复杂运动舰船目标多投影平面InISAR三维重建方法

李宁; 牛金发; 王玮斌; 胡兴旺; 毋琳

doi:10.11999/JEIT251268

复杂运动舰船目标多投影平面InISAR三维重建方法

doi: 10.11999/JEIT251268 cstr: 32379.14.JEIT251268

李宁^{1, 2, 3, ,},
牛金发^{1, 2, 3},
王玮斌⁴,
胡兴旺⁴,
毋琳^{1, 2, 3}

1.
河南大学计算机与信息工程学院开封 475004
2.
河南大学河南省大数据分析与处理重点实验室开封 475004
3.
河南大学河南省空间信息处理工程研究中心开封 475004
4.
中山大学电子与通信工程学院深圳 518107

基金项目: 河南省自然科学基金(编号: 242300421170)

详细信息

作者简介:
李宁：男，教授，研究方向为合成孔径雷达成像与对抗

牛金发：男，硕士生，研究方向为ISAR信号处理

王玮斌：男，硕士生，研究方向为ISAR信号处理

胡兴旺：男，硕士生，研究方向为SAR信号处理

毋琳：女，教授，研究方向为为SAR图像处理

通讯作者:
李宁　hedalining@henu.edu.cn

中图分类号: TN951
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- 被引次数: 0
出版历程
- 修回日期: 2026-03-09
- 录用日期: 2026-03-09
- 网络出版日期: 2026-03-16

Multi-projection plane InISAR 3D reconstruction method for complex moving ship targets

LI Ning^{1, 2, 3
, ,},
NIU Jinfa^{1, 2, 3},
WANG Weibin⁴,
HU Xingwang⁴,
WU Lin^{1, 2, 3}

1.
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
2.
Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China
3.
Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng 475004, China
4.
School of Electronics and Communication Engineering, Sun Yat-Sen University, Shenzhen 518107, China

Funds: Natural Science Foundation of Henan under Grant (242300421170)

摘要

摘要: 干涉逆合成孔径雷达(InISAR)是一种非合作目标三维重建技术。然而，在对具有复杂运动特性的舰船目标成像时，复杂三维旋转运动会导致目标多普勒频率变化不稳定，直接影响目标三维重建质量，同时，ISAR成像不可避免地存在目标叠掩和遮挡问题，致使单一投影平面的InISAR技术无法实现目标三维信息完全重建。针对上述问题，该文提出了一种复杂运动舰船目标多投影平面InISAR三维重建方法。首先，在分析高海情下长相干积累时间内舰船目标多维复杂运动特性的基础上，结合主成分分析算法选择多个不同成像投影平面的成像时间段，获取多个不同投影平面的高质量舰船目标逆合成孔径雷达(ISAR)图像及其三维重建结果。其次，结合加权随机采样一致性与分层迭代最近点方法，高精度提取和匹配多投影平面三维图像的同名特征点，实现多投影平面InISAR三维图像的高效高精度配准与融合。舰船点目标散射模型和电磁仿真模型的实验结果表明，与单一投影平面下的目标三维重建结果相比，该文所提方法获得的InISAR三维重建质量得到了显著提升。
- ISAR成像 /
- InISAR重建 /
- 舰船目标 /
- 多投影平面 /
- RANSAC-ICP
Abstract: Objective Interferometric Inverse Synthetic Aperture Radar (InISAR) is a Three Dimensions (3D) reconstruction technique for non-cooperative target. However, the complex 3D rotational motion of the ship target causes unstable Doppler frequency changes, and Inverse Synthetic Aperture Radar (ISAR) imaging inevitably suffers from target overlap and occlusion problems, making high-precision complete 3D reconstruction difficult under a single projection plane. Thus, a multi-projection planes InISAR 3D reconstruction method of complex moving ship targets based on point cloud fusion is proposed. Through efficient and high-precision point clouds registration and fusion supplement target 3D information, significantly improving the 3D reconstruction quality. Methods This method fully leverages the advantages of multi-plane observation from the severe movement of ship targets, extracts the ship’s centerline and estimates the vertical rotation vector via Principal Component Analysis (PCA), to select the optimal imaging time corresponding to different Imaging Projection Planes, completes ISAR imaging and InISAR 3D reconstruction. Secondly, a point cloud fusion algorithm combining Weighted Random Sampling Consensus (RANSAC) and Hierarchical Iterative Closest Point (ICP) is proposed. The random sampling process is optimized through a feature stability weighting strategy, efficiently extracting and matching corresponding feature points in InISAR images, achieving high-precision multi- Imaging Projection Plane (IPP) point cloud fusion. Results and Discussions Experimental results demonstrate that the proposed method significantly enhances reconstruction accuracy and target completeness. For simulated ship point target data, Fig 7 shows excellent results, with a significant reduction in reconstruction error. Signal-to-noise ratio (SNR) analysis reveals that 3D fusion imaging quality improves continuously as SNR increases from –10 dB to 10 dB, maintaining robust fusion performance even under low SNR conditions. For simulated destroyer radar cross section data, this method achieved significant registration results, and the detail recovery and structural integrity of the fused image were significantly improved, effectively solving the problem of incomplete 3D information reconstruction caused by overlapping and occlusion of scattering points. Conclusions To address the issues of low reconstruction accuracy and information loss caused by target rotation, overlapping, and occlusion in traditional InISAR methods for 3D reconstruction of complex moving ship targets, this paper proposes a multi-IPP InISAR 3D reconstruction method based on point cloud fusion. This method employs a PCA optimal imaging time selection strategy, By employing weighted RANSAC and hierarchical ICP algorithms to achieve efficient and high-precision registration and fusion of InISAR point clouds under multiple IPPs, obtaining high-quality 3D reconstruction results. This paper conducts multi-scenario experiments by constructing a ship model with ideal scattering points and an electromagnetic simulation RCS model with occlusion effects, verifying the accuracy of the proposed method under ideal conditions and its applicability in complex real-world scenarios.
- ISAR imaging /
- InISAR reconstruction /
- ship target /
- multi-projection plane /
- RANSAC-ICP

HTML全文

图 1 L型三天线InISAR几何模型

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图 2 P点云与Q点云采样点在各自空间的欧氏距离对应模型

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图 3 复杂运动舰船目标多投影平面InISAR三维重建方法

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图 4 具有 301 个理想散射点的舰船模型

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图 5 海况1、海况2的舰船目标仿真、估计旋转矢量变化曲线

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图 6 不同时间段天线A $ \mathrm{A} $ISAR成像结果及散射点提取结果、不同时间段舰船目标的InISAR三维重建结果。

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图 7 RANSAC+ICP点云融合后的InISAR三维重建结果以及与原图对比结果

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图 8 不同海况下信噪比(–10 dB～10 dB)下融合成像结果与原模型误差对比

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图 9 驱逐舰CAD模型

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图 10 不同IPP驱逐舰RCS仿真二维成像结果及该时间段的散射点提取结果

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图 11 PCA-ICP、RANSAC-NDT、所提方法对不同投影平面点云融合成想结果以及与原模型对比结果

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表 1 点云融合算法伪代码

算法：加权 RANSAC		算法：HICP
输入：$ \textit{P} $, $ \textit{Q} $, $ {R}_{1} $, $ {P}_{\text{key}} $, $ {Q}_{\text{key}} $, $ {P}_{\text{FPFH}} $, $ {\textit{Q}}_{\text{FPFH}} $, $ \vartheta $, $ {S}_{\text{num}} $, $ \textit{C}_{\text{in}}^{\text{max}} $, $ {N}_{c} $		输入：$ \textit{P'} $, $ \textit{Q} $, $ \mathrm{vx} $, $ {\textit{N}}_{\text{f}} $, $ {\textit{D}}_{\text{f}} $
For each $ {p}_{\text{k}i} $ in $ {P}_{\text{key}} $		For n=1 to 2
$ {p}_{\text{k}i} $=GetNeibr($ {P}_{\text{key}} $, $ {p}_{\text{k}i} $, $ {R}_{1} $);		If n==1 //低分辨率配准
Obtain $ {\textit{NB}}_{\text{FPFH}} $ by $ {p}_{ij} $ and $ {P}_{\text{FPFH}} $;		$ {\textit{P}}_{\text{cur}} $=dowsample($ \textit{P'} $, $ \mathrm{vx} $);
Obtain $ \textit{S}_{j}^{\text{soc}} $ by $ {p}_{\text{k}i} $ and $ {\textit{NB}}_{\text{FPFH}} $ and (17);		$ {\textit{Q}}_{\text{cur}} $=dowsample($ \textit{Q} $, $ \mathrm{vx} $);
$ p_{\textit{i}}^{\text{wei}} $=normalize(mean($ \textit{S}_{j}^{\text{soc}} $));		End If;
End For; //计算余弦相似度并获取权重		If n==2 //高分辨率配准
For each $ {p}_{\text{k}i} $ in $ {P}_{\text{key}} $		$ {\textit{P}}_{\text{cur}} $=$ {\textit{P}}_{\text{f}} $, $ {\textit{Q}}_{\text{cur}} $=$ \textit{Q} $;
$ [{q}_{\text{bm}},{S}_{\text{best}}]=\text{Match}({p}_{\text{k}i},{P}_{\text{FPFH}},{Q}_{\text{key}},{Q}_{\text{FPFH}}) $;		End If;
$ \text{lib}_{i}^{\text{match}}=[{p}_{\text{k}i},{q}_{\text{bm}},P_{\text{idx}}^{\text{wei}}] $;		For m=1 to $ {\textit{N}}_{\text{f}} $/2+1 //迭代
End For //对关键点进行匹配并配置权重		Obtain $ {q}_{i} $ by KD-tree and $ {p}_{i} $ and $ {\textit{P}}_{\text{cur}} $;
For $ i $=1 to $ {N}_{c} $		$ \text{P}{\text{Q}}_{i} $=[$ {p}_{i} $, $ {q}_{i} $];
$ \text{Sa}{\text{m}}_{\text{match}} $=WeightedSample($ \text{lib}_{i}^{\text{match}} $,4);		[$ {R}_{\text{f}i} $, $ {T}_{\text{f}i} $]=OLS_SVD($ \text{P}{\text{Q}}_{i} $); //求解(19)
Obtain $ \rho $ by $ \text{Sa}{\text{m}}_{\text{match}} $ and (17);		$ {\textit{P}}_{\text{cur}} $=$ {R}_{\text{f}i} $·$ {\textit{P}}_{\text{cur}} $+$ {T}_{\text{f}i} $;
If $ \rho $>$ \vartheta $ //相异向量判断		Obtain Err by $ {\textit{P}}_{\text{cur}} $ and $ {\textit{Q}}_{\text{cur}} $;
$ [{R}_{c},{T}_{c}] $=SVD($ {P}_{\text{sam}} $, $ {Q}_{\text{sam}} $); Count $ C_{\text{in}}^{\text{cur}} $ by $ \textit{P} $ and $ \textit{Q} $;		If Err<$ {\textit{D}}_{\text{f}} $ update $ {\textit{R}}_{\text{f}} $, $ {\textit{T}}_{\text{f}} $; break;
else Continue;		End If; //收敛条件
If $ C_{\text{in}}^{\text{cur}} $>$ \textit{C}_{\text{in}}^{\text{max}} $ update $ {R}_{\text{opt}} $, $ {T}_{\text{opt}} $, $ \textit{C}_{\text{in}}^{\text{max}} $; End If;		End For;
If $ \textit{C}_{\text{in}}^{\text{max}} $>$ {S}_{\text{num}} $ break; End If;		$ {\textit{P}}_{\text{f}} $=$ {\textit{R}}_{\text{f}} $·$ \textit{P'} $+$ {\textit{T}}_{\text{f}} $;
End For; //传统RANSAC算法迭代		End For;
输出：$ \textit{P'}\text{=}{R}_{\text{opt}}\cdot P+{T}_{\text{opt}} $;		输出：$ {\textit{P}}_{f} $

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表 2 InISAR 系统对舰船目标仿真参数

参数	值
中心频率	10 GHz
信号带宽	300 MHz
PRF	800 Hz
雷达速度	150 m/s
雷达高度	2 km
舰船速度	10 Kn
基线长度	5 m
雷达到目标距离	30 km
相干积累时间	2 0s

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表 3 舰船目标三维摇摆参数

海况	海况1			海况2
方向	偏航	纵摇	横摇	偏航	纵摇	横摇
幅度(°)	3.6	1.7	19.2	4.0	3.0	5.0
周期(s)	14.2	6.7	12.2	14.0	7.0	12.0

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表 4 各时间段配准精度

海况	海况1			海况2
误差	平均误差(m)	中值误差(m)	均方根误差(m)	平均误差(m)	中值误差(m)	均方根误差(m)
时间段1和2	2.1011	1.4678	2.7251	2.9879	2.4311	2.6162
时间段3和2	1.7467	1.6310	2.3973	2.2799	1.5976	1.9244
原始模型对比	1.4194	0.9410	1.9523	1.2855	0.9804	1.6035

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表 5 驱逐舰三维成像结果点云配准精度

	平均误差(m)	中值误差(m)	均方根误差(m)
时间段1和2	1.1445	0.9841	1.3466
时间段1和3	1.0965	0.9880	1.2449
时间段1和4	2.2753	1.5635	3.1008

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