Sparse Sensing and Sparsity Sensed in Multi-sensor Array Applications

✍ Scribed by Xiangrong Wang, Xianghua Wang, Weitong Zhai, Kaiquan Cai

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 387
• Category: Library

✦ Synopsis

The book focuses on sparse multi-sensor array systems and design approaches. Both principles and engineering practice have been addressed, with more weight placed on algorithm development. This is achieved by providing an in-depth study on sparse sensing for several major multi-sensor array applications such as beam-pattern synthesis, adaptive beamforming, target detection, arrival angle estimation, and dual-functional radar communications. Sparsity sensed in multi-sensor arrays refers to the sparse property of the spatial spectrum sensed. The exploitation of the sparsity in the sensed can significantly enhance the performance of signal processing systems. The comprehensive and systematic treatment of theory and practice in different array applications is one of the major features of the book, which is particularly suited for readers who are interested to learn practical solutions in array signal processing. The book benefits researchers, engineers, and graduate students in the fields of signal processing, electrical engineering, telecommunications, etc.

✦ Table of Contents

Preface
Contents
Acronyms
Abbreviations
Symbols
Part I Foundation
1 Array Processing Fundamentals
1.1 Arrays and Spatial Filters
1.2 Beampattern Characteristics
1.3 Synthesis of Antenna Arrays
1.3.1 Synthesis of Uniform Arrays
1.3.2 Synthesis of Non-uniform Arrays
1.4 Multi-input Multi-output Array
1.4.1 Introduction of MIMO Array
1.4.2 Colocated MIMO Array
1.5 Summary
2 Multi-sensor Array Applications
2.1 Adaptive Beamformers
2.2 Adaptive Sidelobe Canceller
2.2.1 Mathematical Model
2.2.2 Beamforming Weight Design for SLC
2.3 Direction of Arrival Estimation
2.3.1 CRB for DOA Estimation
2.3.2 Maximum Likelihood
2.3.3 MUSIC
2.3.4 Root-MUSIC
2.3.5 ESPRIT
2.4 Target Detection
2.5 Summary
Part II Sparse Sensing via Antenna Selection
3 Sparse Sensing for Determininic Beamforming
3.1 Introduction
3.2 Sparse Array Beampattern Synthesis
3.2.1 Beampattern Synthesis Algorithms
3.2.2 Simulations
3.3 Sparse Quiescent Beamformer Design with MaxSNR
3.3.1 Problem Formulation
3.3.2 Unconstrained Sparse MSNR Beamformer Design
3.3.3 Unconstrained Sparse Quiescent Beamformer Design
3.3.4 Sparse Quiescent Beamformer Design with MSNR and Controlled Sidelobes
3.3.5 Simulations
3.4 Summary
4 Sparse Sensing for Adaptive Beamforming
4.1 Introduction
4.2 Reconfigurable Sparse Arrays in Single-Source Case
4.2.1 Spatial Correlation Coefficient
4.2.2 Antenna Selection for Adaptive Beamforming
4.2.3 Simulations
4.3 Reconfigurable Sparse Arrays in Multi-source Case
4.3.1 Problem Formulation
4.3.2 Performance Analysis of MVDR Beamformer
4.3.3 Spatial Separation Between Two Subspaces
4.3.4 Sparse Array Design by Antenna Selection
4.3.5 Simulations
4.4 Summary
5 Cognitive-Driven Optimization of Sparse Sensing
5.1 Introduction
5.2 Adaptive Beamformer Design by Regularized Complementary Antenna Switching
5.2.1 Spatial Filtering Techniques
5.2.2 Deterministic Complementary Sparse Array Design
5.2.3 Regularized Adaptive Sparse Array Design
5.2.4 Simulations
5.3 Cognitive Sparse Beamformer Design via Regularized Switching Network
5.3.1 Sparse Adaptive Beamforming
5.3.2 Quiescent Beamformer Initialization
5.3.3 Cognitive Sparse Beamformer Design
5.3.4 Numerical Analysis
5.4 Summary
6 Sparse Sensing for MIMO Array Radar
6.1 Introduction
6.2 Sparse MIMO Transceiver Design for MaxSINR
6.2.1 Problem Formulation
6.2.2 Sparse Array Transceiver Design
6.2.3 Simulations
6.3 Cognitive-Driven Optimization of Sparse MIMO Beamforming
6.3.1 Full Covariance Construction
6.3.2 Optimal Transceiver Design
6.3.3 New Transceiver Reconfiguration
6.3.4 Simulations
6.4 Summary
7 Sparse Sensing for Target Detection
7.1 Introduction
7.2 Definition of Spatial Spectral Correlation Coefficient
7.2.1 Signal Model
7.2.2 Definition of Spatial Spectral Correlation Coefficient
7.3 Thinned STAP via Antenna-Pulse Selection
7.3.1 Iterative Min-Max Algorithm
7.3.2 D.C. Programming
7.3.3 Modified Correlation Measurement
7.3.4 Simulations
7.3.5 Experimental Results
7.4 Summary
8 Sparse Sensing for Dual-Functional Radar Communications
8.1 Introduction
8.2 Optimum Sparse Array Design for DFRC
8.2.1 System Configuration and Signal Model
8.2.2 Design of Common Array with Single Beamformer
8.2.3 Design of Common Array with Multiple Beamformers
8.2.4 Design of Intertwined Subarrays with Shared Aperture
8.2.5 Simulations
8.3 Sparse Array Reconfiguration for Spatial Index Modulation
8.3.1 System Configuration and Signal Model
8.3.2 Antenna Selection Based Spatial Index Modulation
8.3.3 Combined Antenna Selection and Waveform Permutation
8.3.4 Regularized Selection Based Spatial Index Modulation
8.3.5 Simulations
8.4 Summary
Part III Sparsity Sensed Using Sparse Arrays
9 Sparsity Sensed with Thinned Antenna Array
9.1 Introduction
9.2 Array Thinning for DOA Estimation in Single-Signal Case
9.2.1 Mathematical Model
9.2.2 Optimum PSL Constrained Isotropic Subarray
9.2.3 Optimum Directional Subarray
9.2.4 Simulations
9.3 Array Thinning for DOA Estimation in Multi-Signal Case
9.3.1 Mathematical Model
9.3.2 Sparse Transceiver Design for Enhanced DOA Estimation
9.3.3 Simulations
9.4 Summary
10 Sparsity Sensed for Enhanced DOA Estimation
10.1 Introduction
10.2 DOA Estimation Using Fully Augmentable Arrays
10.2.1 DOA Estimation Based on Coarray Using MOP
10.2.2 Simulations
10.3 DOA Estimation Using Partially Augmentable Arrays
10.3.1 DOA Estimation Based on Difference Coarray
10.3.2 SMV-BCS Based on Covariance Vectorization
10.3.3 Simulations
10.4 Summary
References
Appendix A Linear and Matrix Algebranning
A.1 Definitions
A.2 Special Matrices
A.3 Matrix Manipulation and Formulas
A.4 Theorems
A.5 Eigendecomposition of Matrices
A.6 Inequalities
Appendix B Random Processes and Power Spectrum Estimation
B.1 Random Variables and Vectors
B.1.1 Random Variables
B.1.2 Independence and Correlation
B.1.3 Random Vectors
B.2 Fundamental PDF and Properties
B.3 Random Processes
B.3.1 Definition and Examples
B.3.2 Time Averages
B.3.3 Power Density Spectrum
Appendix References