Academic Press Library in Signal Processing, Volume 7: Array, Radar and Communications Engineering

Description

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. Front Matter Copyright Contributors About the Editors Section Editors Introduction Holistic radar waveform diversity Introduction Practical Radar Waveforms and Pulse Compression Radar Waveforms Waveform Performance Metrics Received Signal Structure Practical Considerations Transmitter Effects Receive Effects Holistic Waveform Implementation and Design Polyphase-Coded FM Spectrum-Shaped FM Waveforms Transmitter-in-the-Loop Optimization Holistic Higher-Dimensional Waveform Diversity Spatial Modulation Holistic Wideband MIMO Radar Conclusions References Geometric foundations for radar signal processing Introduction Geometric Algebra How to Multiply Vectors A Nonassociative Product of Vectors An Associative Product of Vectors The Geometric Product of Vectors Geometric Algebra Geometric Algebra in Two Dimensions Geometric Algebra in Three Dimensions Geometric Algebra in Three Dimensions Caution-The Pseudoscalar is Not Simply -1 in Higher Dimensions Geometric Product of Multivectors What is a Complex Number? Rotation of Vectors via Spinors What is a Complex Vector? N-Dimensional Complex Vector as a 2N-Dimensional Real Vector Geometric Interpretation of a Complex Data Vector as a Spinor Expansion Projecting a Vector into a Subspace Examples What is a Complex Matrix? Geometry of the Matrix Inverse Selected Applications to Radar Signal Processing Hermitian Inner Product The Geometry of Signal Detection Multivariate Gaussian PDF and a Simple Detection Problem A Geometric Approach to Formulating Detectors Geometry of Nulling Directions Linear Processing to Steer Nulls Geometric Approach to Designing a Notch Filter Choosing the Frequencies That Define the Constraint Subspace Generalized Sidelobe Canceller Conclusion-Future Research Opportunities References Foundations of cognitive radar for next-generation radar systems Background Early Research Contributions Enabling Hardware and Processing Technologies Signal Processing Foundations for Cognitive Radar Waveform Design Deterministic, Known Target Impulse Response Random Target Impulse Response Waveform Shape and Constant Modulus Constraints Sequential Hypothesis Testing Binary Sequential Hypothesis Testing Sequential Testing with Multiple Hypotheses Partially Observable Markov Decision Process Canonical Examples Detection of a Target With Known Impulse Response Waveform Design Detection Performance Information Gained Detecting a Known Signal With a Nuisance Parameter Waveform Design Applied to Adaptive Beamshaping Carryover and Adaptation Performance Gains Parallel Estimation Summary Cognitive Radar Experiments References Parameter bounds under misspecified models for adaptive radar detection List of Symbols and Functions Introduction Problem Statement and Motivations A Generalization of the Deterministic Estimation Theory Under Model Misspecification Regular Models MS-Unbiased Estimators and the MCRB The Mismatched Maximum Likelihood (MML) Estimator A Particular Case: The MCRB as a Bound on the Mean Square Error (MSE) The Constrained MCRB: CMCRB The MCRB for the intrinsic parameter vector Existence of ξ0 MS-unbiasedness and MCRB in ξ0 The constrained MCRB (CMCRB) Two Illustrative Examples The MCRB for the Estimation of the Scatter Matrix in the Family of CES Distributions Misspecified Estimation of the Scatter Matrix With Perfectly Known Extra Parameters Case Study 1. Assumed pdf: complex Normal; true pdf: t-student. Case Study 2. Assumed pdf: complex Normal, true pdf: Generalized Gaussian Case Study 3. Assumed pdf: Generalized Gaussian; true pdf: t-student Misspecified Joint Estimation of the Scatter Matrix and of the Extra Parameters Derivation of the constrained MML (CMML) estimator The CMCRB for the joint estimation of the scatter matrix and the power Evaluation of the matrix Aθ0 Evaluation of the matrix Bθ0 Evaluation of the matrix U Performance analysis Hypothesis Testing Problem for Target Detection The ANMF Detector Detection Performance Conclusions A Generalization of the Slepian Formula Under Misspecification A Generalization of the Bangs Formula Under Misspecification Compact Expression for the MCRB in the CES Family Compact Expression for the Matrix Bθ Compact Expression for the Matrix Aθ Compact Expression for the MCRB, MCRB(θ)=M-1Aθ-1Bθ.Aθ-1 (With R=0) References Multistatic radar systems Introduction Characteristics of Multistatic Radar Multistatic Radar Technology Enablers Signal Processing in Multistatic Radar Target Detection Target Resolution Target Localization Synchronization Considerations for Multistatic Radar System Case Study: NetRAD/NeXtRAD NetRAD NeXtRAD Calibration of Multistatic Polarmetric Radar Corner Reflectors FEKO Simulation Conclusions References Sparsity-based radar technique Introduction Temporal Sparsity Sparse Sampling in Range Sparse Sampling in Range and Doppler Spectral Sparsity Recovery of Missing or Corrupted Spectral Information Sub- or Co-prime Sampling in the Spectral Domain Spatial Sparsity Direction-of-Arrival (DOA) DOA with a linear array DOA with a 2D array 3D-SAR Experimental results Group Sparsity Group Model Example: SIMO Radar Network Example: MIMO Radar Network Example: SFN Radar Signal model Verification Conclusion References Further Reading Millimeter-wave integrated radar systems and techniques Integrated Radar: Trends and Challenges System Design Challenges: Size and Cost Single Chip RF System Antenna Systems Interference Challenges Automotive Radar: Trends and Standardization Efforts Channel Modeling for Millimeter-Wave Radar Propagation Properties in Millimeter-Wave Millimeter-Wave Radar Equation Ray Tracing for Millimeter-Wave Radar Clutter in Millimeter-Wave CMOS Radar Waveform and Signal Processing Time-Bandwidth Product and Radar Resolution Linear FM and FMCW Radar Stepped Frequency Radar Pseudo-Random Stepped Frequency Radar Processing a PRSF Waveform Waveform repetition for M-times Adaptive Radar and Computationally Light Processing Techniques Detection of significant Doppler frequencies Robust range-Doppler estimation Intermediate Frequency Processing Technique Stochastic Geometry Technique for Modeling Automotive Consumer Radars Poisson Point Process Model Lattice Model Interference Analysis Interference Statistics Performance Analysis and Optimization Performance Limitations CMOS Technology Limitations Information Theory Limitations Acknowledgments References Signal processing for massive MIMO communications Introduction Overview of Multiantenna Systems: Path to Massive MIMO Point-to-Point MIMO Toward Massive MIMO MU-MIMO UL (reverse link) DL (forward link) Massive MIMO Precoding Basic Precoding Schemes Constant Envelop Precoding Signal Detection Power Control Channel Estimation and Pilot Contamination Channel Estimation Pilot Contamination Mitigating pilot contamination effects Future Research Challenges References Recent advances in network beamforming Introduction End-to-End Channel Modeling One-Way Network Beamforming Networks With Frequency-Flat Channels Single-user networks SNR-maximization with perfect CSI SNR-per-unit-power maximization Partial CSI MSE-minimization and received signal power maximization Multi-user networks Orthogonal user channels With user interference and perfect CSI With user interference and partial CSI Robust designs against CSI errors Networks With Frequency-Selective Channels Single-user networks Multi-user networks Two-Way Network Beamforming Synchronous Networks Total power minimization Max-min SNR approach Sum-rate maximization Individual power constraints TDBC versus MABC Asynchronous Networks End-to-end channel model Multi-carrier equalization Max-min SNR fair design approach Sum-rate maximization approach Single-carrier post-channel equalization Total MSE minimization Sum-rate maximization Total power minimization Single-carrier pre-channel equalization Joint pre-channel and post-channel equalization Networks With Frequency-Selective Transceiver-Relay Links OFDM-based channel equalization Filter-and-forward relaying Miscellaneous Results Numerical Examples One-Way Network Beamforming Two-Way Network Beamforming Summary References Transmit beamforming for simultaneous wireless information and power transfer Introduction Practical SWIPT Receiver Multiantenna SWIPT Joint Information and Energy Beamforming Design for SWIPT Beamforming Design for SWIPT System With Separate IRs and ERs System model Problem formulation Optimal solution via SDR Numerical examples Secrecy Beamforming Design for SWIPT System model Problem formulation Optimal beamforming solution Numerical results Beamforming Design for SWIPT System With Co-Located IRs and ERs System model Problem formulation Optimal solution Numerical results Extensions Multipoint-to-Multipoint SWIPT Wireless Powered Communication Network CSI Acquisition at Transmitter Conclusion References Sparse methods for direction-of-arrival estimation Introduction Data Model Data Model The Role of Array Geometry Parameter Identifiability Sparse Representation and DOA Estimation Sparse Representation and Compressed Sensing Problem formulation Convex relaxation q optimization Maximum likelihood estimation (MLE) Sparse Representation and DOA Estimation: The Link and the Gap On-Grid Sparse Methods Data Model 2,0 optimization Convex Relaxation 2,1 optimization Dimensionality reduction via 2,1-SVD Another dimensionality reduction technique 2,q optimization Sparse Iterative Covariance-Based Estimation (SPICE) Generalized least squares SPICE Maximum Likelihood Estimation Remarks on Grid Selection Off-Grid Sparse Methods Fixed Grid Data model 1 optimization Sparse Bayesian learning Dynamic Grid Data model Algorithms Gridless Sparse Methods Data Model Vandermonde Decomposition of Toeplitz Covariance Matrices The Single Snapshot Case A general framework for deterministic methods Atomic 0 norm Atomic norm Hankel-based nuclear norm Connection between ANM and EMaC Covariance fitting method: Gridless SPICE (GLS) Connection between ANM and GLS The Multiple Snapshot Case: Covariance Fitting Methods Gridless SPICE (GLS) SMV-based atomic norm minimization (ANM-SMV) Nuclear norm minimization followed by MUSIC (NNM-MUSIC) Comparison of GLS, ANM-SMV, and NNM-MUSIC The Multiple Snapshot Case: Deterministic Methods A general framework Atomic 0 norm Atomic norm Hankel-based nuclear norm Reweighted Atomic Norm Minimization A smooth surrogate for ZA,0 A locally convergent iterative algorithm Interpretation as RAM Connections Between ANM and GLS The case of L < M The case of L M Computational Issues and Solutions Dimensionality reduction Alternating direction method of multipliers (ADMM) Future Research Challenges Conclusions References Beamforming techniques using microphone arrays Introduction Problem Formulation Narrowband Beamforming Wideband Beamforming Basic Approaches in Wideband Beamforming Superdirective Beamformer Linearly Constrained Minimum Variance (LCMV)-Based Adaptive Beamforming Techniques Practical Considerations in Covariance Matrix Estimation in LCMV-Based Beamformers Postfilter by PSD Estimation in Beamspace Problem Setup Beamforming and Its Output PSD PSD Estimation in Beamspace Postfiltering for Source Separation Conclusions References Index A B C D E F G H I J K L M N O P Q R S T U V W Z