Spatial spectrum estimation based on sparse Bayesian learning for towed linear array

被引：0

作者：

Yuan J. ^{[1
]}

Xiao H. ^{[2
]}

Cai Z. ^{[1
]}

Xi C. ^{[1
]}

机构：

[1] College of Electronics Engineering, Navel University of Engineering, Wuhan

[2] Department of Early-Warning Technology, Air Force Early Warning Academy, Wuhan

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2019年 / 41卷 / 06期

关键词：

Expectation maximization; Sparse Bayesian learning; Spatial spectrum estimation; Target port-starboard ambiguity; Towed linear array;

D O I：

10.3969/j.issn.1001-506X.2019.06.05

中图分类号：

O21 [概率论与数理统计];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：1202 / 1209

页数：7

共 24 条

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