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Adaptive radar signal processing [[electronic resource] /] / edited by Simon Haykin
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
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Adaptive radar signal processing [[electronic resource] /] / edited by Simon Haykin
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
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Adaptive radar signal processing / / edited by Simon Haykin
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 9786610721511
9781280721519
1280721510
9780470069127
0470069120
9780470069110
0470069112
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-9911019805803321
Hoboken, N.J., : Wiley-Interscience, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Adaptive signal processing : next generation solutions / / edited by Tèulay Adali, Simon Haykin
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  
New York : , : IEEE, Institute of Electrical and Electronics Engineers, , c2010
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Adaptive signal processing : next generation solutions / / [edited by] Tulay Adali, Simon S. Haykin
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 9786612656507
9781282656505
1282656503
9780470575758
0470575751
9780470575741
0470575743
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
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fundamentals of cognitive radio / / Peyman Setoodeh, Simon Haykin
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->  
Hoboken, New Jersey : , : John Wiley & Sons, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fundamentals of cognitive radio / / Peyman Setoodeh, Simon Haykin
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->  
Hoboken, New Jersey : , : John Wiley & Sons, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Kalman filtering and neural networks [[electronic resource] /] / edited by Simon Haykin
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
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Kalman filtering and neural networks / / edited by Simon Haykin
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 9786610367566
9781280367564
1280367563
9780470312261
0470312262
9780471464211
047146421X
9780471221548
0471221546
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-9911019785303321
New York, : Wiley, c2001
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Kernel adaptive filtering [[electronic resource] ] : a comprehensive introduction / / Jose C. Principe, Weifeng Liu, Simon Haykin
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.)  
Hoboken, N.J., : Wiley, c2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui