LEADER 05840nam 2200781 a 450 001 9910821850803321 005 20240313194054.0 010 $a1-118-53970-2 010 $a1-118-53992-3 035 $a(CKB)2670000000340410 035 $a(EBL)1166811 035 $a(OCoLC)850209477 035 $a(SSID)ssj0000856001 035 $a(PQKBManifestationID)11488949 035 $a(PQKBTitleCode)TC0000856001 035 $a(PQKBWorkID)10807484 035 $a(PQKB)10435396 035 $a(OCoLC)843193425 035 $a(MiAaPQ)EBC1166811 035 $a(Au-PeEL)EBL1166811 035 $a(CaPaEBR)ebr10718826 035 $a(CaONFJC)MIL497765 035 $a(MiAaPQ)EBC7103786 035 $a(Au-PeEL)EBL7103786 035 $a(PPN)188242651 035 $a(JP-MeL)3000111677 035 $a(EXLCZ)992670000000340410 100 $a20150303d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDetection estimation and modulation theory$hPart I$iDetection, estimation, and filtering theory /$fHarry L. Van Trees, Kristine L. Bell ; with Zhi Tian 205 $a2nd ed. 210 $aHoboken, N.J. $cWiley$dc2013 215 $a1 online resource (1175 p.) 225 0 $6880-03$aDetection, estimation, and modulation theory 300 $aIncludes bibliographical references (p. 1125-1143) and index 311 $a0-470-54296-9 320 $aIncludes bibliographical references and index. 327 $aDetection, Estimation, and Modulation Theory: Part I -Detection, Estimation, and Filtering Theory; Contents; Preface; Preface to the First Edition; 1 Introduction; 1.1 Introduction; 1.2 Topical Outline; 1.3 Possible Approaches; 1.4 Organization; 2 Classical Detection Theory; 2.1 Introduction; 2.2 Simple Binary Hypothesis Tests; 2.2.1 Decision Criteria; 2.2.2 Performance: Receiver Operating Characteristic; 2.3 M Hypotheses; 2.4 Performance Bounds and Approximations; 2.5 Monte Carlo Simulation; 2.5.1 Monte Carlo Simulation Techniques; 2.5.2 Importance Sampling; 2.5.2.1 Simulation of PF 327 $a2.5.2.2 Simulation of PM2.5.2.3 Independent Observations; 2.5.2.4 Simulation of the ROC; 2.5.2.5 Examples; 2.5.2.6 Iterative Importance Sampling; 2.5.3 Summary; 2.6 Summary; 2.7 Problems; 3 General Gaussian Detection; 3.1 Detection of Gaussian Random Vectors; 3.1.1 Real Gaussian Random Vectors; 3.1.2 Circular Complex Gaussian Random Vectors; 3.1.3 General Gaussian Detection; 3.1.3.1 Real Gaussian Vectors; 3.1.3.2 Circular Complex Gaussian Vectors; 3.1.3.3 Summary; 3.2 Equal Covariance Matrices; 3.2.1 Independent Components with Equal Variance 327 $a3.2.2 Independent Components with Unequal Variances3.2.3 General Case: Eigendecomposition; 3.2.4 Optimum Signal Design; 3.2.5 Interference Matrix: Estimator-Subtractor; 3.2.6 Low-Rank Models; 3.2.7 Summary; 3.3 Equal Mean Vectors; 3.3.1 Diagonal Covariance Matrix on H0: Equal Variance; 3.3.1.1 Independent, Identically Distributed Signal Components; 3.3.1.2 Independent Signal Components: Unequal Variances; 3.3.1.3 Correlated Signal Components; 3.3.1.4 Low-Rank Signal Model; 3.3.1.5 Symmetric Hypotheses, Uncorrelated Noise; 3.3.2 Nondiagonal Covariance Matrix on H0; 3.3.2.1 Signal on H1 Only 327 $a3.3.2.2 Signal on Both Hypotheses3.3.3 Summary; 3.4 General Gaussian; 3.4.1 Real Gaussian Model; 3.4.2 Circular Complex Gaussian Model; 3.4.3 Single Quadratic Form; 3.4.4 Summary; 3.5 M Hypotheses; 3.6 Summary; 3.7 Problems; 4 Classical Parameter Estimation; 4.1 Introduction; 4.2 Scalar Parameter Estimation; 4.2.1 Random Parameters: Bayes Estimation; 4.2.2 Nonrandom Parameter Estimation; 4.2.3 Bayesian Bounds; 4.2.3.1 Lower Bound on the MSE; 4.2.3.2 Asymptotic Behavior; 4.2.4 Case Study; 4.2.5 Exponential Family; 4.2.5.1 Nonrandom Parameters; 4.2.5.2 Random Parameters 327 $a4.2.6 Summary of Scalar Parameter Estimation4.3 Multiple Parameter Estimation; 4.3.1 Estimation Procedures; 4.3.1.1 Random Parameters; 4.3.1.2 Nonrandom Parameters; 4.3.2 Measures of Error; 4.3.2.1 Nonrandom Parameters; 4.3.2.2 Random Parameters; 4.3.3 Bounds on Estimation Error; 4.3.3.1 Nonrandom Parameters; 4.3.3.2 Random Parameters; 4.3.4 Exponential Family; 4.3.4.1 Nonrandom Parameters; 4.3.4.2 Random Parameters; 4.3.5 Nuisance Parameters; 4.3.5.1 Nonrandom Parameters; 4.3.5.2 Random Parameters; 4.3.5.3 Hybrid Parameters; 4.3.6 Hybrid Parameters; 4.3.6.1 Joint ML and MAP Estimation 327 $a4.3.6.2 Nuisance Parameters 330 $aOriginally published in 1968, Harry Van Trees's Detection, Estimation, and Modulation Theory, Part I is one of the great time-tested classics in the field of signal processing. Highly readable and practically organized, it is as imperative today for professionals, researchers, and students in optimum signal processing as it was over thirty years ago. The second edition is a thorough revision and expansion almost doubling the size of the first edition and accounting for the new developments thus making it again the most comprehensive and up-to-date treatment of the subject. With a wide range 410 0$aNew York Academy of Sciences 606 $aSignal theory (Telecommunication) 606 $aModulation (Electronics) 606 $aEstimation theory 615 0$aSignal theory (Telecommunication) 615 0$aModulation (Electronics) 615 0$aEstimation theory. 676 $a621.382/2 686 $a547.1$2njb/09 686 $a621.381536$2njb/09 700 $aVan Trees$b Harry L$02732 701 $aBell$b Kristine L$0323713 701 $aTian$b Zhi$f1972-$01682669 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910821850803321 996 $aDetection estimation and modulation theory$94052951 997 $aUNINA