基于虚拟共阵扩充的互质阵列欠定DOA估计方法

发布时间：2025-02-08 21:08

　　在传感器数量有限且传感器分布空间不足等有限资源的情况下,如何寻找比传感器更多的源(称为欠定DOA估计)非常重要。本论文的主要重点是在阵列处理中考虑欠定的观测模型,其中阵列接收的信号源的数量可以大于物理阵元的数量。显然,均匀线性阵列(ULA)中存在更高程度的冗余,应该将其解决为最小冗余。用于信号接收和空间谱估计的阵列设计中反复出现的问题是如何有益地部署稀疏阵列的阵元来最佳的采样空间频谱。因此,在确保最佳空间样本采样间隔的同时,如何寻找具有连续整数和最小冗余的阵元位置是一个挑战。本研究的重点在于合理的部署阵列,来检测比阵元数目更多的信号源,利用阵列结构来获得尽可能高的分辨率并增加可检测的信号源数量。为了使阵列配置具有成本效益,对于给定的成本,最大化阵列孔径尺寸同时放置最少传感器来实现更高分辨率是先决条件。本论文的主要贡献是从有限数量的阵元来增加阵列孔径长度,以达到尽可能高的分辨率,减少相关矩阵中的冗余元素。最小冗余阵列(MRA)能够实现最小冗余,但是这类阵列需要穷举搜索程序来确定阵元的位置。此外,没有系统的方法或指定的公式来设计具有未知阵元分布的MRA。共阵在欠定DOA估计的稀疏结构中起着关...

【文章页数】：228 页

【学位级别】：博士

【文章目录】：
摘要
ABSTRACT
Notation
Chapter 1 Introduction
    1.1 Background
    1.2 Literature Review
    1.3 Main Task
    1.4 Outline and Scope of this Thesis
Chapter 2 Array Model and Related Materials
    2.1 Background
    2.2 The Array Signal Model
    2.3 Autocorrelation and Spectral Estimation
    2.4 Parametric Estimation
    2.5 Spectral Estimation
    2.6 Sampling and Underdetermined Estimation
    2.7 Different Array Model
        2.7.1 Linear Arrays
        2.7.2 Planar Arrays
        2.7.3 Circular Arrays
        2.7.4 Coprime Arrays
    2.8 Different fields of view and frequency bands
        2.8.1 Far Field
        2.8.2 Near Field
        2.8.3 Narrow Band
        2.8.4 Wide Band
    2.9 Chapter Summary
Chapter 3 Popular Methods for DOA Estimation
    3.1 Background
    3.2 Classical Beamforming Method
    3.3 MUSIC Algorithm
    3.4 ESPRIT Algorithm
    3.5 Genetic Algorithm
    3.6 Particle swarm optimization (PSO) algorithm
    3.7 Sparse Bayesian Learning Method
    3.8 Chapter Summary
Chapter 4 Virtual Extension exploiting Difference and Sum
    4.1 Introduction
    4.2 Signal Model
    4.3 Proposed Methodology
        4.3.1 Extension of Vitual Arrays Exploiting Difference and Sum Co-array
        4.3.2 MUSIC based DOA estimation
    4.4 Simulation Results
        4.4.1 Case Ⅰ: RMSE for different SNR
        4.4.2 Case Ⅱ: RMSE for different number of Snapshots
        4.4.3 Case Ⅲ: RMSE for different number of Sources
    4.5 Chapter Summary
Chapter 5 Novel Array Structure using Translocation，Axes Rotation and Compression
    5.1 Introduction
    5.2 Signal Model
    5.3 Proposed Methodology
        5.3.1 Conventional Coprime Array Configuration
        5.3.2 Proposed Coprime Array Configuration
        5.3.3 The Difference Co-array of Proposed Method
        5.3.4 Interpolation with Iterative Power Factorization
        5.3.5 MUSIC based DOA estimation
    5.4 Simulation Results
        5.4.1 Case Ⅰ: RMSE for different SNR
        5.4.2 Case Ⅱ: RMSE for different number of Snapshots
        5.4.3 Case Ⅲ: RMSE for different number of Sources
        5.4.4 Number of Lags vs Number of Sensors
    5.5 Chapter Summary
Chapter 6 Novel Array Structure unifying Trio Subarray and FOD
    6.1 Introduction
    6.2 Signal Model
    6.3 Proposed Methodology
        6.3.1 Conventional Coprime Array Configuration
        6.3.2 Proposed Coprime Array Configuration
        6.3.3 The Fourth Order Difference Co-Array of Proposed Method
        6.3.4 Sparse Baysian Learning Based DOA Estimation
    6.4 Simulation Results
        6.4.1 Case Ⅰ: RSME for Different SNR
        6.4.2 Case II: RMSE for Different Number Of Snapshots
        6.4.3 Case Ⅲ: RMSE for Different Number of Sources
        6.4.4 Case Ⅳ: RMSE for Lower Angular DOA Estimation
        6.4.5 Number of Lags vs Number of Sensors
    6.5 Chapter Summary
Chapter 7 Novel Array Structure comprising Triplet Coprime Array
    7.1 Introduction
    7.2 Signal Model
    7.3 Proposed Methodology
        7.3.1 Proposed Coprime Array Configuration
        7.3.2 The Second Order Difference Co-Array of Proposed Method
        7.3.3 MUSIC DOA Estimation
        7.3.4 Sparse Baysian Learning Based DOA Estimation
        7.3.5 The Fourth Order Difference Co-Array of Proposed Method
        7.3.6 Sparse Baysian Learning Based DOA Estimation
    7.4 Simulation Results
        7.4.1 Case Ⅰ: RSME for Different SNR
        7.4.2 Case Ⅱ: RMSE for Different Number Of Snapshots
        7.4.3 Case Ⅲ: RMSE for Different Number of Sources
        7.4.4 Case Ⅳ: RMSE for Lower Angular DOA Estimation
        7.4.5 Number of Lags vs Number of Sensors
    7.5 Chapter Summary
Chapter 8 Conclusion
    8.1 Summary
        8.1.1 VECADS
        8.1.2 CATARCS
        8.1.3 VEFODCI
        8.1.4 TiCADD
    8.2 Future Works
Bibliography
Appendix A
Acknowledgements
Publications



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