动态海面SAR成像高精度仿真方法及其典型应用

王小青; 祁瑞; 姚晓楠; 徐诚极; 晁小朋

doi:10.12000/JR24255

动态海面SAR成像高精度仿真方法及其典型应用

DOI: 10.12000/JR24255 CSTR: 32380.14.JR24255

1.
中山大学电子与通信工程学院深圳 518107
2.
中山大学系统科学与工程学院广州 510275
3.
大连海事大学信息科学技术学院大连 116026

基金项目: 国家重点研发计划(2023YFB3904905)

详细信息

作者简介:
王小青，博士，教授，博士生导师，主要研究方向为海洋微波遥感、雷达信号处理等

祁　瑞，博士生，主要研究方向为SAR海洋遥感

姚晓楠，博士，讲师，主要研究方向为海洋微波遥感、雷达信号处理等

徐诚极，博士生，主要研究方向为SAR海洋遥感

晁小朋，博士生，主要研究方向为SAR海洋遥感

通讯作者:
姚晓楠 yaoxn@dlmu.edu.cn

责任主编：吴尚蓉 Corresponding Editor: WU Shangrong
中图分类号: TN955
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出版历程
- 收稿日期: 2024-12-22
- 修回日期: 2025-03-23

High-precision Simulation of Dynamic Oceans Synthetic Aperture Radar Imaging and its Typical Application

1.
School of Electronic and Communication Engineering, Sun Yat sen University, Shenzhen 518107, China
2.
School of Systems Science and Engineering, Sun Yat sen University, Guangzhou 510275, China
3.
Information Science and Technology College, Dalian Maritime University, Dalian 116026, China

Funds: The National Key R&D Program of China (2023YFB3904905)

More Information

Corresponding author: YAO Xiaonan, yaoxn@dlmu.edu.cn

摘要

摘要: 合成孔径雷达(SAR)海洋遥感仿真是面向海洋应用的SAR系统设计的重要分析工具，同时也可以为复杂海洋现象SAR图像的检测、识别提供训练样本，为海洋参数反演提供正演模型，因此在SAR海洋遥感的SAR系统设计和应用中扮演着非常重要的角色。海面的运动特性、时变特性、去相干特性使得SAR海洋遥感的仿真难度和计算量要远远大于陆地固定目标的仿真，如何在保证仿真精度的情况下提升仿真效率是实现SAR海洋成像高精度、高效率仿真的关键。该文介绍了动态海面SAR成像仿真的主要方法以及发展现状和主要问题，给出了动态海面SAR成像高精度仿真中若干关键问题的实现方法，该仿真方法在保证较高的逼真度情况下(典型工况下仿真SAR图像谱谱峰误差3%，谱宽误差4%)，能在10分钟内完成4 m分辨率情况下400 km²场景的仿真。实现并介绍了动态海面SAR成像仿真在波浪谱反演、基于深度对消网络的海浪纹理抑制以及基于Wake2Wake网络的舰船尾迹检测方面的典型应用。这些应用案例一方面验证了该文给出的动态海面SAR成像高精度仿真的逼真度能够达到智能化仿真训练的要求，另一方面也说明了高精度仿真在SAR海洋图像智能化应用有很好的前景，可以成为解决SAR海洋遥感智能化应用中样本数据瓶颈的重要手段。
- 动态海面 /
- SAR回波仿真 /
- 波浪纹理抑制 /
- 舰船尾迹检测 /
- 波浪谱反演
Abstract: Synthetic Aperture Radar (SAR) ocean remote sensing simulation is an important analytical tool for designing SAR systems for ocean applications. It can also provide training samples for detecting and recognizing SAR images of complex ocean phenomena. Therefore, it plays an important role in the design and application of SAR ocean remote sensing systems. The motion, time-varying, and decoherence characteristics of the sea surface caused the simulation difficulty and calculation amount of SAR ocean remote sensing to be much larger than those of fixed land targets. Therefore, improving the simulation efficiency while ensuring the simulation accuracy is key to achieving high-precision and high-efficiency simulation of SAR ocean imaging. This study introduces the main methods, development status, and main problems of dynamic ocean SAR imaging simulation and provides methods for realizing key problems in high-precision simulation of dynamic ocean SAR imaging. The method can complete the simulation of a 4 m resolution at a 400 km² scene within 10 min while ensuring high fidelity. Under typical working conditions, the spectral peak error of a simulated SAR image is 3%, and the spectral width error is 4%. The typical applications of dynamic ocean surface SAR imaging simulation in wave spectrum inversion, wave texture suppression based on depth cancellation networks, and ship wake detection based on the Wake2Wake network are introduced. On the one hand, these applications verify that the fidelity of the high-precision simulation of dynamic sea SAR imaging presented in this study can satisfy the requirements of intelligent simulation training. On the other hand, the high-precision simulation offers a good prospect for intelligent application of SAR ocean images and can be an important method for providing samples for intelligent application of SAR ocean remote sensing.
- Dynamic ocean /
- SAR echo simulation /
- Wave texture suppression /
- Ship wake detection /
- Wave spectrum inversion

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图 1 SAR海面回波模块组成示意图

Figure 1. Diagram of components of SAR ocean echo simulation

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图 2 考虑和不考虑海面时变性的交叉谱对比

Figure 2. Comparison of the cross spectrum with and without sea surface variability

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图 3 流场拟合结果和M4S仿真结果对比

Figure 3. Comparison between current field fitting results and M4S simulation results

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图 4 ZMNL方法示意图

Figure 4. diagram of ZMNL method

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图 5 BAS算法框架示意图(图中不同颜色代表不同模式信号处理流程)

Figure 5. Diagram of BAS framework (different colors in the figure represent different mode signal processing processes)

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图 6 基于全链路仿真的SAR海浪方向谱反演方法示意图

Figure 6. Schematic diagram of SAR wave directional spectrum inversion method based on full-link simulation

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图 7 3种条件下仿真SAR图像与Sentinel-1观测SAR图像的比较

Figure 7. Comparison of simulated SAR images and Sentinel-1 observed SAR images under three conditions

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图 8 与图7对应的SAR图像谱

Figure 8. SAR image spectrum corresponding to Fig. 7

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图 9 基于浮标谱仿真的SAR图像谱与MPI解析化谱和真实SAR图像谱对比

Figure 9. Comparison of SAR image spectrum based on buoy spectrum simulation with MPI analytical spectrum and real SAR image spectrum

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图 10 反演得到的海浪参数和相应的浮标数据数据对比

Figure 10. Comparison between the inverted wave parameters and the corresponding buoy data

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图 11 有/无海浪杂波干扰SAR图像样本对比

Figure 11. Comparison of SAR image samples with/without wave clutter interference

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图 12 对消器网络结构

Figure 12. Network structure of the eliminator

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图 13 海浪纹理抑制效果对比

Figure 13. Comparison of wave clutter suppression effects

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图 14 典型尾迹示意图及典型尾迹SAR图像

Figure 14. Typical wake diagram and typical wake SAR image

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图 15 典型开尔文尾迹特征

Figure 15. Typical Kelvin wake characteristics

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图 16 湍流定常等效示意图

Figure 16. Steady equivalent diagram of turbulence

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图 17 典型尾迹仿真图像与真实图像对比

Figure 17. Comparison between typical wake simulation images and real images

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图 18 距离舰船尾部不同位置处的湍流尾迹切面图像强度值对比，对应图17(a)和图17(b)

Figure 18. Comparison of intensity values of turbulent wake section images at variant positions behind the ship tail corresponding to Fig. 17 (a) and Fig. 17(b)

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图 19 距离舰船尾部后方不同位置处的湍流尾迹切面图像强度值对比，对应图17(c)和图17(d)

Figure 19. Comparison of intensity values of turbulent wake section images at variant positions behind the ship tail corresponding to Fig. 17 (c) and Fig. 17(d)

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图 20 Wake2Wake检测网络结构示意图

Figure 20. Schematic diagram of Wake2Wake detection network structure

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表 1 流场调制系数拟合精度表

Table 1. Current field modulation coefficient fitting accuracy table

参数	数值
R²	0.74
RMSE	0.06
MAE	0.04
Bias	0

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表 2 基于全链路仿真反演结果与MPI方法反演结果对比

Table 2. Comparison of inversion results based on full link simulation and MPI method

方法	RMSE (Bias)			平均运行时间
方法	波向(°)	波周期(s)	有效波高(m)	平均运行时间
本研究的反演方法	7.646(–1.873)	0.691(0.078)	0.273(–0.049)	8 h
MPI方法	12.774(0.065)	0.978(0.198)	0.453(0.396)	2 min

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表 3 对应图17中3个尾迹的仿真参数

Table 3. Simulation parameters for the three wakes in Fig. 17

参数	参数/名称
参数	尾迹1	尾迹2	尾迹3
SAR平台	TerraSAR-X	TerraSAR-X	Sentinel-1
雷达波段	X	X	C
风速(m/s)	8.9	14.6	7.5
舰长(m)	100	60	220
船宽(m)	17.0	16.5	32.0
吃水(m)	2.7	2.2	8.3
船速(m/s)	17.0	14.5	19.6

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表 4 不同类型旋转目标检测器在实测数据集以及本文所提出的混合数据集上的检测性能

Table 4. Detection performance of different types of rotating object detectors on measured datasets and the hybrid dataset proposed in this paper

数据集	旋转目标检测器(召回率/mAp)
数据集	Rotated RepPoints	S2A-Net	Oriented R-CNN
OpenSARWake	57.4/39.9	46.4/34.0	48.7/35.6
OpenSARWake & Simulated Wake	59.1/41.1	48.9/36.4	50.2/37.9

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