Volume 52 Issue 3
Mar.  2023
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Zhang Xiaoyu, Wang Fengxiang, Guo Ying, Wang Wenjuan, Luo Yongfeng, Wu Wen, Hou Jia, Jiang Ziqing, Peng Ziqiang, Huang Genghua, Shu Rong. Research on linear array scanning lidar and photon signal processing technology based on InGaAs single-photon detector[J]. Infrared and Laser Engineering, 2023, 52(3): 20220474. doi: 10.3788/IRLA20220474
Citation: Zhang Xiaoyu, Wang Fengxiang, Guo Ying, Wang Wenjuan, Luo Yongfeng, Wu Wen, Hou Jia, Jiang Ziqing, Peng Ziqiang, Huang Genghua, Shu Rong. Research on linear array scanning lidar and photon signal processing technology based on InGaAs single-photon detector[J]. Infrared and Laser Engineering, 2023, 52(3): 20220474. doi: 10.3788/IRLA20220474

Research on linear array scanning lidar and photon signal processing technology based on InGaAs single-photon detector

doi: 10.3788/IRLA20220474
Funds:  Major Municipal Science And Technology Projects in Shanghai (2019SHZDZX01); Deve-lopment of Ship Situation Intelligent Perception System (MC-201920-X01); Shanghai Natural Science Foundation (19ZR1466000)
  • Received Date: 2022-07-08
  • Rev Recd Date: 2022-08-21
  • Available Online: 2023-03-20
  • Publish Date: 2023-03-25
  •   Objective   With the development of the detection system, the photon-counting imaging lidar based on single-photon detection technology has greatly improved the detection sensitivity of the echo optical signal, effectively reduced the demand of the system for laser power, and made it possible for miniaturized, long-distance, high-resolution, and high-precision laser 3D imaging equipment, which is widely used in the field of long-distance ranging and imaging and has become a research hotspot.Since the number of echo photons in long-distance laser detection is only the order of single photons, the detection performance of the detector is highly required. So at present, most photon-counting lidar remote imaging generally adopts the method of area array staring detection or unit micro-mirror scanning to accumulate the fixed target for a long time to improve the signal-to-noise ratio, which is not conducive to real-time dynamic measurement of large-scale and large-range targets.Although there are many schemes using SPAD (Single-photon Avalanche Diode) to carry out ranging and imaging experiments at home and abroad, Si-based SPAD in the visible band is mainly used. Compared with InGaAs/InP SPAD, the dark count, detection efficiency, afterpulse probability, dead time and other indicators are not ideal. In order to achieve higher single-photon detection performance in near-infrared band, InGaAs/InP SPAD mainly adopts gated quenching mode, which is more suitable for the situation where the target distance is known, while not suitable for the situation where the target range is large, and the relevant research on the range measurement and imaging experiment using the active quenching mode InGaAs/InP SPAD of the free-running system is less, which is only at the stage of principle prototype. Therefore, this study proposes a linear array imaging lidar scheme based on InGaAs/InP single-photon detector.  Methods   Aiming at the working requirements of the eye safety band, based on the free-running mode InGaAs/InP SPAD, a set of remote linear array photon-counting lidar scanning imaging prototype system with multiple transceivers is designed, 128 units of InGaAs/InP SPAD are spliced into a linear array arrangement, the working band of the system is 1 550 nm, the laser repetition frequency is 20 kHz and the laser scanning imaging in the horizontal 200° range is realized through scanning in 2 seconds (Fig.3, Tab.5).At the same time, the factors affecting the detection probability of the detector in the sunlight background are analyzed, and the optimal working point of the system is obtained by combining with the active quenching circuit design (Fig.1) and the adjustment of the working temperature and bias voltage. Point cloud filtering and afterpulse preprocessing algorithms are used to reduce the original data rate of a single receiving channel and characterize isolated targets in the scanning field of view (Fig.2).  Results and Discussions   By analyzing the characteristics of afterpulse and noise, it can be seen that the background noise signal, including the background noise and the dark count signal, is randomly distributed in the whole space. With the increase of the number of echo statistics per unit ranging period, the background noise will increase. Behind the light count signal, there are two levels of obvious afterpulse signal, and the afterpulse caused by the dark count will also lead to the increase of the background noise (Fig.5). After the processing of the point cloud filtering and afterpulse preprocessing algorithm, the original data rate of a single receiving channel is reduced from 200 kbps to less than 1 kbps, and the obvious afterpulse signal behind the target point cloud is removed. Compared with recording single echo, recording four echoes in a single ranging cycle can increase the effective data volume by about 5%. After the processing of the imaging algorithm, the system successfully realizes three-dimensional imaging of multi-range targets under daylight conditions, the maximum detection distance is more than 3 km, and the imaging targets are clear (Fig.7).  Conclusions   In this study, a long-distance imaging lidar system based on photon-counting detection technology is designed, and the detection performance of single-photon detector is studied. After that, the noise and after-pulse characteristics, imaging clarity, and other indicators of the lidar are verified by fixed-point ranging experiments and scanning imaging experiments. The experimental results show that the system can successfully detect multi-range target information, and can detect long-distance targets of more than 3 km. The final reconstructed image is clear, and the noise suppression effect is perfect. The system is suitable for three-dimensional point cloud imaging of long-distance targets. However, due to the significant after-pulse effect of InGaAs/InP SPAD, the increase of dark count will seriously affect the detection performance. The afterpulse and background noise can be significantly filtered by point cloud filtering and afterpulse preprocessing algorithm, and the point cloud data can be compressed for subsequent processing.
  • [1] Dabney P, Harding D, Abshire J, et al. The slope imaging multi-polarization photon-counting lidar: Development and performance results[C]//2010 IEEE International Geoscience and Remote Sensing Symposium. New York: IEEE, 2010: 653-656.
    [2] Jingjing G, Xiaoyan F, Peng G, et al. High-resolution three-dimensional imaging based on all-fiber photon-counting Lidar system [J]. Infrared and Laser Engineering, 2021, 50(7): 20210162. (in Chinese) doi:  10.3788/IRLA20210162
    [3] Marino R M, Davis W R, Rich G C, et al. High-resolution 3D imaging laser radar flight test experiments[C]//Laser Radar Technology and Applications X. SPIE, 2005, 5791: 138-151.
    [4] Marino R M, Davis W R. Jigsaw: a foliage-penetrating 3D imaging laser radar system [J]. Lincoln Laboratory Journal, 2005, 15(1): 23-36.
    [5] Knowlton R. Airborne ladar imaging research testbed[R]. Lexington, MA, United States: MIT Lincoln Laboratory, 2011.
    [6] Gray G. High Altitude Lidar Operations Experiment (HALOE)–Part 1, system design and operation[C]//Proc of Military Sensing Symposium, Active Electro-Optic Systems, 2011: 12-14.
    [7] Albota M A, Gurjar R S, Mangognia A V D, et al. The airborne optical systems testbed (AOSTB)[R]. Lexington, MA, United States: MIT Lincoln Laboratory, 2017.
    [8] Clifton W E, Steele B, Nelson G, et al. Medium altitude airborne Geiger-mode mapping LIDAR system[C]//Laser Radar Tech-nology and Applications XX and Atmospheric Propagation XII. SPIE, 2015, 9465: 39-46.
    [9] Sun Jianfeng, Jiang Peng, Zhang Xiuchuan, et al. Experimental research of 32×32 InGaAs Gm-APD arrays laser active imaging [J]. Infrared and Laser Engineering, 2016, 45(12): 1206006. (in Chinese) doi:  10.3788/IRLA201645.1206006
    [10] Du B, Pang C, Wu D, et al. High-speed photon-counting laser ranging for broad range of distances [J]. Scientific Reports, 2018, 8(1): 1-6. doi:  10.1038/s41598-018-22675-1
    [11] Li Z P, Ye J T, Huang X, et al. Single-photon imaging over 200 km [J]. Optica, 2021, 8(3): 344-349. doi:  10.1364/OPTICA.408657
    [12] Itzler M A, Entwistle M, Owens M, et al. Geiger-mode avalanche photodiode focal plane arrays for three-dimensional imaging LADAR[C]//Infrared Remote Sensing and Instrumentation XVIII. SPIE, 2010, 7808: 75-88.
    [13] Jiang X, Itzler M, O’Donnell K, et al. InP-based single-photon detectors and Geiger-mode APD arrays for quantum communications applications [J]. IEEE Journal of Selected Topics in Quantum Electronics, 2014, 21(3): 5-16. doi:  10.1109/JSTQE.2014.2358685
    [14] Itzler M A, Jiang X, Entwistle M, et al. Single-photon detectors based on InP avalanche diodes: status and prospects [J]. Advanced Photon Counting Techniques II, 2010, 7681: 195-206.
    [15] Libing H. Research on key technologies of photon counting imaging lidar in moving conditions[D]. Beijing: University of Chinese Academy of Sciences, 2013. (in Chinese)
    [16] Cova S, Ghioni M, Lotito A, et al. Evolution and prospects for single-photon avalanche diodes and quenching circuits [J]. Journal of Modern Optics, 2004, 51(9-10): 1267-1288. doi:  10.1080/09500340410001670839
    [17] Buller G S, Collins R J. Single-photon detectors for infrared wavelengths in the range 1-1.7 μm[M]//Advanced Photon Counting. Cham: Springer, 2014: 43-69.
    [18] Lacaita A, Zappa F, Cova S, et al. Single-photon detection beyond 1 μm: performance of commercially available InGaAs/InP detectors [J]. Applied Optics, 1996, 35(16): 2986-2996. doi:  10.1364/ao.35.002986
    [19] Milstein A B, Jiang L A, Luu J X, et al. Acquisition algorithm for direct-detection ladars with Geiger-mode avalanche photodiodes [J]. Applied Optics, 2008, 47(2): 296-311. doi:  10.1364/AO.47.000296
    [20] Brunt K M, Neumann T A, Walsh K M, et al. Determination of local slope on the greenland ice sheet using a multibeam photon-counting lidar in preparation for the ICESat-2 mission [J]. IEEE Geoscience and Remote Sensing Letters, 2013, 11(5): 935-939.
    [21] Horan K H, Kerekes J P. An automated statistical analysis approach to noise reduction for photon-counting lidar systems[C]//IGARSS, 2013: 4336-4339.
    [22] Zhang J, Kerekes J, Csatho B, et al. A clustering approach for detection of ground in micropulse photon-counting LiDAR altimeter data[C]//2014 IEEE Geoscience and Remote Sensing Symposium. New York: IEEE, 2014: 177-180.
    [23] Magruder L A, Wharton III M E, Stout K D, et al. Noise filtering techniques for photon-counting ladar data[C]//Laser Radar Technology and Applications XVII. SPIE, 2012, 8379: 237-245.
    [24] Awadallah M, Ghannam S, Abbott L, et al. Active contour models for extracting ground and forest canopy curves from discrete laser altimeter data[C]//Proceedings: 13th International Conference on LiDAR Applications for Assessing Forest Ecosystems, 2013: 129-136.
    [25] Awadallah M S T. Image analysis techniques for LiDAR point cloud segmentation and surface estimation[D]. Virginia, US: Virginia Tech, 2016.
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Research on linear array scanning lidar and photon signal processing technology based on InGaAs single-photon detector

doi: 10.3788/IRLA20220474
  • 1. Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. Shanghai Research Center for Quantum Sciences, Shanghai 201315, China
  • 4. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Fund Project:  Major Municipal Science And Technology Projects in Shanghai (2019SHZDZX01); Deve-lopment of Ship Situation Intelligent Perception System (MC-201920-X01); Shanghai Natural Science Foundation (19ZR1466000)

Abstract:   Objective   With the development of the detection system, the photon-counting imaging lidar based on single-photon detection technology has greatly improved the detection sensitivity of the echo optical signal, effectively reduced the demand of the system for laser power, and made it possible for miniaturized, long-distance, high-resolution, and high-precision laser 3D imaging equipment, which is widely used in the field of long-distance ranging and imaging and has become a research hotspot.Since the number of echo photons in long-distance laser detection is only the order of single photons, the detection performance of the detector is highly required. So at present, most photon-counting lidar remote imaging generally adopts the method of area array staring detection or unit micro-mirror scanning to accumulate the fixed target for a long time to improve the signal-to-noise ratio, which is not conducive to real-time dynamic measurement of large-scale and large-range targets.Although there are many schemes using SPAD (Single-photon Avalanche Diode) to carry out ranging and imaging experiments at home and abroad, Si-based SPAD in the visible band is mainly used. Compared with InGaAs/InP SPAD, the dark count, detection efficiency, afterpulse probability, dead time and other indicators are not ideal. In order to achieve higher single-photon detection performance in near-infrared band, InGaAs/InP SPAD mainly adopts gated quenching mode, which is more suitable for the situation where the target distance is known, while not suitable for the situation where the target range is large, and the relevant research on the range measurement and imaging experiment using the active quenching mode InGaAs/InP SPAD of the free-running system is less, which is only at the stage of principle prototype. Therefore, this study proposes a linear array imaging lidar scheme based on InGaAs/InP single-photon detector.  Methods   Aiming at the working requirements of the eye safety band, based on the free-running mode InGaAs/InP SPAD, a set of remote linear array photon-counting lidar scanning imaging prototype system with multiple transceivers is designed, 128 units of InGaAs/InP SPAD are spliced into a linear array arrangement, the working band of the system is 1 550 nm, the laser repetition frequency is 20 kHz and the laser scanning imaging in the horizontal 200° range is realized through scanning in 2 seconds (Fig.3, Tab.5).At the same time, the factors affecting the detection probability of the detector in the sunlight background are analyzed, and the optimal working point of the system is obtained by combining with the active quenching circuit design (Fig.1) and the adjustment of the working temperature and bias voltage. Point cloud filtering and afterpulse preprocessing algorithms are used to reduce the original data rate of a single receiving channel and characterize isolated targets in the scanning field of view (Fig.2).  Results and Discussions   By analyzing the characteristics of afterpulse and noise, it can be seen that the background noise signal, including the background noise and the dark count signal, is randomly distributed in the whole space. With the increase of the number of echo statistics per unit ranging period, the background noise will increase. Behind the light count signal, there are two levels of obvious afterpulse signal, and the afterpulse caused by the dark count will also lead to the increase of the background noise (Fig.5). After the processing of the point cloud filtering and afterpulse preprocessing algorithm, the original data rate of a single receiving channel is reduced from 200 kbps to less than 1 kbps, and the obvious afterpulse signal behind the target point cloud is removed. Compared with recording single echo, recording four echoes in a single ranging cycle can increase the effective data volume by about 5%. After the processing of the imaging algorithm, the system successfully realizes three-dimensional imaging of multi-range targets under daylight conditions, the maximum detection distance is more than 3 km, and the imaging targets are clear (Fig.7).  Conclusions   In this study, a long-distance imaging lidar system based on photon-counting detection technology is designed, and the detection performance of single-photon detector is studied. After that, the noise and after-pulse characteristics, imaging clarity, and other indicators of the lidar are verified by fixed-point ranging experiments and scanning imaging experiments. The experimental results show that the system can successfully detect multi-range target information, and can detect long-distance targets of more than 3 km. The final reconstructed image is clear, and the noise suppression effect is perfect. The system is suitable for three-dimensional point cloud imaging of long-distance targets. However, due to the significant after-pulse effect of InGaAs/InP SPAD, the increase of dark count will seriously affect the detection performance. The afterpulse and background noise can be significantly filtered by point cloud filtering and afterpulse preprocessing algorithm, and the point cloud data can be compressed for subsequent processing.

    • 激光雷达是一种将传统雷达技术与现代激光技术相结合的主动探测技术,其可以高效率高分辨率完成目标的三维结构数据的获取,广泛用于各类高新科技领域。随着探测体系的发展,基于单光子探测技术的光子计数三维成像激光雷达使得回波光信号的探测灵敏度大大提高,使得小型化、远距离、高分辨、高精度的激光三维成像设备成为可能,但其技术难度较高,已成为目前的研究热点[1-2]

      美国麻省理工学院的林肯实验室(MIT/LL)是最早开展光子计数远距离成像激光雷达相关研究的研究机构,研制了多个机载激光雷达原理样机,其中包括Gen系列三代激光雷达系统[3]、Jigsaw[4]、ALIRT[5]、HALOE[6]、MACHETE、AOSTB[7]等,验证了全天时激光雷达三维成像能力。2015年,Harris公司以林肯实验室的技术为基础,基于128×32规模InGaAs SPAD阵列研制了世界上第一款商用盖革模式激光雷达系统IntelliEarth,探测距离达到6 km,实现了大视场机载三维地形测绘[8]。2016年,哈尔滨工业大学在国内自研32×32面阵InGaAs SPAD基础上,搭建了1570 nm激光主动成像实验平台,在单脉冲能量2 mJ条件下,获得了外场3.9 km目标的轮廓像[9]。2018年,华东师范大学采用单元InGaAs/InP SPAD,通过多重复频率激光脉冲来确定飞行时间的相关性的方法实现了21 km的室外激光测距[10]。2020年,中国科学技术大学研制的1550 nm单光子三维成像系统,实现200 km的远距离单光子三维成像,首次将成像距离从10 km突破到百公里量级[11]。远距离激光探测由于回波光子数量仅为单光子量级,对探测器的探测性能的要求极高,所以目前大多数光子计数激光雷达远距离成像普遍采用面阵凝视探测或单元微振镜扫描的方式对固定目标长时间累计以提高信噪比,不利于对大尺度大范围的目标进行实时动态测量。

      虽然国内外已有许多采用SPAD来进行测距及成像实验的方案,但主要采用可见光波段的Si基SPAD,与之相比,InGaAs/InP SPAD的暗计数、探测效率、后脉冲概率、死时间等指标并不理想。为了在近红外波段实现更高的单光子探测性能,InGaAs/InP SPAD主要采用门控淬灭模式,这种模式比较适合目标距离已知的情况,并不适合目标距离范围较大的场合,而采用自由运转体制的主动淬灭模式InGaAs/InP SPAD进行测距及成像实验的相关研究较少,而且仅停留在原理样机阶段;国外由于器件技术的长期发展,已经基本实现大规模阵列InGaAs/InP SPAD器件技术的商业化,其主要由MIT/LL和Princeton Lightwave公司主导[1214]

      文中提出了一种新型的线阵单光子成像雷达,采用128个单元InGaAs/InP SPAD拼接为线阵排列,通过扫描实现水平200°范围的激光扫描成像。

    • 相较于传统面阵凝视及微振镜扫描型的光子计数激光雷达只需提取每一像素点的累计峰值即可获得固定目标表面的三维结构信息,此激光雷达系统采用多元InGaAs/InP SPAD配合机械扫描的方式实时动态获取大角度大尺度范围目标表面的三维信息,无法通过提取光子计数峰值的方式获取目标信号,需要对获取的光子点云图像进行滤波去噪。为了提高信噪比,需要尽可能提高目标的扫描次数即点云密度,而目标越远,点云密度越小,相邻两次探测的空间间隔由公式(1)表示:

      式中:$\omega $为电机转速;$R$为目标距离;${f_L}$为激光重频。采用自由运转模式SPAD可以大大提高探测器的探测频率。

      自由运转模式是单光子探测器针对目标距离未知情况的一种探测方式,SPAD始终处于雪崩就绪状态,等待光子信号触发,当探测到光子事件发生雪崩效应之后,通过主被动混合淬灭模式进行雪崩淬灭,以实现单光子探测器的高效率连续探测。但由于受到噪声和死时间的影响,单光子探测器无法保证每次探测都能产生有效的回波信号。光子计数激光雷达的噪声主要来自单光子探测器的背景光噪声和探测器本身的暗计数两部分,两者是完全独立的两种随机过程,所以可以用下式表示[15]

      式中:$ {N_t} $为总的平均噪声光电子数,代表了在单位时间内探测器响应的噪声光电子数的平均值;$ {N_b} $为背景光噪声的平均光电子数;$ {N_d} $为探测器的暗计数。暗计数$ {N_d} $是由于热运动、隧道效应和势阱捕获效应等原因导致在没有任何光照时在电场作用下也会产生自触发的雪崩效应,其中由于势阱捕获载流子后释放产生的后脉冲效应是暗计数的主要原因之一,也是限制InGaAs/InP SPAD性能的主要因素[16]。为了抑制InGaAs SPAD后脉冲效应,需要通过延长死时间来使被捕获的载流子从倍增区域释放,从而减小触发后脉冲的可能性。然而,施加较长的死时间限制了探测器的饱和计数率从而降低了探测效率,所以死时间大小的选择是后脉冲抑制和最大计数率之间的权衡[17]。相比于Si基器件,III-V族器件中具有非常高密度的少数载流子俘获能级,这也是InGaAs/InP器件总体性能远低于Si基器件的主要原因之一[18]。背景光噪声$ {N_b} $包括背景自身的热辐射光噪声和太阳背景辐射光噪声,而由于文中系统工作在近红外波段,背景自身的热辐射光噪声可以忽略不计,而太阳背景辐射光噪声包括目标对太阳光的反射和太阳光的大气散射两部分,在系统的探测距离范围内,大气透射度较高,大气后向散射噪声趋近于零,因此可忽略大气的后向散射产生的日光噪声,采用下式表示系统背景噪声模型[19]

      式中:$ {N_b} $为单位时间内探测器探测到的背景噪声平均光电子数;$ {\eta _q} $为探测器的量子效率;$\; \rho $为激光波长下第目标反射率;$ {T_a} $为目标到激光雷达系统的大气透过率;$ {T'_a} $为太阳到目标的大气透过率;$ {\eta _r} $为激光雷达的光学接收效率;$ \theta _r^{} $为目标的视场角;$ {A_r} $为接收光学系统的孔径;$ {H_\lambda } $为太阳光对地面的光谱辐照度;$ \Delta \lambda $为滤波带宽;$ \theta $为阳光照射方向和目标表面的法向夹角。

      图1为自由运转模式单光子探测电路的原理结构,主要包括单光子探测器、温控电路、偏压电路、淬灭恢复电路、鉴别整形电路。为了避免淬灭过程中恢复时间长的问题,采用主动淬灭型电路方案,即采用比较器等外部器件构建一个对SPAD偏置电压的负反馈机制。

      Figure 1.  Principle block diagram of free-running SPAD circuit

      SPAD的工作性能(探测效率和暗计数)受工作条件(制冷温度和偏置电压)的影响,需要对探测电路的最佳工作条件进行选择以达到最佳工作性能。单光子探测器在实际应用中应综合考虑探测效率和暗计数指标。

    • 当前针对光子计数激光雷达的数据特点,后续数据处理的难点主要集中在光子点云去噪滤波算法方面。大量的背景噪声、暗计数等噪声点随机分在整个测量范围内,信号点则以线状连续集聚的形式分布在特定的距离上,且密度明显大于噪声点的密度。目前对光子点云的去噪算法可以分为两类,一类是基于直方图、概率统计、聚类等的统计分析方法[2023],另一类是基于边缘检测、图像分割等的图像处理技术[2325]。每种算法各有优势,所适用的场景和解决的问题不尽相同。由于文中系统的噪声密度较大、分布不均且后脉冲噪声明显,单一算法无法获得最佳的处理效果。文中采用分步由粗到精的噪声滤除策略,结合多种算法的优势,针对地面远距离孤立目标的应用场景,提出一种更为鲁棒的噪声滤除方法。

      针对地面远距离孤立目标的应用场景,采用分步由粗到精的噪声滤除策略旨在从以下三个方面进行算法设计。第一,目标可能出现在测量范围的任意距离处,没有任何关于目标距离的先验信息。同时,噪声充满了整个测量范围,因此首先考虑剔除大部分的噪声点以降低后续数据处理量;第二,在地面测量平台下,不同于机载或星载平台,目标并不是以连续线状形式呈现的,而是孤立地存在于自身的空间范围内,因此,精细化处理可根据目标的孤立聚集形态开展;第三,系统存在明显的后脉冲现象,表现为在目标后方存在和目标形态类似的伪信号,需要根据其特征进行鉴别并剔除。

      基于以上分析,设计了适合该场景下的点云滤波及后脉冲预处理算法,图2为算法的处理流程图。具体步骤及结果如下:

      Figure 2.  Algorithm flow chart

      (1)首先,读取原始光子点云数据,按照时间距离组织成二维平面数据,横轴为测量时间,纵轴为测量距离;

      (2)选取合适的时间累积间隔Δt,将数据进行垂直切片,在每一个切片内进行距离向的直方图统计,将大于均值2倍标准差的距离保留,作为初步的目标点候选集合;

      (3)得到粗去噪结果后,进一步使用DBSCAN(Density-Based Spatial Clustering of Applications with Noise)进行基于密度的空间聚类,聚类后可将其中具有高密度相连的区域划分为同一集合簇,并剔除粗去噪结果中的大部分点数较少的点。

      然而基于密度的聚类结果并不能将后脉冲信号识别出,后脉冲噪点簇将被误认为是低密度的目标簇。为了进一步鉴别真实目标信号之后的后脉冲簇,对每一个聚类的点簇集合沿法线向后移动一段距离寻找后脉冲簇,并判断是否存在后脉冲簇,存在时则将后脉冲簇删除。

    • 激光雷达系统采用光子计数体制,主要技术包括窄脉冲高重频短波红外激光技术、InGaAs/InP SPAD单光子探测及主动淬灭技术、光子计数技术及高精度时间测量技术等,组成部分主要包括:激光器模块、收发光学模块、单光子探测器模块、高精度时间测量模块、扫描电机模块、电源模块。系统的主要参数及结构如表1图3所示。

      DevicesParameterValue
      Laser Wavelength 1550 nm
      Total energy 100 μJ
      Energy per beam 0.5 μJ
      Pulse width 1 ns
      Pulse frequency 20 kHz
      Beam splitting scale 128×1
      Emission optics Divergence angle 0.15 mrad
      Maximum measured distance >3 km
      Included angle between beams 1.37 mrad
      Receiving optics FOV 10°
      IFOV 0.3 mrad
      Aperture 34 mm
      F# 4.9
      SPAD Dark count 20 kHz
      Dead time 200 ns
      Detection efficiency 3%@1550 nm

      Table 1.  Key parameters of the lidar system

      Figure 3.  Block diagram of lidar system

      1550 nm光纤激光器产生的激光经过扩束准直后通过衍射光学元件 DOE进行分束,光纤激光器的输出光纤芯径 25 μm,数值孔径 0.10,系统发射波束为 128,每一波束的发散角为 0.15 mrad,相邻波束之间的夹角为 13.7 mrad,衍射元件的尺寸为 38 mm,有效工作范围为 34 mm。接收望远镜采用像方远心的设计,将 128 束回波与线阵光纤进行耦合,之后每根光纤经过中心波长 1550.12 nm、带宽为 0.2 nm 的窄带滤光片滤除背景噪声,最后通过多模光纤与探测器进行耦合。

      激光雷达系统采用的单光子探测器为中国科学院上海技术物理研究所自研的1550 nm波段InGaAs/InP盖革模式雪崩光电二极管,其在1550 nm波段探测效率>3%,内置有三级TEC制冷器,采用 TO-66的 封装结构,耦合50/100 μm多模尾纤,内置有效探测面直径35 μm的盖革模式雪崩光电二极管芯片。接收光纤经光纤滤波器后,各根光纤与单元探测器逐一对准。探测电路集成了主动淬灭、TEC温控、APD偏压和信号读出等功能。探测器工作在自由运转模式下,在每一只InGaAs/InP单光子探测器上独立进行主动淬灭控制,更快地进行雪崩抑制过程,进一步降低噪声对探测概率的影响,同时降低死时间并提高饱和计数率。TEC温控模块将探测器器件的温度稳定在−40~−20 ℃的可调区间,偏压控制单元可以提供稳定可调的独立偏压调节。

      通过MODTRAN软件分析,1550 nm波段大气透过率约为80%,在日光条件下的背景光谱辐照度为H1550 nm=0.26 W/(m2·nm);在夜晚时,背景光谱辐照度为H1550 nm=1.5×10−6 W/(m2·nm)。将以上参数代入公式(3)计算可知,对于30%反射率的目标表面,系统的日光条件背景光子噪声强度约为3 MHz,相当于每个测距周期约150个光子事件;夜晚时背景光子噪声强度在1 kHz以下,相当于每个测距周期约0.05个光子事件。

    • 单光子探测器在实际应用之前,需要对探测器的工作条件进行仔细的选择以达到最佳的探测性能。为表征激光雷达系统在实际工作环境的探测性能,在日光条件下,以固定光功率对约1.6 km固定点目标进行测距实验,在不同制冷温度条件下对探测器的探测效率以及有光入射时的本底噪声计数率随暗计数率的变化进行了测试,如图4所示。可以看出:暗计数率的变化对探测效率的影响并不明显,探测效率基本维持20%幅度上下;而暗计数率升高,则背景噪声计数率也随之增加,之后在243 K与233 K处有小幅度的下降趋势。

      另外,由于对于单元探测器在233~253 K范围内制冷温度每降低10 K,制冷功耗需增加约1.7 W,对于128元探测器便增加约200 W。为了在低功耗和低本底噪声的同时保持较高的探测性能,选取253 K作为探测器制冷温度的最佳工作点,选取5 kHz暗计数所对应的偏压作为探测器的最佳偏置电压。

      Figure 4.  Variation comparison of the PDE (a) and background noise count rate with light incidenting (b) versus DCR under different refrigeration temperatures

    • 为了探究InGaAs/InP SPAD在最佳工作条件下的测距特性及噪声特性,在日光条件下对约1.6 km的固定距离的高层建筑目标进行了对外测距实验。光子计数激光雷达的点云噪声主要包括暗计数以及光信号引起的后脉冲效应。入射光子和暗电流由于被势阱捕获后释放产生的雪崩计数为一级后脉冲,而一级后脉冲引发的雪崩效应同样会引发更多级的后脉冲。选取其中一元,分别对比了单个测距周期内统计1次和4次回波的点云分布图及直方图,给出了累计5 s共100000次测距的结果,点云分布图横坐标为测距次数,纵坐标为测距距离。图5(a)、(b)分别为1次和4次回波的点云记录结果,可以看出:本底噪声信号包括背景噪声及暗计数信号,随机分布于整个空间,随着单位测距周期回波统计次数的增加,背景噪声也会随之增加,在光计数信号之后还会出现两级较为明显的后脉冲,并且暗计数引起的后脉冲会导致本底噪声的增加。光计数信号,一级和二级后脉冲之间间隔分别为18 m和22 m,相应时间间隔为60 ns和73.3 ns。图5(c)为记录4次回波信号的点云数据经过滤波算法及后脉冲预处理后的结果。与记录单次回波的数据相比,经算法处理后的有效数据量提升了约5%;与原始数据相比,经算法处理后,单个接收通道的数据率由200 kbps量级降低到小于1 kbps,同时大大减小了暗计数以及后脉冲等噪声对点云图像处理产生的影响。

      Figure 5.  (a), (b) Point cloud data recording 1 and 4 echoes; (c) Result processed by rough filtering algorithm after recording single echo

    • 对室外高层建筑群进行了扫描成像实验,电机扫描角度为120°,旋转速度为180 (°)/s,扫描4次进行累计,记录单次回波,取其中16元的成像点云数据进行了滤波及后脉冲预处理。从可见光照片图6可以看出:成像区域左侧的建筑目标普遍较远,而右侧的建筑目标距离较近。图7(a)为经过滤波处理后的点云图像,图7(b)和(c)分别为3.3 km距离的一个建筑点云图像和1.5~2.0 km的建筑群的点云图像,其对应于图中白框标记的建筑群。可以看出经过算法处理后的点云图像已经可以较为清晰地还原远距离建筑的结构特征。

      Figure 6.  Photo of measured objects taken with visible light

      Figure 7.  (a) Filtered point cloud image; (b), (c) Point cloud images of a building with a distance of 3.3 km and building group with distance of 1.5-2.0 km respectively

    • 文中设计了一种基于光子计数探测体制的远程激光雷达三维成像系统,研究了单光子探测器的探测性能。之后通过定点测距实验和扫描成像实验验证了激光雷达的噪声及后脉冲特性、成像清晰度等指标。实验结果表明,该系统成功探测多距离目标信息,最高可探测3 km以上的远程目标,最终重建的图像较为清晰,噪声抑制效果较好,该系统适用于远距离目标的三维点云成像,但由于InGaAs/InP SPAD显著的后脉冲效应导致的暗计数上升会严重影响探测性能。通过点云滤波及后脉冲预处理算法可显著滤除后脉冲以及本底噪声,同时压缩点云数据以便进行后续处理。今后也将进一步探索远程光子计数激光雷达系统提升探测距离以及改进成像效果的方法。

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