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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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
Detection estimation and modulation theory . Part I Detection, estimation, and filtering theory / / Harry L. Van Trees, Kristine L. Bell ; with Zhi Tian
Detection estimation and modulation theory . Part I Detection, estimation, and filtering theory / / 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-9910821850803321
Van Trees Harry L  
Hoboken, N.J., : Wiley, c2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Detection, Estimation, & Modulation Theory, Part III, Radar-Sonar Signal Processing & Gaussian Signals in Noise
Detection, Estimation, & Modulation Theory, Part III, Radar-Sonar Signal Processing & Gaussian Signals in Noise
Autore Van Trees Harry L
Pubbl/distr/stampa John Wiley & Sons, Inc. / Engineering
ISBN 0-471-44678-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910512190403321
Van Trees Harry L  
John Wiley & Sons, Inc. / Engineering
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Detection, estimation, and modulation theory . Part III Rasar-sonor signal processing and Gaussian signals in noise [[electronic resource] /] / Harry L. Van Trees
Detection, estimation, and modulation theory . Part III Rasar-sonor signal processing and Gaussian signals in noise [[electronic resource] /] / Harry L. Van Trees
Autore Van Trees Harry L
Pubbl/distr/stampa New York, : Wiley, 2001
Descrizione fisica 1 online resource (647 p.)
Disciplina 621.381536
Soggetto topico Signal theory (Telecommunication)
Modulation (Electronics)
Estimation theory
ISBN 1-280-54185-7
9786610541850
0-470-34665-5
0-471-46381-7
0-471-22109-0
1-60119-557-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; 1 Introduction; 1.1 Review of Parts I and II; 1.2 Random Signals in Noise; 1.3 Signal Processing in Radar-Sonar Systems; References; 2 Detection of Gaussian Signals in White Gaussian Noise; 2.1 Optimum Receivers; 2.1.1 Canonical Realization No. 1: Estimator-Correlator; 2.1.2 Canonical Realization No. 2: Filter-Correlator Receiver; 2.1.3 Canonical Realization No. 3: Filter-Squarer-Integrator (FSI) Receiver; 2.1.4 Canonical Realization No. 4: Optimum Realizable Filter Receiver; 2.1.5 Canonical Realization No. 4S: State-variable Realization; 2.1.6 Summary: Receiver Structures
2.2 Performance2.2.1 Closed-form Expression for μ(s); 2.2.2 Approximate Error Expressions; 2.2.3 An Alternative Expression for μ[sub(R)](S); 2.2.4 Performance for a Typical System; 2.3 Summary: Simple Binary Detection; 2.4 Problems; References; 3 General Binary Detection: Gaussian Processes; 3.1 Model and Problem Classification; 3.2 Receiver Structures; 3.2.1 Whitening Approach; 3.2.2 Various Implementations of the Likelihood Ratio Test; 3.2.3 Summary: Receiver Structures; 3.3 Performance; 3.4 Four Special Situations; 3.4.1 Binary Symmetric Case; 3.4.2 Non-zero Means
3.4.3 Stationary ""Carrier-symmetric"" Bandpass Problems3.4.4 Error Probability for the Binary Symmetric Bandpass Problem; 3.5 General Binary Case: White Noise Not Necessarily Present: Singular Tests; 3.5.1 Receiver Derivation; 3.5.2 Performance: General Binary Case; 3.5.3 Singularity; 3.6 Summary: General Binary Problem; 3.7 Problems; References; 4 Special Categories of Detection Problems; 4.1 Stationary Processes: Long Observation Time; 4.1.1 Simple Binary Problem; 4.1.2 General Binary Problem; 4.1.3 Summary: SPLOT Problem; 4.2 Separable Kernels; 4.2.1 Separable Kernel Model
4.2.2 Time Diversity4.2.3 Frequency Diversity; 4.2.4 Summary: Separable Kernels; 4.3 Low-Energy-Coherence (LEC) Case; 4.4 Summary; 4.5 Problems; References; 5 Discussion: Detection of Gaussian Signals; 5.1 Related Topics; 5.1.1 M-ary Detection: Gaussian Signals in Noise; 5.1.2 Suboptimum Receivers; 5.1.3 Adaptive Receivers; 5.1.4 Non-Gaussian Processes; 5.1.5 Vector Gaussian Processes; 5.2 Summary of Detection Theory; 5.3 Problems; References; 6 Estimation of the Parameters of a Random Process; 6.1 Parameter Estimation Model; 6.2 Estimator Structure
6.2.1 Derivation of the Likelihood Function6.2.2 Maximum Likelihood and Maximum A-Posteriori Probability Equations; 6.3 Performance Analysis; 6.3.1 A Lower Bound on the Variance; 6.3.2 Calculation of J[sup(2)](A); 6.3.3 Lower Bound on the Mean-Square Error; 6.3.4 Improved Performance Bounds; 6.4 Summary; 6.5 Problems; References; 7 Special Categories of Estimation Problems; 7.1 Stationary Processes: Long Observation Time; 7.1.1 General Results; 7.1.2 Performance of Truncated Estimates; 7.1.3 Suboptimum Receivers; 7.1.4 Summary; 7.2 Finite-State Processes; 7.3 Separable Kernels
7.4 Low-Energy-Coherence Case
Record Nr. UNISA-996201055703316
Van Trees Harry L  
New York, : Wiley, 2001
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Detection, estimation, and modulation theory . Part 1 Detection, estimation, and linear modulation theory [[electronic resource] /] / Harry L. Van Trees
Detection, estimation, and modulation theory . Part 1 Detection, estimation, and linear modulation theory [[electronic resource] /] / Harry L. Van Trees
Autore Van Trees Harry L
Pubbl/distr/stampa New York, : Wiley, c2001
Descrizione fisica 1 online resource (714 p.)
Disciplina 621.381536
Soggetto topico Signal theory (Telecommunication)
Modulation theory
Estimation theory
ISBN 1-280-54186-5
9786610541867
0-470-34071-1
0-471-22108-2
0-471-46382-5
1-60119-558-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; 1 Introduction; 1.1 Topical Outline; 1.2 Possible Approaches; 1.3 Organization; 2 Classical Detection and Estimation Theory; 2.1 Introduction; 2.2 Simple Binary Hypothesis Tests; Decision Criteria; Performance: Receiver Operating Characteristic; 2.3 M Hypotheses; 2.4 Estimation Theory; Random Parameters: Bayes Estimation; Real (Nonrandom) Parameter Estimation; Multiple Parameter Estimation; Summary of Estimation Theory; 2.5 Composite Hypotheses; 2.6 The General Gaussian Problem; Equal Covariance Matrices; Equal Mean Vectors; Summary; 2.7 Performance Bounds and Approximations
2.8 Summary2.9 Problems; References; 3 Representations of Random Processes; 3.1 Introduction; 3.2 Deterministic Functions: Orthogonal Representations; 3.3 Random Process Characterization; Random Processes: Conventional Characterizations; Series Representation of Sample Functions of Random Processes; Gaussian Processes; 3.4 Homogeneous Integral Equations and Eigenfunctions; Rational Spectra; Bandlimited Spectra; Nonstationary Processes; White Noise Processes; The Optimum Linear Filter; Properties of Eigenfunctions and Eigenvalues; 3.5 Periodic Processes
3.6 Infinite Time Interval: Spectral DecompositionSpectral Decomposition; An Application of Spectral Decomposition: MAP Estimation of a Gaussian Process; 3.7 Vector Random Processes; 3.8 Summary; 3.9 Problems; References; 4 Detection of Signals-Estimation of Signal Parameters; 4.1 Introduction; Models; Format; 4.2 Detection and Estimation in White Gaussian Noise; Detection of Signals in Additive White Gaussian Noise; Linear Estimation; Nonlinear Estimation; Summary : Known Signals in White Gaussian Noise; 4.3 Detection and Estimation in Nonwhite Gaussian Noise; ""Whitening"" Approach
A Direct Derivation Using the Karhunen-Loeve ExpansionA Direct Derivation with a Sufficient Statistic; Detection Performance; Estimation; Solution Techniques for Integral Equations; Sensitivity; Known Linear Channels; 4.4 Signals with Unwanted Parameters: The Composite Hypothesis Problem; Random Phase Angles; Random Amplitude and Phase; 4.5 Multiple Channels; Formulation; Application; 4.6 Multiple Parameter Estimation; Additive White Gaussian Noise Channel; Extensions; 4.7 Summary and Omissions; Summary; Topics Omitted; 4.8 Problems; References; 5 Estimation of Continuous Waveforms
5.1 Introduction5.2 Derivation of Estimator Equations; No-Memory Modulation Systems; Modulation Systems with Memory; 5.3 A Lower Bound on the Mean-Square Estimation Error; 5.4 Multidimensional Waveform Estimation; Examples of Multidimensional Problems; Problem Formulation; Derivation of Estimator Equations; Lower Bound on the Error Matrix; Colored Noise Estimation; 5.5 Nonrandom Waveform Estimation; 5.6 Summary; 5.7 Problems; References; 6 Linear Estimation; 6.1 Properties of Optimum Processors; 6.2 Realizable Linear Filters: Stationary Processes, Infinite Past: Wiener Filters
Solution of Wiener-Hopf Equation
Record Nr. UNISA-996203312203316
Van Trees Harry L  
New York, : Wiley, c2001
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Detection, estimation, and modulation theory . Part III Rasar-sonor signal processing and Gaussian signals in noise [[electronic resource] /] / Harry L. Van Trees
Detection, estimation, and modulation theory . Part III Rasar-sonor signal processing and Gaussian signals in noise [[electronic resource] /] / Harry L. Van Trees
Autore Van Trees Harry L
Pubbl/distr/stampa New York, : Wiley, 2001
Descrizione fisica 1 online resource (647 p.)
Disciplina 621.381536
Soggetto topico Signal theory (Telecommunication)
Modulation (Electronics)
Estimation theory
ISBN 1-280-54185-7
9786610541850
0-470-34665-5
0-471-46381-7
0-471-22109-0
1-60119-557-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; 1 Introduction; 1.1 Review of Parts I and II; 1.2 Random Signals in Noise; 1.3 Signal Processing in Radar-Sonar Systems; References; 2 Detection of Gaussian Signals in White Gaussian Noise; 2.1 Optimum Receivers; 2.1.1 Canonical Realization No. 1: Estimator-Correlator; 2.1.2 Canonical Realization No. 2: Filter-Correlator Receiver; 2.1.3 Canonical Realization No. 3: Filter-Squarer-Integrator (FSI) Receiver; 2.1.4 Canonical Realization No. 4: Optimum Realizable Filter Receiver; 2.1.5 Canonical Realization No. 4S: State-variable Realization; 2.1.6 Summary: Receiver Structures
2.2 Performance2.2.1 Closed-form Expression for μ(s); 2.2.2 Approximate Error Expressions; 2.2.3 An Alternative Expression for μ[sub(R)](S); 2.2.4 Performance for a Typical System; 2.3 Summary: Simple Binary Detection; 2.4 Problems; References; 3 General Binary Detection: Gaussian Processes; 3.1 Model and Problem Classification; 3.2 Receiver Structures; 3.2.1 Whitening Approach; 3.2.2 Various Implementations of the Likelihood Ratio Test; 3.2.3 Summary: Receiver Structures; 3.3 Performance; 3.4 Four Special Situations; 3.4.1 Binary Symmetric Case; 3.4.2 Non-zero Means
3.4.3 Stationary ""Carrier-symmetric"" Bandpass Problems3.4.4 Error Probability for the Binary Symmetric Bandpass Problem; 3.5 General Binary Case: White Noise Not Necessarily Present: Singular Tests; 3.5.1 Receiver Derivation; 3.5.2 Performance: General Binary Case; 3.5.3 Singularity; 3.6 Summary: General Binary Problem; 3.7 Problems; References; 4 Special Categories of Detection Problems; 4.1 Stationary Processes: Long Observation Time; 4.1.1 Simple Binary Problem; 4.1.2 General Binary Problem; 4.1.3 Summary: SPLOT Problem; 4.2 Separable Kernels; 4.2.1 Separable Kernel Model
4.2.2 Time Diversity4.2.3 Frequency Diversity; 4.2.4 Summary: Separable Kernels; 4.3 Low-Energy-Coherence (LEC) Case; 4.4 Summary; 4.5 Problems; References; 5 Discussion: Detection of Gaussian Signals; 5.1 Related Topics; 5.1.1 M-ary Detection: Gaussian Signals in Noise; 5.1.2 Suboptimum Receivers; 5.1.3 Adaptive Receivers; 5.1.4 Non-Gaussian Processes; 5.1.5 Vector Gaussian Processes; 5.2 Summary of Detection Theory; 5.3 Problems; References; 6 Estimation of the Parameters of a Random Process; 6.1 Parameter Estimation Model; 6.2 Estimator Structure
6.2.1 Derivation of the Likelihood Function6.2.2 Maximum Likelihood and Maximum A-Posteriori Probability Equations; 6.3 Performance Analysis; 6.3.1 A Lower Bound on the Variance; 6.3.2 Calculation of J[sup(2)](A); 6.3.3 Lower Bound on the Mean-Square Error; 6.3.4 Improved Performance Bounds; 6.4 Summary; 6.5 Problems; References; 7 Special Categories of Estimation Problems; 7.1 Stationary Processes: Long Observation Time; 7.1.1 General Results; 7.1.2 Performance of Truncated Estimates; 7.1.3 Suboptimum Receivers; 7.1.4 Summary; 7.2 Finite-State Processes; 7.3 Separable Kernels
7.4 Low-Energy-Coherence Case
Record Nr. UNINA-9910830603303321
Van Trees Harry L  
New York, : Wiley, 2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Detection, estimation, and modulation theory . Part III Rasar-sonor signal processing and Gaussian signals in noise / / Harry L. Van Trees
Detection, estimation, and modulation theory . Part III Rasar-sonor signal processing and Gaussian signals in noise / / Harry L. Van Trees
Autore Van Trees Harry L
Pubbl/distr/stampa New York, : Wiley, 2001
Descrizione fisica 1 online resource (647 p.)
Disciplina 621.381536
Soggetto topico Signal theory (Telecommunication)
Modulation (Electronics)
Estimation theory
ISBN 1-280-54185-7
9786610541850
0-470-34665-5
0-471-46381-7
0-471-22109-0
1-60119-557-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; 1 Introduction; 1.1 Review of Parts I and II; 1.2 Random Signals in Noise; 1.3 Signal Processing in Radar-Sonar Systems; References; 2 Detection of Gaussian Signals in White Gaussian Noise; 2.1 Optimum Receivers; 2.1.1 Canonical Realization No. 1: Estimator-Correlator; 2.1.2 Canonical Realization No. 2: Filter-Correlator Receiver; 2.1.3 Canonical Realization No. 3: Filter-Squarer-Integrator (FSI) Receiver; 2.1.4 Canonical Realization No. 4: Optimum Realizable Filter Receiver; 2.1.5 Canonical Realization No. 4S: State-variable Realization; 2.1.6 Summary: Receiver Structures
2.2 Performance2.2.1 Closed-form Expression for μ(s); 2.2.2 Approximate Error Expressions; 2.2.3 An Alternative Expression for μ[sub(R)](S); 2.2.4 Performance for a Typical System; 2.3 Summary: Simple Binary Detection; 2.4 Problems; References; 3 General Binary Detection: Gaussian Processes; 3.1 Model and Problem Classification; 3.2 Receiver Structures; 3.2.1 Whitening Approach; 3.2.2 Various Implementations of the Likelihood Ratio Test; 3.2.3 Summary: Receiver Structures; 3.3 Performance; 3.4 Four Special Situations; 3.4.1 Binary Symmetric Case; 3.4.2 Non-zero Means
3.4.3 Stationary ""Carrier-symmetric"" Bandpass Problems3.4.4 Error Probability for the Binary Symmetric Bandpass Problem; 3.5 General Binary Case: White Noise Not Necessarily Present: Singular Tests; 3.5.1 Receiver Derivation; 3.5.2 Performance: General Binary Case; 3.5.3 Singularity; 3.6 Summary: General Binary Problem; 3.7 Problems; References; 4 Special Categories of Detection Problems; 4.1 Stationary Processes: Long Observation Time; 4.1.1 Simple Binary Problem; 4.1.2 General Binary Problem; 4.1.3 Summary: SPLOT Problem; 4.2 Separable Kernels; 4.2.1 Separable Kernel Model
4.2.2 Time Diversity4.2.3 Frequency Diversity; 4.2.4 Summary: Separable Kernels; 4.3 Low-Energy-Coherence (LEC) Case; 4.4 Summary; 4.5 Problems; References; 5 Discussion: Detection of Gaussian Signals; 5.1 Related Topics; 5.1.1 M-ary Detection: Gaussian Signals in Noise; 5.1.2 Suboptimum Receivers; 5.1.3 Adaptive Receivers; 5.1.4 Non-Gaussian Processes; 5.1.5 Vector Gaussian Processes; 5.2 Summary of Detection Theory; 5.3 Problems; References; 6 Estimation of the Parameters of a Random Process; 6.1 Parameter Estimation Model; 6.2 Estimator Structure
6.2.1 Derivation of the Likelihood Function6.2.2 Maximum Likelihood and Maximum A-Posteriori Probability Equations; 6.3 Performance Analysis; 6.3.1 A Lower Bound on the Variance; 6.3.2 Calculation of J[sup(2)](A); 6.3.3 Lower Bound on the Mean-Square Error; 6.3.4 Improved Performance Bounds; 6.4 Summary; 6.5 Problems; References; 7 Special Categories of Estimation Problems; 7.1 Stationary Processes: Long Observation Time; 7.1.1 General Results; 7.1.2 Performance of Truncated Estimates; 7.1.3 Suboptimum Receivers; 7.1.4 Summary; 7.2 Finite-State Processes; 7.3 Separable Kernels
7.4 Low-Energy-Coherence Case
Record Nr. UNINA-9910877696003321
Van Trees Harry L  
New York, : Wiley, 2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Detection, estimation, and modulation theory . Part 1 Detection, estimation, and linear modulation theory / / Harry L. Van Trees
Detection, estimation, and modulation theory . Part 1 Detection, estimation, and linear modulation theory / / Harry L. Van Trees
Autore Van Trees Harry L
Pubbl/distr/stampa New York, : Wiley, c2001
Descrizione fisica 1 online resource (714 p.)
Disciplina 621.381536
Soggetto topico Signal theory (Telecommunication)
Modulation theory
Estimation theory
ISBN 1-280-54186-5
9786610541867
0-470-34071-1
0-471-22108-2
0-471-46382-5
1-60119-558-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; 1 Introduction; 1.1 Topical Outline; 1.2 Possible Approaches; 1.3 Organization; 2 Classical Detection and Estimation Theory; 2.1 Introduction; 2.2 Simple Binary Hypothesis Tests; Decision Criteria; Performance: Receiver Operating Characteristic; 2.3 M Hypotheses; 2.4 Estimation Theory; Random Parameters: Bayes Estimation; Real (Nonrandom) Parameter Estimation; Multiple Parameter Estimation; Summary of Estimation Theory; 2.5 Composite Hypotheses; 2.6 The General Gaussian Problem; Equal Covariance Matrices; Equal Mean Vectors; Summary; 2.7 Performance Bounds and Approximations
2.8 Summary2.9 Problems; References; 3 Representations of Random Processes; 3.1 Introduction; 3.2 Deterministic Functions: Orthogonal Representations; 3.3 Random Process Characterization; Random Processes: Conventional Characterizations; Series Representation of Sample Functions of Random Processes; Gaussian Processes; 3.4 Homogeneous Integral Equations and Eigenfunctions; Rational Spectra; Bandlimited Spectra; Nonstationary Processes; White Noise Processes; The Optimum Linear Filter; Properties of Eigenfunctions and Eigenvalues; 3.5 Periodic Processes
3.6 Infinite Time Interval: Spectral DecompositionSpectral Decomposition; An Application of Spectral Decomposition: MAP Estimation of a Gaussian Process; 3.7 Vector Random Processes; 3.8 Summary; 3.9 Problems; References; 4 Detection of Signals-Estimation of Signal Parameters; 4.1 Introduction; Models; Format; 4.2 Detection and Estimation in White Gaussian Noise; Detection of Signals in Additive White Gaussian Noise; Linear Estimation; Nonlinear Estimation; Summary : Known Signals in White Gaussian Noise; 4.3 Detection and Estimation in Nonwhite Gaussian Noise; ""Whitening"" Approach
A Direct Derivation Using the Karhunen-Loeve ExpansionA Direct Derivation with a Sufficient Statistic; Detection Performance; Estimation; Solution Techniques for Integral Equations; Sensitivity; Known Linear Channels; 4.4 Signals with Unwanted Parameters: The Composite Hypothesis Problem; Random Phase Angles; Random Amplitude and Phase; 4.5 Multiple Channels; Formulation; Application; 4.6 Multiple Parameter Estimation; Additive White Gaussian Noise Channel; Extensions; 4.7 Summary and Omissions; Summary; Topics Omitted; 4.8 Problems; References; 5 Estimation of Continuous Waveforms
5.1 Introduction5.2 Derivation of Estimator Equations; No-Memory Modulation Systems; Modulation Systems with Memory; 5.3 A Lower Bound on the Mean-Square Estimation Error; 5.4 Multidimensional Waveform Estimation; Examples of Multidimensional Problems; Problem Formulation; Derivation of Estimator Equations; Lower Bound on the Error Matrix; Colored Noise Estimation; 5.5 Nonrandom Waveform Estimation; 5.6 Summary; 5.7 Problems; References; 6 Linear Estimation; 6.1 Properties of Optimum Processors; 6.2 Realizable Linear Filters: Stationary Processes, Infinite Past: Wiener Filters
Solution of Wiener-Hopf Equation
Altri titoli varianti Detection, estimation, and linear modulation theory
Record Nr. UNINA-9910142558803321
Van Trees Harry L  
New York, : Wiley, c2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Optimum array processing [[electronic resource] /] / Harry L. Van Trees
Optimum array processing [[electronic resource] /] / Harry L. Van Trees
Autore Van Trees Harry L
Pubbl/distr/stampa New York, : Wiley-Interscience, 2002
Descrizione fisica 1 online resource (1470 p.)
Disciplina 621.381536
621.3822
Soggetto topico Signal theory (Telecommunication)
Electric interference
Signal processing
ISBN 1-280-27271-6
9786610272716
0-470-34672-8
0-471-22110-4
0-471-46383-3
1-60119-387-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; Preface; 1 Introduction; 1.1 Array Processing; 1.2 Applications; 1.2.1 Radar; 1.2.2 Radio Astronomy; 1.2.3 Sonar; 1.2.4 Communications; 1.2.5 Direction Finding; 1.2.6 Seismology; 1.2.7 Tomography; 1.2.8 Array Processing Literature; 1.3 Organization of the Book; 1.4 Interactive Study; 2 Arrays and Spatial Filters; 2.1 Introduction; 2.2 Frequency-wavenumber Response and Beam Patterns; 2.3 Uniform Linear Arrays; 2.4 Uniformly Weighted Linear Arrays; 2.4.1 Beam Pattern Parameters; 2.5 Array Steering; 2.6 Array Performance Measures; 2.6.1 Directivity
2.6.2 Array Gain vs. Spatially White Noise (A[sub(w)])2.6.3 Sensitivity and the Tolerance Factor; 2.6.4 Summary; 2.7 Linear Apertures; 2.7.1 Frequency-wavenumber Response; 2.7.2 Aperture Sampling; 2.8 Non-isotropic Element Patterns; 2.9 Summary; 2.10 Problems; 3 Synthesis of Linear Arrays and Apertures; 3.1 Spectral Weighting; 3.2 Array Polynomials and the z-Transform; 3.2.1 z-Transform; 3.2.2 Real Array Weights; 3.2.3 Properties of the Beam Pattern Near a Zero; 3.3 Pattern Sampling in Wavenumber Space; 3.3.1 Continuous Aperture; 3.3.2 Linear Arrays; 3.3.3 Discrete Fourier Transform
3.3.4 Norms3.3.5 Summary; 3.4 Minimum Beamwidth for Specified Sidelobe Level; 3.4.1 Introduction; 3.4.2 Dolph-Chebychev Arrays; 3.4.3 Taylor Distribution; 3.4.4 Villeneuve n Distribution; 3.5 Least Squares Error Pattern Synthesis; 3.6 Minimax Design; 3.6.1 Alternation Theorem; 3.6.2 Parks-McClellan-Rabiner Algorithm; 3.6.3 Summary; 3.7 Null Steering; 3.7.1 Null Constraints; 3.7.2 Least Squares Error Pattern Synthesis with Nulls; 3.8 Asymmetric Beams; 3.9 Spatially Non-uniform Linear Arrays; 3.9.1 Introduction; 3.9.2 Minimum Redundancy Arrays; 3.9.3 Beam Pattern Design Algorithm
3.10 Beamspace Processing3.10.1 Full-dimension Beamspace; 3.10.2 Reduced-dimension Beamspace; 3.10.3 Multiple Beam Antennas; 3.10.4 Summary; 3.11 Broadband Arrays; 3.12 Summary; 3.13 Problems; 4 Planar Arrays and Apertures; 4.1 Rectangular Arrays; 4.1.1 Uniform Rectangular Arrays; 4.1.2 Array Manifold Vector; 4.1.3 Separable Spectral Weightings; 4.1.4 2-D z-Transforms; 4.1.5 Least Squares Synthesis; 4.1.6 Circularly Symmetric Weighting and Windows; 4.1.7 Wavenumber Sampling and 2-D DFT; 4.1.8 Transformations from One Dimension to Two Dimensions; 4.1.9 Null Steering; 4.1.10 Related Topics
4.2 Circular Arrays4.2.1 Continuous Circular Arrays (Ring Apertures); 4.2.2 Circular Arrays; 4.2.3 Phase Mode Excitation Beamformers; 4.3 Circular Apertures; 4.3.1 Separable Weightings; 4.3.2 Taylor Synthesis for Circular Apertures; 4.3.4 Difference Beams; 4.3.5 Summary; 4.4 Hexagonal Arrays; 4.4.1 Introduction; 4.4.2 Beam Pattern Design; 4.4.3 Hexagonal Grid to Rectangular Grid Transformation; 4.4.4 Summary; 4.5 Nonplanar Arrays; 4.5.1 Cylindrical Arrays; 4.5.2 Spherical Arrays; 4.6 Summary; 4.7 Problems; 5 Characterization of Space-time Processes; 5.1 Introduction; 5.2 Snapshot Models
5.2.1 Frequency-domain Snapshot Models
Record Nr. UNISA-996201055803316
Van Trees Harry L  
New York, : Wiley-Interscience, 2002
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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