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

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