王琦森^{1, 2},
余华^{3, 2, 1},
李杰^3,,,
董超^{2, 4},
季飞^{3, 1},
陈焱琨^{2, 4}
1.华南理工大学土木与交通学院 广州 510640
2.自然资源部海洋环境探测技术与应用重点实验室 广州 510300
3.华南理工大学电子与信息学院 广州 510640
4.国家海洋局南海调查技术中心 广州 510300
基金项目:国家自然科学基金(U1809211, 61771202, 61971198)，广东省海洋经济发展专项资金重点项目(粤自然资合[2020]009号)，广东省基础与应用基础研究基金(2019A151501104)，自然资源部海洋环境探测技术与应用重点实验室开放基金课题(MESTA-2020-A005)

详细信息

作者简介:王琦森：男，1996年生，博士生，研究方向为水声信号处理
余华：男，1973年生，教授，研究方向为无线通信与网络、水声通信网络、水声信号处理等
李杰：男，1984 年生，副研究员，研究方向为阵列信号处理、水声通信等
董超：男，1982年生，副研究员，研究方向为海洋无人智能装备
季飞：女，1970年生，教授，研究方向为无线通信与网络、水声通信等
陈焱琨：女，1982年生，工程师，研究方向为水声信号处理

通讯作者:李杰　eejli@scut.edu.cn

¹⁾算法matlab代码在链接：https://pan.baidu.com/s/1JkWTe1JxPSszPpGAik6Fmg 提取码：MSBL
中图分类号:TN911.72

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出版历程

收稿日期:2020-08-03
修回日期:2021-01-25
网络出版日期:2021-02-04
刊出日期:2021-03-22

Sparse Bayesian Learning Based Algorithm for DOA Estimation of Closely Spaced Signals

Qisen WANG^{1, 2},
Hua YU^{3, 2, 1},
Jie LI^3,,,
Chao DONG^{2, 4},
Fei JI^{3, 1},
Yankun CHEN^{2, 4}
1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China
2. Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China
3. School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
4. South China Sea Marine Survey and Technology Center, Ministry of Natural Resources, Guangzhou 510300, China
Funds:The National Natural Science Foundation of China (U1809211, 61771202, 61971198), The Key Program of Marine Economy Development Special Foundation of Guangdong Province (GDNRC [2020]009), Guangdong Basic and Applied Basic Research Foundation (2019A151501104), Open Funding Project of Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources (MESTA-2020-A005)


摘要
摘要:离格(off-grid)波达方向(DOA)估计解决的是实际DOA和假设网格点的失配问题。对于空间紧邻信号的DOA，稀疏的网格点会导致精度和分辨率的下降，密集的网格点虽然可以提高估计精度却显著增加计算负担。针对此问题，该文提出基于稀疏贝叶斯学习(SBL)的空间紧邻信号DOA估计算法，主要包括3个步骤。首先，通过最大化阵列输出的边缘似然函数，推导了信号在拉普拉斯先验下的新不动点迭代方法，进行超参数的预估计，相比其他经典SBL算法提高了收敛速度；其次，利用新网格插值方法优化网格点集，并二次估计噪声方差和信号功率以分辨空间紧邻信号的DOA；最后，推导了似然函数关于角度的最大化公式以改进离格DOA搜索。仿真表明该算法比其他经典SBL类算法对空间紧邻信号的DOA具有更高的精度和分辨率，同时有计算效率的提升。
关键词:波达方向估计/
离格/
稀疏贝叶斯学习/
空间紧邻
Abstract:Off-grid Direction Of Arrival (DOA) estimation aims to handle the mismatch between the actual DOA and the presumed grid points. For DOAs of closely spaced signals, sparse grid points leads to degradation of accuracy and resolution, although dense grid points can improve the estimation accuracy, it significantly increases the computational burden. To solve this problem, this paper proposes a Sparse Bayesian Learning (SBL) based algorithm for DOA estimation of closely spaced signals, which consists of three steps. Firstly, a novel fixed point iterative method for signal of Laplace priori is derived to pre-estimate the hyper-parameters by maximizing the array’s marginal likelihood function, which results in faster convergence speed compared to other classical SBL algorithms. Secondly, a new grid interpolation method is implemented to optimize a set of grid points, and signal power and noise variance are estimated again to resolve closely spaced DOAs. Finally, an expression of maximum likelihood function with respect to angle is derived to improve the search of the off-grid DOA. Simulation results show that the proposed algorithm has higher accuracy and resolution for closely spaced DOAs with higher computational efficiency compared with other classical algorithms based on SBL.
Key words:Direction Of Arrival estimation/
Off-grid/
Sparse Bayesian Learning (SBL)/
Closely spaced
注释:
1) ¹⁾算法matlab代码在链接：https://pan.baidu.com/s/1JkWTe1JxPSszPpGAik6Fmg 提取码：MSBL



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