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Detection estimation and modulation theory . Part I Detection, estimation, and filtering theory [[electronic resource] /] / Harry L. Van Trees, Kristine L. Bell ; with Zhi Tian
| Detection estimation and modulation theory . Part I Detection, estimation, and filtering theory [[electronic resource] /] / Harry L. Van Trees, Kristine L. Bell ; with Zhi Tian |
| Autore | Van Trees Harry L |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2013 |
| Descrizione fisica | 1 online resource (1175 p.) |
| Disciplina | 621.382/2 |
| Altri autori (Persone) |
BellKristine L
TianZhi <1972-> |
| Soggetto topico |
Signal theory (Telecommunication)
Modulation (Electronics) Estimation theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-118-53970-2
1-118-53992-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Detection, 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
2.5.2.2 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 3.2.2 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 3.3.2.2 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 4.2.6 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 4.3.6.2 Nuisance Parameters |
| Record Nr. | UNINA-9910462883903321 |
Van Trees Harry L
|
||
| Hoboken, N.J., : Wiley, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Detection estimation and modulation theory . Part I Detection, estimation, and filtering theory [[electronic resource] /] / Harry L. Van Trees, Kristine L. Bell ; with Zhi Tian
| Detection estimation and modulation theory . Part I Detection, estimation, and filtering theory [[electronic resource] /] / Harry L. Van Trees, Kristine L. Bell ; with Zhi Tian |
| Autore | Van Trees Harry L |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2013 |
| Descrizione fisica | 1 online resource (1175 p.) |
| Disciplina | 621.382/2 |
| Altri autori (Persone) |
BellKristine L
TianZhi <1972-> |
| Collana | Detection, estimation, and modulation theory |
| Soggetto topico |
Signal theory (Telecommunication)
Modulation (Electronics) Estimation theory |
| ISBN |
1-118-53970-2
1-118-53992-3 |
| Classificazione |
547.1
621.381536 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Detection, 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
2.5.2.2 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 3.2.2 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 3.3.2.2 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 4.2.6 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 4.3.6.2 Nuisance Parameters |
| Record Nr. | UNINA-9910786249403321 |
|
Van Trees Harry L
|
||
| Hoboken, N.J., : Wiley, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||