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基于稀疏贝叶斯学习的拖曳线列阵空间谱估计
引用本文:袁骏,肖卉,蔡志明,奚畅.基于稀疏贝叶斯学习的拖曳线列阵空间谱估计[J].系统工程与电子技术,2019,41(6):1202-1209.
作者姓名:袁骏  肖卉  蔡志明  奚畅
作者单位:1. 海军工程大学电子工程学院, 湖北 武汉 430033; 2. 空军预警学院预警技术系, 湖北 武汉 430019
基金项目:大学科研发展基金(425517K175)资助课题
摘    要:针对常规拖曳线列阵目标方位估计中存在的左右舷模糊问题,提出了联合多个时刻机动拖曳线列阵信号模型的稀疏贝叶斯学习空间谱重构估计方法。首先,建立了机动拖曳线列阵的阵元域信号超完备稀疏表示模型;然后,根据稀疏贝叶斯学习原理将目标的空间角度稀疏特性通过信号双层先验假设进行隐性描述;最后,对目标空间谱的变化过程采用隐马尔可夫模型进行描述,并将空间谱连续慢变的客观规律应用到目标信号超参数的概率密度计算中,构建基于多个时刻阵列信号模型的空间谱稀疏重构模型。计算机仿真研究和海试数据验证结果表明:所提方法在拖曳线列阵机动条件下,能够有效抑制固有的左右舷模糊,同时具有更好的重构精度,从而实现拖曳线列阵空间谱的优效估计。

关 键 词:拖曳线列阵  空间谱估计  稀疏贝叶斯学习  目标左右舷模糊  期望最大化

Spatial spectrum estimation based on sparse Bayesian learning for towed linear array
YUAN Jun,XIAO Hui,CAI Zhiming,XI Chang.Spatial spectrum estimation based on sparse Bayesian learning for towed linear array[J].System Engineering and Electronics,2019,41(6):1202-1209.
Authors:YUAN Jun  XIAO Hui  CAI Zhiming  XI Chang
Institution:1. College of Electronics Engineering, Navel University of Engineering, Wuhan 430033, China;; 2. Department of Early Warning Technology, Air Force Early Warning Academy, Wuhan 430019, China
Abstract:The port starboard ambiguity in the conventional single towed linear array sonar is one of the most deceiving obstacles which exists in the way of development of spatial spectrum estimation. This paper proposes a spatial spectrum reconstruction estimation method based on sparse Bayesian learning using multiple signal models of the maneuvering towed linear array. Firstly, the signal over complete sparse representation model of the maneuvering towed linear array is established. Then, based on the principle of sparse Bayesian learning, the sparse characteristics of target angle is described implicitly by hypothesis of hierarchical prior. Finally, the change of spatial spectrum is modeled using a hidden Markov model, the objective law of slowly varying of spatial spectrum is applied to the calculation of probability density of the signal hyperparameters and the sparse reconstruction model based on multiple array signal models is established. Simulation and sea trial results demonstrate that the proposed algorithm has evident advantages in ambiguity suppression ratio and accuracy of reconstruction and achieve superior spatial spectrum estimation for towed linear array.
Keywords:towed linear array  spatial spectrum estimation  sparse Bayesian learning  target port starboard ambiguity  expectation maximization
  
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