Radar Array Design using Optimization Theory (Radar, Sonar and Navigation)

Cover
Contents
Foreword
Notation
About the editors
1 Machine learning-based antenna selection for sparse array reconfiguration
	1.1 Introduction
	1.2 Sparse array beamformer design via machine learning
		1.2.1 Mathematical model
		1.2.2 Optimization-based sparse array design
		1.2.3 Machine learning-based sparse array design
		1.2.4 Simulations
	1.3 CNN-based sparse array design for enhanced DOA estimation
		1.3.1 Mathematical model
		1.3.2 CNN for antenna selection
		1.3.3 Numerical results
	1.4 Conclusion
2 Beampattern synthesis via the constrained subarray layout optimization
	2.1 Introduction
	2.2 Array model
		2.2.1 Subarray layout model
		2.2.2 Thinned array beampattern
	2.3 Problem formulation
		2.3.1 Power beampattern constraints
		2.3.2 Subarray layout constraints
		2.3.3 Optimization problem
	2.4 Optimization method
		2.4.1 Update k+1
		2.4.2 Update yk+1
		2.4.3 Update tH, zk+1
		2.4.4 Update ek+1
	2.5 Simulation results
		2.5.1 Beampattern synthesis with single mainlobe
		2.5.2 Beampattern synthesis with multiple mainlobes
		2.5.3 Reliability analysis
	2.6 Conclusion
3 Reconfigurable array beampattern synthesis via conceptual sensor network modeling and computation
	3.1 Introduction
	3.2 Problem formulation
	3.3 Proposed method
		3.3.1 Sensor network modeling
		3.3.2 Sensor network computation
	3.4 Extension to uniform amplitude array synthesis
	3.5 Numerical examples
		3.5.1 Focused beam-shaped beam synthesis
		3.5.2 Shaped beam-shaped beam synthesis
		3.5.3 Multiple beam scanning
		3.5.4 Uniform amplitude array synthesis for beam scanning
		3.5.5 Practical performance of the proposed method simulated with Ansys HFSS
	3.6 Conclusion
4 Shaped power pattern synthesis with minimization of dynamic range ratio
	4.1 Introduction
	4.2 Problem formulation
	4.3 Proposed algorithm
		4.3.1 Computational complexity
		4.3.2 Convergence analysis
	4.4 Numerical examples
		4.4.1 Flat-top pattern synthesis
		4.4.2 Focused beampattern synthesis
		4.4.3 Flat-top beampattern synthesis with notching
		4.4.4 Multibeam synthesis with notching
		4.4.5 Power pattern synthesis for uniform rectangular array
		4.4.6 Power pattern synthesis for uniform circular array
	4.5 Conclusion
5 Array beampattern synthesis with shape constraints and excitation range control
	5.1 Background and introduction
	5.2 Previous methods based on convex optimization
	5.3 Problem formulation
	5.4 Beampattern shaping with excitation range control
		5.4.1 Step 1: update {rs(k+1),(k+1)}
		5.4.2 Step 2: update vm(k+1)
		5.4.3 Step 3: update w(k+1)
		5.4.4 Convergence and computational complexity
	5.5 Numerical examples
		5.5.1 Experiment 1: focused beampatterns
		5.5.2 Experiment 2: flat-top beampatterns
		5.5.3 Experiment 3: cosecant square beampatterns
		5.5.4 Experiment 4: extension to linear arbitrary arrays
		5.5.5 Experiment 5: performance evaluation of the proposed method via using Ansys HFSS software
		5.5.6 Experiment 6: 2-D planar array beampattern synthesis
		5.5.7 Experiment 7: array beampattern synthesis with null formation
	5.6 Conclusions
6 Pattern synthesis via array response control
	6.1 Introduction
	6.2 Array response control
		6.2.1 Adaptive array theory
		6.2.2 A2RC refa2rc
		6.2.3 MA2RC and M2A2RC 2017-2
		6.2.4 OPARC p1,p3
		6.2.5 WORD word
		6.2.6 C2-WORD 1994-3
		6.2.7 Robust C2-WORD 1994-3
		6.2.8 FARCOP farcop
	6.3 Simulations
		6.3.1 Nonuniform sidelobe synthesis for a large ULA
		6.3.2 Uniform sidelobe synthesis for a nonisotropic random array
	6.4 Conclusion
7 Wideband beampattern synthesis using single digital beamformer with integer time delay filters
	7.1 Introduction
	7.2 System model
	7.3 Problem formulation
		7.3.1 Design criteria of beamforming
		7.3.2 White noise gain constraint
		7.3.3 Mainlobe level constraint
		7.3.4 Optimization problem
	7.4 AO algorithm
		7.4.1 Step 1: update w(k + 1)
		7.4.2 Step 2: update yq,p( k + 1 ), ( k + 1 )
		7.4.3 Step 3: update y0( k + 1 )
		7.4.4 Performance analysis
	7.5 CA algorithm
		7.5.1 Approximate mainlobe level constraint
		7.5.2 Approximate white noise gain constraint
		7.5.3 Performance analysis
	7.6 Numerical results
		7.6.1 Structure rationality analysis
		7.6.2 Model validity analysis
		7.6.3 Algorithm performance comparison
	7.7 Conclusion
8 Hybrid beamforming design for dual-function radar-communication system
	8.1 Introduction
	8.2 HBF design for single-carrier DFRC system
		8.2.1 System model and problem formulation
		8.2.2 Proposed HBF algorithm for multi-carrier DFRC system
		8.2.3 Simulation results
	8.3 HBF design for multi-carrier DFRC system
		8.3.1 System model and problem formulation
		8.3.2 Proposed HBF algorithm for multi-carrier DFRC system
		8.3.3 HBF-based DOA estimation
		8.3.4 Simulation results
	8.4 Conclusion
9 Robust beamforming design for dual-function radar-communication system
	9.1 Introduction
	9.2 Signal model of MIMO-DFRC system
	9.3 SINR-constrained robust beamforming design
		9.3.1 Problem formulation
		9.3.2 SDP reformulation
		9.3.3 Solution of problem (9.18)
		9.3.4 Extensions of the design
	9.4 Outage-constrained robust beamforming design
		9.4.1 Problem formulation
		9.4.2 ADMM-based solution
	9.5 Simulation results
		9.5.1 Results of SINR-constrained design
		9.5.2 Results of outage-constrained design
	9.6 Conclusion
10 Optimization of sparse MIMO array for enhanced sensing
	10.1 Introduction
	10.2 Sparse MIMO transceiver design for enhanced DOA estimation
		10.2.1 Cramer–Rao bound of multi-source DOA estimation
		10.2.2 Sparse MIMO array transceiver design in the metric of CRB
		10.2.3 Simulations
	10.3 Sparse MIMO transceiver design for MaxSINR with known environmental information
		10.3.1 Problem formulation
		10.3.2 Sparse array transceiver design
		10.3.3 Simulation
	10.4 Conclusion
11 Transmit–receive beamforming for distributed phased-MIMO radar system
	11.1 Introduction
	11.2 Distributed phased-MIMO radar system model
		11.2.1 Transmit signal model
		11.2.2 Receive signal model
		11.2.3 Matched-filtering operation and receive beamforming
	11.3 Problem formulation and optimization algorithm
		11.3.1 Design of l(k)
		11.3.2 Design of wT(k)
		11.3.3 Design of wR(k)
		11.3.4 JSLRBIF algorithm
	11.4 Simulation results
		11.4.1 CD algorithm for solving subarray layout vector
		11.4.2 CA optimization approach
		11.4.3 Joint design of subarray layout and weighting coefficients
		11.4.4 Transmit beampattern and virtual beampattern ofdistributed phased-MIMO radar
	11.5 Conclusions
Index
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