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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 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
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 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
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 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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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