基于关键参数先验的车载雷达镜头杂散光抑制模型研究

张丽芝; 陆秋萍; 段帆琳; 戴幸; 乔大勇

doi:10.37188/CO.2025-0074

基于关键参数先验的车载雷达镜头杂散光抑制模型研究

doi: 10.37188/CO.2025-0074

cstr: 32171.14.CO.2025-0074

张丽芝^{1, 2,},
陆秋萍^2,,
段帆琳^2,,
戴幸^2,,
乔大勇^{1, 3, ,}

1.
西北工业大学空天微纳系统教育部重点实验室, 陕西西安 710072
2.
宁波永新光学股份有限公司, 浙江宁波 315048
3.
西北工业大学宁波研究院, 浙江宁波 315103

基金项目: 国家自然科学基金（No. U21B2035，No. 62074128）

详细信息

作者简介:
张丽芝（1986—），女，山东淄博人，博士研究生，高级工程师，2012于浙江大学获得硕士学位，主要从事成像光学系统设计及开发方面的研究。E-mail：zlz@yxopt.com

乔大勇（1977—），男，山东青岛人，教授，博士生导师，2007年于西北工业大学获得博士学位，主要从事光学MEMS芯片及其应用、先进MEMS制造工艺等研究。E-mail：dyqiao@nwpu.edu.cn

中图分类号: 0439
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出版历程
- 网络出版日期: 2025-08-21

Research on stray light of vehicle LiDAR lens based on key parameter priors

ZHANG Li-zhi^{1, 2
,},
LU Qiu-ping^2
,,
DUAN Fan-lin^2
,,
DAI Xing^2
,,
QIAO Da-yong^{1, 3
, ,}

1.
Key Laboratory of Micro/Nano Systems for Aerospace, Ministry of Education, Northwestern Polytechnical University, Xi’an 710072, Shanxi, China
2.
Ningbo Yongxin Optics Co. Ltd., Ningbo 315048, China
3.
Ningbo Institute of Northwestern Polytechnical University, Ningbo, 315103, Zhejiang, China

Funds: Supported by the National Natural Science Foundation of China (No. U21B2035, No. 62074128)

More Information

Corresponding author: dyqiao@nwpu.edu.cn

摘要

摘要:
车载激光雷达受杂散光干扰会导致信噪比降低和探测效率下降。因此，本文提出了一种基于光谱功率密度函数和总积分散射的表面散射建模方法，拟合了不同材料表面的双向反射分布函数（BRDF）。模型计算结果与实测BRDF数据高度吻合，验证了该方法的有效性。基于此模型，本文系统分析了车载激光雷达长焦接收镜头的杂散光来源及传播路径，包括机盒内壁、镜片边缘、隔圈表面散射等。根据仿真结果，本文提出了多项杂光抑制措施，如采用低散射材料结构件、镜片表面镀增透膜、透镜非工作区涂覆消光油墨等，并且从光学设计、信号处理及工程优化等多维度评估了该激光雷达接收光学系统的杂光抑制水平。实验结果显示，优化后系统杂散辐射水平显著降低：成像视场外的点源透过率（PST）从1e+0降至1e-5，视场内PST从1e+2降至1e-1，杂散光与目标信号对比度控制在10e-4以下。此外，探测回波信号强度提升显著，有效提高了激光雷达的探测性能。本研究为车载激光雷达的杂散光抑制提供了理论模型和实用解决方案，对高灵敏度光学系统的设计与优化具有一定的参考价值。
- 车载激光雷达 /
- 长焦光学系统 /
- 杂散光抑制 /
- 点源透过率 /
- 消光处理
Abstract:
Stray light interference in vehicular LiDAR systems can reduce the signal-to-noise ratio and degrade detection efficiency. To mitigate this issue, this paper proposed a surface scattering modeling method based on the spectral power density function and total integrated scattering, which fits the bidirectional reflectance distribution function (BRDF) for various material surfaces. The model calculation results were highly consistent with the measured BRDF data, verifying the effectiveness of the method. Based on this model, the study systematically analyzed the sources and propagation paths of stray light in the long-focal-length receiving optics of vehicular LiDAR, with specific attention to scattering from the housing's inner walls, lens edges, and spacer ring surfaces. According to the simulation results, this paper put forward a number of stray light suppression measures, such as using structural components made of low-scattering materials, coating anti-reflection films on lens surfaces, and applying light-absorbing ink to the non-optical areas of lenses, etc. Furthermore, from optical design, signal processing and engineering optimization, the stray light suppression level of this LiDAR receiving optical system was evaluated in multiple dimensions. The results of the experiment showed that the level of stray radiation in the optimized system was significantly reduced: the point source transmittance (PST) outside the imaging field of view was reduced from 1e+0 to 1e-5, the PST in the field of view was reduced from 1e+2 to 1e-1, and the stray light contrast with the target signal was controlled below 10e-4. Additionally, the intensity of the detected echo signal is significantly improved, thereby effectively enhancing the detection performance of the LiDAR. This study provides a theoretical model and practical solutions for stray light suppression in vehicle-mounted LiDAR, offering valuable references for the design and optimization of high-sensitivity optical systems.
- vehicle-mounted LiDAR /
- telephoto optical system /
- stray light suppression /
- point source transmittance /
- anti-reflection treatment

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图 1 PSD拟合结果

Figure 1. PSD fitting results

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图 2 镜片BRDF测量与拟合

Figure 2. Lens BRDF measurement and fitting

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图 3 不同结构件BRDF拟合结果

Figure 3. Different structures BRDF fitting results

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图 4 激光雷达整机结构示意图

Figure 4. Structure diagram of LiDAR

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图 5 镜头结构示意图

Figure 5. Structure diagram of lens

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图 6 机盒内不同表面的光线路径及辐照度

Figure 6. Ray path in the box and irradiance map on surfaces

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图 7 镜头内杂光分析

Figure 7. Stray light analysis in the lens

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图 8 不同抑制方案的效果

Figure 8. Results of different plans

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图 9 镜头信号接收实验

Figure 9. Lens signal reception experiment

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表 1 机盒内主要光线路径

Table 1. Primary ray propagation paths within housing

序号	光线路径	入射光通量与光源之比/%	在其接收面的占比/%	光线数/根
1	光源-反射镜1-视窗	94.9240	98.90	119401
2	光源-反射镜1-视窗-目标-视窗	0.0426	0.01	1129
3	光源-反射镜1-视窗-目标-视窗-反射镜2-G1	0.0035	29.75	107
4	光源-反射镜1-视窗-反射镜1-光源-反射镜2-G1	0.0027	22.54	1326
5	光源-反射镜1-视窗内部反射-反射镜1-光源-反射镜2-G1	0.0024	20.56	1342
6	光源-反射镜1-视窗-目标-视窗-反射镜2-G1-G2-G3-G4-G5-探测器	0.0019	86.44	77
7	光源-反射镜1-视窗-目标-视窗-反射镜2-G1-G2-G3-G4-G5非工作区-探测器	0.0003	13.47	12

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表 2 镜头内主要光线路径

Table 2. Dominant optical paths through lens system

序号	光线路径	入射光通量与光源之比/%	在接收面的占比/%	光线数/根
1	目标-G1-G2-G3-G4-G5-探测器	11.3395	68.3153	17924
2	目标-G1-G2-G3-G4-G5非工作区-探测器	3.9552	23.8282	6252
3	目标-G1-G2-G3-G4-B5-G5-探测器	0.0602	0.3627	1910
4	目标-G1-G2非工作区-G3-G4-G5非工作区-探测器	0.0112	0.0673	18
5	目标-G1-G2非工作区-G3-G4-G5-探测器	0.0099	0.0599	16
6	目标-G1-G2-G3S1-G2S2-G3-G4-G5-探测器	0.0077	0.0464	16535
7	目标-G1-G2-G3-G4-G5非工作区-H1-探测器	0.0075	0.0450	236
8	目标-G1-G2-G3内部反射一次-G4-G5-探测器	0.0074	0.0448	15974
9	目标-G1-G2-G3S2-G2S2-G3-G4-G5-探测器	0.0069	0.0417	15792

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表 3 分析与抑制方案设计

Table 3. Design of analysis and inhibition protocol

方案序号	结构件表面		光学件表面		其它
方案序号	黑漆	黑漆喷涂	普通镀膜	增透膜	镜片非工作区涂墨	消光螺纹
#1	√	-	√	-	-	-
#2	-	√	√	-	-	-
#3	-	√	-	√	-	-
#4	-	√	√	-	√	-
#5	-	√	√	-	-	√
#6	-	√	-	√	√	-
#7	-	√	-	√	-	√
#8	-	√	√	-	√	√
#9	-	√	-	√	√	√

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