基于鲁棒主成分分析的多域联合杂波抑制算法

李相平; 王明泽; 但波; 李蔚; 马俊伟

doi:10.11999/JEIT210676

基于鲁棒主成分分析的多域联合杂波抑制算法

doi: 10.11999/JEIT210676

1.
海军航空大学岸防兵学院烟台 264001
2.
海军航空大学航空作战勤务学院烟台 264001
3.
海军航空大学航空保障专业兵训练基地青岛 266109

基金项目: 山东省自然科学基金(ZR2020MF090)

详细信息

作者简介:
李相平：男，1963年生，教授，研究方向为精确制导技术与信息通信

王明泽：男，1997年生，硕士生，研究方向为穿墙雷达成像处理技术

但波：男，1985年生，讲师，研究方向为精确制导技术与机器学习

李蔚：女，1990年生，讲师，研究方向为电子对抗技术

马俊伟：男，1989年生，助理工程师，研究方向为控制科学与工程

通讯作者:
王明泽　m18766634763_1@163.com

中图分类号: TN957.52
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- 被引次数: 7
出版历程
- 收稿日期: 2021-07-06
- 修回日期: 2021-10-28
- 网络出版日期: 2021-11-05
- 刊出日期: 2022-04-18

The Multi-domain Union Clutter Suppression Algorithm Based on Robust Principal Component Analysis

1.
Coast Guard College, Naval Aviation University, Yantai 264001, China
2.
Air Combat Service College, Naval Aviation University, Yantai 264001, China
3.
Aviation Support Professional Training Base, Naval Aviation University, Qingdao 266109, China

Funds: The Natural Science Foundation of Shandong Province (ZR2020MF090)

摘要

摘要: 奇异值分解等传统算法在处理穿墙成像中的杂波抑制问题时，杂波消除不够彻底，目标成像质量不高，严重影响后续的目标检测与识别。为解决这一问题，该文基于鲁棒主成分分析理论，在回波域和图像域分别建立联合低秩稀疏模型，以光滑化快速交替线性化(SFAL)方法来求解模型，并对目标图像进行指数加权联乘多域图像融合处理，从而得到最终成像结果。仿真结果表明，该算法速度快、精度高，可有效改善目标成像质量，并能较好地满足穿墙成像的实时性和准确性要求。
- 穿墙成像雷达 /
- 杂波抑制 /
- 鲁棒主成分分析 /
- 联合低秩稀疏模型 /
- 多域联合
Abstract: In through-the-wall imaging, the clutter can not be eliminated completely through traditional algorithms, and affects seriously the subsequent target detection and recognition. To solve the problem, based on robust principal component analysis theory, a joint low-rank and sparse model is established in echo and image domain respectively. The models are solved by Smoothing Fast Alternating Linearization (SFAL) method. Then, the target images are dealt with exponentially weighted multiply multi-domain image fusion to obtain the final image. The simulation results indicate that the algorithm has great speed and accuracy with effective improvement on imaging quality of targets.
- Through-the-Wall Imaging Radar(TWIR) /
- Clutter suppression /
- Robust Principal Component Analysis(RPCA) /
- Joint low-rank and sparse model /
- Multi-domain union

HTML全文

图 1 杂波抑制算法流程图

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图 2 穿墙场景示意图

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图 3 原始回波成像

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图 4 回波域目标图像

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图 5 图像域目标图像

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图 6 超均值像素数变化曲线

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图 7 多域联合成像

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图 8 背景对消成像

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图 9 SVD算法成像

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表 1 SFAL方法

输入：2维图像矩阵 ${\mathbf{I}} \in {{\mathbf{R}}^{P \times Q}}$ ，凸函数 $f({\mathbf{x}}) = {\left\\| {\mathbf{x}} \right\\|_ * }$ ，凸函数 $g({\mathbf{x}}) = \gamma {\left\\| {{\mathbf{I}} - {\mathbf{x}}} \right\\|_1}$ ，正则化参数 $\gamma = 1/\sqrt {\max (P,Q)}$ ；
输出：杂波分量矩阵 ${ {\mathbf{I} }_{\text{w} } } = { {\mathbf{x} }^{k{{ - } }1} }$ ，目标分量矩阵 ${ {\mathbf{I} }_{ {\text{tg} } } } = {\mathbf{I} } - { {\mathbf{x} }^{k{{ - } }1} }$ 。
(1) 初始化参数： $\alpha = \beta = {10^{ - 6}}$ , ${{\mathbf{x}}^0} = {{\mathbf{y}}^0} = {{\mathbf{z}}^1} = 0$ , ${\mu _f} = {\mu _g} = 1$ , ${\eta _1} = 1$ ； $k = 1$ 。
(2) 根据式(12)和式(14)进行光滑化处理。
(3) 迭代解未收敛时执行步骤(4)到(7)
(4) 根据式(18)和式(19)进行交替迭代；
(5) 根据式(20)更新 $\eta$ ；
(6) 根据式(21)更新 ${\mathbf{z}}$ ；
(7) $k \leftarrow k + 1$
(8) 结束循环

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表 2 各方法性能对比

算法类型	SFAL	APG	EALM	IALM
目标杂波比(dB)	12.15	11.22	11.95	11.27
迭代次数(次)	8	163	13	39
迭代时间(s)	0.1327	1.4603	8.3925	0.5512

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表 3 各情况下的目标杂波比(dB)

	原始回波成像	多域联合成像	背景对消成像	SVD算法成像
目标杂波比	3.89	25.21	16.27	13.17
较原始成像改善	0	21.32	12.38	9.28

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