激光雷达超分辨成像技术研究现状与发展趋势

郭玉麟; 胡以华; 方佳节; 徐世龙; 张鑫源

doi:10.12000/JR25028

激光雷达超分辨成像技术研究现状与发展趋势

DOI: 10.12000/JR25028 CSTR: 32380.14.JR25028

郭玉麟^{1, 2},
胡以华^{1, 2, 3, ,},
方佳节^{1, 2},
徐世龙^{1, 2, 3},
张鑫源^{1, 2, 4, ,}

1.
国防科技大学先进激光技术安徽省实验室合肥 230037
2.
国防科技大学电子制约技术安徽省重点实验室合肥 230037
3.
合肥综合性国家科学中心信息安全研究中心合肥 230037
4.
国科大杭州高等研究院物理与光电工程学院杭州 310024

基金项目: 国家自然科学基金(61871389)，国防科技大学自主创新科学基金(24-ZZCX-JDZ-43, 22-ZZCX-07)，国防科技大学青年自主创新科学基金(ZK23-45)

详细信息

作者简介:
郭玉麟，博士生，主要研究方向为反射层析激光雷达成像技术

胡以华，博士，教授，主要研究方向为光电探测技术

方佳节，博士，副研究员，主要研究方向为激光雷达信号处理

徐世龙，博士，副教授，主要研究方向为激光雷达图像处理

张鑫源，博士，助理研究员，主要研究方向为远程激光雷达成像理论及应用

通讯作者:
胡以华 skl_hyh@163.com

张鑫源 skl_zxy@163.com

责任主编：汪丙南 Corresponding Editor: WANG Bingnan
中图分类号: O439; TN958.98
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出版历程
- 收稿日期: 2025-02-14
- 修回日期: 2025-05-09
- 网络出版日期: 2025-05-29

Research Status and Development Trend of LiDAR Super-resolution Imaging Technology

GUO Yulin^{1, 2},
HU Yihua^{1, 2, 3
, ,},
FANG Jiajie^{1, 2},
XU Shilong^{1, 2, 3},
ZHANG Xinyuan^{1, 2, 4
, ,}

1.
Advanced Laser Technology Laboratory of Anhui Province, National University of Defense Technology, Hefei 230037, China
2.
Anhui Province Key Laboratory of Electronic Restriction, National University of Defense Technology, Hefei 230037, China
3.
Information Security Research Center, Hefei Comprehensive National Science Center, Hefei 230037, China
4.
Hangzhou Institute for Advanced Study, School of Physics and Optoclectronic Engincering, Hangzhou 310024, China

Funds: The National Natural Science Foundation of China (61871389), The Scientific Research Project of National University of Defense Technology (24-ZZCX-JDZ-43, 22-ZZCX-07), The Youth Independent Innovation Science Fundation Project of National University of Defense Technology (ZK23-45)

More Information

Corresponding author: HU Yihua, skl_hyh@163.com; ZHANG Xinyuan, skl_zxy@163.com

摘要

摘要: 随着我国空间利益拓展与在轨资产规模增长，非合作空间暗弱目标的高精度探测已成为空间安全防御与碎片清除的核心瓶颈。传统光学或雷达探测手段受限于衍射极限与信噪比约束，对“快、远、小、暗”目标的探测与识别精度不足，激光雷达凭借其高精度、抗干扰等优势，逐渐成为空间目标高精度探测的核心技术手段。通过突破传统激光雷达系统的物理限制，亚像素扫描、合成孔径和反射层析等技术能够实现远距离超分辨成像。该文从总结梳理关键问题和关键技术出发，追踪了3种激光雷达超分辨成像技术的关键技术研究进展，分析了典型实验系统和实验结果，并结合空间探测、遥感测绘等任务需求，阐述了各体制的特点、优势和不足，展望了其应用前景和发展趋势。
- 激光雷达 /
- 超分辨成像 /
- 亚像素扫描 /
- 合成孔径 /
- 反射层析
Abstract: With the expansion of China’s space interests and the growth in the scale of on-orbit assets, high-precision detection of dark and weak targets in noncooperative space has become the core bottleneck in space security defense and debris removal. Traditional optical or radar detection technologies are limited by diffraction limit and signal-to-noise ratio constraints, and the detection and identification accuracy of “fast, far, small, and dark” targets is insufficient. Light Detection and Ranging (LiDAR), with its high precision and anti-jamming advantages, has gradually become the core technical means of accurately detecting space targets. Technologies such as sub-pixel scanning, synthetic aperture, and reflective tomography enable long-range super-resolution imaging by breaking through the physical limitations of conventional LiDAR systems. This paper begins by summarizing and sorting the critical problems associated with LiDAR super-resolution technology. The key technological research progress is then reported, typical experimental systems and experimental results are analyzed, and the characteristics, advantages, and shortcomings of each system are described with respect to requirements of space exploration, remote sensing, and mapping missions. Finally, the application prospects and development trends are presented.
- Light Detection and Ranging (LiDAR) /
- Super-resolution imaging /
- Sub-pixel scanning /
- Synthetic aperture /
- Reflective tomography

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图 1 激光雷达超分辨成像超分辨原理与关键技术框架

Figure 1. Mechanism and key technology framework of LiDAR super-resolution imaging technology

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图 2 光束偏转机制与细分实现技术

Figure 2. Beam deflection mechanism and subdivision implementation technology

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图 3 准直照明与聚焦照明成像原理^[27](a)准直照明(b)聚焦照明

Figure 3. Collimated and Focused Illumination Imaging Principles (a) Collimated illumination (b) Focused illumination

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图 4 高效光子重建算法与ML估计算法对比 SBR-信号与背景比

Figure 4. Efficient photon reconstruction algorithm vs. ML estimation algorithm SBR-signal to background ratio

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图 5 Jigsaw 机载激光雷达^[42](a) Jigsaw机载激光雷达光学探测部分，(b) 彩色编码高度三维点云成像，选通合适高度点云，树底目标清晰可见

Figure 5. Jigsaw Airborne^[42] (a) LiDAR jigsaw sensor optical head (b) Color-coded altitude 3D point cloud imaging, selecting the right altitude point cloud for a clear view of the target at the base of the tree

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图 6 Pawlikowska三维单光子激光雷达对6.8 km目标重建效果^[44](a) 可见光波段近景图像(b)和(c)为通过最小二乘寻峰法对6.8 km 桥塔深度强度重建图，(b)和(c)设置阈值不同

Figure 6. Effect of Pawlikowska 3D single-photon LiDAR on 6.8 km target reconstruction^[44] (a) Close-up image in the visible band (b) and (c) Depth intensity reconstruction of the 6.8 km bridge tower by the least-squares peak-finding method, with different thresholds in (b) and (c)

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图 7 中国科学技术大学徐飞虎团队机载单光子激光雷达系统^[45](a) 系统总体外观 (b) 系统与陀螺稳定件 (c) 光学收发装置细节 (d) 实例230 m×470 m 成像结果

Figure 7. Airborne single-photon LiDAR system by USTC Feihu Xu’s team^[45] (a) Overall design of the airborne single-photon LiDAR system (b) Main components of the system installed on the gyro stabilization mount (c) Details of the transceiver optics (d) Example 3D imaging result of a 230 m × 470 m region

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图 8 相位噪声补偿的FMCW激光雷达实验示意图^[58]

Figure 8. Schematic of the experimental FMCW ladar for phase noise compensation^[58]

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图 9 正交基线干涉处理方法^[67](a) “一发四收”视场布局示意图，(b) 系统结构图

Figure 9. Orthogonal interferometry^[67] (a) Schematic diagram of the layout of one transmitting and four receiving in inner FOV (b) System schematic diagram

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图 10 阵列SAL^[72] (a) 通道阵列SAL光学系统示意图，(b) 10路阵列激光远场椭圆光斑分布示意图

Figure 10. Array SAL^[72] (a) Schematic diagram of 10-channel array SAL optical system, (b) Schematic diagram of far-field elliptical spot distribution of a 10-channel array laser

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图 11 7 km ISAL外场成像结果对比^[76](a) 靶标 (b) RDA恢复图像 (c) MMBIR恢复图像

Figure 11. 7 km ISAL outfield imaging results comparison^[76] (a) Target (b) RDA recovery image (c) MMBIR recovery image

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图 12 Buck团队^[77]机载SAL实验(a) 机载平台，(b) 机载系统外观，(c) 机载系统内部结构，(d) 目标光学图像，(e) 无角反射器SAL成像结果，(f) 使用角反射器校正相位误差SAL成像结果

Figure 12. Buck’s team^[77] airborne SAL experiment (a) Airborne platform, (b) Airborne system outside, (c) Inside, (d) Target optical image, (e) SAL imaging results without corner, (f) SAL imaging results using corner reflector to correct for phase error reflector

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图 13 刘立人团队^[78,79]直视SAL实验结果(a) 地面目标 (b) 3 km机载SAL成像结果 (c) 无人机目标 (d) 3.4 km无人机目标ISAL侧面成像结果

Figure 13. Liu’s team^[78,79] down look SAL experiment results (a) Ground target (b) 3 km airborne SAL imaging results (c) UAV target (d) 3.4 km UAV target ISAL side imaging results

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图 14 机载1 km实验^[80](a) 字母“E”实物 (b) SAL成像结果 (c) SAL成像分辨率分析

Figure 14. Airborne 1 km experiment^[80] (a) Letter “E”object (b) SAL imaging results (c) SAL imaging resolution analysis

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图 15 阵列SAL 4.6 km实验^[72](a) 汉字“月”实物图 (b) 4个通道分别成像结果 (c) 拼接后整个图像

Figure 15. Array SAL 4.6 km experiment^[72] (a) Physical image of the Chinese character “yue” (b) Imaging results of each of the four channels (c) The whole imageafter stitching

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图 16 百公里级SAL成像实验^[84](a) 实验场景 (b) 目标及SAL成像结果 (c) 6个角锥的摆放场景(其中两端角锥相距11 m)以及SAL成像结果

Figure 16. SAL experimental scene and imaging results^[84] (a) Experimental scene (b) The targets and their SAL imaging result (c) The scene showing the placement of 6 pyramids (where the two end pyramids are 11 m apart) and the SAL imaging

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图 17 1 km RTL远场实验不同算法对比^[102](a) 重建目标 (b) 全角度采样重建 (c) 间隔20°采样重建数字1～5代表不同重建算法(1) Inverse Radon (2) FBP (3) ART (4) sparse ART with OMP (5) TV sparse reconstruction with ART

Figure 17. Comparison of different algorithms for 1 km RTL outfield experiments^[102] (a) Reconstruction target (b) Full angle sampling reconstruction (c) 20° spaced sampling reconstruction (Numbers 1～5 represent different reconstruction algorithms) (1) Inverse Radon (2) FBP (3) ART (4) Sparse ART with OMP (5) TV sparse reconstruction with ART

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图 18 对在轨卫星RTL成像结果^[113](a) LACE卫星外观 (b) RTL重建图像 (c) 阈值处理后重建图像

Figure 18. Results of RTL imaging of on-orbit satellites^[113] (a) LACE satellite appearance (b) RTL reconstructed image (c) Reconstructed image after thresholding

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图 19 Murray等人^[98]22.4 km编码孔径激光反射层析实验(a) 实验装置 (b) Radon变换 (c) 重建图像

Figure 19. Murray et al.^[98] 22.4 km coded aperture laser reflection chromatography experiment (a) Test configuration (b) Radon transform (c) Reconstructed image

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图 20 胡以华团队^[119]10.38 km激光反射层析室外实验(a) “NUDT”艺术字 (b) 艺术字重建结果 (c) 空间碎片目标 (d) 空间碎片重建结果，(e) 实验装置

Figure 20. 10.38 km laser reflection tomography outdoor experiment by Hu Yihu’s team^[119] (a) “NUDT”artwork (b) Artwork reconstruction result (c) Space debris target (d) Space debris reconstruction result (e) Experimental set-up

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表 1 典型激光扫描成像系统对比

Table 1. Comparison of typical laser scanning imaging system

文献	年份	距离(km)	分辨率	光源	探测器	系统	图像重建方法
[43]	2013	4.5	—	1550 nm 1.6 mW	InGaAs/InP PDE 26% DCR 16000/cps	Galvanometer Scan mirror	pixel-by-pixel time-correlated cross-correlation
[44]	2017	10.5	0.28 m	1550 nm 10 mW	InGaAs/InP PDE 30% —	Galvanometer Scan mirror	Image mosaicking TV restoration
[46]	2017	0.9	—	532 nm 28 mW	Si PDE 55% —	Galvanometer Scan mirror Aperture 78 mm	Modified TV restoration
[24]	2020	45.0	0.6 m	1550 nm 120 mW	InGaAs/InP PDE 15% DCR 2200/cps	Galvanometer Scan mirror Aperture 279 mm	Sub-pixel scanning 3D- Deconvolution
[25]	2020	201.5	—	1550 nm 600 mW	InGaAs/InP PDE 29% DCR ～100/cps	Galvanometer Scan mirror Aperture 279 mm	Sub-pixel scanning 3D-Deconvolution
[47]	2021	3.1	0.11 m	1064 nm 6 μJ	Si — DCR 100/cps	All-fiber Rotation platform Aperture 20.5 mm	Sub-pixel scanning 3D-Deconvolution
[45]	2024	2.1	0.4 m	1550 nm 150 mW	InGaAs/InP PDE 20% DCR 2000/cps	Three 64×64 SPAD arrays Aperture 47 mm	Sub-pixel scanning 3D-Deconvolution
*PDE: Photon Detection Efficiency, DCR: Dark Count Rate

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表 2 典型SAL系统对比

Table 2. Comparison of typical SAL systems

文献	年份	分辨率	距离	激光参数	相位补偿与成像方法	系统参数
[77]	2011	3.3 cm	1.6 km	1.5 W @1550 nm Phase coding 7 GHz	PGA Conventional	Airborne SAL Swath 1～2 m (1.67 mrad) Single- channel
[78]	2016	～ 5 cm	3 km	20 W @1030 nm Galvanometer-frequency 650 Hz	Attitude compensation Cross-Track Resampling	Airborne Down look SAL Swath 14.5 m (4.8 mrad) Single-channel
[79]	2018	12.7 mm×9.2 mm	3.4 km	3 W @1030 nm Galvanometer-frequency 760 Hz	PGA Envelope alignment	Ground down look ISAL Single-channel
[81]	2016	1.8 cm	1.1 km	10 W@1550 nm EOM 4 GHz	— —	Ground ISAL Single-channel
[80]	2016	4 cm	2 km	1550 nm EOM 4 GHz	Sub-aperture PGA splicing	Airbone SAL Single-channel
[72]	2021	～1 cm	4.3 km	20 W@1550 nm 5 GHz	— Time-space stitching	Ground ISAL 10-channel array
[84]	2024	15.6 mm×1.7 mm	101.8 km	101.3 W 10 GHz	— —	Ground ISAL 4×4 micromirror-lens array Swath 11 m
*Res: Resolution, EOM: Electro-Optic Modulation

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表 3 典型RTL成像系统对比

Table 3. Comparison of typical RTL imaging systems

文献	年份	距离	分辨率	回波探测方法	激光波长/脉宽	回波处理方法/重构算法/后处理技术
[99]	1989	5.4 km	—	Doppler detection	10.6 μm —	— Inverse Radon transform —
[113]	2001	500 km	0.2 m	Heterodyne detection	11.15 μm 1.5 ns	— FBP Thresholding
[98]	2010	22.4 km	0.15 m	Coded aperture	— —	— Inverse Radon transform —
[115]	2012	53 m	—	TCSPC	1.5 μm 4 ps	— FBP Convex hull mask
[119]	2022	10.4 km	0.02 m	Direct detection	1.06 μm 73 ps	Feature tracking FBP —
[102]	2022	1 km	—	Direct detection	1.06 μm 100 ps	Feature tracking TV-ART —
*TCSPC：Time-Correlated Single Photon Counting

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表 4 激光雷达超分辨成像技术对比

Table 4. Comparison of LiDAR super-resolution imaging techniques

成像技术	超分辨原理	优势	局限
扫描成像	二维亚像素扫描	技术成熟、效果稳定、成像灵活	分辨率受扫描步进、阵列像素间距、激光光斑大小等因素限制，随探测距离下降
SAL	多个成像口径相干合成	理论分辨率与探测距离无关	需要平台与目标相对运动，对平台稳定度、激光相干性等要求高，工程难度大
RTL	多角度层析重建	理论分辨率与探测距离无关，可与多体制结合	非直视平面成像，需要采集尽可能多的角度回波信息，平台与目标需相对旋转

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