Adaptive radar signal processing [[electronic resource] /] / edited by Simon Haykin |
Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
Descrizione fisica | 1 online resource (248 p.) |
Disciplina | 621.3848 |
Altri autori (Persone) | HaykinSimon S. <1931-> |
Soggetto topico |
Radar
Adaptive signal processing |
Soggetto genere / forma | Electronic books. |
ISBN |
1-280-72151-0
9786610721511 0-470-06912-0 0-470-06911-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Adaptive Radar Signal Processing; Contents; Preface; Acknowledgments; Contributors List; 1. Introduction; Experimental Radar Facilities; Organization of the Book; Part I Radar Spectral Analysis; 2. Angle-of-Arrival Estimation in the Presence of Multipath; 2.1 Introduction; 2.2 The Low-Angle Tracking Radar Problem; 2.3 Spectrum Estimation Background; 2.3.1 The Fundamental Equation of Spectrum Estimation; 2.4 Thomson's Multi-Taper Method; 2.4.1 Prolate Spheroidal Wavefunctions and Sequences; 2.5 Test Dataset and a Comparison of Some Popular Spectrum Estimation Procedures
2.5.1 Classical Spectrum Estimation2.5.2 MUSIC and MFBLP; 2.6 Multi-taper Spectrum Estimation; 2.6.1 The Adaptive Spectrum; 2.6.2 The Composite Spectrum; 2.6.3 Computing the Crude, Adaptive, and Composite Spectra; 2.7 F-Test for the Line Components; 2.7.1 Brief Outline of the F-Test; 2.7.2 The Point Regression Single-Line F-Test; 2.7.3 The Integral Regression Single-Line F-Test; 2.7.4 The Point Regression Double-Line F-Test; 2.7.5 The Integral Regression Double-Line F-Test; 2.7.6 Line Component Extraction; 2.7.7 Prewhitening; 2.7.8 Multiple Snapshots 2.7.9 Multiple Snapshot, Single-Line, Point-Regression F-Tests2.7.10 Multiple-Snapshot, Double-Line Point-Regression F-Tests; 2.8 Experimental Data Description for a Low-Angle Tracking Radar Study; 2.9 Angle-of-Arrival (AOA) Estimation; 2.10 Diffuse Multipath Spectrum Estimation; 2.11 Discussion; References; 3. Time-Frequency Analysis of Sea Clutter; 3.1 Introduction; 3.2 An Overview of Nonstationary Behavior and Time-Frequency Analysis; 3.3 Theoretical Background on Nonstationarity; 3.3.1 Multi-taper Estimates; 3.3.2 Spectrum Estimation as an Inverse Problem 3.4 High-Resolution Multi-taper Spectrograms3.4.1 Nonstationary Quadratic-Inverse Theory; 3.4.2 Multi-taper Estimates of the Loève Spectrum; 3.5 Spectrum Analysis of Radar Signals; 3.6 Discussion; 3.6.1 Target Detection Rooted in Learning; References; Part II Dynamic Models; 4. Dynamics of Sea Clutter; 4.1 Introduction; 4.2 Statistical Nature of Sea Clutter: Classical Approach; 4.2.1 Background; 4.2.2 Current Models; 4.3 Is There a Radar Clutter Attractor?; 4.3.1 Nonlinear Dynamics; 4.3.2 Chaotic Invariants; 4.3.3 Inconclusive Experimental Results on the Chaotic Invariants of Sea Clutter 4.3.4 Dynamic Reconstruction4.3.5 Chaos, a Self-Fulfilling Prophecy?; 4.4 Hybrid AM/FM Model of Sea Clutter; 4.4.1 Radar Return Plots; 4.4.2 Rayleigh Fading; 4.4.3 Time-Doppler Spectra; 4.4.4 Evidence for Amplitude Modulation, Frequency Modulation, and More; 4.4.5 Modeling Sea Clutter as a Nonstationary Complex Autoregressive Process; 4.5 Discussion; 4.5.1 Nonlinear Dynamics of Sea Clutter; 4.5.2 Autoregressive Modeling of Sea Clutter; 4.5.3 State-Space Theory; 4.5.4 Nonlinear Dynamical Approach Versus Classical Statistical Approach; 4.5.5 Stochastic Chaos; References Appendix A Specifications of the Three Sea-Clutter Sets Used in This Chapter |
Record Nr. | UNINA-9910143406403321 |
Hoboken, N.J., : Wiley-Interscience, c2007 | ||
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Lo trovi qui: Univ. Federico II | ||
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Adaptive radar signal processing [[electronic resource] /] / edited by Simon Haykin |
Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
Descrizione fisica | 1 online resource (248 p.) |
Disciplina | 621.3848 |
Altri autori (Persone) | HaykinSimon S. <1931-> |
Soggetto topico |
Radar
Adaptive signal processing |
ISBN |
1-280-72151-0
9786610721511 0-470-06912-0 0-470-06911-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Adaptive Radar Signal Processing; Contents; Preface; Acknowledgments; Contributors List; 1. Introduction; Experimental Radar Facilities; Organization of the Book; Part I Radar Spectral Analysis; 2. Angle-of-Arrival Estimation in the Presence of Multipath; 2.1 Introduction; 2.2 The Low-Angle Tracking Radar Problem; 2.3 Spectrum Estimation Background; 2.3.1 The Fundamental Equation of Spectrum Estimation; 2.4 Thomson's Multi-Taper Method; 2.4.1 Prolate Spheroidal Wavefunctions and Sequences; 2.5 Test Dataset and a Comparison of Some Popular Spectrum Estimation Procedures
2.5.1 Classical Spectrum Estimation2.5.2 MUSIC and MFBLP; 2.6 Multi-taper Spectrum Estimation; 2.6.1 The Adaptive Spectrum; 2.6.2 The Composite Spectrum; 2.6.3 Computing the Crude, Adaptive, and Composite Spectra; 2.7 F-Test for the Line Components; 2.7.1 Brief Outline of the F-Test; 2.7.2 The Point Regression Single-Line F-Test; 2.7.3 The Integral Regression Single-Line F-Test; 2.7.4 The Point Regression Double-Line F-Test; 2.7.5 The Integral Regression Double-Line F-Test; 2.7.6 Line Component Extraction; 2.7.7 Prewhitening; 2.7.8 Multiple Snapshots 2.7.9 Multiple Snapshot, Single-Line, Point-Regression F-Tests2.7.10 Multiple-Snapshot, Double-Line Point-Regression F-Tests; 2.8 Experimental Data Description for a Low-Angle Tracking Radar Study; 2.9 Angle-of-Arrival (AOA) Estimation; 2.10 Diffuse Multipath Spectrum Estimation; 2.11 Discussion; References; 3. Time-Frequency Analysis of Sea Clutter; 3.1 Introduction; 3.2 An Overview of Nonstationary Behavior and Time-Frequency Analysis; 3.3 Theoretical Background on Nonstationarity; 3.3.1 Multi-taper Estimates; 3.3.2 Spectrum Estimation as an Inverse Problem 3.4 High-Resolution Multi-taper Spectrograms3.4.1 Nonstationary Quadratic-Inverse Theory; 3.4.2 Multi-taper Estimates of the Loève Spectrum; 3.5 Spectrum Analysis of Radar Signals; 3.6 Discussion; 3.6.1 Target Detection Rooted in Learning; References; Part II Dynamic Models; 4. Dynamics of Sea Clutter; 4.1 Introduction; 4.2 Statistical Nature of Sea Clutter: Classical Approach; 4.2.1 Background; 4.2.2 Current Models; 4.3 Is There a Radar Clutter Attractor?; 4.3.1 Nonlinear Dynamics; 4.3.2 Chaotic Invariants; 4.3.3 Inconclusive Experimental Results on the Chaotic Invariants of Sea Clutter 4.3.4 Dynamic Reconstruction4.3.5 Chaos, a Self-Fulfilling Prophecy?; 4.4 Hybrid AM/FM Model of Sea Clutter; 4.4.1 Radar Return Plots; 4.4.2 Rayleigh Fading; 4.4.3 Time-Doppler Spectra; 4.4.4 Evidence for Amplitude Modulation, Frequency Modulation, and More; 4.4.5 Modeling Sea Clutter as a Nonstationary Complex Autoregressive Process; 4.5 Discussion; 4.5.1 Nonlinear Dynamics of Sea Clutter; 4.5.2 Autoregressive Modeling of Sea Clutter; 4.5.3 State-Space Theory; 4.5.4 Nonlinear Dynamical Approach Versus Classical Statistical Approach; 4.5.5 Stochastic Chaos; References Appendix A Specifications of the Three Sea-Clutter Sets Used in This Chapter |
Record Nr. | UNINA-9910830633403321 |
Hoboken, N.J., : Wiley-Interscience, c2007 | ||
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Lo trovi qui: Univ. Federico II | ||
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Adaptive radar signal processing / / edited by Simon Haykin |
Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
Descrizione fisica | 1 online resource (248 p.) |
Disciplina | 621.3848 |
Altri autori (Persone) | HaykinSimon S. <1931-> |
Soggetto topico |
Radar
Adaptive signal processing |
ISBN |
1-280-72151-0
9786610721511 0-470-06912-0 0-470-06911-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Adaptive Radar Signal Processing; Contents; Preface; Acknowledgments; Contributors List; 1. Introduction; Experimental Radar Facilities; Organization of the Book; Part I Radar Spectral Analysis; 2. Angle-of-Arrival Estimation in the Presence of Multipath; 2.1 Introduction; 2.2 The Low-Angle Tracking Radar Problem; 2.3 Spectrum Estimation Background; 2.3.1 The Fundamental Equation of Spectrum Estimation; 2.4 Thomson's Multi-Taper Method; 2.4.1 Prolate Spheroidal Wavefunctions and Sequences; 2.5 Test Dataset and a Comparison of Some Popular Spectrum Estimation Procedures
2.5.1 Classical Spectrum Estimation2.5.2 MUSIC and MFBLP; 2.6 Multi-taper Spectrum Estimation; 2.6.1 The Adaptive Spectrum; 2.6.2 The Composite Spectrum; 2.6.3 Computing the Crude, Adaptive, and Composite Spectra; 2.7 F-Test for the Line Components; 2.7.1 Brief Outline of the F-Test; 2.7.2 The Point Regression Single-Line F-Test; 2.7.3 The Integral Regression Single-Line F-Test; 2.7.4 The Point Regression Double-Line F-Test; 2.7.5 The Integral Regression Double-Line F-Test; 2.7.6 Line Component Extraction; 2.7.7 Prewhitening; 2.7.8 Multiple Snapshots 2.7.9 Multiple Snapshot, Single-Line, Point-Regression F-Tests2.7.10 Multiple-Snapshot, Double-Line Point-Regression F-Tests; 2.8 Experimental Data Description for a Low-Angle Tracking Radar Study; 2.9 Angle-of-Arrival (AOA) Estimation; 2.10 Diffuse Multipath Spectrum Estimation; 2.11 Discussion; References; 3. Time-Frequency Analysis of Sea Clutter; 3.1 Introduction; 3.2 An Overview of Nonstationary Behavior and Time-Frequency Analysis; 3.3 Theoretical Background on Nonstationarity; 3.3.1 Multi-taper Estimates; 3.3.2 Spectrum Estimation as an Inverse Problem 3.4 High-Resolution Multi-taper Spectrograms3.4.1 Nonstationary Quadratic-Inverse Theory; 3.4.2 Multi-taper Estimates of the Loève Spectrum; 3.5 Spectrum Analysis of Radar Signals; 3.6 Discussion; 3.6.1 Target Detection Rooted in Learning; References; Part II Dynamic Models; 4. Dynamics of Sea Clutter; 4.1 Introduction; 4.2 Statistical Nature of Sea Clutter: Classical Approach; 4.2.1 Background; 4.2.2 Current Models; 4.3 Is There a Radar Clutter Attractor?; 4.3.1 Nonlinear Dynamics; 4.3.2 Chaotic Invariants; 4.3.3 Inconclusive Experimental Results on the Chaotic Invariants of Sea Clutter 4.3.4 Dynamic Reconstruction4.3.5 Chaos, a Self-Fulfilling Prophecy?; 4.4 Hybrid AM/FM Model of Sea Clutter; 4.4.1 Radar Return Plots; 4.4.2 Rayleigh Fading; 4.4.3 Time-Doppler Spectra; 4.4.4 Evidence for Amplitude Modulation, Frequency Modulation, and More; 4.4.5 Modeling Sea Clutter as a Nonstationary Complex Autoregressive Process; 4.5 Discussion; 4.5.1 Nonlinear Dynamics of Sea Clutter; 4.5.2 Autoregressive Modeling of Sea Clutter; 4.5.3 State-Space Theory; 4.5.4 Nonlinear Dynamical Approach Versus Classical Statistical Approach; 4.5.5 Stochastic Chaos; References Appendix A Specifications of the Three Sea-Clutter Sets Used in This Chapter |
Record Nr. | UNINA-9910877470303321 |
Hoboken, N.J., : Wiley-Interscience, c2007 | ||
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Lo trovi qui: Univ. Federico II | ||
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Adaptive signal processing : next generation solutions / / edited by Tèulay Adali, Simon Haykin |
Autore | Adali Tülay |
Edizione | [1st edition] |
Pubbl/distr/stampa | New York : , : IEEE, Institute of Electrical and Electronics Engineers, , c2010 |
Descrizione fisica | 1 online resource (428 p.) |
Disciplina |
621.382/2
621.3822 |
Altri autori (Persone) | HaykinSimon S. <1931-> |
Collana | Adaptive and learning systems for signal processing, communications and control series |
Soggetto topico | Adaptive signal processing |
ISBN |
1-282-65650-3
9786612656507 0-470-57575-1 0-470-57574-3 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Preface -- Contributors -- Chapter 1 Complex-Valued Adaptive Signal Processing -- 1.1 Introduction -- -- 1.2 Preliminaries -- 1.3 Optimization in the Complex Domain -- 1.4 Widely Linear Adaptive Filtering -- 1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons -- 1.6 Complex Independent Component Analysis -- 1.7 Summary -- 1.8 Acknowledgment -- 1.9 Problems -- References -- Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors -- 2.1 Introduction -- 2.2 Statistical Characterization of Complex Random Vectors -- 2.3 Complex Elliptically Symmetric (CES) Distributions -- 2.4 Tools to Compare Estimators -- 2.5 Scatter and Pseudo-Scatter Matrices -- 2.6 Array Processing Examples -- 2.7 MVDR Beamformers Based on M-Estimators -- 2.8 Robust ICA -- 2.9 Conclusion -- 2.10 Problems -- References -- Chapter 3 Turbo Equalization -- 3.1 Introduction -- 3.2 Context -- 3.3 Communication Chain -- 3.4 Turbo Decoder: Overview -- 3.5 Forward-Backward Algorithm -- 3.6 Simplified Algorithm: Interference Canceler -- 3.7 Capacity Analysis -- 3.8 Blind Turbo Equalization -- 3.9 Convergence -- 3.10 Multichannel and Multiuser Settings -- 3.11 Concluding Remarks -- 3.12 Problems -- References -- Chapter 4 Subspace Tracking for Signal Processing -- 4.1 Introduction -- 4.2 Linear Algebra Review -- 4.3 Observation Model and Problem Statement -- 4.4 Preliminary Example: Oja's Neuron -- 4.5 Subspace Tracking -- 4.6 Eigenvectors Tracking -- 4.7 Convergence and Performance Analysis Issues -- 4.8 Illustrative Examples -- 4.9 Concluding Remarks -- 4.10 Problems -- References -- Chapter 5 Particle Filtering -- 5.1 Introduction -- 5.2 Motivation for Use of Particle Filtering -- 5.3 The Basic Idea -- 5.4 The Choice of Proposal Distribution and Resampling -- 5.5 Some Particle Filtering Methods -- 5.6 Handling Constant Parameters -- 5.7 Rao-Blackwellization -- 5.8 Prediction -- 5.9 Smoothing -- 5.10 Convergence Issues -- 5.11 Computational Issues and Hardware Implementation -- 5.12 Acknowledgments.
5.13 Exercises -- References -- Chapter 6 Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems -- 6.1 Introduction -- 6.2 Back-Propagation and Support Vector Machine-Learning Algorithms: Review -- 6.3 Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation -- 6.4 The Extended Kalman Filter -- 6.5 Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms -- 6.6 Concluding Remarks -- 6.7 Problems -- References -- Chapter 7 Bandwidth Extension of Telephony Speech -- 7.1 Introduction -- 7.2 Organization of the Chapter -- 7.3 Nonmodel-Based Algorithms for Bandwidth Extension -- 7.4 Basics -- 7.5 Model-Based Algorithms for Bandwidth Extension -- 7.6 Evaluation of Bandwidth Extension Algorithms -- 7.7 Conclusion -- 7.8 Problems -- References -- Index. |
Record Nr. | UNISA-996203788603316 |
Adali Tülay
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New York : , : IEEE, Institute of Electrical and Electronics Engineers, , c2010 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Adaptive signal processing : next generation solutions / / [edited by] Tulay Adali, Simon S. Haykin |
Edizione | [1st edition] |
Pubbl/distr/stampa | Hoboken, N.J., : Wiley-IEEE, c2010 |
Descrizione fisica | 1 online resource (428 p.) |
Disciplina | 621.382/2 |
Altri autori (Persone) |
AdaliTulay
HaykinSimon S. <1931-> |
Collana | Adaptive and learning systems for signal processing, communications and control series |
Soggetto topico | Adaptive signal processing |
ISBN |
1-282-65650-3
9786612656507 0-470-57575-1 0-470-57574-3 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Preface -- Contributors -- Chapter 1 Complex-Valued Adaptive Signal Processing -- 1.1 Introduction -- -- 1.2 Preliminaries -- 1.3 Optimization in the Complex Domain -- 1.4 Widely Linear Adaptive Filtering -- 1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons -- 1.6 Complex Independent Component Analysis -- 1.7 Summary -- 1.8 Acknowledgment -- 1.9 Problems -- References -- Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors -- 2.1 Introduction -- 2.2 Statistical Characterization of Complex Random Vectors -- 2.3 Complex Elliptically Symmetric (CES) Distributions -- 2.4 Tools to Compare Estimators -- 2.5 Scatter and Pseudo-Scatter Matrices -- 2.6 Array Processing Examples -- 2.7 MVDR Beamformers Based on M-Estimators -- 2.8 Robust ICA -- 2.9 Conclusion -- 2.10 Problems -- References -- Chapter 3 Turbo Equalization -- 3.1 Introduction -- 3.2 Context -- 3.3 Communication Chain -- 3.4 Turbo Decoder: Overview -- 3.5 Forward-Backward Algorithm -- 3.6 Simplified Algorithm: Interference Canceler -- 3.7 Capacity Analysis -- 3.8 Blind Turbo Equalization -- 3.9 Convergence -- 3.10 Multichannel and Multiuser Settings -- 3.11 Concluding Remarks -- 3.12 Problems -- References -- Chapter 4 Subspace Tracking for Signal Processing -- 4.1 Introduction -- 4.2 Linear Algebra Review -- 4.3 Observation Model and Problem Statement -- 4.4 Preliminary Example: Oja's Neuron -- 4.5 Subspace Tracking -- 4.6 Eigenvectors Tracking -- 4.7 Convergence and Performance Analysis Issues -- 4.8 Illustrative Examples -- 4.9 Concluding Remarks -- 4.10 Problems -- References -- Chapter 5 Particle Filtering -- 5.1 Introduction -- 5.2 Motivation for Use of Particle Filtering -- 5.3 The Basic Idea -- 5.4 The Choice of Proposal Distribution and Resampling -- 5.5 Some Particle Filtering Methods -- 5.6 Handling Constant Parameters -- 5.7 Rao-Blackwellization -- 5.8 Prediction -- 5.9 Smoothing -- 5.10 Convergence Issues -- 5.11 Computational Issues and Hardware Implementation -- 5.12 Acknowledgments.
5.13 Exercises -- References -- Chapter 6 Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems -- 6.1 Introduction -- 6.2 Back-Propagation and Support Vector Machine-Learning Algorithms: Review -- 6.3 Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation -- 6.4 The Extended Kalman Filter -- 6.5 Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms -- 6.6 Concluding Remarks -- 6.7 Problems -- References -- Chapter 7 Bandwidth Extension of Telephony Speech -- 7.1 Introduction -- 7.2 Organization of the Chapter -- 7.3 Nonmodel-Based Algorithms for Bandwidth Extension -- 7.4 Basics -- 7.5 Model-Based Algorithms for Bandwidth Extension -- 7.6 Evaluation of Bandwidth Extension Algorithms -- 7.7 Conclusion -- 7.8 Problems -- References -- Index. |
Record Nr. | UNINA-9910140741803321 |
Hoboken, N.J., : Wiley-IEEE, c2010 | ||
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Lo trovi qui: Univ. Federico II | ||
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Fundamentals of cognitive radio / / Peyman Setoodeh, Simon Haykin |
Autore | Setoodeh Peyman <1974-> |
Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2017 |
Descrizione fisica | 1 online resource (242 pages) : illustrations |
Disciplina | 621.384 |
Collana | Adaptive and cognitive dynamic systems: signal processing, learning, communications and control |
Soggetto topico |
Cognitive radio networks
Wireless communication systems |
ISBN |
1-119-40584-X
1-119-40583-1 1-119-40581-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Game theory -- Cognitive radio transceiver -- Cognitive radio networks -- Sustainability of the spectrum supply chain network -- Cognitive heterogeneous networks. |
Record Nr. | UNINA-9910271011803321 |
Setoodeh Peyman <1974->
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Hoboken, New Jersey : , : John Wiley & Sons, , 2017 | ||
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Lo trovi qui: Univ. Federico II | ||
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Fundamentals of cognitive radio / / Peyman Setoodeh, Simon Haykin |
Autore | Setoodeh Peyman <1974-> |
Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2017 |
Descrizione fisica | 1 online resource (242 pages) : illustrations |
Disciplina | 621.384 |
Collana | Adaptive and cognitive dynamic systems: signal processing, learning, communications and control |
Soggetto topico |
Cognitive radio networks
Wireless communication systems |
ISBN |
1-119-40584-X
1-119-40583-1 1-119-40581-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Game theory -- Cognitive radio transceiver -- Cognitive radio networks -- Sustainability of the spectrum supply chain network -- Cognitive heterogeneous networks. |
Record Nr. | UNINA-9910828331703321 |
Setoodeh Peyman <1974->
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Hoboken, New Jersey : , : John Wiley & Sons, , 2017 | ||
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Lo trovi qui: Univ. Federico II | ||
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Kalman filtering and neural networks [[electronic resource] /] / edited by Simon Haykin |
Pubbl/distr/stampa | New York, : Wiley, c2001 |
Descrizione fisica | 1 online resource (302 p.) |
Disciplina |
006.3/2
621.3815324 |
Altri autori (Persone) | HaykinSimon S. <1931-> |
Collana | Adaptive and learning systems for signal processing, communications, and control |
Soggetto topico |
Kalman filtering
Neural networks (Computer science) |
ISBN |
1-280-36756-3
9786610367566 0-470-31226-2 0-471-46421-X 0-471-22154-6 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
KALMAN FILTERING AND NEURAL NETWORKS; CONTENTS; Preface; Contributors; 1 Kalman Filters; 1.1 Introduction; 1.2 Optimum Estimates; 1.3 Kalman Filter; 1.4 Divergence Phenomenon: Square-Root Filtering; 1.5 Rauch-Tung-Striebel Smoother; 1.6 Extended Kalman Filter; 1.7 Summary; References; 2 Parameter-Based Kalman Filter Training: Theory and Implementation; 2.1 Introduction; 2.2 Network Architectures; 2.3 The EKF Procedure; 2.3.1 Global EKF Training; 2.3.2 Learning Rate and Scaled Cost Function; 2.3.3 Parameter Settings; 2.4 Decoupled EKF (DEKF); 2.5 Multistream Training
2.5.1 Some Insight into the Multistream Technique2.5.2 Advantages and Extensions of Multistream Training; 2.6 Computational Considerations; 2.6.1 Derivative Calculations; 2.6.2 Computationally Efficient Formulations for Multiple-Output Problems; 2.6.3 Avoiding Matrix Inversions; 2.6.4 Square-Root Filtering; 2.7 Other Extensions and Enhancements; 2.7.1 EKF Training with Constrained Weights; 2.7.2 EKF Training with an Entropic Cost Function; 2.7.3 EKF Training with Scalar Errors; 2.8 Automotive Applications of EKF Training; 2.8.1 Air/Fuel Ratio Control; 2.8.2 Idle Speed Control 2.8.3 Sensor-Catalyst Modeling2.8.4 Engine Misfire Detection; 2.8.5 Vehicle Emissions Estimation; 2.9 Discussion; 2.9.1 Virtues of EKF Training; 2.9.2 Limitations of EKF Training; 2.9.3 Guidelines for Implementation and Use; References; 3 Learning Shape and Motion from Image Sequences; 3.1 Introduction; 3.2 Neurobiological and Perceptual Foundations of our Model; 3.3 Network Description; 3.4 Experiment 1; 3.5 Experiment 2; 3.6 Experiment 3; 3.7 Discussion; References; 4 Chaotic Dynamics; 4.1 Introduction; 4.2 Chaotic (Dynamic) Invariants; 4.3 Dynamic Reconstruction 4.4 Modeling Numerically Generated Chaotic Time Series4.4.1 Logistic Map; 4.4.2 Ikeda Map; 4.4.3 Lorenz Attractor; 4.5 Nonlinear Dynamic Modeling of Real-World Time Series; 4.5.1 Laser Intensity Pulsations; 4.5.2 Sea Clutter Data; 4.6 Discussion; References; 5 Dual Extended Kalman Filter Methods; 5.1 Introduction; 5.2 Dual EKF-Prediction Error; 5.2.1 EKF-State Estimation; 5.2.2 EKF-Weight Estimation; 5.2.3 Dual Estimation; 5.3 A Probabilistic Perspective; 5.3.1 Joint Estimation Methods; 5.3.2 Marginal Estimation Methods; 5.3.3 Dual EKF Algorithms; 5.3.4 Joint EKF 5.4 Dual EKF Variance Estimation5.5 Applications; 5.5.1 Noisy Time-Series Estimation and Prediction; 5.5.2 Economic Forecasting-Index of Industrial Production; 5.5.3 Speech Enhancement; 5.6 Conclusions; Acknowledgments; Appendix A: Recurrent Derivative of the Kalman Gain; Appendix B: Dual EKF with Colored Measurement Noise; References; 6 Learning Nonlinear Dynamical System Using the Expectation-Maximization Algorithm; 6.1 Learning Stochastic Nonlinear Dynamics; 6.1.1 State Inference and Model Learning; 6.1.2 The Kalman Filter; 6.1.3 The EM Algorithm; 6.2 Combining EKS and EM 6.2.1 Extended Kalman Smoothing (E-step) |
Record Nr. | UNINA-9910830752003321 |
New York, : Wiley, c2001 | ||
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Lo trovi qui: Univ. Federico II | ||
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Kalman filtering and neural networks / / edited by Simon Haykin |
Pubbl/distr/stampa | New York, : Wiley, c2001 |
Descrizione fisica | 1 online resource (302 p.) |
Disciplina |
006.3/2
621.3815324 |
Altri autori (Persone) | HaykinSimon S. <1931-> |
Collana | Adaptive and learning systems for signal processing, communications, and control |
Soggetto topico |
Kalman filtering
Neural networks (Computer science) |
ISBN |
1-280-36756-3
9786610367566 0-470-31226-2 0-471-46421-X 0-471-22154-6 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
KALMAN FILTERING AND NEURAL NETWORKS; CONTENTS; Preface; Contributors; 1 Kalman Filters; 1.1 Introduction; 1.2 Optimum Estimates; 1.3 Kalman Filter; 1.4 Divergence Phenomenon: Square-Root Filtering; 1.5 Rauch-Tung-Striebel Smoother; 1.6 Extended Kalman Filter; 1.7 Summary; References; 2 Parameter-Based Kalman Filter Training: Theory and Implementation; 2.1 Introduction; 2.2 Network Architectures; 2.3 The EKF Procedure; 2.3.1 Global EKF Training; 2.3.2 Learning Rate and Scaled Cost Function; 2.3.3 Parameter Settings; 2.4 Decoupled EKF (DEKF); 2.5 Multistream Training
2.5.1 Some Insight into the Multistream Technique2.5.2 Advantages and Extensions of Multistream Training; 2.6 Computational Considerations; 2.6.1 Derivative Calculations; 2.6.2 Computationally Efficient Formulations for Multiple-Output Problems; 2.6.3 Avoiding Matrix Inversions; 2.6.4 Square-Root Filtering; 2.7 Other Extensions and Enhancements; 2.7.1 EKF Training with Constrained Weights; 2.7.2 EKF Training with an Entropic Cost Function; 2.7.3 EKF Training with Scalar Errors; 2.8 Automotive Applications of EKF Training; 2.8.1 Air/Fuel Ratio Control; 2.8.2 Idle Speed Control 2.8.3 Sensor-Catalyst Modeling2.8.4 Engine Misfire Detection; 2.8.5 Vehicle Emissions Estimation; 2.9 Discussion; 2.9.1 Virtues of EKF Training; 2.9.2 Limitations of EKF Training; 2.9.3 Guidelines for Implementation and Use; References; 3 Learning Shape and Motion from Image Sequences; 3.1 Introduction; 3.2 Neurobiological and Perceptual Foundations of our Model; 3.3 Network Description; 3.4 Experiment 1; 3.5 Experiment 2; 3.6 Experiment 3; 3.7 Discussion; References; 4 Chaotic Dynamics; 4.1 Introduction; 4.2 Chaotic (Dynamic) Invariants; 4.3 Dynamic Reconstruction 4.4 Modeling Numerically Generated Chaotic Time Series4.4.1 Logistic Map; 4.4.2 Ikeda Map; 4.4.3 Lorenz Attractor; 4.5 Nonlinear Dynamic Modeling of Real-World Time Series; 4.5.1 Laser Intensity Pulsations; 4.5.2 Sea Clutter Data; 4.6 Discussion; References; 5 Dual Extended Kalman Filter Methods; 5.1 Introduction; 5.2 Dual EKF-Prediction Error; 5.2.1 EKF-State Estimation; 5.2.2 EKF-Weight Estimation; 5.2.3 Dual Estimation; 5.3 A Probabilistic Perspective; 5.3.1 Joint Estimation Methods; 5.3.2 Marginal Estimation Methods; 5.3.3 Dual EKF Algorithms; 5.3.4 Joint EKF 5.4 Dual EKF Variance Estimation5.5 Applications; 5.5.1 Noisy Time-Series Estimation and Prediction; 5.5.2 Economic Forecasting-Index of Industrial Production; 5.5.3 Speech Enhancement; 5.6 Conclusions; Acknowledgments; Appendix A: Recurrent Derivative of the Kalman Gain; Appendix B: Dual EKF with Colored Measurement Noise; References; 6 Learning Nonlinear Dynamical System Using the Expectation-Maximization Algorithm; 6.1 Learning Stochastic Nonlinear Dynamics; 6.1.1 State Inference and Model Learning; 6.1.2 The Kalman Filter; 6.1.3 The EM Algorithm; 6.2 Combining EKS and EM 6.2.1 Extended Kalman Smoothing (E-step) |
Record Nr. | UNINA-9910877678003321 |
New York, : Wiley, c2001 | ||
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Lo trovi qui: Univ. Federico II | ||
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Kernel adaptive filtering [[electronic resource] ] : a comprehensive introduction / / Jose C. Principe, Weifeng Liu, Simon Haykin |
Autore | Príncipe J. C (José C.) |
Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2010 |
Descrizione fisica | 1 online resource (236 p.) |
Disciplina | 621.382/23 |
Altri autori (Persone) |
LiuWeifeng
HaykinSimon S. <1931-> |
Collana | Adaptive and Learning Systems for Signal Processing, Communications and Control Series |
Soggetto topico |
Adaptive filters
Kernel functions |
ISBN |
1-118-21121-9
1-282-54977-4 9786612549779 0-470-60859-5 0-470-60858-7 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | KERNEL ADAPTIVE FILTERING; CONTENTS; PREFACE; ACKNOWLEDGMENTS; NOTATION; ABBREVIATIONS AND SYMBOLS; 1 BACKGROUND AND PREVIEW; 2 KERNEL LEAST-MEAN-SQUARE ALGORITHM; 3 KERNEL AFFINE PROJECTION ALGORITHMS; 4 KERNEL RECURSIVE LEAST-SQUARES ALGORITHM; 5 EXTENDED KERNEL RECURSIVE LEAST-SQUARES ALGORITHM; 6 DESIGNING SPARSE KERNEL ADAPTIVE FILTERS; EPILOGUE; APPENDIX; A MATHEMATICAL BACKGROUND; B APPROXIMATE LINEAR DEPENDENCY AND SYSTEM STABILITY; REFERENCES; INDEX |
Record Nr. | UNINA-9910140616303321 |
Príncipe J. C (José C.)
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Hoboken, N.J., : Wiley, c2010 | ||
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Lo trovi qui: Univ. Federico II | ||
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