基于Bessel先验快速稀疏贝叶斯学习的互质阵列DOA估计

冯明月; 何明浩; 陈昌孝; 韩俊

doi:10.11999/JEIT170951

基于Bessel先验快速稀疏贝叶斯学习的互质阵列DOA估计

doi: 10.11999/JEIT170951

(空军预警学院武汉 430019)

基金项目:

国家自然科学基金(61703430)，湖北省自然科学基金(2016CFB288)

详细信息

作者简介:
冯明月：男，1988年生，博士生，研究方向为电子对抗信息处理. 何明浩：男，1963年生，教授，博士生导师，研究方向为电子对抗信息处理. 陈昌孝：男，1982年生，博士，研究方向为电子对抗信息处理. 韩俊：男，1983年生，博士，研究方向为电子对抗信息处理、雷达信号处理.

中图分类号: TN911.7
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出版历程
- 收稿日期: 2017-10-16
- 修回日期: 2018-01-16
- 刊出日期: 2018-07-19

DOA Estimation for Co-prime Array Based on Fast Sparse Bayesian Learning Using Bessel Priors

FENG Mingyue HE Minghao CHEN Changxiao HAN Jun

Funds:

The National Natural Science Foundation of China (61703430), The Natural Science Foundation of Hubei Province (2016CFB288)

摘要

摘要: 为提高低采样点条件下互质阵列DOA估计精度，该文提出基于Bessel先验快速稀疏贝叶斯学习算法。该方法针对互质阵列输出的多采样点复数数据，首先构建了基于Bessel先验的多量测分层模型；其次推导了模型所涉超参数的对数似然函数，根据最大似然估计准则得到了超参数的迭代公式；最后提出了快速实现方案，提高了运算效率。仿真结果表明，该方法不依赖先验信息，在低采样点条件下具有更高的DOA估计精度和分辨率，能够对相干信号进行高精度DOA估计，并具有较高的运算效率。此外，该文探究了虚拟阵列扩展与互质阵列测向自由度扩展间的关联，为后续阵列误差条件下互质阵列DOA研究估计提供参考。
- 互质阵列 /
- DOA估计 /
- 稀疏贝叶斯学习 /
- 贝塞尔先验 /
- 低采样点
Abstract: In order to improve DOA estimation accuracy of co-prime array while the number of snapshots is small, a novel fast Sparse Bayesian Learning (SBL) algorithm using Bessel priors is proposed. Focusing on the multi-snapshots complex output data of coprime array, a multiple measurement vectors hierarchical model based on Bessel priors is firstly built. Then the log-likelihood function of model hyperparameters is derived, and the iterative formulas of hyperparameters are derived based on the criterion of maximum likelihood estimation. Finally, a fast implementation scheme is developed in order to improve the computation efficiency. Simulation experiments show that the proposed algorithm is independent on prior information. Under the condition of small number of snapshots, higher DOA estimation accuracy and resolution of uncorrelated and correlated signals can be achieved with proper computational efficiency. Further more, the necessity between virtual array extension and DOA estimation freedom of co-prime array is explored, which provides reference to DOA estimation for co-prime array under array perturbation conditions.
- Co-prime array、Direction Of Arrival (DOA) estimation、Sparse Bayesian Learning (SBL)、Bessel priors、Small number of snapshots /

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参考文献(19)

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