Vehicle SAR Image Simulation: Progress, Application and Challenge

doi:10.16182/j.issn1004731x.joss.25-1037

Abstract

Abstract:

SAR image simulation technology can efficiently generate large-scale annotated data by flexibly configuring system parameters and scene conditions, breaking through the inherent limitations of high cost and long acquisition cycles associated with real SAR images, and effectively mitigating the problem of data scarcity. To elaborate on the overall research status of vehicle SAR image simulation, a classification framework for simulation methods is first established based on technical principles and methodological approaches. Subsequently, a comprehensive review of research progress is provided, offering an in-depth analysis of the theoretical foundations, practical applications, and suitable scenarios of various methods. Finally, based on current development trends and technical challenges in the field, potential breakthrough directions for future research are discussed.

Key words: synthetic aperture radar, image simulation, signal simulation, computational electromagnetics, computer graphics

CLC Number:

TN955+.2

Luo Shengjie, Liu Zhigang, Li Xihai, Wang Yiting, Zeng Xiaoniu, Tan Xiaofeng, Li Hongru, Liu Tianyou, Zhang Yun. Vehicle SAR Image Simulation: Progress, Application and Challenge[J]. Journal of System Simulation, 2026, 38(9): 2535-2558.

Figures/Tables 17

Fig. 1

Fig. 2

Table 1

Low frequency methods

低频法	类型	计算复杂度	空间复杂度	计算精度
FDTD	时域微分	$O (N t N x y z)$	$O (N x y z)$	较高
FEM	频域微分	$O (N ∼ N 3)$	$O (N ∼ N 2)$	高
MoM	频域积分	$O (N 2 ∼ N 3)$	$O (N 2)$	高
MLFMA	频域积分	$O (N l g N)$	$O (N)$	高

Table 1

Table 2

High frequency methods

方法	优点	缺点	适用场景
几何光学法(GO)	计算简单，效率高	无法处理绕射、相位；在阴影边界、焦散区和源区出现奇异性	适用于表面和简单几何结构的目标，如镜面反射场景
物理光学法(PO)	避免了阴影区和焦散区的奇异性问题	仅考虑一次反射，无法处理多次反射、绕射和极化效应	适用于电大尺寸目标的光滑曲面散射计算，如机身、船身、船底等
弹跳射线法(SBR)	能模拟多次反射和复杂路径；结合了GO和PO法的优点	计算复杂度随反射次数增加；难以处理绕射效应	适用于复杂几何结构的目标，如车辆、船舶、建筑物等，尤其是多次反射场景
几何绕射法(GTD)	能处理边缘、尖点和曲面不连续处的绕射效应	仅能计算Keller锥面方向的散射场；在阴影边界和焦散区出现无穷大值。对复杂几何结构的适应性有限	适用于边缘、尖锥、平板角点、曲面绕射效应显著的目标，如天线、棱角结构等
物理绕射法(PTD)	通过边缘电流修正补偿PO法的几何不连续性；消除GTD阴影边界的奇异点	仅能计算Keller锥面方向的散射场；对复杂几何结构的适应性有限	同上，且边缘散射计算更精确
一致性绕射法(UTD)	改进GTD，解决阴影边界处的无穷大问题；适用范围更广	焦散区的奇异性仍存在	同上，且在过渡区域表现更好，适用于需要高精度绕射计算的场景，如天线设计、复杂目标的RCS计算
等效电磁流法(MEC)	克服GTD和PTD的局限性；能处理焦散区奇异性问题；绕射场不局限于Keller锥面方向；适用于复杂几何形状目标	计算复杂度较高；难以处理阻抗边缘和尖顶等结构	同上，适用于需要高精度边缘和尖端散射计算的场景，如隐身目标设计、复杂结构分析等
增量长度绕射系数(ILDC)	改进了PTD，绕射场不局限于Keller锥面方向；在反射、阴影边界的过渡区为有限值	表达形式复杂，对任何边缘都要分别计算物理光学积分和边缘贡献	同上，且边缘散射计算更精确

Table 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 3

SAR image simulation of vehicle targets based on electromagnetic models

文献	年份	电磁计算方法	仿真方法	成像方法	仿真质量评估
[45]	2008	PO+ILDC	图像/GRECO	映射投影	模板匹配
[46]	2010	PO+MEC	图像/像素法	映射投影	视觉分析
[51]	2015	SBR+MEC	图像	映射投影	PAC
[45]	2008	PO+ILDC	信号	RD	模板匹配
[46]	2010	PO+MEC	信号/转台成像	PFA	视觉分析
[47]	2010	PO+PTD+GO/PO	信号/转台成像	2D-IFFT	NCC、Hausdorff、RCS分析
[48]	2012	PO+SBR+MEC	信号/频域方法	RD\RM	NCC、Hausdorff、RCS分析
[76]	2014	GO	信号	$ω - k$	SSIM分类
[49]	2015	SBR+MEC	信号/频域方法	RD	NCC、PAC、径向积分
[14]	2016	SBR+PTD	信号/频域方法	BP	分类
[74]	2018	SBR	信号/转台成像	RD	灰度均值、灰度共生矩阵
[12]	2019	高频射线追踪	信号	PFA	分类模型
[72]	2020	PO	信号/转台成像	2D-IFFT	SVM分类
[17]	2021	SBR	信号/双站ISAR	2D-IFFT	SSIM分类
[52]	2021	SBR+PTD	信号/RFPC	RD\ $ω - k$	视觉分析
[53]	2022	PO+ SBR+ PTD	信号/RFPC	RD	COSS、RCS分析
[98]	2023	SBR+MEC+KA			相关系数、RCS分析

Table 3

Fig. 8

Table 4

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Table 5

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类别	方法	适用范围
理论模型	基尔霍夫近似(KA)	大尺度粗糙表面
	微扰法(SPM)	小尺度粗糙度表面
	双尺度方法(TSM)	存在大尺度粗糙度和小尺度粗糙度的多尺度粗糙表面
	小斜率近似(SSA)	多尺度粗糙表面，尤其是中等粗糙度表面
	积分方程模型(IEM)	各种粗糙度表面
	随机散射法(SSA)	各种粗糙度表面
经验/ 半经验模型	Ulaby模型	裸露土壤和稀疏草地、植被、城市、雪地
	Oh模型	各种湿度的裸露土壤或低植被覆盖的地表
	Dubois模型	裸露土壤，特别是干燥或中等湿度的土壤