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Adaptive filter theory / / Simon Haykin ; international edition contributions by Telagarapu Prabhakar
Adaptive filter theory / / Simon Haykin ; international edition contributions by Telagarapu Prabhakar
Autore Haykin Simon S. <1931->
Edizione [Fifth edition, International edition.]
Pubbl/distr/stampa Upper Saddle River : , : Pearson, , [2014]
Descrizione fisica 1 online resource (912 pages) : illustrations (some color)
Disciplina 621.3815324
Collana Always learning
Soggetto topico Adaptive filters
ISBN 0-273-77572-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title -- Contents -- Preface -- Acknowledgments -- Background and Preview -- 1. The Filtering Problem -- 2. Linear Optimum Filters -- 3. Adaptive Filters -- 4. Linear Filter Structures -- 5. Approaches to the Development of Linear Adaptive Filters -- 6. Adaptive Beamforming -- 7. Four Classes of Applications -- 8. Historical Notes -- Chapter 1 Stochastic Processes and Models -- 1.1 Partial Characterization of a Discrete-Time Stochastic Process -- 1.2 Mean Ergodic Theorem -- 1.3 Correlation Matrix -- 1.4 Correlation Matrix of Sine Wave Plus Noise -- 1.5 Stochastic Models -- 1.6 Wold Decomposition -- 1.7 Asymptotic Stationarity of an Autoregressive Process -- 1.8 Yule-Walker Equations -- 1.9 Computer Experiment: Autoregressive Process of Order Two -- 1.10 Selecting the Model Order -- 1.11 Complex Gaussian Proceses -- 1.12 Power Spectral Density -- 1.13 Propert ies of Power Spectral Density -- 1.14 Transmission of a Stationary Process Through a Linear Filter -- 1.15 Cramér Spectral Representation for a Stationary Process -- 1.16 Power Spectrum Estimation -- 1.17 Other Statistical Characteristics of a Stochastic Process -- 1.18 Polyspectra -- 1.19 Spectral-Correlation Density -- 1.20 Summary and Discussion -- Problems -- Chapter 2 Wiener Filters -- 2.1 Linear Optimum Filtering: Statement of the Problem -- 2.2 Principle of Orthogonality -- 2.3 Minimum Mean-Square Error -- 2.4 Wiener-Hopf Equations -- 2.5 Error-Performance Surface -- 2.6 Multiple Linear Regression Model -- 2.7 Example -- 2.8 Linearly Constrained Minimum-Variance Filter -- 2.9 Generalized Sidelobe Cancellers -- 2.10 Summary and Discussion -- Problems -- Chapter 3 Linear Prediction -- 3.1 Forward Linear Prediction -- 3.2 Backward Linear Prediction -- 3.3 Levinson-Durbin Algorithm -- 3.4 Properties of Prediction-Error Filters -- 3.5 Schur-Cohn Test.
3.6 Autoregressive Modeling of a Stationary Stochastic Process -- 3.7 Cholesky Factorization -- 3.8 Lattice Predictors -- 3.9 All-Pole, All-Pass Lattice Filter -- 3.10 Joint-Process Estimation -- 3.11 Predictive Modeling of Speech -- 3.12 Summary and Discussion -- Problems -- Chapter 4 Method of Steepest Descent -- 4.1 Basic Idea of the Steepest-Descent Algorithm -- 4.2 The Steepest-Descent Algorithm Applied to the Wiener Filter -- 4.3 Stability of the Steepest-Descent Algorithm -- 4.4 Example -- 4.5 The Steepest-Descent Algorithm Viewed as a Deterministic Search Method -- 4.6 Virtue and Limitation of the Steepest-Descent Algorithm -- 4.7 Summary and Discussion -- Problems -- Chapter 5 Method of Stochastic Gradient Descent -- 5.1 Principles of Stochastic Gradient Descent -- 5.2 Application 1: Least-Mean-Square (LMS) Algorithm -- 5.3 Application 2: Gradient-Adaptive Lattice Filtering Algorithm -- 5.4 Other Applications of Stochastic Gradient Descent -- 5.5 Summary and Discussion -- Problems -- Chapter 6 The Least-Mean-Square (LMS) Algorithm -- 6.1 Signal-Flow Graph -- 6.2 Optimality Considerations -- 6.3 Applications -- 6.4 Statistical Learning Theory -- 6.5 Transient Behavior and Convergence Considerations -- 6.6 Efficiency -- 6.7 Computer Experiment on Adaptive Prediction -- 6.8 Computer Experiment on Adaptive Equalization -- 6.9 Computer Experiment on a Minimum-Variance Distortionless-Response Beamformer -- 6.10 Summary and Discussion -- Problems -- Chapter 7 Normalized Least-Mean-Square (LMS) Algorithm and Its Generalization -- 7.1 Normalized LMS Algorithm: The Solution to a Constrained Optimization Problem -- 7.2 Stability of the Normalized LMS Algorithm -- 7.3 Step-Size Control for Acoustic Echo Cancellation -- 7.4 Geometric Considerations Pertaining to the Convergence Process for Real-Valued Data -- 7.5 Affine Projection Adaptive Filters.
7.6 Summary and Discussion -- Problems -- Chapter 8 Block-Adaptive Filters -- 8.1 Block-Adaptive Filters: Basic Ideas -- 8.2 Fast Block LMS Algorithm -- 8.3 Unconstrained Frequency-Domain Adaptive Filters -- 8.4 Self-Orthogonalizing Adaptive Filters -- 8.5 Computer Experiment on Adaptive Equalization -- 8.6 Subband Adaptive Filters -- 8.7 Summary and Discussion -- Problems -- Chapter 9 Method of Least-Squares -- 9.1 Statement of the Linear Least-Squares Estimation Problem -- 9.2 Data Windowing -- 9.3 Principle of Orthogonality Revisited -- 9.4 Minimum Sum of Error Squares -- 9.5 Normal Equations and Linear Least-Squares Filters -- 9.6 Time-Average Correlation Matrix Φ -- 9.7 Reformulation of the Normal Equations in Terms of Data Matrices -- 9.8 Properties of Least-Squares Estimates -- 9.9 Minimum-Variance Distortionless Response (MVDR) Spectrum Estimation -- 9.10 Regularized MVDR Beamforming -- 9.11 Singular-Value Decomposition -- 9.12 Pseudoinverse -- 9.13 Interpretation of Singular Values and Singular Vectors -- 9.14 Minimum-Norm Solution to the Linear Least-Squares Problem -- 9.15 Normalized LMS Algorithm Viewed as the Minimum-Norm Solution to an Underdetermined Least-Squares Estimation Problem -- 9.16 Summary and Discussion -- Problems -- Chapter 10 The Recursive Least-Squares (RLS) Algorithm -- 10.1 Some Preliminaries -- 10.2 The Matrix Inversion Lemma -- 10.3 The Exponentially Weighted RLS Algorithm -- 10.4 Selection of the Regularization Parameter -- 10.5 Updated Recursion for the Sum of Weighted Error Squares -- 10.6 Example: Single-Weight Adaptive Noise Canceller -- 10.7 Statistical Learning Theory -- 10.8 Efficiency -- 10.9 Computer Experiment on Adaptive Equalization -- 10.10 Summary and Discussion -- Problems -- Chapter 11 Robustness -- 11.1 Robustness, Adaptation, and Disturbances.
11.2 Robustness: Preliminary Considerations Rooted in H∞ Optimization -- 11.3 Robustness of the LMS Algorithm -- 11.4 Robustness of the RLS Algorithm -- 11.5 Comparative Evaluations of the LMS and RLS Algorithms from the Perspective of Robustness -- 11.6 Risk-Sensitive Optimality -- 11.7 Trade-Offs Between Robustness and Efficiency -- 11.8 Summary and Discussion -- Problems -- Chapter 12 Finite-Precision Effects -- 12.1 Quantization Errors -- 12.2 Least-Mean-Square (LMS) Algorithm -- 12.3 Recursive Least-Squares (RLS) Algorithm -- 12.4 Summary and Discussion -- Problems -- Chapter 13 Adaptation in Nonstationary Environments -- 13.1 Causes and Consequences of Nonstationarity -- 13.2 The System Identification Problem -- 13.3 Degree of Nonstationarity -- 13.4 Criteria for Tracking Assessment -- 13.5 Tracking Performance of the LMS Algorithm -- 13.6 Tracking Performance of the RLS Algorithm -- 13.7 Comparison of the Tracking Performance of LMS and RLS Algorithms -- 13.8 Tuning of Adaptation Parameters -- 13.9 Incremental Delta-Bar-Delta (IDBD) Algorithm -- 13.10 Autostep Method -- 13.11 Computer Experiment: Mixture of Stationary and Nonstationary Environmental Data -- 13.12 Summary and Discussion -- Problems -- Chapter 14 Kalman Filters -- 14.1 Recursive Minimum Mean-Square Estimation for Scalar Random Variables -- 14.2 Statement of the Kalman Filtering Problem -- 14.3 The Innovations Process -- 14.4 Estimation of the State Using the Innovations Process -- 14.5 Filtering -- 14.6 Initial Conditions -- 14.7 Summary of the Kalman Filter -- 14.8 Optimality Criteria for Kalman Filtering -- 14.9 Kalman Filter as the Unifying Basis for RLS Algorithms -- 14.10 Covariance Filtering Algorithm -- 14.11 Information Filtering Algorithm -- 14.12 Summary and Discussion -- Problems -- Chapter 15 Square-Root Adaptive Filtering Algorithms.
15.1 Square-Root Kalman Filters -- 15.2 Building Square-Root Adaptive Filters on the Two Kalman Filter Variants -- 15.3 QRD-RLS Algorithm -- 15.4 Adaptive Beamforming -- 15.5 Inverse QRD-RLS Algorithm -- 15.6 Finite-Precision Effects -- 15.7 Summary and Discussion -- Problems -- Chapter 16 Order-Recursive Adaptive Filtering Algorithm -- 16.1 Order-Recursive Adaptive Filters Using Least-Squares Estimation: An Overview -- 16.2 Adaptive Forward Linear Prediction -- 16.3 Adaptive Backward Linear Prediction -- 16.4 Conversion Factor -- 16.5 Least-Squares Lattice (LSL) Predictor -- 16.6 Angle-Normalized Estimation Errors -- 16.7 First-Order State-Space Models for Lattice Filtering -- 16.8 QR-Decomposition-Based Least-Squares Lattice (QRD-LSL) Filters -- 16.9 Fundamental Properties of the QRD-LSL Filter -- 16.10 Computer Experiment on Adaptive Equalization -- 16.11 Recursive (LSL) Filters Using A Posteriori Estimation Errors -- 16.12 Recursive LSL Filters Using A Priori Estimation Errors with Error Feedback -- 16.13 Relation Between Recursive LSL and RLS Algorithms -- 16.14 Finite-Precision Effects -- 16.15 Summary and Discussion -- Problems -- Chapter 17 Blind Deconvolution -- 17.1 Overview of Blind Deconvolution -- 17.2 Channel Identifiability Using Cyclostationary Statistics -- 17.3 Subspace Decomposition for Fractionally Spaced Blind Identification -- 17.4 Bussgang Algorithm for Blind Equalization -- 17.5 Extension of the Bussgang Algorithm to Complex Baseband Channels -- 17.6 Special Cases of the Bussgang Algorithm -- 17.7 Fractionally Spaced Bussgang Equalizers -- 17.8 Estimation of Unknown Probability Distribution Function of Signal Source -- 17.9 Summary and Discussion -- Problems -- Epilogue -- 1. Robustness, Efficiency, and Complexity -- 2. Kernel-Based Nonlinear Adaptive Filtering -- Appendix A Theory of Complex Variables.
A.1 Cauchy-Riemann Equations.
Record Nr. UNINA-9910150209303321
Haykin Simon S. <1931->  
Upper Saddle River : , : Pearson, , [2014]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cognitive dynamic systems : perception--action cycle, radar, and radio / / Simon Haykin [[electronic resource]]
Cognitive dynamic systems : perception--action cycle, radar, and radio / / Simon Haykin [[electronic resource]]
Autore Haykin Simon S. <1931->
Pubbl/distr/stampa Cambridge : , : Cambridge University Press, , 2012
Descrizione fisica 1 online resource (xii, 309 pages) : digital, PDF file(s)
Disciplina 003/.7
Soggetto topico Self-organizing systems
Cognitive radio networks
ISBN 1-107-21273-1
1-280-87888-6
1-139-12259-2
9786613720191
1-139-11468-9
0-511-81836-X
1-139-11249-X
1-139-12751-9
1-139-11685-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 2. The perception-action cycle -- 3. Power-spectrum estimation for sensing the environment -- 4. Bayesian filtering for state estimation of the environment -- 5. Dynamic programming for action in the environment -- 6. Cognitive radar -- 7. Cognitive radio -- 8. Epilogue.
Record Nr. UNINA-9910462648103321
Haykin Simon S. <1931->  
Cambridge : , : Cambridge University Press, , 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cognitive dynamic systems : perception--action cycle, radar, and radio / / Simon Haykin [[electronic resource]]
Cognitive dynamic systems : perception--action cycle, radar, and radio / / Simon Haykin [[electronic resource]]
Autore Haykin Simon S. <1931->
Pubbl/distr/stampa Cambridge : , : Cambridge University Press, , 2012
Descrizione fisica 1 online resource (xii, 309 pages) : digital, PDF file(s)
Disciplina 003/.7
Soggetto topico Self-organizing systems
Cognitive radio networks
ISBN 1-107-21273-1
1-280-87888-6
1-139-12259-2
9786613720191
1-139-11468-9
0-511-81836-X
1-139-11249-X
1-139-12751-9
1-139-11685-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 2. The perception-action cycle -- 3. Power-spectrum estimation for sensing the environment -- 4. Bayesian filtering for state estimation of the environment -- 5. Dynamic programming for action in the environment -- 6. Cognitive radar -- 7. Cognitive radio -- 8. Epilogue.
Record Nr. UNINA-9910790302503321
Haykin Simon S. <1931->  
Cambridge : , : Cambridge University Press, , 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cognitive dynamic systems : perception--action cycle, radar, and radio / / Simon Haykin
Cognitive dynamic systems : perception--action cycle, radar, and radio / / Simon Haykin
Autore Haykin Simon S. <1931->
Edizione [1st ed.]
Pubbl/distr/stampa Cambridge ; ; New York, : Cambridge University Press, 2012
Descrizione fisica 1 online resource (xii, 309 pages) : digital, PDF file(s)
Disciplina 003/.7
Soggetto topico Self-organizing systems
Cognitive radio networks
ISBN 1-107-21273-1
1-280-87888-6
1-139-12259-2
9786613720191
1-139-11468-9
0-511-81836-X
1-139-11249-X
1-139-12751-9
1-139-11685-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 2. The perception-action cycle -- 3. Power-spectrum estimation for sensing the environment -- 4. Bayesian filtering for state estimation of the environment -- 5. Dynamic programming for action in the environment -- 6. Cognitive radar -- 7. Cognitive radio -- 8. Epilogue.
Record Nr. UNINA-9910815147203321
Haykin Simon S. <1931->  
Cambridge ; ; New York, : Cambridge University Press, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook on array processing and sensor networks / / [edited by] Simon Haykin, K.J. Ray Liu
Handbook on array processing and sensor networks / / [edited by] Simon Haykin, K.J. Ray Liu
Autore Haykin Simon S. <1931->
Edizione [1st edition]
Pubbl/distr/stampa [Piscataway, New Jersey] : , : IEEE, , c2009
Descrizione fisica 1 online resource (924 p.)
Disciplina 621.382/4
621.3822
621.3824
Altri autori (Persone) LiuK. J. Ray
HaykinSimon S
Collana Adaptive and learning systems for signal processing, communications and control series
Soggetto topico Sensor networks
Antenna arrays
Array processors
ISBN 1-282-54932-4
9786612549328
1-61344-501-6
0-470-48706-2
0-470-48705-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface (Simon Haykin and K. J. Ray Liu) -- Contributors -- Introduction (Simon Haykin) -- PART I: FUNDAMENTAL ISSUES IN ARRAY SIGNAL PROCESSING -- 1. Wavefields. (Alfred Hanssen) -- 1.1 Introduction -- 1.2 Harmonizable Stochastic Processes -- 1.3 Stochastic Wavefields -- 1.4 Wave Dispersion -- 1.5 Conclusions -- 1.6 Acknowledgements -- References. -- 2. Spatial Spectrum Estimation (Petar M. Djuri) -- 2.1 Introduction -- 2.2 Fundamentals -- 2.3 Temporal Spectrum Estimation -- 2.4 Spatial Spectrum Estimation -- 2.5 Final Remarks -- References. -- 3. MIMO Radio Propagation (Tricia J. Willink) -- 3.1 Introduction -- 3.2 Space-Time Propagation Environment -- 3.3 Propagation Models -- 3.4 Measured Channel Characteristics -- 3.5 Stationarity -- 3.6 Summary -- References. -- 4. Robustness Issues in Sensor Array Processing (Alex B. Gershman) -- 4.1 Introduction -- 4.2 Direction-of-Arrival Estimation -- 4.3 Adaptive Beamforming -- 4.4 Conclusions -- Acknowledgments -- References. -- 5. Wireless Communication and Sensing in Multipath Environments Using Multiantenna Transceivers (Akbar M. Sayeed and Thiagarajan Sivanadyan) -- 5.1 Introduction and Overview -- 5.2 Multipath Wireless Channel Modeling in Time, Frequency and Space -- 5.3 Point-to-Point MIMO Wireless Communication Systems -- 5.4 Active Wireless Sensing with Wideband MIMO Transceivers -- 5.5 Concluding Remarks -- References -- PART II: NOVEL TECHNIQUES FOR AND APPLICATIONS OF ARRAY SIGNAL PROCESSING. -- 6. Implicit Training and Array Processing for Digital Communication Systems (Aldo G. Orozco-Lugo, Mauricio Lara, and Desmond C. McLernon) -- 6.1 Introduction -- 6.2 Classification of Implicit Training Methods -- 6.3 IT-Based Estimation for a Single User -- 6.4 IT-Based Estimation for Multiple Users Exploiting Array Processing: Continuous Transmission -- 6.5 IT-Based Estimation for Multiple Users Exploiting Array Processing: Packet Transmission -- 6.6 Open Research Problems -- Acknowledgments -- References -- 7. Unitary Design of Radar Waveform Diversity Sets (Michael D. Zoltowski, Tariq R. Qureshi, Robert Calderbank, and Bill Moran).
7.1 Introduction -- 7.2 2 x 2 Space-Time Diversity Waveform Design -- 7.3 4 x 4 Space-Time Diversity Waveform Design -- 7.4 Waveform Families Based on Kronecker Products -- 7.5 Introduction to Data-Dependent Waveform Design -- 7.6 3 x 3 and 6 x 6 Waveform Scheduling -- 7.7 Summary -- References. -- 8. Acoustic Array Processing for Speech Enhancement (Markus Buck, Eberhard H Ansler, Mohamed Krini, Gerhard Schmidt and Tobias Wolff) -- 8.1 Introduction -- 8.2 Signal Processing in the Subband Domain -- 8.3 Multichannel Echo Cancelation -- 8.4 Speaker Localization -- 8.5 Beamforming -- 8.6 Sensor Calibration -- 8.7 Postprocessing -- 8.8 Conclusions -- References -- 9. Acoustic Beamforming for Hearing Aid Applications (Simon Doclo, Sharon Gannot, Marc Moonen and Ann Spriet) -- 9.1. Introduction -- 9.2. Overview of noise reduction techniques -- 9.3. Monaural beamforming -- 9.4. Binaural beamforming -- 9.5. Conclusion -- 10. Undetermined Blind Source Separation Using Acoustic Arrays (Shoji Makino, Shoko Araki, Stefan Winter and Hiroshi Sawada) -- 10.1 Introduction -- 10.2 Underdetermined Blind Source Separation of Speeches in Reverberant Environments -- 10.3 Sparseness of Speech Sources -- 10.4 Binary Mask Approach to Underdetermined BSS -- 10.5 MAP-Based Two-Stage Approach to Underdetermined BSS -- 10.6 Experimental Comparison with Binary Mask Approach and MAP-Based Two-Stage Approach -- 10.7 Concluding Remarks -- References -- 11. Array Processing in Astronomy (Douglas C.-J. Bock) -- 11.1 Introduction -- 11.2 Correlation Arrays -- 11.3 Aperture Plane Phased Arrays -- 11.4 Future Directions -- 11.5 Conclusion -- References. -- 12. Digital 3D/4D Ultrasound Imaging Array (Stergios Stergiopoulos) -- 12.1 Background -- 12.2 Next Generation 3D/4D Ultrasound Imaging Technology -- 12.3 Computing Architecture and Implementation Issues -- 12.4 An Experimental Planar Array Ultrasound Imaging System -- 12.5 Conclusion -- References -- PART III: FUNDAMENTAL ISSUES IN DISTRIBUTED SENSOR NETWORKS.
13. Self-Localization of Sensor Networks (Josh N. Ash and Randolph L. Moses) -- 13.1 Introduction -- 13.2 Measurement Types and Performance Bounds -- 13.3 Localization Algorithms -- 13.4 Relative and Transformation Error Decomposition -- 13.5 Conclusions -- References. -- 14. Multitarget Tracking and Classification in Collaborative Sensor Networks via Sequential Monte Carlo (Tom Vercauteren and Xiaodong Wang) -- 14.1 Introduction -- 14.2 System Description and Problem Formulation -- 14.3 Sequential Monte Carlo Methods -- 14.4 Joint Single-Target Tracking and Classification -- 14.5 Multiple-Target Tracking and Classification -- 14.6 Sensor Selection -- 14.7 Simulation Results -- Conclusion -- Appendix: Derviations of (14.38 and (14.40) -- References -- 15. Energy-Efficient Decentralized Estimation (Jin-Jun Xiao, Shuguang Cui and Zhi-Quan Luo) -- 15.5 Introduction -- 15.2 System Model -- 15.3 Digital Approaches -- 15.4 Analog Approaches -- 15.5 Analog versus Digital -- 15.6 Extension to Vector Model -- 15.7 Concluding Remarks -- Acknowledgments -- References. -- 16. Sensor Data Fusion with Application to Multitarget Tracking (R. Tharmarasa, K. Punithakumar, T. Kirubarajan and Y. Bar-Shalom) -- 16.1 Introduction -- 16.2 Tracking Filters -- 16.3 Data Association -- 16.4 Out-of-Sequence Measurements -- 16.5 Results with Real Data -- 16.6 Summary -- References. -- 17. Distributed Algorithms in Sensor Networks (Usman A. Khan, Soummya Kar and Jos A A Moura) -- 17.1 Introduction -- 17.2 Preliminaries -- 17.3 Distributed Detection -- 17.4 Consensus Algorithms -- 17.5 Zero-Dimension (Average) Consensus -- 17.6 Consensus in Higher Dimensions -- 17.7 Leader-Follower (Type) Algorithms -- 17.8 Localization in Sensor Networks -- 17.9 Linear System of Equations: Distributed Algorithm -- 17.10 Conclusions -- References. -- 18. Cooperative Sensor Communications (Ahmed K. Sadek, Weifeng Su and K. J. Ray Liu) -- 18.1 Introduction -- 18.2 Cooperative Relay Protocols -- 18.3 SER Analysis and Optimal Power Allocation.
18.4 Energy Efficiency in Cooperative Sensor Networks -- 18.5 Experimental Results -- 18.6 Conclusions -- References. -- 19. Distributed Source Coding (Zixiang Xiong, Angelos D. Liveris and Yang Yang) -- 19.1 Introduction -- 19.2 Theoretical Background -- 19.3 Code Designs -- 19.4 Applications -- 19.5 Conclusions -- References. -- 20. Network Coding for Sensor Networks (Christina Fragouli) -- 20.1 Introduction -- 20.2 How Can We Implement Network Coding in a Practical Sensor Network? -- 20.3 Data Collection and Coupon Collector Problem -- 20.4 Distributed Storage and Sensor Network Data Persistence -- 20.5 Decentralized Operation and Untuned Radios -- 20.6 Broadcasting and Multipath Diversity -- 20.7 Network, Channel and Source Coding -- 20.8 Identity-Aware Sensor Networks -- 20.9 Discussion -- Acknowledgments -- References. -- 21. Information-Theoretic Studies of Wireless Sensor Networks (Liang-Liang Xie and P. R. Kumar) -- 21.1 Introduction -- 21.2 Information-Theoretic Studies -- 21.3 Relay Schemes -- 21.4 Wireless Network Coding -- 21.5 Concluding Remarks -- Acknowledgments -- References. -- PART IV: NOVEL TECHNIQUES FOR AND APPLICATIONS OF DISTRIBUTED SENSOR NETWORKS -- 22. Distributed Adaptive Learning Mechanisms (Ali H. Sayed and Federico S. Cattivelli) -- 22.1 Introduction -- 22.2 Motivation -- 22.3 Incremental Adaptive Solutions -- 22.4 Diffusion Adaptive Solutions -- 22.5 Concluding Remarks -- Acknowledgments -- References -- 23. Routing for Statistical Inference in Sensor Networks (A. Anandkumar, A. Ephremides, A. Swami and L. Tong) -- 23.1 Introduction -- 23.2 Spatial Data Correlation -- 23.3 Statistical Inference of Markov Random Fields -- 23.4 Optimal Routing for Inference with Local Processing -- 23.5 Conclusion and Future Work -- 23.6 Bibliographic Notes -- References. -- 24. Spectral Estimation in Cognitive Radios (Behrouz Farhang-Boroujeny) -- 24.1 Filter Bank Formulation of Spectral Estimators -- 24.2 Polyphase Realization of Uniform Filter Banks.
24.3 Periodogram Spectral Estimator -- 24.4 Multitaper Spectral Estimator -- 24.5 Filter Bank Spectral Estimator -- 24.6 Distributed Spectrum Sensing -- 24.7 Discussion -- Appendix A: Effective Degree of Freedom -- Appendix B: Explanation to the Results of Table 24.1 -- References -- 25. Nonparametric Techniques for Pedestrian Tracking in Wireless Local Area Networks (Azadeh Kushki and Kostas N. Plataniotis) -- 25.1 Introduction -- 25.2 WLAN Positioning Architectures -- 25.3 Signal Models -- 25.4 Zero-Memory Positioning -- 25.5 Dynamic Positioning Systems -- 25.6 Cognition and Feedback -- 25.7 Tracking Example -- 25.8 Conclusions -- References -- 26. Reconfigurable Self-Activating Ion-Channel-Based Biosensors Vikram Krishnamurthy and Bruce Cornell) -- 26.1 Introduction -- 26.2 Biosensors Built of Ion Channels -- 26.3 Joint Input Excitation and Concentration Classification for Biosensor -- 26.4 Decentralized Deployment of Dense Network of Biosensors -- 26.5 Discussion and Extensions -- References. -- 27. Biochemical Transport Modeling, Estimation and Detection in Realistic Environments (Mathias Ortner and Arye Nehorai ) -- 27.1 Introduction -- 27.2 Physical and Statistical Models -- 27.3 Transport Modeling Using Monte Carlo Approximation -- 27.4 Localizing the Source(s) -- 27.5 Sequential Detection -- 27.6 Conclusion -- References -- 28. Security and Privacy for Sensor Networks (Wade Trappe, Peng Ning and Adrian Perrig) -- 28.1 Introduction -- 28.2 Security and Privacy Challenges -- 28.3 Ensuring Integrity of Measurement Process -- 28.4 Availability Attacks against the Wireless Link -- 28.5 Ensuring Privacy of Routing Contexts -- 28.6 Conclusion -- References -- Index.
Record Nr. UNINA-9910140619803321
Haykin Simon S. <1931->  
[Piscataway, New Jersey] : , : IEEE, , c2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook on array processing and sensor networks / / [edited by] Simon Haykin, K.J. Ray Liu
Handbook on array processing and sensor networks / / [edited by] Simon Haykin, K.J. Ray Liu
Autore Haykin Simon S. <1931->
Edizione [1st edition]
Pubbl/distr/stampa [Piscataway, New Jersey] : , : IEEE, , c2009
Descrizione fisica 1 online resource (924 p.)
Disciplina 621.382/4
621.3822
621.3824
Altri autori (Persone) LiuK. J. Ray
HaykinSimon S
Collana Adaptive and learning systems for signal processing, communications and control series
Soggetto topico Sensor networks
Antenna arrays
Array processors
ISBN 1-282-54932-4
9786612549328
1-61344-501-6
0-470-48706-2
0-470-48705-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface (Simon Haykin and K. J. Ray Liu) -- Contributors -- Introduction (Simon Haykin) -- PART I: FUNDAMENTAL ISSUES IN ARRAY SIGNAL PROCESSING -- 1. Wavefields. (Alfred Hanssen) -- 1.1 Introduction -- 1.2 Harmonizable Stochastic Processes -- 1.3 Stochastic Wavefields -- 1.4 Wave Dispersion -- 1.5 Conclusions -- 1.6 Acknowledgements -- References. -- 2. Spatial Spectrum Estimation (Petar M. Djuri) -- 2.1 Introduction -- 2.2 Fundamentals -- 2.3 Temporal Spectrum Estimation -- 2.4 Spatial Spectrum Estimation -- 2.5 Final Remarks -- References. -- 3. MIMO Radio Propagation (Tricia J. Willink) -- 3.1 Introduction -- 3.2 Space-Time Propagation Environment -- 3.3 Propagation Models -- 3.4 Measured Channel Characteristics -- 3.5 Stationarity -- 3.6 Summary -- References. -- 4. Robustness Issues in Sensor Array Processing (Alex B. Gershman) -- 4.1 Introduction -- 4.2 Direction-of-Arrival Estimation -- 4.3 Adaptive Beamforming -- 4.4 Conclusions -- Acknowledgments -- References. -- 5. Wireless Communication and Sensing in Multipath Environments Using Multiantenna Transceivers (Akbar M. Sayeed and Thiagarajan Sivanadyan) -- 5.1 Introduction and Overview -- 5.2 Multipath Wireless Channel Modeling in Time, Frequency and Space -- 5.3 Point-to-Point MIMO Wireless Communication Systems -- 5.4 Active Wireless Sensing with Wideband MIMO Transceivers -- 5.5 Concluding Remarks -- References -- PART II: NOVEL TECHNIQUES FOR AND APPLICATIONS OF ARRAY SIGNAL PROCESSING. -- 6. Implicit Training and Array Processing for Digital Communication Systems (Aldo G. Orozco-Lugo, Mauricio Lara, and Desmond C. McLernon) -- 6.1 Introduction -- 6.2 Classification of Implicit Training Methods -- 6.3 IT-Based Estimation for a Single User -- 6.4 IT-Based Estimation for Multiple Users Exploiting Array Processing: Continuous Transmission -- 6.5 IT-Based Estimation for Multiple Users Exploiting Array Processing: Packet Transmission -- 6.6 Open Research Problems -- Acknowledgments -- References -- 7. Unitary Design of Radar Waveform Diversity Sets (Michael D. Zoltowski, Tariq R. Qureshi, Robert Calderbank, and Bill Moran).
7.1 Introduction -- 7.2 2 x 2 Space-Time Diversity Waveform Design -- 7.3 4 x 4 Space-Time Diversity Waveform Design -- 7.4 Waveform Families Based on Kronecker Products -- 7.5 Introduction to Data-Dependent Waveform Design -- 7.6 3 x 3 and 6 x 6 Waveform Scheduling -- 7.7 Summary -- References. -- 8. Acoustic Array Processing for Speech Enhancement (Markus Buck, Eberhard H Ansler, Mohamed Krini, Gerhard Schmidt and Tobias Wolff) -- 8.1 Introduction -- 8.2 Signal Processing in the Subband Domain -- 8.3 Multichannel Echo Cancelation -- 8.4 Speaker Localization -- 8.5 Beamforming -- 8.6 Sensor Calibration -- 8.7 Postprocessing -- 8.8 Conclusions -- References -- 9. Acoustic Beamforming for Hearing Aid Applications (Simon Doclo, Sharon Gannot, Marc Moonen and Ann Spriet) -- 9.1. Introduction -- 9.2. Overview of noise reduction techniques -- 9.3. Monaural beamforming -- 9.4. Binaural beamforming -- 9.5. Conclusion -- 10. Undetermined Blind Source Separation Using Acoustic Arrays (Shoji Makino, Shoko Araki, Stefan Winter and Hiroshi Sawada) -- 10.1 Introduction -- 10.2 Underdetermined Blind Source Separation of Speeches in Reverberant Environments -- 10.3 Sparseness of Speech Sources -- 10.4 Binary Mask Approach to Underdetermined BSS -- 10.5 MAP-Based Two-Stage Approach to Underdetermined BSS -- 10.6 Experimental Comparison with Binary Mask Approach and MAP-Based Two-Stage Approach -- 10.7 Concluding Remarks -- References -- 11. Array Processing in Astronomy (Douglas C.-J. Bock) -- 11.1 Introduction -- 11.2 Correlation Arrays -- 11.3 Aperture Plane Phased Arrays -- 11.4 Future Directions -- 11.5 Conclusion -- References. -- 12. Digital 3D/4D Ultrasound Imaging Array (Stergios Stergiopoulos) -- 12.1 Background -- 12.2 Next Generation 3D/4D Ultrasound Imaging Technology -- 12.3 Computing Architecture and Implementation Issues -- 12.4 An Experimental Planar Array Ultrasound Imaging System -- 12.5 Conclusion -- References -- PART III: FUNDAMENTAL ISSUES IN DISTRIBUTED SENSOR NETWORKS.
13. Self-Localization of Sensor Networks (Josh N. Ash and Randolph L. Moses) -- 13.1 Introduction -- 13.2 Measurement Types and Performance Bounds -- 13.3 Localization Algorithms -- 13.4 Relative and Transformation Error Decomposition -- 13.5 Conclusions -- References. -- 14. Multitarget Tracking and Classification in Collaborative Sensor Networks via Sequential Monte Carlo (Tom Vercauteren and Xiaodong Wang) -- 14.1 Introduction -- 14.2 System Description and Problem Formulation -- 14.3 Sequential Monte Carlo Methods -- 14.4 Joint Single-Target Tracking and Classification -- 14.5 Multiple-Target Tracking and Classification -- 14.6 Sensor Selection -- 14.7 Simulation Results -- Conclusion -- Appendix: Derviations of (14.38 and (14.40) -- References -- 15. Energy-Efficient Decentralized Estimation (Jin-Jun Xiao, Shuguang Cui and Zhi-Quan Luo) -- 15.5 Introduction -- 15.2 System Model -- 15.3 Digital Approaches -- 15.4 Analog Approaches -- 15.5 Analog versus Digital -- 15.6 Extension to Vector Model -- 15.7 Concluding Remarks -- Acknowledgments -- References. -- 16. Sensor Data Fusion with Application to Multitarget Tracking (R. Tharmarasa, K. Punithakumar, T. Kirubarajan and Y. Bar-Shalom) -- 16.1 Introduction -- 16.2 Tracking Filters -- 16.3 Data Association -- 16.4 Out-of-Sequence Measurements -- 16.5 Results with Real Data -- 16.6 Summary -- References. -- 17. Distributed Algorithms in Sensor Networks (Usman A. Khan, Soummya Kar and Jos A A Moura) -- 17.1 Introduction -- 17.2 Preliminaries -- 17.3 Distributed Detection -- 17.4 Consensus Algorithms -- 17.5 Zero-Dimension (Average) Consensus -- 17.6 Consensus in Higher Dimensions -- 17.7 Leader-Follower (Type) Algorithms -- 17.8 Localization in Sensor Networks -- 17.9 Linear System of Equations: Distributed Algorithm -- 17.10 Conclusions -- References. -- 18. Cooperative Sensor Communications (Ahmed K. Sadek, Weifeng Su and K. J. Ray Liu) -- 18.1 Introduction -- 18.2 Cooperative Relay Protocols -- 18.3 SER Analysis and Optimal Power Allocation.
18.4 Energy Efficiency in Cooperative Sensor Networks -- 18.5 Experimental Results -- 18.6 Conclusions -- References. -- 19. Distributed Source Coding (Zixiang Xiong, Angelos D. Liveris and Yang Yang) -- 19.1 Introduction -- 19.2 Theoretical Background -- 19.3 Code Designs -- 19.4 Applications -- 19.5 Conclusions -- References. -- 20. Network Coding for Sensor Networks (Christina Fragouli) -- 20.1 Introduction -- 20.2 How Can We Implement Network Coding in a Practical Sensor Network? -- 20.3 Data Collection and Coupon Collector Problem -- 20.4 Distributed Storage and Sensor Network Data Persistence -- 20.5 Decentralized Operation and Untuned Radios -- 20.6 Broadcasting and Multipath Diversity -- 20.7 Network, Channel and Source Coding -- 20.8 Identity-Aware Sensor Networks -- 20.9 Discussion -- Acknowledgments -- References. -- 21. Information-Theoretic Studies of Wireless Sensor Networks (Liang-Liang Xie and P. R. Kumar) -- 21.1 Introduction -- 21.2 Information-Theoretic Studies -- 21.3 Relay Schemes -- 21.4 Wireless Network Coding -- 21.5 Concluding Remarks -- Acknowledgments -- References. -- PART IV: NOVEL TECHNIQUES FOR AND APPLICATIONS OF DISTRIBUTED SENSOR NETWORKS -- 22. Distributed Adaptive Learning Mechanisms (Ali H. Sayed and Federico S. Cattivelli) -- 22.1 Introduction -- 22.2 Motivation -- 22.3 Incremental Adaptive Solutions -- 22.4 Diffusion Adaptive Solutions -- 22.5 Concluding Remarks -- Acknowledgments -- References -- 23. Routing for Statistical Inference in Sensor Networks (A. Anandkumar, A. Ephremides, A. Swami and L. Tong) -- 23.1 Introduction -- 23.2 Spatial Data Correlation -- 23.3 Statistical Inference of Markov Random Fields -- 23.4 Optimal Routing for Inference with Local Processing -- 23.5 Conclusion and Future Work -- 23.6 Bibliographic Notes -- References. -- 24. Spectral Estimation in Cognitive Radios (Behrouz Farhang-Boroujeny) -- 24.1 Filter Bank Formulation of Spectral Estimators -- 24.2 Polyphase Realization of Uniform Filter Banks.
24.3 Periodogram Spectral Estimator -- 24.4 Multitaper Spectral Estimator -- 24.5 Filter Bank Spectral Estimator -- 24.6 Distributed Spectrum Sensing -- 24.7 Discussion -- Appendix A: Effective Degree of Freedom -- Appendix B: Explanation to the Results of Table 24.1 -- References -- 25. Nonparametric Techniques for Pedestrian Tracking in Wireless Local Area Networks (Azadeh Kushki and Kostas N. Plataniotis) -- 25.1 Introduction -- 25.2 WLAN Positioning Architectures -- 25.3 Signal Models -- 25.4 Zero-Memory Positioning -- 25.5 Dynamic Positioning Systems -- 25.6 Cognition and Feedback -- 25.7 Tracking Example -- 25.8 Conclusions -- References -- 26. Reconfigurable Self-Activating Ion-Channel-Based Biosensors Vikram Krishnamurthy and Bruce Cornell) -- 26.1 Introduction -- 26.2 Biosensors Built of Ion Channels -- 26.3 Joint Input Excitation and Concentration Classification for Biosensor -- 26.4 Decentralized Deployment of Dense Network of Biosensors -- 26.5 Discussion and Extensions -- References. -- 27. Biochemical Transport Modeling, Estimation and Detection in Realistic Environments (Mathias Ortner and Arye Nehorai ) -- 27.1 Introduction -- 27.2 Physical and Statistical Models -- 27.3 Transport Modeling Using Monte Carlo Approximation -- 27.4 Localizing the Source(s) -- 27.5 Sequential Detection -- 27.6 Conclusion -- References -- 28. Security and Privacy for Sensor Networks (Wade Trappe, Peng Ning and Adrian Perrig) -- 28.1 Introduction -- 28.2 Security and Privacy Challenges -- 28.3 Ensuring Integrity of Measurement Process -- 28.4 Availability Attacks against the Wireless Link -- 28.5 Ensuring Privacy of Routing Contexts -- 28.6 Conclusion -- References -- Index.
Record Nr. UNISA-996204858203316
Haykin Simon S. <1931->  
[Piscataway, New Jersey] : , : IEEE, , c2009
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Handbook on array processing and sensor networks / / [edited by] Simon Haykin, K.J. Ray Liu
Handbook on array processing and sensor networks / / [edited by] Simon Haykin, K.J. Ray Liu
Autore Haykin Simon S. <1931->
Edizione [1st edition]
Pubbl/distr/stampa [Piscataway, New Jersey] : , : IEEE, , c2009
Descrizione fisica 1 online resource (924 p.)
Disciplina 621.382/4
621.3822
621.3824
Altri autori (Persone) LiuK. J. Ray
HaykinSimon S
Collana Adaptive and learning systems for signal processing, communications and control series
Soggetto topico Sensor networks
Antenna arrays
Array processors
ISBN 1-282-54932-4
9786612549328
1-61344-501-6
0-470-48706-2
0-470-48705-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface (Simon Haykin and K. J. Ray Liu) -- Contributors -- Introduction (Simon Haykin) -- PART I: FUNDAMENTAL ISSUES IN ARRAY SIGNAL PROCESSING -- 1. Wavefields. (Alfred Hanssen) -- 1.1 Introduction -- 1.2 Harmonizable Stochastic Processes -- 1.3 Stochastic Wavefields -- 1.4 Wave Dispersion -- 1.5 Conclusions -- 1.6 Acknowledgements -- References. -- 2. Spatial Spectrum Estimation (Petar M. Djuri) -- 2.1 Introduction -- 2.2 Fundamentals -- 2.3 Temporal Spectrum Estimation -- 2.4 Spatial Spectrum Estimation -- 2.5 Final Remarks -- References. -- 3. MIMO Radio Propagation (Tricia J. Willink) -- 3.1 Introduction -- 3.2 Space-Time Propagation Environment -- 3.3 Propagation Models -- 3.4 Measured Channel Characteristics -- 3.5 Stationarity -- 3.6 Summary -- References. -- 4. Robustness Issues in Sensor Array Processing (Alex B. Gershman) -- 4.1 Introduction -- 4.2 Direction-of-Arrival Estimation -- 4.3 Adaptive Beamforming -- 4.4 Conclusions -- Acknowledgments -- References. -- 5. Wireless Communication and Sensing in Multipath Environments Using Multiantenna Transceivers (Akbar M. Sayeed and Thiagarajan Sivanadyan) -- 5.1 Introduction and Overview -- 5.2 Multipath Wireless Channel Modeling in Time, Frequency and Space -- 5.3 Point-to-Point MIMO Wireless Communication Systems -- 5.4 Active Wireless Sensing with Wideband MIMO Transceivers -- 5.5 Concluding Remarks -- References -- PART II: NOVEL TECHNIQUES FOR AND APPLICATIONS OF ARRAY SIGNAL PROCESSING. -- 6. Implicit Training and Array Processing for Digital Communication Systems (Aldo G. Orozco-Lugo, Mauricio Lara, and Desmond C. McLernon) -- 6.1 Introduction -- 6.2 Classification of Implicit Training Methods -- 6.3 IT-Based Estimation for a Single User -- 6.4 IT-Based Estimation for Multiple Users Exploiting Array Processing: Continuous Transmission -- 6.5 IT-Based Estimation for Multiple Users Exploiting Array Processing: Packet Transmission -- 6.6 Open Research Problems -- Acknowledgments -- References -- 7. Unitary Design of Radar Waveform Diversity Sets (Michael D. Zoltowski, Tariq R. Qureshi, Robert Calderbank, and Bill Moran).
7.1 Introduction -- 7.2 2 x 2 Space-Time Diversity Waveform Design -- 7.3 4 x 4 Space-Time Diversity Waveform Design -- 7.4 Waveform Families Based on Kronecker Products -- 7.5 Introduction to Data-Dependent Waveform Design -- 7.6 3 x 3 and 6 x 6 Waveform Scheduling -- 7.7 Summary -- References. -- 8. Acoustic Array Processing for Speech Enhancement (Markus Buck, Eberhard H Ansler, Mohamed Krini, Gerhard Schmidt and Tobias Wolff) -- 8.1 Introduction -- 8.2 Signal Processing in the Subband Domain -- 8.3 Multichannel Echo Cancelation -- 8.4 Speaker Localization -- 8.5 Beamforming -- 8.6 Sensor Calibration -- 8.7 Postprocessing -- 8.8 Conclusions -- References -- 9. Acoustic Beamforming for Hearing Aid Applications (Simon Doclo, Sharon Gannot, Marc Moonen and Ann Spriet) -- 9.1. Introduction -- 9.2. Overview of noise reduction techniques -- 9.3. Monaural beamforming -- 9.4. Binaural beamforming -- 9.5. Conclusion -- 10. Undetermined Blind Source Separation Using Acoustic Arrays (Shoji Makino, Shoko Araki, Stefan Winter and Hiroshi Sawada) -- 10.1 Introduction -- 10.2 Underdetermined Blind Source Separation of Speeches in Reverberant Environments -- 10.3 Sparseness of Speech Sources -- 10.4 Binary Mask Approach to Underdetermined BSS -- 10.5 MAP-Based Two-Stage Approach to Underdetermined BSS -- 10.6 Experimental Comparison with Binary Mask Approach and MAP-Based Two-Stage Approach -- 10.7 Concluding Remarks -- References -- 11. Array Processing in Astronomy (Douglas C.-J. Bock) -- 11.1 Introduction -- 11.2 Correlation Arrays -- 11.3 Aperture Plane Phased Arrays -- 11.4 Future Directions -- 11.5 Conclusion -- References. -- 12. Digital 3D/4D Ultrasound Imaging Array (Stergios Stergiopoulos) -- 12.1 Background -- 12.2 Next Generation 3D/4D Ultrasound Imaging Technology -- 12.3 Computing Architecture and Implementation Issues -- 12.4 An Experimental Planar Array Ultrasound Imaging System -- 12.5 Conclusion -- References -- PART III: FUNDAMENTAL ISSUES IN DISTRIBUTED SENSOR NETWORKS.
13. Self-Localization of Sensor Networks (Josh N. Ash and Randolph L. Moses) -- 13.1 Introduction -- 13.2 Measurement Types and Performance Bounds -- 13.3 Localization Algorithms -- 13.4 Relative and Transformation Error Decomposition -- 13.5 Conclusions -- References. -- 14. Multitarget Tracking and Classification in Collaborative Sensor Networks via Sequential Monte Carlo (Tom Vercauteren and Xiaodong Wang) -- 14.1 Introduction -- 14.2 System Description and Problem Formulation -- 14.3 Sequential Monte Carlo Methods -- 14.4 Joint Single-Target Tracking and Classification -- 14.5 Multiple-Target Tracking and Classification -- 14.6 Sensor Selection -- 14.7 Simulation Results -- Conclusion -- Appendix: Derviations of (14.38 and (14.40) -- References -- 15. Energy-Efficient Decentralized Estimation (Jin-Jun Xiao, Shuguang Cui and Zhi-Quan Luo) -- 15.5 Introduction -- 15.2 System Model -- 15.3 Digital Approaches -- 15.4 Analog Approaches -- 15.5 Analog versus Digital -- 15.6 Extension to Vector Model -- 15.7 Concluding Remarks -- Acknowledgments -- References. -- 16. Sensor Data Fusion with Application to Multitarget Tracking (R. Tharmarasa, K. Punithakumar, T. Kirubarajan and Y. Bar-Shalom) -- 16.1 Introduction -- 16.2 Tracking Filters -- 16.3 Data Association -- 16.4 Out-of-Sequence Measurements -- 16.5 Results with Real Data -- 16.6 Summary -- References. -- 17. Distributed Algorithms in Sensor Networks (Usman A. Khan, Soummya Kar and Jos A A Moura) -- 17.1 Introduction -- 17.2 Preliminaries -- 17.3 Distributed Detection -- 17.4 Consensus Algorithms -- 17.5 Zero-Dimension (Average) Consensus -- 17.6 Consensus in Higher Dimensions -- 17.7 Leader-Follower (Type) Algorithms -- 17.8 Localization in Sensor Networks -- 17.9 Linear System of Equations: Distributed Algorithm -- 17.10 Conclusions -- References. -- 18. Cooperative Sensor Communications (Ahmed K. Sadek, Weifeng Su and K. J. Ray Liu) -- 18.1 Introduction -- 18.2 Cooperative Relay Protocols -- 18.3 SER Analysis and Optimal Power Allocation.
18.4 Energy Efficiency in Cooperative Sensor Networks -- 18.5 Experimental Results -- 18.6 Conclusions -- References. -- 19. Distributed Source Coding (Zixiang Xiong, Angelos D. Liveris and Yang Yang) -- 19.1 Introduction -- 19.2 Theoretical Background -- 19.3 Code Designs -- 19.4 Applications -- 19.5 Conclusions -- References. -- 20. Network Coding for Sensor Networks (Christina Fragouli) -- 20.1 Introduction -- 20.2 How Can We Implement Network Coding in a Practical Sensor Network? -- 20.3 Data Collection and Coupon Collector Problem -- 20.4 Distributed Storage and Sensor Network Data Persistence -- 20.5 Decentralized Operation and Untuned Radios -- 20.6 Broadcasting and Multipath Diversity -- 20.7 Network, Channel and Source Coding -- 20.8 Identity-Aware Sensor Networks -- 20.9 Discussion -- Acknowledgments -- References. -- 21. Information-Theoretic Studies of Wireless Sensor Networks (Liang-Liang Xie and P. R. Kumar) -- 21.1 Introduction -- 21.2 Information-Theoretic Studies -- 21.3 Relay Schemes -- 21.4 Wireless Network Coding -- 21.5 Concluding Remarks -- Acknowledgments -- References. -- PART IV: NOVEL TECHNIQUES FOR AND APPLICATIONS OF DISTRIBUTED SENSOR NETWORKS -- 22. Distributed Adaptive Learning Mechanisms (Ali H. Sayed and Federico S. Cattivelli) -- 22.1 Introduction -- 22.2 Motivation -- 22.3 Incremental Adaptive Solutions -- 22.4 Diffusion Adaptive Solutions -- 22.5 Concluding Remarks -- Acknowledgments -- References -- 23. Routing for Statistical Inference in Sensor Networks (A. Anandkumar, A. Ephremides, A. Swami and L. Tong) -- 23.1 Introduction -- 23.2 Spatial Data Correlation -- 23.3 Statistical Inference of Markov Random Fields -- 23.4 Optimal Routing for Inference with Local Processing -- 23.5 Conclusion and Future Work -- 23.6 Bibliographic Notes -- References. -- 24. Spectral Estimation in Cognitive Radios (Behrouz Farhang-Boroujeny) -- 24.1 Filter Bank Formulation of Spectral Estimators -- 24.2 Polyphase Realization of Uniform Filter Banks.
24.3 Periodogram Spectral Estimator -- 24.4 Multitaper Spectral Estimator -- 24.5 Filter Bank Spectral Estimator -- 24.6 Distributed Spectrum Sensing -- 24.7 Discussion -- Appendix A: Effective Degree of Freedom -- Appendix B: Explanation to the Results of Table 24.1 -- References -- 25. Nonparametric Techniques for Pedestrian Tracking in Wireless Local Area Networks (Azadeh Kushki and Kostas N. Plataniotis) -- 25.1 Introduction -- 25.2 WLAN Positioning Architectures -- 25.3 Signal Models -- 25.4 Zero-Memory Positioning -- 25.5 Dynamic Positioning Systems -- 25.6 Cognition and Feedback -- 25.7 Tracking Example -- 25.8 Conclusions -- References -- 26. Reconfigurable Self-Activating Ion-Channel-Based Biosensors Vikram Krishnamurthy and Bruce Cornell) -- 26.1 Introduction -- 26.2 Biosensors Built of Ion Channels -- 26.3 Joint Input Excitation and Concentration Classification for Biosensor -- 26.4 Decentralized Deployment of Dense Network of Biosensors -- 26.5 Discussion and Extensions -- References. -- 27. Biochemical Transport Modeling, Estimation and Detection in Realistic Environments (Mathias Ortner and Arye Nehorai ) -- 27.1 Introduction -- 27.2 Physical and Statistical Models -- 27.3 Transport Modeling Using Monte Carlo Approximation -- 27.4 Localizing the Source(s) -- 27.5 Sequential Detection -- 27.6 Conclusion -- References -- 28. Security and Privacy for Sensor Networks (Wade Trappe, Peng Ning and Adrian Perrig) -- 28.1 Introduction -- 28.2 Security and Privacy Challenges -- 28.3 Ensuring Integrity of Measurement Process -- 28.4 Availability Attacks against the Wireless Link -- 28.5 Ensuring Privacy of Routing Contexts -- 28.6 Conclusion -- References -- Index.
Record Nr. UNINA-9910830400203321
Haykin Simon S. <1931->  
[Piscataway, New Jersey] : , : IEEE, , c2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook on array processing and sensor networks / / Simon S. Haykin, K.J. Ray Liu
Handbook on array processing and sensor networks / / Simon S. Haykin, K.J. Ray Liu
Autore Haykin Simon S. <1931->
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : John Wiley & Sons, 2009
Descrizione fisica 1 online resource (924 p.)
Disciplina 621.382/4
Altri autori (Persone) LiuK. J. Ray <1961->
Collana Adaptive and learning systems for signal processing, communications and control series
Soggetto topico Sensor networks
Antenna arrays
Array processors
ISBN 1-282-54932-4
9786612549328
1-61344-501-6
0-470-48706-2
0-470-48705-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface (Simon Haykin and K. J. Ray Liu) -- Contributors -- Introduction (Simon Haykin) -- PART I: FUNDAMENTAL ISSUES IN ARRAY SIGNAL PROCESSING -- 1. Wavefields. (Alfred Hanssen) -- 1.1 Introduction -- 1.2 Harmonizable Stochastic Processes -- 1.3 Stochastic Wavefields -- 1.4 Wave Dispersion -- 1.5 Conclusions -- 1.6 Acknowledgements -- References. -- 2. Spatial Spectrum Estimation (Petar M. Djuri) -- 2.1 Introduction -- 2.2 Fundamentals -- 2.3 Temporal Spectrum Estimation -- 2.4 Spatial Spectrum Estimation -- 2.5 Final Remarks -- References. -- 3. MIMO Radio Propagation (Tricia J. Willink) -- 3.1 Introduction -- 3.2 Space-Time Propagation Environment -- 3.3 Propagation Models -- 3.4 Measured Channel Characteristics -- 3.5 Stationarity -- 3.6 Summary -- References. -- 4. Robustness Issues in Sensor Array Processing (Alex B. Gershman) -- 4.1 Introduction -- 4.2 Direction-of-Arrival Estimation -- 4.3 Adaptive Beamforming -- 4.4 Conclusions -- Acknowledgments -- References. -- 5. Wireless Communication and Sensing in Multipath Environments Using Multiantenna Transceivers (Akbar M. Sayeed and Thiagarajan Sivanadyan) -- 5.1 Introduction and Overview -- 5.2 Multipath Wireless Channel Modeling in Time, Frequency and Space -- 5.3 Point-to-Point MIMO Wireless Communication Systems -- 5.4 Active Wireless Sensing with Wideband MIMO Transceivers -- 5.5 Concluding Remarks -- References -- PART II: NOVEL TECHNIQUES FOR AND APPLICATIONS OF ARRAY SIGNAL PROCESSING. -- 6. Implicit Training and Array Processing for Digital Communication Systems (Aldo G. Orozco-Lugo, Mauricio Lara, and Desmond C. McLernon) -- 6.1 Introduction -- 6.2 Classification of Implicit Training Methods -- 6.3 IT-Based Estimation for a Single User -- 6.4 IT-Based Estimation for Multiple Users Exploiting Array Processing: Continuous Transmission -- 6.5 IT-Based Estimation for Multiple Users Exploiting Array Processing: Packet Transmission -- 6.6 Open Research Problems -- Acknowledgments -- References -- 7. Unitary Design of Radar Waveform Diversity Sets (Michael D. Zoltowski, Tariq R. Qureshi, Robert Calderbank, and Bill Moran).
7.1 Introduction -- 7.2 2 x 2 Space-Time Diversity Waveform Design -- 7.3 4 x 4 Space-Time Diversity Waveform Design -- 7.4 Waveform Families Based on Kronecker Products -- 7.5 Introduction to Data-Dependent Waveform Design -- 7.6 3 x 3 and 6 x 6 Waveform Scheduling -- 7.7 Summary -- References. -- 8. Acoustic Array Processing for Speech Enhancement (Markus Buck, Eberhard H Ansler, Mohamed Krini, Gerhard Schmidt and Tobias Wolff) -- 8.1 Introduction -- 8.2 Signal Processing in the Subband Domain -- 8.3 Multichannel Echo Cancelation -- 8.4 Speaker Localization -- 8.5 Beamforming -- 8.6 Sensor Calibration -- 8.7 Postprocessing -- 8.8 Conclusions -- References -- 9. Acoustic Beamforming for Hearing Aid Applications (Simon Doclo, Sharon Gannot, Marc Moonen and Ann Spriet) -- 9.1. Introduction -- 9.2. Overview of noise reduction techniques -- 9.3. Monaural beamforming -- 9.4. Binaural beamforming -- 9.5. Conclusion -- 10. Undetermined Blind Source Separation Using Acoustic Arrays (Shoji Makino, Shoko Araki, Stefan Winter and Hiroshi Sawada) -- 10.1 Introduction -- 10.2 Underdetermined Blind Source Separation of Speeches in Reverberant Environments -- 10.3 Sparseness of Speech Sources -- 10.4 Binary Mask Approach to Underdetermined BSS -- 10.5 MAP-Based Two-Stage Approach to Underdetermined BSS -- 10.6 Experimental Comparison with Binary Mask Approach and MAP-Based Two-Stage Approach -- 10.7 Concluding Remarks -- References -- 11. Array Processing in Astronomy (Douglas C.-J. Bock) -- 11.1 Introduction -- 11.2 Correlation Arrays -- 11.3 Aperture Plane Phased Arrays -- 11.4 Future Directions -- 11.5 Conclusion -- References. -- 12. Digital 3D/4D Ultrasound Imaging Array (Stergios Stergiopoulos) -- 12.1 Background -- 12.2 Next Generation 3D/4D Ultrasound Imaging Technology -- 12.3 Computing Architecture and Implementation Issues -- 12.4 An Experimental Planar Array Ultrasound Imaging System -- 12.5 Conclusion -- References -- PART III: FUNDAMENTAL ISSUES IN DISTRIBUTED SENSOR NETWORKS.
13. Self-Localization of Sensor Networks (Josh N. Ash and Randolph L. Moses) -- 13.1 Introduction -- 13.2 Measurement Types and Performance Bounds -- 13.3 Localization Algorithms -- 13.4 Relative and Transformation Error Decomposition -- 13.5 Conclusions -- References. -- 14. Multitarget Tracking and Classification in Collaborative Sensor Networks via Sequential Monte Carlo (Tom Vercauteren and Xiaodong Wang) -- 14.1 Introduction -- 14.2 System Description and Problem Formulation -- 14.3 Sequential Monte Carlo Methods -- 14.4 Joint Single-Target Tracking and Classification -- 14.5 Multiple-Target Tracking and Classification -- 14.6 Sensor Selection -- 14.7 Simulation Results -- Conclusion -- Appendix: Derviations of (14.38 and (14.40) -- References -- 15. Energy-Efficient Decentralized Estimation (Jin-Jun Xiao, Shuguang Cui and Zhi-Quan Luo) -- 15.5 Introduction -- 15.2 System Model -- 15.3 Digital Approaches -- 15.4 Analog Approaches -- 15.5 Analog versus Digital -- 15.6 Extension to Vector Model -- 15.7 Concluding Remarks -- Acknowledgments -- References. -- 16. Sensor Data Fusion with Application to Multitarget Tracking (R. Tharmarasa, K. Punithakumar, T. Kirubarajan and Y. Bar-Shalom) -- 16.1 Introduction -- 16.2 Tracking Filters -- 16.3 Data Association -- 16.4 Out-of-Sequence Measurements -- 16.5 Results with Real Data -- 16.6 Summary -- References. -- 17. Distributed Algorithms in Sensor Networks (Usman A. Khan, Soummya Kar and Jos A A Moura) -- 17.1 Introduction -- 17.2 Preliminaries -- 17.3 Distributed Detection -- 17.4 Consensus Algorithms -- 17.5 Zero-Dimension (Average) Consensus -- 17.6 Consensus in Higher Dimensions -- 17.7 Leader-Follower (Type) Algorithms -- 17.8 Localization in Sensor Networks -- 17.9 Linear System of Equations: Distributed Algorithm -- 17.10 Conclusions -- References. -- 18. Cooperative Sensor Communications (Ahmed K. Sadek, Weifeng Su and K. J. Ray Liu) -- 18.1 Introduction -- 18.2 Cooperative Relay Protocols -- 18.3 SER Analysis and Optimal Power Allocation.
18.4 Energy Efficiency in Cooperative Sensor Networks -- 18.5 Experimental Results -- 18.6 Conclusions -- References. -- 19. Distributed Source Coding (Zixiang Xiong, Angelos D. Liveris and Yang Yang) -- 19.1 Introduction -- 19.2 Theoretical Background -- 19.3 Code Designs -- 19.4 Applications -- 19.5 Conclusions -- References. -- 20. Network Coding for Sensor Networks (Christina Fragouli) -- 20.1 Introduction -- 20.2 How Can We Implement Network Coding in a Practical Sensor Network? -- 20.3 Data Collection and Coupon Collector Problem -- 20.4 Distributed Storage and Sensor Network Data Persistence -- 20.5 Decentralized Operation and Untuned Radios -- 20.6 Broadcasting and Multipath Diversity -- 20.7 Network, Channel and Source Coding -- 20.8 Identity-Aware Sensor Networks -- 20.9 Discussion -- Acknowledgments -- References. -- 21. Information-Theoretic Studies of Wireless Sensor Networks (Liang-Liang Xie and P. R. Kumar) -- 21.1 Introduction -- 21.2 Information-Theoretic Studies -- 21.3 Relay Schemes -- 21.4 Wireless Network Coding -- 21.5 Concluding Remarks -- Acknowledgments -- References. -- PART IV: NOVEL TECHNIQUES FOR AND APPLICATIONS OF DISTRIBUTED SENSOR NETWORKS -- 22. Distributed Adaptive Learning Mechanisms (Ali H. Sayed and Federico S. Cattivelli) -- 22.1 Introduction -- 22.2 Motivation -- 22.3 Incremental Adaptive Solutions -- 22.4 Diffusion Adaptive Solutions -- 22.5 Concluding Remarks -- Acknowledgments -- References -- 23. Routing for Statistical Inference in Sensor Networks (A. Anandkumar, A. Ephremides, A. Swami and L. Tong) -- 23.1 Introduction -- 23.2 Spatial Data Correlation -- 23.3 Statistical Inference of Markov Random Fields -- 23.4 Optimal Routing for Inference with Local Processing -- 23.5 Conclusion and Future Work -- 23.6 Bibliographic Notes -- References. -- 24. Spectral Estimation in Cognitive Radios (Behrouz Farhang-Boroujeny) -- 24.1 Filter Bank Formulation of Spectral Estimators -- 24.2 Polyphase Realization of Uniform Filter Banks.
24.3 Periodogram Spectral Estimator -- 24.4 Multitaper Spectral Estimator -- 24.5 Filter Bank Spectral Estimator -- 24.6 Distributed Spectrum Sensing -- 24.7 Discussion -- Appendix A: Effective Degree of Freedom -- Appendix B: Explanation to the Results of Table 24.1 -- References -- 25. Nonparametric Techniques for Pedestrian Tracking in Wireless Local Area Networks (Azadeh Kushki and Kostas N. Plataniotis) -- 25.1 Introduction -- 25.2 WLAN Positioning Architectures -- 25.3 Signal Models -- 25.4 Zero-Memory Positioning -- 25.5 Dynamic Positioning Systems -- 25.6 Cognition and Feedback -- 25.7 Tracking Example -- 25.8 Conclusions -- References -- 26. Reconfigurable Self-Activating Ion-Channel-Based Biosensors Vikram Krishnamurthy and Bruce Cornell) -- 26.1 Introduction -- 26.2 Biosensors Built of Ion Channels -- 26.3 Joint Input Excitation and Concentration Classification for Biosensor -- 26.4 Decentralized Deployment of Dense Network of Biosensors -- 26.5 Discussion and Extensions -- References. -- 27. Biochemical Transport Modeling, Estimation and Detection in Realistic Environments (Mathias Ortner and Arye Nehorai ) -- 27.1 Introduction -- 27.2 Physical and Statistical Models -- 27.3 Transport Modeling Using Monte Carlo Approximation -- 27.4 Localizing the Source(s) -- 27.5 Sequential Detection -- 27.6 Conclusion -- References -- 28. Security and Privacy for Sensor Networks (Wade Trappe, Peng Ning and Adrian Perrig) -- 28.1 Introduction -- 28.2 Security and Privacy Challenges -- 28.3 Ensuring Integrity of Measurement Process -- 28.4 Availability Attacks against the Wireless Link -- 28.5 Ensuring Privacy of Routing Contexts -- 28.6 Conclusion -- References -- Index.
Record Nr. UNINA-9910876967403321
Haykin Simon S. <1931->  
Hoboken, N.J., : John Wiley & Sons, 2009
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