LEADER 05541nam 22007213u 450 001 9911007080403321 005 20230802010955.0 010 $a0-486-13689-2 010 $a1-62198-604-7 035 $a(CKB)2550000001186864 035 $a(EBL)1894611 035 $a(SSID)ssj0001002760 035 $a(PQKBManifestationID)12415446 035 $a(PQKBTitleCode)TC0001002760 035 $a(PQKBWorkID)10997569 035 $a(PQKB)11581975 035 $a(MiAaPQ)EBC1894611 035 $a(Au-PeEL)EBL1894611 035 $a(CaONFJC)MIL566235 035 $a(OCoLC)868272595 035 $a(EXLCZ)992550000001186864 100 $a20141222d2012|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOptimal Filtering 205 $a1st ed. 210 $aNewburyport $cDover Publications$d2012 215 $a1 online resource (695 p.) 225 1 $aDover Books on Electrical Engineering 300 $aDescription based upon print version of record. 311 08$a0-486-43938-0 311 08$a1-306-34984-2 327 $aTitle Page; Copyright Page; Table of Contents; PREFACE; CHAPTER 1 - INTRODUCTION; 1.1 FILTERING; 1.2 HISTORY OF SIGNAL FILTERING; 1.3 SUBJECT MATTER OF THIS BOOK; 1.4 OUTLINE OF THE BOOK; REFERENCES; CHAPTER 2 - FILTERING, LINEAR SYSTEMS, AND ESTIMATION; 2.1 SYSTEMS, NOISE, FILTERING, SMOOTHING, AND PREDICTION; 2.2 THE GAUSS-MARKOV DISCRETE-TIME MODEL; 2.3 ESTIMATION CRITERIA; REFERENCES; CHAPTER 3 - THE DISCRETE-TIME; 3.1 THE KALMAN FILTER; 3.2 BEST LINEAR ESTIMATOR PROPERTY OF THE KALMAN FILTER; 3.3 IDENTIFICATION AS A KALMAN FILTERING PROBLEM; 3.4 APPLICATION OF KALMAN FILTERS; REFERENCES 327 $aCHAPTER 4 - TIME-INVARIANT FILTERS4.1 BACKGROUND TO TIME INVARIANCE OF THE FILTER; 4.2 STABILITY PROPERTIES OF LINEAR, DISCRETE-TIME SYSTEMS; 4.3 STATIONARY BEHAVIOUR OF LINEAR SYSTEMS; 4.4 TIME INVARIANCE AND ASYMPTOTIC STABILITY OF THE FILTER; 4.5 FREQUENCY DOMAIN FORMULAS; REFERENCES; CHAPTER 5 - KALMAN FILTER PROPERTIES; 5.1 INTRODUCTION; 5.2 MINIMUM VARIANCE AND LINEAR MINIMUM VARIANCE ESTIMATION; ORTHOGONALITY AND PROJECTION; 5.3 THE INNOVATIONS SEQUENCE; 5.4 THE KALMAN FILTER; 5.5 TRUE FILTERED ESTIMATES AND THE SIGNAL-TO-NOISE RATIO IMPROVEMENT PROPERTY 327 $a5.6 INVERSE PROBLEMS: WHEN IS A FILTER OPTIMAL?REFERENCES; CHAPTER 6 - COMPUTATIONAL ASPECTS; 6.1 SIGNAL MODEL ERRORS, FILTER DIVERGENCE, AND DATA SATURATION; 6.2 EXPONENTIAL DATA WEIGHTING-A FILTER WITH PRESCRIBED DEGREE OF STABILITY; 6.3 THE MATRIX INVERSION LEMMA AND THE INFORMATION FILTER; 6.4 SEQUENTIAL PROCESSING; 6.5 SQUARE ROOT FILTERING; 6.6 THE HIGH MEASUREMENT NOISE CASE; 6.7 CHANDRASEKHAR-TYPE, DOUBLING, AND NONRECURSIVE ALGORITHMS; REFERENCES; CHAPTER 7 - SMOOTHING OF DISCRETE-TIME SIGNALS; 7.1 INTRODUCTION TO SMOOTHING; 7.2 FIXED-POINT SMOOTHING; 7.3 FIXED-LAG SMOOTHING 327 $a7.4 FIXED-INTERVAL SMOOTHINGREFERENCES; CHAPTER 8 - APPLICATIONS IN NONLINEAR FILTERING; 8.1 NONLINEAR FILTERING; 8.2 THE EXTENDED KALMAN FILTER; 8.3 A BOUND OPTIMAL FILTER; 8.4 GAUSSIAN SUM ESTIMATORS; REFERENCES; CHAPTER 9 - INNOVATIONS REPRESENTATIONS, SPECTRAL FACTORIZATION, WIENER AND LEVINSON FILTERING; 9.1 INTRODUCTION; 9.2 KALMAN FILTER DESIGN FROM COVARIANCE DATA; 9.3 INNOVATIONS REPRESENTATIONS WITH FINITE INITIAL TIME; 9.4 STATIONARY INNOVATIONS REPRESENTATIONS AND SPECTRAL FACTORIZATION; 9.5 WIENER FILTERING; 9.6 LEVINSON FILTERS; REFERENCES 327 $aCHAPTER 10 - PARAMETER IDENTIFICATION AND ADAPTIVE ESTIMATION10.1 ADAPTIVE ESTIMATION VIA PARALLEL PROCESSING; 10.2 ADAPTIVE ESTIMATION VIA EXTENDED LEAST SQUARES; REFERENCES; CHAPTER 11 - COLORED NOISE AND SUBOPTIMAL REDUCED ORDER FILTERS; 11.1 GENERAL APPROACHES TO DEALING WITH COLORED NOISE; 11.2 FILTER DESIGN WITH MARKOV OUTPUT NOISE; 11.3 FILTER DESIGN WITH SINGULAR OR NEAR-SINGULAR OUTPUT NOISE; 11.4 SUBOPTIMAL DESIGN GIVEN COLORED INPUT OR MEASUREMENT NOISE; 11.5 SUBOPTIMAL FILTER DESIGN BY MODEL ORDER REDUCTION; REFERENCES; APPENDIX A - BRIEF REVIEW OF RESULTS OF PROBABILITY THEORY 327 $aAPPENDIX B - BRIEF REVIEW OF SOME RESULTS OF MATRIX THEORY 330 $a This graduate-level text augments and extends beyond undergraduate studies of signal processing, particularly in regard to communication systems and digital filtering theory. Vital for students in the fields of control and communications, its contents are also relevant to students in such diverse areas as statistics, economics, bioengineering, and operations research.Topics include filtering, linear systems, and estimation; the discrete-time Kalman filter; time-invariant filters; properties of Kalman filters; computational aspects; and smoothing of discrete-time signals. Additional subjects e 410 0$aDover Books on Electrical Engineering 606 $aSignal processing 606 $aElectric filters 606 $aElectrical & Computer Engineering$2HILCC 606 $aEngineering & Applied Sciences$2HILCC 606 $aTelecommunications$2HILCC 615 0$aSignal processing. 615 0$aElectric filters. 615 7$aElectrical & Computer Engineering 615 7$aEngineering & Applied Sciences 615 7$aTelecommunications 676 $a621.382/2 700 $aAnderson$b Brian D. O$025113 701 $aMoore$b John B$0339582 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9911007080403321 996 $aOptimal filtering$9333948 997 $aUNINA