阵列互耦情况下基于稀疏贝叶斯学习的离网格DOA估计  

Off-grid DOA estimation based on sparse Bayesian learning under interaction among array elements

作　　者：王绪虎 白浩东[1] 张群飞 田雨[1] WANG Xuhu;BAI Haodong;ZHANG Qunfei;TIAN Yu(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China;School of Marine Science and Technology,Northwestern Polytechnic University,Xi’an 710072,China)

机构地区：[1]青岛理工大学信息与控制工程学院,青岛266520 [2]西北工业大学航海学院,西安710072

出　　处：《振动与冲击》2022年第17期303-312,共10页Journal of Vibration and Shock

基　　金：国家自然科学基金重点项目(61531015);山东省自然科学基金面上项目(ZR2017MF024)。

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。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;确定离网格误差和互耦系数向量的先验分布;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。Here,aiming at the problem of interaction among hydrophone array elements of actual sonar array causing estimation performance droppingof the array’s direction of arrival(DOA),a DOA estimation method under uncertain interaction of array elementswas proposed.Firstly,based on the sparse Bayesian learning(SBL)model,a spatial domain was discretized into a uniform grid,and the off-grid error was introduced.For interactionamong array elements,the interaction coefficient vector was introduced.Secondly,prior distributions of off-grid error and interaction coefficient vector were determined.Finally,the expectation maximization algorithm of Bayesian learning was used to iteratively update unknown parameters,and obtain the target space spectrum.Simulation results showed that the proposed method can have higher estimation accuracy and stronger multi-target resolving ability under larger unknown interaction of array elements.

关 键 词：波达方向估计 稀疏贝叶斯学习 互耦 离网格 

分 类 号：TB56[交通运输工程—水声工程] TN911.7[理学—物理]