综合孔径微波辐射计的射频干扰源空间角度稀疏贝叶斯估计方法

张娟; 庄乐慧; 李一楠; 李虹; 窦昊锋

doi:10.11999/JEIT231367

综合孔径微波辐射计的射频干扰源空间角度稀疏贝叶斯估计方法

doi: 10.11999/JEIT231367

1.
西安电子科技大学雷达信号处理全国重点实验室西安 710071
2.
中国空间技术研究院西安分院西安 710100

基金项目: 实验室稳定支持计划项目(HTKJ2022KL504015)

详细信息

作者简介:
张娟：女，博士，教授，研究方向为雷达系统建模与仿真、雷达信号检测与自适应信号处理技术

庄乐慧：女，硕士生，研究方向为综合孔径微波辐射计射频干扰检测与定位

李一楠：男，高级工程师，研究方向为被动微波遥感、综合孔径微波辐射计系统设计等

李虹：女，硕士生，研究方向为综合孔径辐射计射频干扰抑制处理

窦昊锋：男，博士后，研究方向为微波遥感、信号处理

通讯作者:
张娟　jzhang@xidian.edu.cn

中图分类号: TN911.73; TP79
计量
- 文章访问数: 32
- HTML全文浏览量: 20
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-11
- 修回日期: 2024-03-14
- 网络出版日期: 2024-03-29

A Method for Radio Frequency Interference Space Angle Sparse Bayesian Estimating in Synthetic Aperture Microwave Radiometer

1.
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
China Academy of Space Technology (Xi’an), Xi’an 710100, China

Funds: The Laboratory Stabilization Support Program Project (HTKJ2022KL504015)

摘要

摘要: 该文提出一种综合孔径微波辐射计射频干扰源(RFI)空间稀疏贝叶斯估计方法。首先建立了综合孔径微波辐射计可见度函数干涉测量模型，观测数据表示为综合孔径天线基线对相关导向矢量观测矩阵与视场亮温的乘积，由于相关导向矢量观测矩阵的正交性和RFI空间角度分布的稀疏性，亮温在基线对相关导向矢量观测矩阵正交基所构成的支撑域中的变换系数是稀疏的。该文在稀疏贝叶斯学习(SBL)框架下对亮温进行稀疏重构。该方法在无需稀疏度和正则化参数等先验信息前提下也能获得较高的重构性能。计算机仿真验证了该方法的有效性。
- 综合孔径微波辐射计 /
- 射频干扰源 /
- 稀疏贝叶斯 /
- 空间角度估计.
Abstract: A sparse Bayesian estimation for spatial Radio Frequency Interference (RFI) of synthetic aperture microwave radiometers is proposed in this paper. Firstly, an interferometry measurement model of the visibility function for synthetic aperture microwave radiometers is established. The observed data are expressed as the product of the observation matrix of the aperture synthesis antenna baseline correlation steering vector and the brightness temperature of the field of view. Due to the orthogonality of the observation matrix and the sparsity of the RFI spatial angle distribution, the transformation coefficients of brightness temperature in the support domain are sparse. Under the Sparse Bayesian Learning (SBL) framework, brightness temperature is sparsely reconstructed. Notably, this method can obtain high reconstruction performance without the prior information of sparsity and regularization parameters. The effectiveness of this method is verified through computer simulations.
- Synthetic aperture microwave radiometers /
- Radio Frequency Interference(RFI) /
- Sparse Bayesian /
- Spatial angle estimation.

HTML全文

图 1 陆地背景添加不同强度的RFI反演图像

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图 2 MUSIC算法和SBL算法定位结果图

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图 3 海陆交替背景添加不同强度的RFI反演图像

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图 4 MUSIC算法和SBL算法定位结果图

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图 5 海陆交替背景下蒙特卡洛实验结果

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图 6 18-Aug-2013 00:01:58获取的L1a数据反演图像

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图 7 SMOS添加400K RFI后定位结果

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图 8 SMOS背景下蒙特卡洛实验结果

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图 9 18-Aug-2013 00:28:37获取的L1a数据反演图像

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表 1 陆地背景定位结果

算法	400 K	1 000 K
MUSIC	(0.406 5,0.208 6)	(0.406 5,0.208 6)
SBL	(0.406 5,0.208 6)	(0.406 5,0.208 6)

下载: 导出CSV

表 2 海陆交替背景定位结果

算法	400 K	1 000 K
MUSIC	(0.361 3,0.260 8)	(0.383 9,0.195 6)
SBL	(0.406 5,0.208 6)	(0.406 5,0.208 6)

下载: 导出CSV

表 3 SMOS背景定位结果

算法	(0.4,0.2)	方位均方误差
MUSIC	(0.199 5,0.303 7)	0.353 2
SBL	(0.399 1,0.209 5)	0.009 5

下载: 导出CSV

表 4 SMOS数据定位结果

算法	1	2	3	4
MUSIC	(–0.0544, –0.3037)	(–0.0726, –0.3142)	(–0.0726, –0.2933)	(0.2358, –0.1362)
SBL	(–0.0363, –0.3561)	(0.2358, –0.1362)	(0.2358, –0.1571)	(0.5079, –0.0209)

下载: 导出CSV

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