基于稀疏贝叶斯学习的低空测角算法

张永顺; 葛启超; 丁姗姗; 郭艺夺

doi:10.11999/JEIT151319

基于稀疏贝叶斯学习的低空测角算法

doi: 10.11999/JEIT151319

2.
(空军工程大学防空反导学院西安 710051) ②(信息感知技术协同创新中心西安 710077)

基金项目:

国家自然科学基金(61372033, 61501501)

计量
- 文章访问数: 1394
- HTML全文浏览量: 112
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- 被引次数: 0
出版历程
- 收稿日期: 2015-11-25
- 修回日期: 2016-04-18
- 刊出日期: 2016-09-19

Low-angle Estimation Method via Sparse Bayesian Learning

2.
(Air and Missile Defense College, Air Force Engineering University, Xi&rsquo

Funds:

The National Natural Science Fouudation of China (61372033, 61501501)

摘要

摘要: 为解决米波雷达低空测角的精度问题，该文结合稀疏贝叶斯学习方法，利用相邻快拍稀疏结构的相似性，将多观测向量模型通过Kronecker积变换成具有块稀疏结构的单观测向量模型，同时通过矩阵变换解决了贝叶斯准则在复数域中的应用。通过稀疏贝叶斯学习的不断迭代恢复出了信号在感知矩阵下的系数矩阵，得到了信源的角度信息。仿真实验验证了该方法相对于广义MUSIC和M-FOCUSS算法具有更好的性能，并且分析了快拍数变化对算法性能的影响。
- 米波雷达 /
- 多径 /
- 压缩感知 /
- 稀疏贝叶斯学习 /
- 多观测向量
Abstract: In order to improve the accuracy of low-angle estimation in meter-wave radars, combined with sparse Bayesian learning, this paper makes use of the Kronecker product and the similarity of the sparse structure between adjacent snapshots to transform the multiple measurement vector model into a single measurement vector model. The angle of the source is obtained by the coefficient matrix of the sensing matrix related to signal and the coefficient matrix is recovered by the continuous iteration in sparse Bayesian learning. Simulation experiments show that the proposed method has better performance than the generalized MUSIC algorithm and M-FOCUSS algorithm, the influence on algorithm performance caused by the snapshot change is obtained.
- Meter-wave radars /
- Multipath /
- Compressive sensing /
- Sparse Bayesian learning /
- Multiple measurement vector

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参考文献(27)

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