基于具有时序结构的稀疏贝叶斯学习的水声目标DOA估计研究

doi:10.3969/j.issn.0372-2112.2016.03.030

电子学报 ›› 2016, Vol. 44 ›› Issue (3): 693-698.DOI: 10.3969/j.issn.0372-2112.2016.03.030

基于具有时序结构的稀疏贝叶斯学习的水声目标DOA估计研究

王彪, 朱志慧, 戴跃伟

江苏科技大学电子信息学院, 江苏镇江 212003

收稿日期:2014-04-10 修回日期:2014-08-29 出版日期:2016-03-25
- 作者简介:
- 王彪男,1980年3月出生于甘肃张掖,毕业于中国科学院声学所获博士学位,现在江苏科技大学工作,副教授,硕士生导师. 研究方向是水声阵列信号处理及水声通信技术. E-mail:mail-wb@163.com;朱志慧 女,1991年1月出生于安徽池州,现在江苏科技大学攻读硕士学位.主要研究方向为水声阵列信号处理. E-mail:mail_zzh33@163.com;戴跃伟 男,1962年10月出生于江苏镇江,毕业于南京理工大学获博士学位,现在江苏科技大学工作,教授,博士生导师. 研究领域为信息处理、系统工程等. E-mail:daiywei@163.com
- 基金资助:
- 国家自然科学基金 (No.11204109,No.61401180,No.11574120); 江苏省高校自然科学基金 (No.12KJB510003,No.13KJB510007); 江苏省高校优势学科建设工程; 江苏科技大学深蓝工程青年学者计划资助课题; 江苏省"青蓝工程"资助课题

Direction of Arrival Estimation Research for Underwater Acoustic Target Based on Sparse Bayesian Learning with Temporally Correlated Source Vectors

WANG Biao, ZHU Zhi-hui, DAI Yue-wei

School of Electronic and Information, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China

Received:2014-04-10 Revised:2014-08-29 Online:2016-03-25 Published:2016-03-25
- Supported by:
- National Natural Science Foundation of China (No.11204109, No.61401180, No.11574120); Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No.12KJB510003, No.13KJB510007); Advantageous Discipline Construction Project of Colleges and Universities in Jiangsu Province; Supported by the Shenlan Young Scholars Funding Program of Jiangsu University of Science and Technology; Funded by Blue Project in Jiangsu Province

摘要/Abstract

摘要：

现有的基于CS-MMV(Compressed Sensing-Multiple Measurement Vectors)模型的DOA估计一般都假定信号源为独立同分布(i.i.d),算法建立在信号的空间结构上进行分析,而当处理具有时序结构的源信号时表现出性能和鲁棒性差的问题,为此该文提出一种具有时序结构的稀疏贝叶斯学习的DOA算法,该方法通过建立一阶自回归过程(AR)来描述具有时序结构的水声信号,将信号源的时间结构特性充分应用到DOA估计模型中,然后采用针对多测量矢量的稀疏贝叶斯学习(Muti-vectors Sparse Bayesian Learning)算法重构信号空间谱,建立多重测量向量中恢复未知稀疏源的信号的CS(Compressed Sensing)模型,最终完成DOA估计.仿真结果表明该方法相对于传统的算法具有更高的空间分辨率和估计精度的特点,且抗干扰能力强.

关键词: CS-MMV模型, DOA估计, 时序结构, 稀疏贝叶斯学习

Abstract:

Assuming independently but identically distributed sources,most existing DOA algorithms based on the CS-MMV model are analyzed according to the spatial structure of the signals. The temporal correlation between the sources,however,results in poor performance and robustness. To overcome this problem,we propose a DOA estimation algorithm based on Sparse Bayesian Learning (SBL) with temporally correlated source vectors. In this method,an underwater acoustic source is regarded as a first-order autoregressive process,with time structure characteristics being applied to DOA estimation model. After that,the multi-vector SBL algorithm is used to reconstruct the signal spatial spectrum. Then the CS-MMV model of the unknown sparse vector signal sources is established to estimate the DOA. Through simulation,it shows that the proposed algorithm provides a higher spatial resolution and estimation accuracy in comparison to many other current algorithms.

Key words: CS-MMV model, DOA estimation, temporally structure, sparse bayesian learning

王彪, 朱志慧, 戴跃伟. 基于具有时序结构的稀疏贝叶斯学习的水声目标DOA估计研究[J]. 电子学报, 2016, 44(3): 693-698.

WANG Biao, ZHU Zhi-hui, DAI Yue-wei . Direction of Arrival Estimation Research for Underwater Acoustic Target Based on Sparse Bayesian Learning with Temporally Correlated Source Vectors[J]. Acta Electronica Sinica, 2016, 44(3): 693-698.

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基于具有时序结构的稀疏贝叶斯学习的水声目标DOA估计研究

Direction of Arrival Estimation Research for Underwater Acoustic Target Based on Sparse Bayesian Learning with Temporally Correlated Source Vectors

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