003c\/p\u003e \u003cp\u003e18.2 Complex-Valued Data.\u003c\/p\u003e \u003cp\u003e18.3 Simulation Results.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19. Performance of RLS and Other Filters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Performance of RLS.\u003c\/p\u003e \u003cp\u003e19.2 Performance of Other Filters.\u003c\/p\u003e \u003cp\u003e19.3 Performance Table for Small Step-Sizes.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20. Nonstationary Environments.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Motivation.\u003c\/p\u003e \u003cp\u003e20.2 Nonstationary Data Model.\u003c\/p\u003e \u003cp\u003e20.3 Energy Conservation Relation.\u003c\/p\u003e \u003cp\u003e20.4 Variance Relation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21. Tracking Performance.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Performance of LMS.\u003c\/p\u003e \u003cp\u003e21.2 Performance of NLMS.\u003c\/p\u003e \u003cp\u003e21.3 Performance of Sign-Error LMS.\u003c\/p\u003e \u003cp\u003e21.4 Performance of RLS.\u003c\/p\u003e \u003cp\u003e21.5 Comparison of Tracking Performance.\u003c\/p\u003e \u003cp\u003e21.6 Comparing RLS and LMS.\u003c\/p\u003e \u003cp\u003e21.7 Performance of Other Filters.\u003c\/p\u003e \u003cp\u003e21.8 Performance Table for Small Step-Sizes.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART V: TRANSIENT PERFORMANCE.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22. Weighted Energy Conservation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Data Model.\u003c\/p\u003e \u003cp\u003e22.2 Data-Normalized Adaptive Filters.\u003c\/p\u003e \u003cp\u003e22.3 Weighted Energy Conservation Relation.\u003c\/p\u003e \u003cp\u003e22.4 Weighted Variance Relation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23. LMS with Gaussian Regressors.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Mean and Variance Relations.\u003c\/p\u003e \u003cp\u003e23.2 Mean Behavior.\u003c\/p\u003e \u003cp\u003e23.3 Mean-Square Behavior.\u003c\/p\u003e \u003cp\u003e23.4 Mean-Square Stability.\u003c\/p\u003e \u003cp\u003e23.5 Steady-State Performance.\u003c\/p\u003e \u003cp\u003e23.6 Small Step-Size Approximations.\u003c\/p\u003e \u003cp\u003e23.A Convergence Time.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24. LMS with non-Gaussian Regressors.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Mean and Variance Relations.\u003c\/p\u003e \u003cp\u003e24.2 Mean-Square Stability and Performance.\u003c\/p\u003e \u003cp\u003e24.3 Small Step-Size Approximations.\u003c\/p\u003e \u003cp\u003e24.A Independence and Averaging Analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25. Data-Normalized Filters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 NLMS Filter.\u003c\/p\u003e \u003cp\u003e25.2 Data-Normalized Filters.\u003c\/p\u003e \u003cp\u003e25.A Stability Bound.\u003c\/p\u003e \u003cp\u003e25.B Stability of NLMS.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VI: BLOCK ADAPTIVE FILTERS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26. Transform Domain Adaptive Filters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Transform-Domain Filters.\u003c\/p\u003e \u003cp\u003e26.2 DFT-Domain LMS.\u003c\/p\u003e \u003cp\u003e26.3 DCT-Domain LMS.\u003c\/p\u003e \u003cp\u003e26.A DCT-Transformed Regressors.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27. Efficient Block Convolution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Motivation.\u003c\/p\u003e \u003cp\u003e27.2 Block Data Formulation.\u003c\/p\u003e \u003cp\u003e27.3 Block Convolution.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28. Block and Subband Adaptive Filters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 DFT Block Adaptive Filters.\u003c\/p\u003e \u003cp\u003e28.2 Subband Adaptive Filters.\u003c\/p\u003e \u003cp\u003e28.A Another Constrained DFT Block Filter.\u003c\/p\u003e \u003cp\u003e28.B Overlap-Add Block Adaptive Filters.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VII: LEAST-SQUARES METHODS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29. Least-Squares Criterion.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 Least-Squares Problem.\u003c\/p\u003e \u003cp\u003e29.2 Geometric Argument.\u003c\/p\u003e \u003cp\u003e29.3 Algebraic Arguments.\u003c\/p\u003e \u003cp\u003e29.4 Properties of Least-Squares Solution.\u003c\/p\u003e \u003cp\u003e29.5 Projection Matrices.\u003c\/p\u003e \u003cp\u003e29.6 Weighted Least-Squares.\u003c\/p\u003e \u003cp\u003e29.7 Regularized Least-Squares.\u003c\/p\u003e \u003cp\u003e29.8 Weighted Regularized Least-Squares.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30. Recursive Least-Squares.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e30.1 Motivation.\u003c\/p\u003e \u003cp\u003e30.2 RLS Algorithm.\u003c\/p\u003e \u003cp\u003e30.3 Regularization.\u003c\/p\u003e \u003cp\u003e30.4 Conversion Factor.\u003c\/p\u003e \u003cp\u003e30.5 Time-Update of the Minimum Cost.\u003c\/p\u003e \u003cp\u003e30.6 Exponentially-Weighted RLS Algorithm.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31. Kalman Filtering and RLS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e31.1 Equivalence in Linear Estimation.\u003c\/p\u003e \u003cp\u003e31.2 Kalman Filtering and Recursive Least-Squares.\u003c\/p\u003e \u003cp\u003e31.A Extended RLS Algorithms.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32. Order and Time-Update Relations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e32.1 Backward Order-Update Relations.\u003c\/p\u003e \u003cp\u003e32.2 Forward Order-Update Relations.\u003c\/p\u003e \u003cp\u003e32.3 Time-Update Relation.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VIII: ARRAY ALGORITHMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33. Norm and Angle Preservation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e33.1 Some Difficulties.\u003c\/p\u003e \u003cp\u003e33.2 Square-Root Factors.\u003c\/p\u003e \u003cp\u003e33.3 Norm and Angle Preservation.\u003c\/p\u003e \u003cp\u003e33.4 Motivation for Array Methods.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e34. Unitary Transformations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e34.1 Givens Rotations.\u003c\/p\u003e \u003cp\u003e34.2 Householder Transformations.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e35. QR and Inverse QR Algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e35.1 Inverse QR Algorithm.\u003c\/p\u003e \u003cp\u003e35.2 QR Algorithm.\u003c\/p\u003e \u003cp\u003e35.3 Extended QR Algorithm.\u003c\/p\u003e \u003cp\u003e35.A Array Algorithms for Kalman Filtering.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IX: FAST RLS ALGORITHMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e36. Hyperbolic Rotations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e36.1 Hyperbolic Givens Rotations.\u003c\/p\u003e \u003cp\u003e36.2 Hyperbolic Householder Transformations.\u003c\/p\u003e \u003cp\u003e36.3 Hyperbolic Basis Rotations.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e37. Fast Array Algorithm.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e37.1 Time-Update of the Gain Vector.\u003c\/p\u003e \u003cp\u003e37.2 Time-Update of the Conversion Factor.\u003c\/p\u003e \u003cp\u003e37.3 Initial Conditions.\u003c\/p\u003e \u003cp\u003e37.4 Array Algorithm.\u003c\/p\u003e \u003cp\u003e37.A Chandrasekhar Filter.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e38. Regularized Prediction Problems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e38.1 Regularized Backward Prediction.\u003c\/p\u003e \u003cp\u003e38.2 Regularized Forward Prediction.\u003c\/p\u003e \u003cp\u003e38.3 Low-Rank Factorization.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e39. Fast Fixed-Order Filters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e39.1 Fast Transversal Filter.\u003c\/p\u003e \u003cp\u003e39.2 FAEST Filter.\u003c\/p\u003e \u003cp\u003e39.3 Fast Kalman Filter.\u003c\/p\u003e \u003cp\u003e39.4 Stability Issues.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART X: LATTICE FILTERS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e40. Three Basic Estimation Problems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e40.1 Motivation for Lattice Filters.\u003c\/p\u003e \u003cp\u003e40.2 Joint Process Estimation.\u003c\/p\u003e \u003cp\u003e40.3 Backward Estimation Problem.\u003c\/p\u003e \u003cp\u003e40.4 Forward Estimation Problem.\u003c\/p\u003e \u003cp\u003e40.5 Time and Order-Update Relations.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e41. Lattice Filter Algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e41.1 Significance of Data Structure.\u003c\/p\u003e \u003cp\u003e41.2 A Posteriori-Based Lattice Filter.\u003c\/p\u003e \u003cp\u003e41.3 A Priori-Based Lattice Filter.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e42. Error-Feedback Lattice Filters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e42.1 A Priori Error-Feedback Lattice Filter.\u003c\/p\u003e \u003cp\u003e42.2 A Posteriori Error-Feedback Lattice Filter.\u003c\/p\u003e \u003cp\u003e42.3 Normalized Lattice Filter.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e43. Array Lattice Filters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e43.1 Order-Update of Output Estimation Errors.\u003c\/p\u003e \u003cp\u003e43.2 Order-Update of Backward Estimation Errors.\u003c\/p\u003e \u003cp\u003e43.3 Order-Update of Forward Estimation Errors.\u003c\/p\u003e \u003cp\u003e43.4 Significance of Data Structure.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART XI: ROBUST FILTERS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e44. Indefinite Least-Squares.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e44.1 Indefinite Least-Squares.\u003c\/p\u003e \u003cp\u003e44.2 Recursive Minimization Algorithm.\u003c\/p\u003e \u003cp\u003e44.3 Time-Update of the Minimum Cost.\u003c\/p\u003e \u003cp\u003e44.4 Singular Weighting Matrices.\u003c\/p\u003e \u003cp\u003e44.A Stationary Points.\u003c\/p\u003e \u003cp\u003e44.B Inertia Conditions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e45. Robust Adaptive Filters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e45.1 A Posteriori-Based Robust Filters.\u003c\/p\u003e \u003cp\u003e45.2 \u0026amp;epsi;-NLMS Algorithm.\u003c\/p\u003e \u003cp\u003e45.3 A Priori-Based Robust Filters.\u003c\/p\u003e \u003cp\u003e45.4 LMS Algorithm.\u003c\/p\u003e \u003cp\u003e45.A H1 Filters.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e46. Robustness Properties.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e46.1 Robustness of LMS.\u003c\/p\u003e \u003cp\u003e46.2 Robustness of \u0026amp;epsi;NLMS.\u003c\/p\u003e \u003cp\u003e46.3 Robustness of RLS.\u003c\/p\u003e \u003cp\u003eSummary and Notes.\u003c\/p\u003e \u003cp\u003eProblems and Computer Projects.\u003c\/p\u003e \u003cp\u003eREFERENCES AND INDICES.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eAuthor Index.\u003c\/p\u003e \u003cp\u003eSubject Index.\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Brand New","offer_id":52509145628952,"sku":"9780470253885","price":122.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470253885.jpg?v=1786493898","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/adaptive-filters-hardback-9780470253885","provider":"Freshly Printed Books","version":"1.0","type":"link"}