以全球导航卫星系统为辐射源的前向散射雷达发展综述

郑雨晴; 艾小锋; 王满喜; 徐志明; 肖顺平

doi:10.11999/JEIT231255

以全球导航卫星系统为辐射源的前向散射雷达发展综述

doi: 10.11999/JEIT231255

1.
国防科技大学电子科学学院长沙 410073
2.
电子信息系统复杂电磁环境效应国家重点实验室洛阳 471003

基金项目: 国家自然科学基金(62071475)，国家自然科学基金重大项目课题(61890541, 61890542)

详细信息

作者简介:
郑雨晴：女，博士生，研究方向为前向散射雷达目标运动参数估计

艾小锋：男，副研究员，研究方向为双基地雷达成像、特征提取

王满喜：男，副研究员，研究方向为通信对抗

徐志明：男，讲师，研究方向为双基地雷达目标特性与特征提取

肖顺平：男，教授，研究方向为雷达极化信息处理及应用、电子信息系统仿真建模评估

通讯作者:
郑雨晴　zhengyuqing@nudt.edu.cn

中图分类号: TN959
计量
- 文章访问数: 17
- HTML全文浏览量: 10
- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2023-11-14
- 修回日期: 2024-07-16
- 网络出版日期: 2024-07-24

GNSS Forward Scatter Radar: A Review

1.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
2.
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China

Funds: National Natural Science Foundation of China (62071475), National Natural Science Foundation of China major program topics (61890541, 61890542)

摘要

摘要: 前向散射雷达(FSR)可获得高水平雷达截面积(RCS)的特性使其在反隐身中占据重要地位。利用全球导航卫星系统(GNSS)作为辐射源，具有全天时全天候全地域覆盖的优势，通过部署多个接收节点可构建地面/海上/空中目标监视网络。该文针对基于GNSS的FSR发展现状，从目标检测、目标参数估计、阴影逆合成孔径雷达(SISAR)成像及目标分类识别等方面对关键技术和现存问题进行概述，并从组网探测、多目标定位、布站优化和极化信息获取等方面对基于GNSS的FSR发展趋势提出展望。
- 前向散射雷达 /
- GNSS /
- 信号建模 /
- 目标检测 /
- 参数估计 /
- 侧影成像 /
- 目标识别
Abstract: Forward Scatter Radar (FSR) can obtain high level Radar Cross Section (RCS), so it plays an important role in anti-stealth. The Global Navigation Satellite System (GNSS) has the advantage of all-weather coverage throughout the day as a radiation source and the ground/sea/air target surveillance network can be built by deploying multiple receiving nodes. According to the development status of GNSS-based FSR, the key technologies and the existing problems in target detection, target parameter estimation, Shadow Inverse Synthetic Aperture Radar (SISAR) imaging, and target classification are summarized. What’s more, the development trend of GNSS-based FSR is prospected from the aspects of network detection, multi-target location, station optimization and polarization information acquisition.
- Forward Scatter Radar(FSR) /
- Global Navigation Satellite System(GNSS) /
- Signal modeling /
- Target detection /
- Parameter estimation /
- Shadow Inverse Synthetic Aperture Radar(SISAR) /
- Target recognition

HTML全文

图 1 基于GNSS的前向散射雷达网探测示意图

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图 2 基于GNSS的前向散射雷达系统目标探测可行性验证历程

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图 3 前向散射波与直达波之间的干涉示意图

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图 4 暗室中金属球穿越基线时的回波特性^[28]

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图 5 数字式GPS接收机典型跟踪环路^[29]

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图 6 3维空间目标穿越基线示意图^[33]

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图 7 时域和频域的FVD^[35]

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图 8 矩形空间碎片目标多普勒频散分析^[37]

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图 9 晶体视频检测器

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图 10 两段分离桥架阴影信号测量的实验场景^[44]

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图 11 两段分离桥架阴影信号检测^[44]

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图 12 基于GPS L5信号的检测流程^[46]

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图 13 GPS-FSR检测结果^[48]

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图 14 基于Rényi熵的前向散射信号检测性能分析^[50]

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图 15 单基线系统目标运动参数估计流程^[54]

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图 16 双基线系统结构示意图

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图 17 多普勒谱图中对两目标分辨能力分析^[55]

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图 18 目标运动参数估计实验^[57]

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图 19 双基线系统目标运动参数估计方法总结

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图 20 基于穿越时刻的参数估计方法估计精度影响因素分析^[64]

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图 21 基于GNSS的前向散射雷达拓扑结构

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图 22 汽车实测数据SISAR成像结果^[69]

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图 23 基于FSSR的不规则形状目标SISAR成像^[75]

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图 24 基于GNSS的SISAR像实验结果^[31]

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图 25 车辆目标阴影信号分类^[93]

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表 1 用于FSR目标识别的特征归纳表

特征	特征公式	变量含义
目标阴影长度特征	$ dT = {T_2} - {T_1} $	$ {T_1} $和$ {T_2} $为时域阴影信号的开始和结束时刻，由操作员手动估计
峰值信噪比特征	$ {\text{SN}}{{\text{R}}_{{\text{peak}}}}\left[ {{\text{dB}}} \right] = {\text{mean}}\left( {{P_{\text{n}}}} \right) - \min \left( {{P_{\text{s}}}} \right) $	区间$\left[ {{T_1},{T_2}} \right]$内平均噪声功率与阴影信号最小值之间的差值，$ {P_{\text{n}}} $为噪声功率，$ {P_{\text{s}}} $为阴影信号功率。
目标阴影平均功率特征	$ {P_{{\text{ave}}}}\left[ {{\text{dB}}} \right] = \lg \left( {mean\left( {{P_{{\text{s}},i}}} \right)} \right),i = 1,2,\cdots,N $	$ N $为信号采样点数
目标阴影平均能量特征	$ {E_{{\text{ave}}}} = {P_{{\text{ave}}}}N $	平均功率与阴影长度的乘积
阴影信号功率谱主瓣宽度	$W = P\left( i \right)$	$P$表示信号功率谱，$i$为功率谱第1个极小值点的位置
目标侧影像特征	$ H\left( \eta \right) = \left\| {\dot H\left( \eta \right)} \right\| = \left\{ \begin{gathered} \sin \left( {\dfrac{{k{n_1}}}{{2\pi }}h\left( \eta \right)} \right) \cdot \dfrac{\lambda }{{{n_1}}},{n_1} \ne 0 \\ h\left( \eta \right)\quad \quad \quad \quad \;,{n_1} = 0 \\ \end{gathered} \right. $	$ {n_1} $为垂直方向余弦，$ H\left( \eta \right) $为复侧影像$ \dot H\left( \eta \right) $的模值，也称目标的侧影像，反映目标上下边沿高度差，$ h\left( \eta \right) $为目标侧影轮廓高度差，由目标几何结构决定，$ \lambda $为信号波长，$k = \dfrac{{2\pi }}{\lambda }$
目标侧影轮廓中线相位差分特征	${\bar \phi _c}\left( \eta \right) = \dfrac{{{\phi _c}\left( \eta \right) - \min \left( {{\phi _c}\left( \eta \right)} \right)}}{{\max \left( {{\phi _c}\left( \eta \right)} \right) - \min \left( {{\phi _c}\left( \eta \right)} \right)}}$	${\phi _c}\left( \eta \right)$由$\phi \left( \eta \right) = {\text{angle}}\left( {\dot H\left( \eta \right)} \right)$进行处理后得到，可反映目标侧影轮廓中线特征
侧影像归一化极点距离特征	$ {\bar D_i} = {{D_i}}/{\mathop {\max \left( {{D_i}} \right)}\limits_{i = 1,\cdots,M - 1} } $	$M$为侧影像极点个数，它们之间的距离为${D_1},{D_2},\cdots,{D_i},\cdots{D_{M - 1}}$，其中${D_i}$为侧影像起始点与第$i + 1$个极点间的距离，$ \mathop {\max \left( {{D_i}} \right)}\limits_{i = 1,\cdots,M - 1} $是取距离序列中的最大值

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表 2 FSR目标分类研究现状总结

辐射源	分类目标	种类	输入特征	分类方法
GPS^[79,94]	船/汽车	3	阴影信号持续时间阴影信号平均功率阴影信号平均能量	决策树(J48)、随机森林、贝叶斯分类器(NaiveBayes和BayesNet)、最近邻算法、规则学习(rule learning)(OneR和JRip)、神经网络 (多层感知器MLP)
LTE^[85,86]	人/不同高度的无人机(2 m,3 m)	4/2	去噪后阴影信号PSD的PCA结果	聚类
地面雷达发射机^[87]	人类活动(坐在椅子上和前倾跌落)	2	STFT平均值	SVM
地面雷达发射机^[88]	汽车	5	去噪后阴影信号Z-Score标准化(zero-mean normalization)和PCA结果	KNN、决策树、判别分析
北斗^[95]	飞行器	3	阴影信号	稀疏自动编码器、卷积神经网络

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