Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing (Artech House Signal Processing Library)

Signal processing is an essential topic for all practicing and aspiring electrical engineers to understand no matter what specific area they are involved in. Originally published by McGraw-Hill\* and now reissued by Artech House, this definitive volume offers a unified, comprehensive and practical treatment of statistical and adaptive signal processing. Written by leading experts in industry and academia, the book covers the most important aspects of the subject, such as spectral estimation, signal modeling, adaptive filtering, and array processing. This unique resource provides balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike. The book presents clear examples, problem sets, and computer experiments that help readers master the material and learn how to implement various methods presented in the chapters. This invaluable reference also includes a set of Matlab[registered] functions that engineers can use to solve real-world problems in the field. The book is packed with over 3,000 equations and more than 300 illustrations. ABOUT THE AUTHORS......Page 9 CONTENTS......Page 10 ORGANIZATION OF THE BOOK......Page 17 THEORYAND PRACTICE......Page 18 1.1 RANDOM SIGNALS......Page 19 1.2 SPECTRAL ESTIMATION......Page 26 1.3 SIGNAL MODELING......Page 29 1.4 ADAPTIVE FILTERING......Page 34 1.4.2 Features of Adaptive Filters......Page 41 1.5 ARRAY PROCESSING......Page 43 1.6 ORGANIZATION OF THE BOOK......Page 47 2.1 DISCRETE-TIME SIGNALS......Page 51 2.1.3 Real-World Signals......Page 54 2.2 TRANSFORM-DOMAIN REPRESENTATION OF DETERMINISTIC SIGNALS......Page 55 2.3 DISCRETE-TIME SYSTEMS......Page 65 2.4 MINIMUM PHASE AND SYSTEM INVERTIBILITY......Page 72 2.5 LATTICE FILTER REALIZATIONS......Page 82 PROBLEMS......Page 88 3.1 RANDOM VARIABLES......Page 93 3.2 RANDOM VECTORS......Page 101 3.3 DISCRETE-TIME STOCHASTIC PROCESSES......Page 115 3.4 LINEAR SYSTEMS WITH STATIONARY RANDOM INPUTS......Page 133 3.5 WHITENINGAND INNOVATIONS REPRESENTATION......Page 143 3.6 PRINCIPLES OF ESTIMATION THEORY......Page 151 3.7 SUMMARY......Page 160 PROBLEMS......Page 161 4.1 INTRODUCTION......Page 167 4.2 ALL-POLE MODELS......Page 174 4.3 ALL-ZERO MODELS......Page 190 4.4 POLE-ZERO MODELS......Page 195 4.5 MODELS WITH POLES ON THE UNIT CIRCLE......Page 200 4.6 CEPSTRUM OF POLE-ZERO MODELS......Page 202 PROBLEMS......Page 207 5 Nonparametric Power Spectrum Estimation......Page 213 5.1 SPECTRAL ANALYSIS OF DETERMINISTIC SIGNALS......Page 214 5.2 ESTIMATION OF THE AUTOCORRELATION OF STATIONARY RANDOM SIGNALS......Page 227 5.3 ESTIMATION OF THE POWER SPECTRUM OF STATIONARY RANDOM SIGNALS......Page 230 5.4 JOINT SIGNAL ANALYSIS......Page 255 5.5 MULTITAPER POWER SPECTRUM ESTIMATION......Page 264 5.6 SUMMARY......Page 272 PROBLEMS......Page 273 6.1 OPTIMUM SIGNAL ESTIMATION......Page 279 6.2 LINEAR MEAN SQUARE ERROR ESTIMATION......Page 282 6.3 SOLUTION OF THE NORMAL EQUATIONS......Page 292 6.4 OPTIMUM FINITE IMPULSE RESPONSE FILTERS......Page 296 6.5 LINEAR PREDICTION......Page 304 6.6 OPTIMUM INFINITE IMPULSE RESPONSE FILTERS......Page 313 6.7 INVERSE FILTERING AND DECONVOLUTION......Page 324 6.8 CHANNEL EQUALIZATION IN DATA TRANSMISSION SYSTEMS......Page 328 6.9 MATCHED FILTERS AND EIGENFILTERS......Page 337 PROBLEMS......Page 343 7 Algorithms and Structures for Optimum Linear Filters......Page 351 7.1 FUNDAMENTALS OF ORDER-RECURSIVE ALGORITHMS......Page 352 7.2 INTERPRETATIONS OF ALGORITHMIC QUANTITIES......Page 361 7.3 ORDER-RECURSIVE ALGORITHMS FOR OPTIMUM FIR FILTERS......Page 365 7.4 ALGORITHMS OF LEVINSON AND LEVINSON-DURBIN......Page 373 7.5 LATTICE STRUCTURES FOR OPTIMUM FIR FILTERS AND PREDICTORS......Page 379 7.6 ALGORITHM OF SCHÜR......Page 386 7.7 TRIANGULARIZATION AND INVERSION OF TOEPLITZ MATRICES......Page 392 7.8 KALMAN FILTER ALGORITHM......Page 396 7.9 SUMMARY......Page 405 PROBLEMS......Page 407 8.1 THE PRINCIPLE OF LEAST SQUARES......Page 413 8.2 LINEAR LEAST-SQUARES ERROR ESTIMATION......Page 414 8.3 LEAST-SQUARES FIR FILTERS......Page 424 8.4 LINEAR LEAST-SQUARES SIGNAL ESTIMATION......Page 429 8.5 LS COMPUTATIONS USING THE NORMAL EQUATIONS......Page 434 8.6 LS COMPUTATIONS USING ORTHOGONALIZATION TECHNIQUES......Page 440 8.7 LS COMPUTATIONS USING THE SINGULAR VALUE DECOMPOSITION......Page 449 8.8 SUMMARY......Page 456 PROBLEMS......Page 457 9.1 THE MODELING PROCESS: THEORY AND PRACTICE......Page 463 9.2 ESTIMATION OF ALL-POLE MODELS......Page 467 9.3 ESTIMATION OF POLE-ZERO MODELS......Page 480 9.4 APPLICATIONS......Page 485 9.5 MINIMUM-VARIANCE SPECTRUM ESTIMATION......Page 489 9.6 HARMONIC MODELS AND FREQUENCY ESTIMATION TECHNIQUES......Page 496 9.7 SUMMARY......Page 511 PROBLEMS......Page 512 10 Adaptive Filters......Page 517 10.1 TYPICALAPPLICATIONS OFADAPTIVE FILTERS......Page 518 10.2 PRINCIPLES OF ADAPTIVE FILTERS......Page 524 10.3 METHOD OF STEEPEST DESCENT......Page 534 10.4 LEAST-MEAN-SQUARE ADAPTIVE FILTERS......Page 542 10.5 RECURSIVE LEAST-SQUARES ADAPTIVE FILTERS......Page 566 10.6 RLS ALGORITHMS FOR ARRAY PROCESSING......Page 578 10.7 FAST RLS ALGORITHMS FOR FIR FILTERING......Page 591 10.8 TRACKING PERFORMANCE OF ADAPTIVE ALGORITHMS......Page 608 10.9 SUMMARY......Page 625 PROBLEMS......Page 626 11 Array Processing......Page 639 11.1 ARRAY FUNDAMENTALS......Page 640 11.2 CONVENTIONAL SPATIAL FILTERING: BEAMFORMING......Page 649 11.3 OPTIMUM ARRAY PROCESSING......Page 659 11.4 PERFORMANCE CONSIDERATIONS FOR OPTIMUM BEAMFORMERS......Page 670 11.5 ADAPTIVE BEAMFORMING......Page 677 11.6 OTHER ADAPTIVE ARRAY PROCESSING METHODS......Page 689 11.7 ANGLE ESTIMATION......Page 696 11.8 SPACE-TIME ADAPTIVE PROCESSING......Page 701 11.9 SUMMARY......Page 703 PROBLEMS......Page 704 12.1 HIGHER-ORDER STATISTICS IN SIGNAL PROCESSING......Page 709 12.2 BLIND DECONVOLUTION......Page 715 12.3 UNSUPERVISED ADAPTIVE FILTERS—BLIND EQUALIZERS......Page 720 12.4 FRACTIONALLY SPACED EQUALIZERS......Page 727 12.5 FRACTIONAL POLE-ZERO SIGNAL MODELS......Page 734 12.6 SELF-SIMILAR RANDOM SIGNAL MODELS......Page 743 12.7 SUMMARY......Page 759 PROBLEMS......Page 760 APPENDIX A: Matrix Inversion Lemma......Page 763 B.1 GRADIENT......Page 765 B.2 LAGRANGE MULTIPLIERS......Page 767 APPENDIX C: Matlab Functions......Page 771 D.1 COMPLEX-VALUED VECTOR SPACE......Page 773 D.2 MATRICES......Page 774 D.3 DETERMINANT OF A SQUARE MATRIX......Page 778 D.4 UNITARY MATRICES......Page 780 D.5 POSITIVE DEFINITE MATRICES......Page 782 APPENDIX E: Minimum Phase Test for Polynomials......Page 784 Bibliography......Page 787 Index......Page 805 This authoritative volume on statistical and adaptive signal processing offers you a unified, comprehensive and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing. Packed with over 3,000 equations and more than 300 illustrations, this unique resource provides you with balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike. From the fundamentals of discrete-time signal processing and linear signal models, to optimum linear filters and least-squares filtering and prediction, you get in-depth information on a broad range of critical topics from leading experts in industry and academia. This invaluable reference provides clear examples, problem sets, and computer experiments that help you master the material and learn how to implement various methods presented in the book. You also find a set of Matlab® functions that illustrate the use of various techniques and can be used to solve real-world problems in the field. "This authoritative volume on statistical and adaptive signal processing offers a unified, comprehensive, and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing. Packed with over 3,000 equations and more than 300 illustrations, this unique resource provides balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike. Leading experts in industry and academia present in-depth information on a broad range of critical topics."--Jacket

This authoritative volume on statistical and adaptive signal processing offers you a unified, comprehensive and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing. Packed with over 3,000 equations and more than 300 illustrations, this unique resource provides you with balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike.