基于卷积神经网络的天基预警雷达杂波抑制方法

段克清; 李想; 行坤; 王永良

doi:10.12000/JR21161

基于卷积神经网络的天基预警雷达杂波抑制方法

DOI: 10.12000/JR21161

段克清¹,
李想¹,
行坤²,
王永良^3, ,

1.
中山大学电子与通信工程学院深圳 518107
2.
中国科学院空天信息创新研究院北京 100094
3.
空军预警学院武汉 430019

基金项目: 国家自然科学基金(61871397)

详细信息

作者简介:
段克清(1981–)，男，河北石家庄人，2010年获国防科技大学信号与信息处理专业博士学位，现为中山大学电子与通信工程学院副教授。获国家技术发明二等奖和部委科技进步一等奖各1项，在IEEE Transaction on AES, IEEE Transaction on AC和中国科学等期刊与会议发表论文80余篇(SCI收录28篇)。IEEE会员，中国电子学会高级会员。主要研究方向包括空时自适应处理、机载/星载雷达信号处理和阵列信号处理等

李　想(1997–)，男，广东茂名人，现为中山大学电子与通信工程学院在读硕士生。主要研究方向包括阵列信号处理、空时自适应处理和深度学习等

行　坤(1980–)，男，陕西西安人，2006年获西安电子科技大学信号与信息处理专业硕士学位，现为中国科学院空天信息创新研究院副研究员，中国科学院大学硕士生导师。主要研究方向包括机载多功能雷达、地基监视雷达系统总体设计与性能仿真和动目标检测与跟踪技术等

王永良(1965–)，男，浙江嘉兴人，中国科学院院士，空军预警学院教授、博士生导师。主要研究方向包括空时自适应处理、雷达信号处理和阵列信号处理等

通讯作者:
王永良 ylwangkjld@163.com

责任主编：廖桂生 Corresponding Editor: LIAO Guisheng
中图分类号: TN957.51
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出版历程
- 收稿日期: 2021-10-30
- 修回日期: 2021-12-18
- 网络出版日期: 2022-01-11
- 刊出日期: 2022-06-28

Clutter Mitigation in Space-based Early Warning Radar Using a Convolutional Neural Network

1.
School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3.
Air Force Early Warning Academy, Wuhan 430019, China

Funds: The National Natural Science Foundation of China (61871397)

More Information

Corresponding author: WANG Yongliang, ylwangkjld@163.com

摘要

摘要: 利用天基预警雷达实现动目标指示具有重要的军事应用价值。对于天基预警雷达，其平台高速运动及受地球自转影响导致杂波复杂非平稳性，更大的波束照射区域带来更严重的杂波非均匀性，从而导致适用于机载预警雷达的传统空时自适应处理(STAP)方法无法直接应用。针对上述问题，该文分析了天基预警雷达杂波分布特性，并构建了基于卷积神经网络(CNN)超分辨谱估计的STAP处理框架。首先，利用雷达系统和卫星轨道参数，仿真随机生成不同纬度、距离门、阵元误差、杂波起伏和地貌散射系数的回波数据集；然后，设计并调优了含5个权重层的二维CNN，实现由小样本所估低分辨杂波谱到高分辨谱的非线性映射；最后，基于高分辨空时谱构造空时滤波器实现杂波抑制和目标检测。仿真实验验证所提方法在小样本条件下可实现次最优杂波抑制性能，同时所需在线运算量远低于现有稀疏超分辨类方法，因此适用于天基预警雷达实际应用。
- 天基预警雷达 /
- 卷积神经网络(CNN) /
- 空时自适应处理(STAP) /
- 稀疏恢复 /
- 杂波抑制
Abstract: Moving target indication using space-based early warning radar is important in military applications. For the space-based early warning radar, complicated non-stationary clutter characteristics are induced due to the high-speed movement of the radar platform and Earth’s rotation, and more serious clutter non-homogeneity than the airborne radar scenario is caused by the large beam illumination region. Consequently, traditional Space-Time Adaptive Processing (STAP) methods, which have been widely used in airborne early warning radar, cannot be applied directly to the space-based early warning radar. In this study, we analyze the characteristic of clutter distribution and build a novel STAP framework, where high-resolution clutter spectra used to construct the adaptive weights is estimated via a Convolutional Neural Network (CNN). First, clutter data sets were randomly simulated with different ranges of bin, latitude, spatial error, internal clutter motion, and coefficients of surface scattering, where the radar and satellite parameters were utilized as a priori knowledge. Then, we designed a two-dimensional CNN with five layers that converted a low-resolution clutter spectrum into a high-resolution spectrum. Finally, a space-time adaptive filter was calculated using the estimated high-resolution space-time spectrum and employed for clutter suppression and target detection. The simulation results show that the proposed CNN STAP can achieve sub-optimal performance under limited sample conditions, and a smaller computational load compared with a state-of-the-art sparse recovery STAP method. Therefore, this framework is suitable for practical application in space-based early warning radar.
- Space-time early warning radar /
- Convolutional Neural Network (CNN) /
- Space-Time Adaptive Processing (STAP) /
- Sparse recovery /
- Clutter suppression

HTML全文

图 1 天基预警雷达几何坐标系

Figure 1. Space-based early warning radar viewing geometry

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图 2 天基预警雷达杂波分布曲线

Figure 2. Clutter distribution of space-based early warning radar

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图 3 所提CNN模型结构

Figure 3. Structure of our CNN

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图 4 网络训练收敛性分析

Figure 4. Convergence analysis of our CNN

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图 5 高分辨空时谱对比结果

Figure 5. Comparison of high-resolution angle-Doppler spectra

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图 6 各方法信杂噪比损失曲线对比

Figure 6. Comparison of SCNR Loss curves

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图 7 相对平均SCNR Loss对比结果

Figure 7. Comparison of relative average SCNR Loss

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图 8 运算复杂度对比结果

Figure 8. Comparison of computational complexity

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表 1 仿真参数

Table 1. Parameters of simulation

参数	数值
卫星轨道	506 km
卫星轨道倾角	70^o
卫星速度	7610 m/s
天线尺寸	50 m×2 m
阵元数	384×12
合成通道数	32×1
工作频率	1250 MHz
带宽	3 MHz
主波束方位角	90^o
主波束俯仰角	–30^o
脉冲重复频率	10000 Hz
相参脉冲数	16
峰值辐射功率	25 kW
系统损耗	8 dB

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表 2 稳定性分析

Table 2. Stability analysis

方法	≤1 dB	≤3 dB	≤5 dB	≤7 dB	≤9 dB
FOCUSS STAP	5.4%	35.0%	70.0%	87.0%	94.8%
SBL STAP	44.0%	85.2%	90.0%	97.8%	98.0%
CNN STAP	32.2%	73.6%	84.8%	92.0%	96.3%

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表 3 运算复杂度分析

Table 3. Analysis of computational complexity

方法	运算复杂度
FOCUSS STAP	$\begin{aligned} & {O}\left(\left(NM{N}_{\mathrm{s} }{N}_{\mathrm{d} }+{\left(NM\right)}^{3}+2{\left(NM\right)}^{2}{N}_{\mathrm{s} }{N}_{\mathrm{d} }\right.\right.\\& \left.\left.+NM{\left({N}_{\mathrm{s} }{N}_{\mathrm{d} }\right)}^{2}\right){k}_{\mathrm{F}\mathrm{O}\mathrm{C} }\right)\end{aligned}$
SBL STAP	$\begin{aligned} & {O}\left(\left(NM{N}_{\mathrm{s} }{N}_{\mathrm{d} }+{\left(NM\right)}^{3}+3{\left(NM\right)}^{3}{N}_{\mathrm{s} }{N}_{\mathrm{d} }\right.\right.\\& \left.\left.+2NM{\left({N}_{\mathrm{s} }{N}_{\mathrm{d} }\right)}^{2}\right){k}_{\mathrm{S}\mathrm{B}\mathrm{L} }\right)\end{aligned}$
CNN STAP	${O}\left(28777{N}_{\mathrm{s} }{N}_{\mathrm{d} }\right)$

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表 4 运行时间比较

Table 4. Comparison of running time

方法	离线时间(s)	在线时间(s)
FOCUSS STAP	0	30.84
SBL STAP	0	33.02
CNN STAP	5481	0.013

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