Academic Press Library in Signal Processing, Volume 7

Array, Radar and Communications Engineering
 
 
Elsevier (Verlag)
  • 1. Auflage
  • |
  • erschienen am 13. Dezember 2017
  • |
  • 650 Seiten
 
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-0-12-811888-7 (ISBN)
 

Academic Press Library in Signal Processing, Volume 7: Array, Radar and Communications Engineering is aimed at university researchers, post graduate students and R&D engineers in the industry, providing a tutorial-based, comprehensive review of key topics and technologies of research in Array and Radar Processing, Communications Engineering and Machine Learning. Users will find the book to be an invaluable starting point to their research and initiatives.

With this reference, readers will quickly grasp an unfamiliar area of research, understand the underlying principles of a topic, learn how a topic relates to other areas, and learn of research issues yet to be resolved.

  • Presents a quick tutorial of reviews of important and emerging topics of research
  • Explores core principles, technologies, algorithms and applications
  • Edited and contributed by international leading figures in the field
  • Includes comprehensive references to journal articles and other literature upon which to build further, more detailed knowledge
  • Englisch
  • Saint Louis
  • |
  • Großbritannien
  • 45,39 MB
978-0-12-811888-7 (9780128118887)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Academic Press Library in Signal Processing, Volume 7: Array, Radar and Communications Engineering
  • Copyright
  • Contents
  • Contributors
  • About the Editors
  • Section Editors
  • Introduction
  • Section 1: Radar signal processing
  • Chapter 1: Holistic radar waveform diversity
  • 1.1. Introduction
  • 1.2. Practical Radar Waveforms and Pulse Compression
  • 1.2.1. Radar Waveforms
  • 1.2.2. Waveform Performance Metrics
  • 1.2.3. Received Signal Structure
  • 1.3. Practical Considerations
  • 1.3.1. Transmitter Effects
  • 1.3.2. Receive Effects
  • 1.4. Holistic Waveform Implementation and Design
  • 1.4.1. Polyphase-Coded FM
  • 1.4.2. Spectrum-Shaped FM Waveforms
  • 1.4.3. Transmitter-in-the-Loop Optimization
  • 1.5. Holistic Higher-Dimensional Waveform Diversity
  • 1.5.1. Spatial Modulation
  • 1.5.2. Holistic Wideband MIMO Radar
  • 1.6. Conclusions
  • References
  • Chapter 2: Geometric foundations for radar signal processing
  • 2.1. Introduction
  • 2.2. Geometric Algebra
  • 2.2.1. How to Multiply Vectors
  • 2.2.1.1. A Nonassociative Product of Vectors
  • 2.2.1.2. An Associative Product of Vectors
  • 2.2.1.3. The Geometric Product of Vectors
  • 2.2.2. Geometric Algebra
  • 2.2.2.1. Geometric Algebra in Two Dimensions
  • 2.2.2.2. Geometric Algebra in Three Dimensions
  • 2.2.2.3. Geometric Algebra in Three Dimensions
  • 2.2.2.4. Caution-The Pseudoscalar is Not Simply -1 in Higher Dimensions
  • 2.2.2.5. Geometric Product of Multivectors
  • 2.2.3. What is a Complex Number?
  • 2.2.3.1. Rotation of Vectors via Spinors
  • 2.2.4. What is a Complex Vector?
  • 2.2.4.1. N-Dimensional Complex Vector as a 2N-Dimensional Real Vector
  • 2.2.4.2. Geometric Interpretation of a Complex Data Vector as a Spinor Expansion
  • 2.2.4.3. Projecting a Vector into a Subspace
  • 2.2.4.3.1. Examples
  • 2.2.5. What is a Complex Matrix?
  • 2.2.5.1. Geometry of the Matrix Inverse
  • 2.3. Selected Applications to Radar Signal Processing
  • 2.3.1. Hermitian Inner Product
  • 2.3.2. The Geometry of Signal Detection
  • 2.3.2.1. Multivariate Gaussian PDF and a Simple Detection Problem
  • 2.3.2.2. A Geometric Approach to Formulating Detectors
  • 2.3.3. Geometry of Nulling Directions
  • 2.3.3.1. Linear Processing to Steer Nulls
  • 2.3.3.2. Geometric Approach to Designing a Notch Filter
  • 2.3.3.3. Choosing the Frequencies That Define the Constraint Subspace
  • 2.3.3.4. Generalized Sidelobe Canceller
  • 2.4. Conclusion-Future Research Opportunities
  • References
  • Chapter 3: Foundations of cognitive radar for next-generation radar systems
  • 3.1. Background
  • 3.2. Early Research Contributions
  • 3.3. Enabling Hardware and Processing Technologies
  • 3.4. Signal Processing Foundations for Cognitive Radar
  • 3.4.1. Waveform Design
  • 3.4.1.1. Deterministic, Known Target Impulse Response
  • 3.4.1.2. Random Target Impulse Response
  • 3.4.1.3. Waveform Shape and Constant Modulus Constraints
  • 3.4.2. Sequential Hypothesis Testing
  • 3.4.2.1. Binary Sequential Hypothesis Testing
  • 3.4.2.2. Sequential Testing with Multiple Hypotheses
  • 3.4.3. Partially Observable Markov Decision Process
  • 3.5. Canonical Examples
  • 3.5.1. Detection of a Target With Known Impulse Response
  • 3.5.1.1. Waveform Design
  • 3.5.1.2. Detection Performance
  • 3.5.1.3. Information Gained
  • 3.5.2. Detecting a Known Signal With a Nuisance Parameter
  • 3.5.2.1. Waveform Design Applied to Adaptive Beamshaping
  • 3.5.2.2. Carryover and Adaptation Performance Gains
  • 3.5.3. Parallel Estimation
  • 3.5.4. Summary
  • 3.6. Cognitive Radar Experiments
  • References
  • Chapter 4: Parameter bounds under misspecified models for adaptive radar detection
  • 4.1. List of Symbols and Functions
  • 4.2. Introduction
  • 4.3. Problem Statement and Motivations
  • 4.4. A Generalization of the Deterministic Estimation Theory Under Model Misspecification
  • 4.4.1. Regular Models
  • 4.4.2. MS-Unbiased Estimators and the MCRB
  • 4.4.3. The Mismatched Maximum Likelihood (MML) Estimator
  • 4.4.4. A Particular Case: The MCRB as a Bound on the Mean Square Error (MSE)
  • 4.4.5. The Constrained MCRB: CMCRB
  • 4.4.5.1. The MCRB for the intrinsic parameter vector
  • Existence of ?0
  • MS-unbiasedness and MCRB in ?0
  • 4.4.5.2. The constrained MCRB (CMCRB)
  • 4.5. Two Illustrative Examples
  • 4.6. The MCRB for the Estimation of the Scatter Matrix in the Family of CES Distributions
  • 4.6.1. Misspecified Estimation of the Scatter Matrix With Perfectly Known Extra Parameters
  • 4.6.1.1. Case Study 1. Assumed pdf: complex Normal
  • true pdf: t-student.
  • 4.6.1.2. Case Study 2. Assumed pdf: complex Normal, true pdf: Generalized Gaussian
  • 4.6.1.3. Case Study 3. Assumed pdf: Generalized Gaussian
  • true pdf: t-student
  • 4.6.2. Misspecified Joint Estimation of the Scatter Matrix and of the Extra Parameters
  • 4.6.2.1. Derivation of the constrained MML (CMML) estimator
  • 4.6.2.2. The CMCRB for the joint estimation of the scatter matrix and the power
  • Evaluation of the matrix A?0
  • Evaluation of the matrix B?0
  • Evaluation of the matrix U
  • 4.6.2.3. Performance analysis
  • 4.7. Hypothesis Testing Problem for Target Detection
  • 4.7.1. The ANMF Detector
  • 4.7.2. Detection Performance
  • 4.8. Conclusions
  • Appendix A. A Generalization of the Slepian Formula Under Misspecification
  • Appendix B. A Generalization of the Bangs Formula Under Misspecification
  • Appendix C. Compact Expression for the MCRB in the CES Family
  • Compact Expression for the Matrix B?
  • Compact Expression for the Matrix A?
  • Compact Expression for the MCRB, MCRB?=M-1A?-1B?.A?-1 (With r=0)
  • References
  • Chapter 5: Multistatic radar systems
  • 5.1. Introduction
  • 5.2. Characteristics of Multistatic Radar
  • 5.3. Multistatic Radar Technology Enablers
  • 5.4. Signal Processing in Multistatic Radar
  • 5.5. Target Detection
  • 5.6. Target Resolution
  • 5.7. Target Localization
  • 5.8. Synchronization Considerations for Multistatic Radar
  • 5.9. System Case Study: NetRAD/NeXtRAD
  • 5.9.1. NetRAD
  • 5.9.2. NeXtRAD
  • 5.9.3. Calibration of Multistatic Polarmetric Radar
  • 5.9.4. Corner Reflectors FEKO Simulation
  • 5.10. Conclusions
  • Acknowledgments
  • References
  • Chapter 6: Sparsity-based radar technique
  • 6.1. Introduction
  • 6.2. Temporal Sparsity
  • 6.2.1. Sparse Sampling in Range
  • 6.2.2. Sparse Sampling in Range and Doppler
  • 6.3. Spectral Sparsity
  • 6.3.1. Recovery of Missing or Corrupted Spectral Information
  • 6.3.2. Sub- or Co-prime Sampling in the Spectral Domain
  • 6.4. Spatial Sparsity
  • 6.4.1. Direction-of-Arrival (DOA)
  • 6.4.1.1. DOA with a linear array
  • 6.4.1.2. DOA with a 2D array
  • 6.4.2. 3D-SAR
  • 6.4.2.1. Experimental results
  • 6.5. Group Sparsity
  • 6.5.1. Group Model
  • 6.5.2. Example: SIMO Radar Network
  • 6.5.3. Example: MIMO Radar Network
  • 6.5.4. Example: SFN Radar
  • 6.5.4.1. Signal model
  • 6.5.4.2. Verification
  • 6.6. Conclusion
  • References
  • Further Reading
  • Chapter 7: Millimeter-wave integrated radar systems and techniques
  • 7.1. Integrated Radar: Trends and Challenges
  • 7.1.1. System Design Challenges: Size and Cost
  • 7.1.2. Single Chip RF System
  • 7.1.3. Antenna Systems
  • 7.1.4. Interference Challenges
  • 7.1.5. Automotive Radar: Trends and Standardization Efforts
  • 7.2. Channel Modeling for Millimeter-Wave Radar
  • 7.2.1. Propagation Properties in Millimeter-Wave
  • 7.2.2. Millimeter-Wave Radar Equation
  • 7.2.3. Ray Tracing for Millimeter-Wave Radar
  • 7.2.4. Clutter in Millimeter-Wave CMOS Radar
  • 7.3. Waveform and Signal Processing
  • 7.3.1. Time-Bandwidth Product and Radar Resolution
  • 7.3.2. Linear FM and FMCW Radar
  • 7.3.3. Stepped Frequency Radar
  • 7.3.4. Pseudo-Random Stepped Frequency Radar
  • 7.3.5. Processing a PRSF Waveform
  • 7.3.5.1. Waveform repetition for M-times
  • 7.3.6. Adaptive Radar and Computationally Light Processing Techniques
  • 7.3.6.1. Detection of significant Doppler frequencies
  • 7.3.6.2. Robust range-Doppler estimation
  • 7.3.7. Intermediate Frequency Processing Technique
  • 7.4. Stochastic Geometry Technique for Modeling Automotive Consumer Radars
  • 7.4.1. Poisson Point Process Model
  • 7.4.2. Lattice Model
  • 7.4.3. Interference Analysis
  • 7.4.4. Interference Statistics
  • 7.4.5. Performance Analysis and Optimization
  • 7.5. Performance Limitations
  • 7.5.1. CMOS Technology Limitations
  • 7.5.2. Information Theory Limitations
  • Acknowledgments
  • References
  • Section 2: Communications
  • Chapter 8: Signal processing for massive MIMO communications
  • 8.1. Introduction
  • 8.2. Overview of Multiantenna Systems: Path to Massive MIMO
  • 8.2.1. Point-to-Point MIMO
  • 8.2.2. Toward Massive MIMO
  • 8.2.3. MU-MIMO
  • 8.2.3.1. UL (reverse link)
  • 8.2.3.2. DL (forward link)
  • 8.3. Massive MIMO Precoding
  • 8.3.1. Basic Precoding Schemes
  • 8.3.2. Constant Envelop Precoding
  • 8.4. Signal Detection
  • 8.5. Power Control
  • 8.6. Channel Estimation and Pilot Contamination
  • 8.6.1. Channel Estimation
  • 8.6.2. Pilot Contamination
  • 8.6.2.1. Mitigating pilot contamination effects
  • 8.7. Future Research Challenges
  • References
  • Chapter 9: Recent advances in network beamforming
  • 9.1. Introduction
  • 9.2. End-to-End Channel Modeling
  • 9.3. One-Way Network Beamforming
  • 9.3.1. Networks With Frequency-Flat Channels
  • 9.3.1.1. Single-user networks
  • 9.3.1.2. SNR-maximization with perfect CSI
  • 9.3.1.3. SNR-per-unit-power maximization
  • 9.3.1.4. Partial CSI
  • 9.3.1.5. MSE-minimization and received signal power maximization
  • 9.3.1.6. Multi-user networks
  • 9.3.1.7. Orthogonal user channels
  • 9.3.1.8. With user interference and perfect CSI
  • 9.3.1.9. With user interference and partial CSI
  • 9.3.1.10. Robust designs against CSI errors
  • 9.3.2. Networks With Frequency-Selective Channels
  • 9.3.2.1. Single-user networks
  • 9.3.2.2. Multi-user networks
  • 9.4. Two-Way Network Beamforming
  • 9.4.1. Synchronous Networks
  • 9.4.1.1. Total power minimization
  • 9.4.1.2. Max-min SNR approach
  • 9.4.1.3. Sum-rate maximization
  • 9.4.1.4. Individual power constraints
  • 9.4.1.5. TDBC versus MABC
  • 9.4.2. Asynchronous Networks
  • 9.4.2.1. End-to-end channel model
  • 9.4.2.2. Multi-carrier equalization
  • 9.4.2.3. Max-min SNR fair design approach
  • 9.4.2.4. Sum-rate maximization approach
  • 9.4.2.5. Single-carrier post-channel equalization
  • 9.4.2.6. Total MSE minimization
  • 9.4.2.7. Sum-rate maximization
  • 9.4.2.8. Total power minimization
  • 9.4.2.9. Single-carrier pre-channel equalization
  • 9.4.2.10. Joint pre-channel and post-channel equalization
  • 9.4.3. Networks With Frequency-Selective Transceiver-Relay Links
  • 9.4.3.1. OFDM-based channel equalization
  • 9.4.3.2. Filter-and-forward relaying
  • 9.4.4. Miscellaneous Results
  • 9.5. Numerical Examples
  • 9.5.1. One-Way Network Beamforming
  • 9.5.2. Two-Way Network Beamforming
  • 9.6. Summary
  • References
  • Chapter 10: Transmit beamforming for simultaneous wireless information and power transfer
  • 10.1. Introduction
  • 10.1.1. Practical SWIPT Receiver
  • 10.1.2. Multiantenna SWIPT
  • 10.2. Joint Information and Energy Beamforming Design for SWIPT
  • 10.2.1. Beamforming Design for SWIPT System With Separate IRs and ERs
  • 10.2.1.1. System model
  • 10.2.1.2. Problem formulation
  • 10.2.1.3. Optimal solution via SDR
  • 10.2.1.4. Numerical examples
  • 10.2.2. Secrecy Beamforming Design for SWIPT
  • 10.2.2.1. System model
  • 10.2.2.2. Problem formulation
  • 10.2.2.3. Optimal beamforming solution
  • 10.2.2.4. Numerical results
  • 10.2.3. Beamforming Design for SWIPT System With Co-Located IRs and ERs
  • 10.2.3.1. System model
  • 10.2.3.2. Problem formulation
  • 10.2.3.3. Optimal solution
  • 10.2.3.4. Numerical results
  • 10.3. Extensions
  • 10.3.1. Multipoint-to-Multipoint SWIPT
  • 10.3.2. Wireless Powered Communication Network
  • 10.3.3. CSI Acquisition at Transmitter
  • 10.4. Conclusion
  • References
  • Section 3: Sensor array processing
  • Chapter 11: Sparse methods for direction-of-arrival estimation
  • 11.1. Introduction
  • 11.2. Data Model
  • 11.2.1. Data Model
  • 11.2.2. The Role of Array Geometry
  • 11.2.3. Parameter Identifiability
  • 11.3. Sparse Representation and DOA Estimation
  • 11.3.1. Sparse Representation and Compressed Sensing
  • 11.3.1.1. Problem formulation
  • 11.3.1.2. Convex relaxation
  • 11.3.1.3. lq optimization
  • 11.3.1.4. Maximum likelihood estimation (MLE)
  • 11.3.2. Sparse Representation and DOA Estimation: The Link and the Gap
  • 11.4. On-Grid Sparse Methods
  • 11.4.1. Data Model
  • 11.4.2. l2,0 optimization
  • 11.4.3. Convex Relaxation
  • 11.4.3.1. l2,1 optimization
  • 11.4.3.2. Dimensionality reduction via l2,1-SVD
  • 11.4.3.3. Another dimensionality reduction technique
  • 11.4.4. l2,q optimization
  • 11.4.5. Sparse Iterative Covariance-Based Estimation (SPICE)
  • 11.4.5.1. Generalized least squares
  • 11.4.5.2. SPICE
  • 11.4.6. Maximum Likelihood Estimation
  • 11.4.7. Remarks on Grid Selection
  • 11.5. Off-Grid Sparse Methods
  • 11.5.1. Fixed Grid
  • 11.5.1.1. Data model
  • 11.5.1.2. l1 optimization
  • 11.5.1.3. Sparse Bayesian learning
  • 11.5.2. Dynamic Grid
  • 11.5.2.1. Data model
  • 11.5.2.2. Algorithms
  • 11.6. Gridless Sparse Methods
  • 11.6.1. Data Model
  • 11.6.2. Vandermonde Decomposition of Toeplitz Covariance Matrices
  • 11.6.3. The Single Snapshot Case
  • 11.6.3.1. A general framework for deterministic methods
  • 11.6.3.2. Atomic l0 norm
  • 11.6.3.3. Atomic norm
  • 11.6.3.4. Hankel-based nuclear norm
  • 11.6.3.5. Connection between ANM and EMaC
  • 11.6.3.6. Covariance fitting method: Gridless SPICE (GLS)
  • 11.6.3.7. Connection between ANM and GLS
  • 11.6.4. The Multiple Snapshot Case: Covariance Fitting Methods
  • 11.6.4.1. Gridless SPICE (GLS)
  • 11.6.4.2. SMV-based atomic norm minimization (ANM-SMV)
  • 11.6.4.3. Nuclear norm minimization followed by MUSIC (NNM-MUSIC)
  • 11.6.4.4. Comparison of GLS, ANM-SMV, and NNM-MUSIC
  • 11.6.5. The Multiple Snapshot Case: Deterministic Methods
  • 11.6.5.1. A general framework
  • 11.6.5.2. Atomic 0 norm
  • 11.6.5.3. Atomic norm
  • 11.6.5.4. Hankel-based nuclear norm
  • 11.6.6. Reweighted Atomic Norm Minimization
  • 11.6.6.1. A smooth surrogate for ZA,0
  • 11.6.6.2. A locally convergent iterative algorithm
  • 11.6.6.3. Interpretation as RAM
  • 11.6.7. Connections Between ANM and GLS
  • 11.6.7.1. The case of L < M
  • 11.6.7.2. The case of L M
  • 11.6.8. Computational Issues and Solutions
  • 11.6.8.1. Dimensionality reduction
  • 11.6.8.2. Alternating direction method of multipliers (ADMM)
  • 11.7. Future Research Challenges
  • 11.8. Conclusions
  • Acknowledgments
  • References
  • Section 4: Acoustic Signal Processing
  • Chapter 12: Beamforming techniques using microphone arrays
  • 12.1. Introduction
  • 12.2. Problem Formulation
  • 12.2.1. Narrowband Beamforming
  • 12.2.2. Wideband Beamforming
  • 12.3. Basic Approaches in Wideband Beamforming
  • 12.3.1. Superdirective Beamformer
  • 12.3.2. Linearly Constrained Minimum Variance (LCMV)-Based Adaptive Beamforming Techniques
  • 12.3.3. Practical Considerations in Covariance Matrix Estimation in LCMV-Based Beamformers
  • 12.4. Postfilter by PSD Estimation in Beamspace
  • 12.4.1. Problem Setup
  • 12.4.2. Beamforming and Its Output PSD
  • 12.4.3. PSD Estimation in Beamspace
  • 12.4.4. Postfiltering for Source Separation
  • 12.5. Conclusions
  • References
  • Index
  • Back Cover

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