Global Navigation Satellite System Forward Scatter Radar: A Review
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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。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.
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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 $ {\mathrm{d}}{\rm{T}} = {T_2} - {T_1} $ $ {T_1} $和$ {T_2} $为时域阴影信号开始和结束时刻，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( {{\mathrm{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),\qquad \qquad \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${\bar \phi _{\mathrm{c}}}\left( \eta \right) = \dfrac{{{\phi _{\mathrm{c}}}\left( \eta \right) - \min \left( {{\phi _{\mathrm{c}}}\left( \eta \right)} \right)}}{{\max \left( {{\phi _{\mathrm{c}}}\left( \eta \right)} \right) - \min \left( {{\phi _{\mathrm{c}}}\left( \eta \right)} \right)}}$ ${\phi _{\mathrm{c}}}\left( \eta \right)$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[79,93] 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[84,85] 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[86] 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[87] 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[94] 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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