基于幅相分离的属性散射中心参数估计新方法

蒋文; 李王哲

doi:10.12000/JR18097

基于幅相分离的属性散射中心参数估计新方法

doi: 10.12000/JR18097

蒋文,
李王哲^,

微波成像技术国家级重点实验室北京 100190
中国科学院电子学研究所北京 100190
中国科学院大学北京 100049

基金项目: 国家自然科学基金(61701476, 61690191)

详细信息

作者简介:
蒋　文(1994–)，女，2016年获得西安电子科技大学学士学位，现于中国科学院大学攻读硕士学位。研究方向是微波成像技术。E-mail: jiangwen16@mails.ucas.ac.cn

李王哲(1983–)，男，青年千人，现为中国科学院电子学研究所研究员，微波成像技术国家级重点实验室主任。研究方向有基于光子技术的合成孔径雷达(SAR)，基于微波成像的微波光子传感器，微波光子模块芯片集成。 E-mail: wzli@mail.ie.ac.cn

通讯作者:
李王哲　wzli@mail.ie.ac.cn

中图分类号: TN957.52
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出版历程
- 收稿日期: 2018-11-15
- 修回日期: 2019-04-09
- 网络出版日期: 2019-10-01

A New Method for Parameter Estimation of Attributed Scattering Centers Based on Amplitude-phase Separation

JIANG Wen,
LI Wangzhe^,

National Key Lab of Microwave Imaging Technology, Beijing 100190, China
Institute of Electronics Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (61701476, 61690191)

More Information

Corresponding author: LI Wangzhe, wzli@mail.ie.ac.cn

摘要

摘要: 利用属性散射中心(ASC)参数估计来识别目标上的散射结构是实现合成孔径雷达(SAR)自动目标体识别(ATR)的重要步骤。为提高属性散射中心参数估计的速度并抑制杂散影响，该文首先从图像中提取多个属性散射中心，然后分别估计各个属性散射中心的参数。为提高单个属性散射中心的参数估计速率，考虑到其幅度和相位相关参数可分离，该文提出幅度相位分离的属性散射中心参数估计思想，与传统方法相比，该思想使参数估计算法复杂度和参数估计时间降低了1个数量级。引入迭代半阈值(IHT)算法提高参数估计精度。根据各个属性散射中心的参数估计结果可识别目标上各种散射结构并确定其在目标上的位置分布。仿真数据、实测数据以及MSTAR数据集得到的参数估计的高效性和高准确性，验证了该文所提方法的有效性。
- 属性散射中心模型 /
- SAR自动目标体识别 /
- 幅度相位分离 /
- L_1/2正则化
Abstract: Parameter estimation of Attributed Scattering Centers (ASCs) corresponding to scattering geometries on targets plays an important role in Synthetic Aperture Radar (SAR) imaging-assisted Automatic Target Recognition (ATR). To achieve computational savings and clutter suppression, we extract the measurements of several ASCs and estimate the parameters of each ASC separately. To improve the speed of the estimation process, we propose a method for parameter estimation of ASCs based on amplitude–phase separation that considers a reasonable assumption that the amplitude- and phase-related parameters of an ASC can be estimated separately and independently. Through the proposed method, the complexity and time consumed for parameter estimation are reduced by one order of magnitude than the traditional method. The Iterative Half Thresholding (IHT) algorithm is introduced to enhance the accuracy of parameter estimation. The types and locations of scattering geometries on the target are determined using the estimated ASC parameters. Using simulated data, measured data, and MSTAR data sets, the accuracy and efficiency of parameter estimation are improved and the effectiveness of the proposed method is verified.
- Attributed scattering center model /
- Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) /
- Amplitude-phase separation /
- L_1/2 regularization

HTML全文

图 1 本文所提ASC参数估计流程图

Figure 1. Flow chart of the method for estimation of ASC parameters proposed in this paper

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图 2 原始算法与本文算法性能比较图

Figure 2. Performance comparison of the traditional method and the method this paper proposed

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图 3 实验场景及成像结果

Figure 3. Experimental filed and ISAR images

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图 4 T72光学图像

Figure 4. T72 optical image

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图 5 多幅T72 SAR图像

Figure 5. Various T72 SAR images

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图 6 各个ASC对应散射结构位置分布图

Figure 6. Estimated position distribution map of scattering geometries corresponding to the ASCs

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表 1 简单散射结构α取值表

Table 1. Discrimination of canonical scattering geometries from α

$\alpha $的值	对应的散射结构
1.0	宽边平板，二面角
0.5	单曲表面反射
0	点，球，直边镜面
–0.5	边缘衍射
–1.0	拐角衍射

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表 2 不同L和α对应典型散射结构

Table 2. Discrimination of canonical scattering geometries from L and α

L	$\alpha $
L	1.0	0.5	0
=0	三面角	帽顶	双曲面
>0	二面角	圆柱	直边

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表 3 仿真参数取值

Table 3. Values of the simulation parameters

参数	分辨率	范围	取值个数
\|A\|	0.1	[1, 6]	51
L	0.1 m	[0, 2] m	21
$\alpha $	0.5	[–1, 1]	5
$\bar \phi $	0.5°	[–10.5°, 10.5°]	43
x	0.2 m	[–2, 2] m	41
y	0.2 m	[–2, 2] m	41
phase(A)	0.314	$[{\text{π}} , {\text{π}} ]$	21

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表 4 两种字典构造方法性能对比

Table 4. Performance comparison of the two dictionary construction methods

对比内容	高维联合字典	幅度相位分离字典
运行时间(s)	1029	492
MSE	0.06	0.05

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表 5 基于传统方法的多个ASC参数估计结果

Table 5. Estimation results of ASCs parameters using traditional method

ASC参数	S1	S1估计值	S2	S2估计值	S3	S3估计值
\|A\|	1.00	1.00	6.00	6.12	1.00	0.92
L	0	0	1	1	0	0.2
$\bar \phi $	0	0	0.5°	0.5°	0	0.5°
$\alpha $	0.5	0.5	0.5	0.5	0.5	0.5
x	1	1	0	0	–1	–1
y	0	0	0	1	1	1

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表 6 基于本文方法的多个ASC参数估计结果

Table 6. Estimation results of ASCs parameters using the method this paper proposed

ASC参数	S1	S1估计值	S2	S2估计值	S3	S3估计值
\|A\|	1.00	1.01	6.00	6.00	1.00	0.98
L	0	0	1	1	0	0.1
$\bar \phi $	0	0	0.5°	0.5°	0	0
$\alpha $	0.5	0.5	0.5	0.5	0.5	0.5
x	1	1	0	0	–1	–1
y	0	0	0	0	1	1

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表 7 2种ASC参数估计方法性能对比

Table 7. Performance comparison of the two ASC parameters estimation methods

对比内容	传统方法	本文方法
运行时间(s)	1.47	0.17
MSE	0.3536	0.0170

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表 8 3个ASC对应散射结构识别结果

Table 8. Recognition results of the scattering geometries corresponding to the three ASCs

S1	S2	S3
帽顶	圆柱	帽顶

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表 9 ASC参数估计结果

Table 9. Estimated parameters of the 2 ASCs

参数	1	2
A	–3.132+3.41j	4.0558
L (m)	0.4	0
$\bar \phi $	–0.5°	0.5°
$\gamma $	0	4.1034e–10
$\alpha $	0.5	0
x (m)	119.6248	120.1499
y (m)	–0.29845	0.2863

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表 10 ASC对应散射结构判定结果

Table 10. Recognition results of the scattering geometries corresponding to the 2 ASCs

1	2
圆柱	点

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表 11 ASC参数估计结果及对应散射结构判定结果

Table 11. Estimation results of the ASCs parameters and the recognitionresults of the scattering geometries corresponding to these ASCs

第几个散射中心	1	2	3	4	5	6	7	8
L(m)	2.67	4	0	0	0	0	0	0
$\alpha $	0.5	0.5	–0.5	1	0.5	0	1	1
散射结构类型	圆柱	圆柱	边缘绕射	三角面	帽顶	双曲面	三角面	三角面
第几个散射中心	9	10	11	12	13	14	15	16
L(m)	0.89	0.89	0.44	0	0	0	0	0
$\alpha $	0	0	0	0	1	0	1	0.5
散射结构类型	直边	直边	直边	双曲面	三角面	双曲面	三角面	帽顶

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