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.

2. Spatial Spectrum Estimation (Petar Djuric).

1 Introduction.

2 Fundamentals.

3 Temporal spectrum estimation.

3.1 Non-parametric methods.

4 Spatial spectrum estimation.

4.1 The model.

4.2 Non-parametric methods.

4.3 Parametric methods.

4.4 Estimation of the number of impinging signals.

5 Final remarks.

3. MIMO Radio Propagation (Tricia Willink).

3.1 Introduction.

3.2 The space-time propagation environment.

3.3 Propagation models.

3.4 Measured channel characteristics.

3.5 Stationarity.

3.6 Summary.

4. Robustness Issues in Sensor Array Processing (Alex Gershman).

1 Robustness Issues in Sensor Array Processing.

1.2 Direction-of-arrival estimation.

1.3 Adaptive beamforming.

1.4 Conclusions.

5. Wireless Communication and Sensing in Multipath Environments Using Multi Antenna Transceivers (Akbar Sayeed and Thiagarajan Sivanadyan).

1. Overview.

2. Multipath Wireless Channel Modeling in Time, Frequency and Space.

3. MIMO Wireless Communication Systems.

4. Active Wireless Sensing.

Directions for Future Research.

Concluding Remarks.

6. Implicit Training and Array Processing for Digital Communications Systems (Mauricio Lara, Aldo Orozco and Desmond 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.

6.7 References.

7. Unitary Design of Radar Waveform Diversity Sets (Michael Zoltowski, Robert Calderbank, Tariq R. Qureshi and Bill Moran).

1. Introduction.

2. 2x2 Space-Time Diversity Waveform Design.

3. 4x4 Space-Time Diversity Waveform Design.

4. Waveform Families Based on Kronecker Products.

5. Introduction to Data Dependent Waveform Design.

6. 3x3 and 6x6 Waveform Scheduling.

7. Summary.

8. Acoustic Array Processing for Speech Enhancement (Markus Buck, Eberhard Hänsler, Mohamed Krini, Gerhard Schmidt and Tobias Wolff).

8.1 Introduction.

8.2 Signal Processing in the Subband Domain.

8.3 Multi-Channel 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, Hiroshi Sawada and Stefan Winter).

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.

Index.

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.

12. Digital 3D/4D Ultrasound Imaging Technology (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.

Reference.

13. Self-Localization of Sensor Networks (Josh N. Ash and Randy Moses).

Introduction.

13.1 The Self-localization Problem.

13.2 Algorithm Classifications.

13.3 Measurement Types and Performance Bounds.

13.4 Localization Algorithms.

13.5 Relative and Transformation Error Decomposition.

13.6 Conclusions.

14. Multi-Target Tracking and Classification in Collaborative Sensor Networks via Sequential Monte Carlo (Tom Vercauteren and Xiaodong Wang).

1. Introduction.

2. System Description and Problem Formulation.

3. Sequential Monte Carlo (SMC) Methods.

4. Joint Single Target Tracking And Classification.

5. Multiple Target Tracking and Classification.

6. Sensor Selection.

7. Simulation Results.

8. Conclusion.

15. Energy-Efficient Decentralized Estimation (Jin-Jun Xiao, Shuguang Cui and Zhi-Quan Luo).

1. Motivation and Introduction.

2. Distributed Estimation: Digital Approaches.

3. Distributed Estimation: Analog Approaches.

4. Distributed Anti-jammer Sensing via Game Theory.

5. Future research discussion.

16. Sensor Data Fusion with Application to Multi-Target Tracking (T. Kirubarajan, R. Tharmarasa, K. Punithakumar and Yaakov 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.

17. Distributed Algorithms in Sensor Networks (Soummya Kar, Jose’ Moura and Usman A. Khan).

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.

18. Cooperative Sensor Communications (Ahmed Sadek, Weifeng Su and Ray Liu).

18.1 Introduction.

18.2 Cooperative Relay Protocols.

18.3 Performance Analysis and Optimal Power Allocation.

18.4 Energy Efficiency in Cooperative Sensor Networks.

18.5 Experimental Results.

18.6 Conclusions.

19. Distributed Source Coding (Zixiang Xiong, Angelos Liveris and Yang Yang).

19.1 Introduction.

19.2 Theoretical background.

19.3 Code designs.

19.4 Applications.

19.5 Conclusions.

20. Network Coding for Sensor Networks (Christina Fragouli).

I. Introduction.

II. How can we implement network coding in a practical sensor network.

III. Data collection and the coupon collectors problem.

IV. Distributed storage and sensor network data persistence.

V. Decentralized operation and untuned radios.

VI. Broadcasting and multipath diversity.

VII. Network, channel and source coding.

VIII. Identity aware sensor networks.

IX. Discussion.

21. Information-Theoretic Studies of Wireless Sensor Networks (Liang-Liang Xie and PR Kumar).

1. Information-Theoretic Studies of Wireless Sensor Networks.

1.2 Information-theoretic studies.

1.3 Relay schemes.

1.4 Wireless network coding.

1.5 Concluding remarks.

22. Distributed Adaptive Learning Mechanisms (Ali Sayed and Federico S. Cattivelli).

1 Introduction.

2. Motivation.

3. Incremental Adaptive Solutions.

4. Diffusion Adaptive Solutions.

5. Concluding Remarks.

References

23. Routing for Statistical Inference in Sensor Networks (A. Anandkumar, A. Ephremides, A. Swami and Lang Tong).

22.1 Introduction.

22.2 Spatial Data Correlation.

22.3 Statistical Inference of Markov Random Fields.

22.4 Optimal Routing for Inference with Local Processing.

22.5 Conclusion and Future Work 2.

24. Spectrum Estimation in Cognitive Radios (Behrouz Farhang-Boroujeny).

I Filter Bank Formulation of Spectral Estimators.

II Polyphase Realization of Uniform Filter Banks.

III Periodogram Spectral Estimator.

IV Multitaper Spectral Estimator.

V Filter Bank Spectral Estimator.

VI Distributed Spectrum Sensing.

VII Discussion.

25. Nonparametric Techniques for Pedestrian Tracking in Wireless Local Area Networks (Kostas Plataniotis and Azadeh Kushki).

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 & Feedback.

25.7 Tracking Example.

25.8 Conclusions.

References.

26. Reconfigurable Self-Activating Ion-Channel Based Biosensors: Signal Processing and Networking via the Theory of Global Games (Vikram Krishnamurthy and Bruce Cornell).

26.1 Introduction.

26.2 Biosensors built of ion channels.

26.3 Joint Input Excitation and Concentration Classification of Biosensor.

26.4 Decentralized Deployment of Dense Network of Biosensors.

26.5 Discussion and Extensions..

27. Biochemical Transport Modeling, Estimation and Detection in Realistic Environments (Mathias Ortner and Arye Nehorai ).

1. Introduction.

2. Physical, statistical and numerical models.

3. Localizing the sources.

4. Sequential Detection.

5 Conclusion.

28. Security and Privacy for Sensor Networks (Wade Trappe, Peng Ning and Adrian Perrig).

28.1 Introduction2.

28.2 Security and Privacy Challenges.

28.3 Ensuring the Integrity of the Measurement Process.

28.4 Availability Attacks against the Wireless Link.

28.5 Ensuring Privacy of Routing Contexts.

28.6 Conclusion.