阵列互耦情况下基于稀疏贝叶斯学习的离网格DOA估计

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摘要针对实际声呐基阵水听器阵元间存在互耦导致阵列波达方向(Direction of arrival, DOA)估计性能下降的问题，提出了一种阵列不确定互耦情况下的波达方向估计方法。首先，基于稀疏贝叶斯学习(Sparse Bayesian Learning, SBL)模型，将空间域离散化为均匀的网格，并且引入离网格误差，针对阵元互耦，引入互耦系数向量；其次，确定离网格误差和互耦系数向量的先验分布；最后，使用贝叶斯学习的期望最大化算法，对未知参数进行迭代更新，得到目标空间谱。仿真结果表明，所提方法在阵元未知互耦较大情况下估计精度较高，多目标分辨能力较强。
关键词：波达方向估计；稀疏贝叶斯学习；互耦；离网格

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	王绪虎1
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	白浩东1
	张群飞2
	田雨1

关键词 ：波达方向估计, 稀疏贝叶斯学习, 互耦, 离网格

Abstract：When mutual coupling exists in the sonar array hydrophone array element, the performance of direction of arrival (DOA) estimation will reduced. We proposed a method of direction of arrival estimation to alleviate such problem. Firstly, based on the sparse Bayesian learning (SBL)model, the spatial domain was discretized into a uniform grid, and the off-grid error was introduced. And for solving the problem of mutual coupling of the array elements， the mutual coupling coefficient vector was introduced. Secondly, the prior distribution of off-grid error and mutual coupling coefficient vector are were determined. Finally, Using the expectation maximization algorithm, the unknown parameters iteratively were updated, and spatial spectrum of target signal was obtained. The simulation results show that the proposed method has higher estimation accuracy and stronger multi-target resolution, even when the problem of unknown mutual coupling of array elements.
key words：DOA estimation; SBL; mutual coupling; off-grid

Key words： DOA estimation SBL mutual coupling off-grid

收稿日期: 2021-09-06 出版日期: 2022-09-15

引用本文:

王绪虎1，2，白浩东1，张群飞2，田雨1. 阵列互耦情况下基于稀疏贝叶斯学习的离网格DOA估计[J]. 振动与冲击, 2022, 41(17): 303-312.
WANG Xuhu1,2, BAI Haodong1, ZHANG Qunfei2, TIAN Yu1. Off-grid DOA estimation based on sparse Bayesian learning under interaction among array elements. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(17): 303-312.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I17/303

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