基于稀疏贝叶斯学习的多跳频信号DOA估计方法

郭英; 东润泽; 张坤峰; 眭萍; 杨银松

doi:10.11999/JEIT180435

基于稀疏贝叶斯学习的多跳频信号DOA估计方法

doi: 10.11999/JEIT180435

郭英^{1, 2, ,},
东润泽¹,
张坤峰¹,
眭萍¹,
杨银松¹

1.
空军工程大学信息与导航学院西安 710077
2.
通信网信息传输与分发技术重点实验室石家庄 050081

基金项目: 国家自然科学基金(61601500)

详细信息

作者简介:
郭英：女，1961年生，博士，教授，博士生导师，研究方向为通信信号处理、自适应信号处理等

东润泽：男，1995年生，硕士生，研究方向为跳频信号检测、参数估计

张坤峰：男，1989年生，博士生，研究方向为通信信号侦查处理、阵列信号处理

眭萍：女，1991年生，博士生，研究方向为信号指纹特征识别

杨银松：男，1994年生，硕士生，研究方向为通信信号处理、跳频信号网台分选

通讯作者:
郭英　yguo@163.com

中图分类号: TN911.7
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- 被引次数: 0
出版历程
- 收稿日期: 2018-05-08
- 修回日期: 2018-09-20
- 网络出版日期: 2018-10-23
- 刊出日期: 2019-03-01

Direction of Arrival Estimation for Multiple Frequency Hopping Signals Based on Sparse Bayesian Learning

1.
Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2.
Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Shijiazhuang 050081, China

Funds: The National Natural Science Foundation of China (61601500)

摘要

摘要:
针对多跳频信号空域参数估计问题，该文在稀疏贝叶斯学习(SBL)的基础上，利用跳频信号的空域稀疏性实现了波达方向(DOA)的估计。首先构造空域离散网格，将实际DOA与网格点之间的偏移量建模进离散网格中，建立多跳频信号均匀线阵接收数据模型；然后通过SBL理论得到行稀疏信号矩阵的后验概率分布，用超参数控制偏移量和信号矩阵的行稀疏程度；最后利用期望最大化(EM)算法对超参数进行迭代，得到信号矩阵的最大后验估计以完成DOA的估计。理论分析与仿真实验表明该方法具有良好的估计性能并能适应较少快拍数的情况。
- 信号处理 /
- 跳频 /
- 波达方向 /
- 稀疏贝叶斯学习
Abstract:
To solve the problem of spatial parameter estimation of multi-frequency hopping signals, the sparsity in spatial domain of frequency hopping signals is used to realize the Direction Of Arrival (DOA) estimation based on Sparse Bayesian Learning (SBL). First, the spatial discrete grid is constructed and the offset between the actual DOA and the grid points is modeled into it. The data model of the uniform linear array with multiple frequency hopping signals is established. Then the posterior probability distribution of the sparse signal matrix is obtained by the SBL theory, and the line sparsity of the signal matrix and the offset is controlled by the hyperparameters. Finally, The expectation maximization algorithm is used to iterate the hyper parameters, and the maximum posteriori estimation of the signal matrix is obtained to complete the DOA estimation. Theoretical analysis and simulation experiments show that this method has good estimation performance and can adapt to less snapshots.
- Signal processing /
- Frequency-hopping /
- Direction Of Arrival (DOA) /
- Sparse Bayesian Learning (SBL)

HTML全文

图 1 均匀线阵接收模型

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图 2 不同阵元数下算法性能与运行时间的比较

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图 3 不同网格间隔下算法性能与运行时间的比较

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图 4 各算法的空间谱比较

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图 5 不同快拍数下算法性能的比较

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表 1 不同快拍数下算法运行时间的比较(s)

快拍数 20 80

本文算法所用时间 0.3804 0.5915
稀疏重构算法所用时间 0.4066 0.3935
OGSBI算法所用时间 0.6454 0.8428

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