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对于激光雷达探测,通用的雷达方程为[12]:
$$ P_{R}=\frac{4 P_{T}}{\pi \theta_{T}^{2} R^{2}} \cdot\frac{\displaystyle\int \rho {\rm{d}} A}{\varOmega R^{2}} \cdot\frac{\pi D^{2}}{4} \cdot\eta_{Atm} \cdot\eta_{Sys }+P_{{b}} $$ (1) 式中:PR为接收回波功率;PT为发射激光功率;Pb为背景辐射和噪声功率;R为目标与雷达之间距离;θT为发射光束发散角;ρ为目标表面对激光的反射率;dA为目标表面面元;Ω为目标光散射立体角;D为接收天线孔径;ηAtm为介质的双程传输效率; ηSys为光学系统总效率。
由此,对于单光子激光雷达,当目标简化为朗伯体时,单个脉冲的回波光子数Ns为:
$$ N_{s}=\frac{\lambda \cdot\eta_{Sys } \cdot E_{T} \cdot\rho \cdot A_{T} \cdot D^{2} \cdot T_{a}^{2} \cdot\cos (\phi)}{h \cdot c \cdot \pi \cdot\theta_{T}^{2} \cdot R^{4}} $$ (2) 式中:λ为激光波长;ET为发射激光单脉冲能量;AT为目标截面积(对于扩展目标
$ A_{T}=1 /\left(4 \pi \theta_{T}^{2} R^{2}\right) $ );Ta为雷达到目标之间的单程大气透过率;ϕ为目标平面法线与激光光轴的夹角;h为普朗克常数;c为真空中光速。实际探测时,单光子探测器输出脉冲中既包含有信号光子的响应脉冲,也包含有噪声光子的响应脉冲,它们混杂在一起,仅通过单次探测无法区分出信号光子脉冲。考虑到每次信号光子脉冲出现的时刻一致(目标距离不变),而噪声光子脉冲出现的时刻具有随机性,因此TCSPC[10]的统计方法被用于单光子探测中,通过多脉冲累积来提取出淹没在噪声中的回波光子信号。
多脉冲累积过程实际上相当于相互独立的多次重复试验(伯努利重复试验)。经过M次累积后的探测概率为[13-15]:
$$ \begin{split} P_{D}=& \displaystyle\sum_{k=k_{thr}}^{M} C_{M}^{k}\left(1-P_{D S}\right)^{(M-k)}\left(P_{D S}\right)^{k} \\ & C_{M}^{k}=M ![k !(M-k) !] \\ & P_{D S}=P_{A}\left[1-{\rm{e}}^{-\eta_{qe}\left(n_{s}+n_{n}\right)}\right] \\ & P_{A}=1 /\left(1+\eta_{\mathbb{qe}} n_{n} t_{d} / \Delta T\right) \end{split} $$ (3) 式中:
$ {k}_{thr} $ 为鉴别阈值;$ k $ 为索引变量,其变化范围为0~($ {k}_{thr} $ −1);PDS为单次探测概率;$ {\eta }_{qe} $ 为单光子探测器的量子效率;$ {n}_{s} $ 和$ {n}_{n} $ 分别为一个计时单元内的平均信号光子和噪声光子数;$ {P}_{A} $ 为雪崩概率(也称为阻塞系数);td为探测器死时间;ΔT为探测时间门宽度。由公式(3)可知,M次累积后的探测概率由以下几方面因素共同决定:鉴别阈值、探测器的量子效率、平均信号光子数和噪声光子数、累积次数以及探测器死时间。系统探测概率既与平均回波光子数有关,又与平均噪声光子数有关,随着平均回波光子数的增加而增加,随着噪声光子数的增加而降低。
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光子计数激光雷达的噪声来源主要是背景光噪声以及探测器本身的暗计数噪声,由于暗计数噪声是由器件本身的暗电流产生的,与器件性能有关,这里不作分析。背景光噪声的主要来源是太阳辐射,因此需要对由太阳辐射引起的、最终到达单光子探测器的噪声光子进行分析。
为了简化处理,可以将太阳视作一个温度为5778 K的标准黑体,利用黑体辐射公式可以准确得到某一波段太阳光谱的辐射能量。已知标准的普朗克黑体辐射公式为[77]:
$$ \begin{split} \\ W(\lambda, T)=\frac{2 \pi h c^{2}}{\lambda^{5}} \cdot \frac{1}{\exp \left(\dfrac{h c}{\lambda k T}\right)-1} \end{split} $$ (4) 式中:h为普朗克常数,
$ h=6.626\;196 \times 10^{-34} \mathrm{~J} \cdot \mathrm{s} $ ;c为真空中光速,$c=3 \times 10^{8} \mathrm{~m} / \mathrm{s} $ ;k为玻耳兹曼常数,$ k= 1.380\;622 \times 10^{-23} \mathrm{~J} / \mathrm{K}$ ;T为黑体的温度,在此太阳温度取T=5 778 K,因此,可以得到常用的太阳辐射光谱照度图如图1所示。依据黑体辐射公式和太阳辐射光谱,可以得到某一波长的太阳常数,即日地距离上,大气层顶垂直于太阳光线的单位面积在每秒接收到的太阳辐射功率,其随波长不同而变化。在确定激光雷达系统探测波长后,根据系统的光谱滤波带宽以及该波长太阳常数即可计算出该波段范围内的太阳光谱辐射功率。
激光雷达探测器接收到的目标散射的日光辐射光子数为:
$$ N_{T S}=\frac{\lambda \cdot \eta_R \cdot S_\lambda \cdot \rho \cdot A_R \cdot \varOmega_{FOV} \cdot T_a \cdot T_a^{\sec \theta_s} \cdot \cos (\psi) \cdot \Delta \lambda \cdot \Delta T}{h \cdot c \cdot \pi} $$ (5) 式中:ηR为雷达接收光学效率;Sλ为雷达波长处的太阳辐射常数;AR为雷达接收光学面积;
$\varOmega_{ {FOV}}$ 为雷达接收光学视场立体角($\varOmega_{{FOV }}=\pi \theta_{R}^{2} / 4$ ,$ \theta_{R} $ 为接收视场全角);$\psi $ 为目标平面法线与太阳到目标连线的夹角;Δλ为雷达光谱滤波带宽;$ T_{a}^{\text {sec } \theta_{s}} $ 表示太阳到目标之间的单程大气透过率。$$ T_{a}^{\text {sec } \theta_{s}} =\exp \left(-\sec \theta_{s} \int_{H}^{\infty} \mu(z) {\rm{d}} z\right) $$ (6) 式中:
$ \theta_{s} $ 为太阳天顶角;$\; \mu(z) $ 为高度z处的大气消光系数;H为目标海拔高度。除此之外,位于雷达和目标之间路径上的大气也会对太阳辐射进行散射,整个路径上的大气散射被雷达光学系统接收也会成为噪声光子,这部分辐射的计算十分复杂,与太阳高度角、目标方位角、路径大气消光系数分布等探测时的具体太阳与大气参数密切相关,难以精确计算。对于由路径大气散射引起的噪声光子,可由下式进行简化的评估[79]:
$$ \begin{split} N_{A S}=&\dfrac{\lambda \cdot\eta_{R} \cdot S_{\lambda} \cdot A_{R} \cdot \varOmega_{{FOV}} \cdot\Delta \lambda \cdot \Delta T}{4 \cdot h \cdot c \cdot\pi}\cdot \\ &\left\{\dfrac{1-T_{a} \cdot T_{a}^{\text {sec } \theta_{s}} }{1+\sec \theta_{s}}\right\} \end{split} $$ (7) 对于激光雷达用的单光子探测器的选择,由表1可见,SNSPD探测器在可见到近红外波段的综合探测性能上具有明显优势,制约 SNSPD 的条件主要是其系统需要极低制冷温度(低于 4 K),难以实现系统小型化。另外,目前其使用成本过于昂贵,价格约为半导体探测器的10倍以上。不考虑以上因素时,选用SNSPD 探测器将显著提升雷达探测性能,下文不再将其列入对比分析。此外,对于532 nm探测,由于PMT量子效率高且无死时间效应,在不考虑其他因素的情况下也应优先选用,下文也不再将其列入对比分析。
表 1 单光子探测器的典型参数(基于主流商用探测器参数,来源厂商包括Hamamatsu、Excelitas、PicoQuant、Laser Components、ID Quantique、Aurea、Scontel、Becker & Hickl 等)
Table 1. Typical parameters of single-photon detectors (Based on mainstream commercial detector parameters, source vendors include Hamamatsu, Excelitas, PicoQuant, Laser Components, ID Quantique, Aurea, Scontel, Becker & Hickl, etc.)
Parameter PMT Si APD InGaAs APD SNSPD Quantum efficiency@532 nm 40%-60% 40%-60% - 60%-80% Quantum efficiency@1064 nm - 2%-3% 10%-15% 60%-90% Quantum efficiency@1550 nm - - 20%-25% 60%-90% Dark count/s−1 50-600 100-1000 800-5000 10-50 Dead time/ns - 25-50 200-10000 10-30 为了进行探测性能对比分析,选定10 km距离的扩展目标,按照表2中参数计算不同光谱滤波带宽时不同波长和探测器的探测概率。
表 2 对比分析用雷达参数表
Table 2. Lidar parameters for comparative analysis
Wavelength & Detector 532 nm
Si APD1064 nm Si APD 1064 nm InGaAs APD 1550 nm InGaAs APD Receiving aperture/mm 100 Transmitting efficiency 0.8 Pulse energy/μJ 10 Receiving efficiency 0.6 Receiving FOV/mrad 1 Time bin width/ns 1 Target reflectivity 0.3 Atmospheric visibility/km 12 Target distance/km 10 Solar zenith angle/(°) 30 Detector quantum efficiency 60% 3% 15% 25% Detector dark count/s−1 200 200 2 000 2 000 Detector dead time/ns 40 40 200 200 Solar constant/W·m–2·μm–1 1880 642 642 274 计算中大气辐射采用LOWTRAN软件,夏季中纬度地区标准大气模式,光谱滤波带宽分别选择0.5、1、1.5、2 nm,结果如图2所示。图2(a)为单次探测概率,图2(b)为脉冲累积100次、鉴别阈值为10时的探测概率,图2(a)中还给出了12 km能见度时,10 km距离不同波长的双程大气透过率。由图可见,无论对于哪种波长,光谱滤波带宽都极为重要,带宽2 nm以上白天探测概率急剧下降。虽然532 nm的探测器效率很高,但由于大气衰减以及日光背景较强的影响,其探测性能并不好。而InGaAs探测器由于其死时间过长,导致在强噪声环境下探测性能不佳,因此,虽然1550 nm波长的大气衰减最弱,日光背景最低,但其探测性能仍然不佳。综合来看,白天探测时采用1064 nm波长,Si APD探测器的性能最好,同时光谱滤波带宽需小于2 nm。
图 2 10 km目标不同光谱滤波带宽时不同波长和探测器的探测概率
Figure 2. Detection probability of different wavelengths and detectors under different spectral filtering bandwidths of 10 km target
图3(a)所示为1 nm滤波带宽,1064 nm波长不同探测距离时,单次探测接收到的目标激光回波光子数(红色)、目标散射日光噪声光子数(蓝色)和路径大气散射日光噪声光子数(绿色)。图3(b)所示为几种波长和探测器在不同探测距离上的单次探测概率,红色点线为1064 nm、Si APD探测器在夜晚无日光背景下的探测概率。由图可见,白天受到日光背景噪声的影响,目标的探测概率并不是随距离增大直接降低的,对于扩展目标,在近距离上目标散射的日光噪声为主要影响,甚至会一定程度上导致距离越近,目标探测概率越低,该影响会随距离的增加而降低,而路径大气散射的日光噪声会随距离的增加而增加,并逐渐成为主要噪声源。
图 3 1 nm滤波带宽时不同距离单次探测的噪声信号与探测概率
Figure 3. Noise signal and detection probability of single detection at different distances with 1 nm filtering bandwidth
图4为夜间无日光背景影响时,不同波长和探测器在对不同距离目标的探测概率。由图可见,在无日光背景噪声影响时,探测距离主要受到传输路径大气衰减的影响,1550 nm波长由于大气衰减最弱,可以达到最远的探测距离,1064 nm波长采用InGaAs探测器时量子效率相对Si APD探测器较高,所以探测距离较远。
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经过上节的计算分析不难看出,背景光噪声抑制技术是提升激光雷达全天时工作能力的关键。目前激光雷达系统中采用的光谱滤波技术主要包括窄带干涉滤光片、法布里-珀罗(F-P)标准具[80-82]、光栅滤波器件[83]、原子滤光器等[84-85]方法。为了达到更好的背景抑制效果,光谱滤波器件的滤波带宽越窄越好,带外的抑制能力越高越好,同时为了不影响信号光的接收,其中心波长的透过率越高越好。窄带干涉滤光片是目前激光雷达中应用最为广泛的光谱滤波器件,多数窄带干涉滤光片的滤波带宽为0.5~10 nm,透过率为70%~90%,带宽更窄的干涉滤光片制备困难,且透过率会急剧下降;F-P标准具虽然能够达到几十皮米量级的滤波带宽,但是由于其工作原理的限制,导致其对于温度变化和外界震动异常敏感,需要复杂精密的控制,并且其自由光谱范围较小,须与干涉滤光片结合使用,这些都限制了F-P标准具的实际应用;光栅滤波器件在衍射效率及滤波带宽上存在矛盾,且其带外抑制能力不足。原子滤光器一般只能针对特定波长的入射光产生共振从而透过,同时需要外加温度控制以及磁场,使用时有诸多不便。
为了实现更好的光谱滤波,笔者采用反射式布拉格光栅作为核心滤波器件[86-87],搭建了一个超窄带光谱滤波系统(Ultra-narrowband Spectral Filtering System,UNSFS) ,该系统滤波带宽约为50 pm,透过率约为85%,中心波长为1029 nm。同时,采用波长为1029 nm的Yb:YAG窄线宽脉冲激光器作为激光雷达光源。1029 nm与1064 nm同处于1 μm波段,大气衰减和日光背景辐射水平相当,但对于硅基单光子探测器,在1029 nm处的量子效率约为1064 nm处的三倍(8%)。UNSFS与激光器的光谱匹配关系如图5所示。
图5中,蓝线为滤波器的透过率曲线,FWHM 约为50 pm,中心透过率超过85%,中心波长为1029.07 nm;红线为激光器的光谱功率,其FWHM约为25 pm,两者可以有效配合。
基于该UNSFS搭建了一个单光子激光雷达,系统结构如图6所示。激光重复频率为1.6 kHz,单脉冲能量为20 μJ,脉冲宽度为2 ns,发射束直径为1 mm,发散角为1.08 mrad,光学接收径为25 mm,接收视场为1.3 mrad。透镜收集的光线通过针孔后进入UNSFS,滤波后透射光线耦合到芯径为100 μm的多模光纤中,然后进入单光子探测器。激光的发射和接收光束由带有中心孔的反射镜进行空间合束,在中心孔旁边放置PIN探测器作为发射脉冲同步信号源。
完成雷达系统搭建后,首先进行了单点测距实验,实验目标选择为实验室所能观测到最远的目标——成都市东侧的龙泉山脉,从地图上得知该山脉距离实验地点的直线距离约为25 km。实验时天气晴朗,能见度约22 km。从上午10点到晚上10点,每小时进行一次探测,检测积累时间为1 s (1600次),时间门宽度为16 ns,结果如图7所示。
图 7 (a)激光雷达回波光子计数距离直方图;(b)背景噪声计数率
Figure 7. (a) Lidar echo photon counting distance histogram; (b) Background noise count rate
图7 (a)为激光雷达回波光子计数距离直方图,图7(b)为每次探测的背景噪声计数率(Background Noise Count Rate, BNCR)统计。由图可见,目标实测距离为24.35 km,目标回波信号累积光子计数为16,此时背景光条件下的平均噪声计数(Average Noise Count, ANC)为3.06,噪声计数标准差为0.54。
BNCR由下式给出:
$$ B N C R=\frac{A N C}{P R F \cdot T \cdot \Delta T} $$ (8) 式中:PRF为激光器的重复频率;T为探测累计时间。可以看到,在强烈背景光条件下,激光雷达系统也拥有非常高的信噪比,该系统在全天最大的背景噪声计数率仅为119 kHz,远低于其他的光子计数激光雷达系统(一般为几MHz量级或者更多[42,64])。该实验能够证明采用新型UNSFS激光雷达系统拥有良好的日间工作能力。
在完成了远距离测距实验后,利用一个可二维角度调节的反射镜完成了对城市区域的快速扫描三维成像实验,系统实物如图8所示。
扫描的目标选择了成都市的地标建筑——成都金融城双子塔,该建筑有着较为明显的几何特征。实验时间选择在下午4点阳光强烈的时刻,当天能见度约为10 km,整个扫描视场为1°×1°,扫描的图像分辨率为100×141。得益于该系统优异的背景噪声抑制能力,使得整个探测扫描的时间大为缩短,这对于远距离三维成像来说至关重要。整个扫描时间为85 s,单点累加时间约为6 ms。扫描结果如图9所示。
图9(a)为在实验室由光学相机拍摄到的目标照片,红色线框为激光雷达扫描区域;图9(b)为扫描全景三维点云图像;图9(c)为单个像素在6 ms累加时间下的光子计数直方图;图9(d)为7.1~7.7 km远景三维点云图;图9(e)为3.5~4.2 km近景三维点云图;图9(f)和图9(g)分别为近景和远景区域顶视图。从结果中可以看出,整个点云图分布在3.5~7.7 km范围,能够非常精细地展示出双子塔的表面轮廓(细节处有穿透成像),同时前方建筑群的细节也能够得到有效的展现,证明了该系统能够出色完成日间强烈背景光条件下的快速三维成像。
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在完成了激光雷达的系统搭建和测试实验后,考虑对其探测性能进行评估。人们一般习惯用最大探测距离来评价,然而对于激光雷达系统来说,简单地增加接收光学孔径和发射激光功率总是可以获得更远的探测距离,但这不可避免地会带来对系统资源的更多需求,给雷达的实际应用带来困难。这里引入经济学的概念,体积、质量、功耗等系统资源对于一台激光雷达来说可以认为是各种“投资”,而“回报”就是最大探测距离。在经济学上,人们追求更高的投资回报率(Return on Investment,ROI),对于激光雷达系统同样也应如此。为此,文中提出了一种激光雷达性能评估模型,该模型将激光雷达系统的“投资”参数进行归一化,以此来评价一个激光雷达系统是否更经济,即投入更少的系统资源能否获取更远的探测距离。
对于一个激光雷达系统而言,其光学接收面积和激光发射功率无疑都属于“投资”,而且探测时间也是应考虑的一种时间“投资”。对于激光雷达探测的“回报”,通常习惯以最大探测距离作为评价,并且考虑到距离项在激光雷达方程中是以平方形式给出的,因此,将最大探测距离的平方作为激光雷达系统的“回报”。除了上述参数外,激光雷达的最大探测距离还与传输路径的衰减和目标的有效散射截面有关,但它们都属于外部影响因素,而这里只对激光雷达系统本身进行评估,在此评估模型中不对它们进行考虑。
由此,可以通过在单位接收面积、单位发射功率和单位探测时间情况下系统的最大探测距离平方来得到激光雷达的ROI指数,以如下形式给出:
$$ R O I=\frac{Z^{2}}{A_{R} P T} $$ (9) 式中:Z为激光雷达的最大探测距离。鉴于激光雷达系统中激光器对资源的消耗最终是以平均功率来衡量的,因此P应为激光发射的平均功率。对于脉冲激光器,有:
$$ P=E_{T} \cdot P R F $$ (10) 对于光子计数激光雷达,每个像素的脉冲累积次数N=T×PRF;而对于线性探测激光雷达,其单像素探测时间T=1/PRF,脉冲累积次数N=1。
这样,ROI指数可以写为:
$$ R O I=\frac{Z^{2}}{A_{R} E_{T} N} $$ (11) 式中所有物理量单位都采用国际标准单位,ROI指数的单位为J−1,可以认为是激光雷达系统的“投资回报率”。显然,ROI指数越高,激光雷达的探测效率就越高,或者说激光雷达探测更为“经济”。
需要指出的是,对于N×M面阵探测激光雷达,其单像素探测时间为T/(N×M),而T是其单帧的探测时间。可见,对于面阵探测激光雷达,通过一次探测即可同时获得N×M个像素点的距离值,从而缩短了探测时间。然而,为了保证N×M个像素的同时探测,它必将以更高的发射总功率或更大的接收面积为代价。同样,对于单点激光雷达,它减少了对发射功率或接收面积的需求,但同时付出的是需要扫描N×M个点才能完成三维成像的时间代价。
另外,线性探测激光雷达可以用一个脉冲即完成测量,而光子计数激光雷达要积累一定数量的脉冲才能识别目标获得测量值,这也可以看作是对探测时间上不同的“投资”。
为了展示ROI指数的评估效果,这里除了上文提出的新型单光子激光雷达外,还选择了四个具有代表性的光子计数激光雷达系统作为参考进行评估。表3中,系统1是英国赫瑞·瓦特大学的远距离单光子扫描成像系统[2],能够在800 m~10.5 km (夜间工作)的范围内获取3D图像。系统2是NASA ATLAS系统,部署在ICESat-2卫星上,轨道高度500 km,用于对地观测[41],它可以昼夜工作。系统3是中国科学技术大学的单光子3D成像激光雷达系统,该系统在夜间获得了202 km距离的扫描3D成像[72],并在21.6 km处进行了白天3D成像实验(系统4)。系统5是上文提出的新型单光子激光雷达系统。
需要指出的是,对于上文提出的新型单光子激光雷达系统,在以上评估中使用的探测距离值仅仅是一个实际探测实验中的距离值,而远远不是该激光雷达的最大可探测距离。此外,表3中使用的数据都来自公开发表的文献,并不代表这些激光雷达系统的实际最大可探测距离。因此,表3中计算的ROI指数仅供参考,并不表示这些系统所能达到的实际指数。
表 3 不同激光雷达系统ROI指数对比
Table 3. Comparison of ROI index of different lidar systems
System Institute Receiving aperture/m Pulse energy/J Single pixel detection time/s Distance/km ROI index 1 HWU (Night) 0.21 8×10−10 0.3 10.5 1.06×1014 2 NASA (Day) 0.8 4.8×10−5 1×10−4 500 1.18×1014 3 CSTU (Night) 0.279 1×10−4 0.19 201 5.89×1012 4 CSTU (Day) 0.279 1.2×10−6 2.2×10−2 21.6 2.9×1012 5 IOE (Day) 0.025 2×10−5 6×10−3 7.7 6.03×1014 另外,对于三维成像激光雷达,利用基于目标空间关联性的各种算法可以有效减少单像素的累积时间,从而提高系统的ROI指数。对于图9中的3D成像,由于每个单像素的探测信噪比已经足够好,没有应用任何其他关联算法。
Technical progress and system evaluation of all-time single photon lidar
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摘要: 单光子激光雷达(又称光子计数激光雷达)具有单光子量级的探测灵敏度,相比于传统的线性探测激光雷达,能够获得更远的探测距离,已经成为激光雷达探测技术的前沿和发展趋势。然而,极高的探测灵敏度也使单光子激光雷达在探测中极易受到背景噪声光子的干扰,这在很大程度上降低了其在白天工作的性能,也极大地限制了其适用范围。文中从单光子激光雷达的探测原理出发,简要回顾了其技术发展,分析了全天时工作对单光子激光雷达探测系统的需求,在此基础上,采用一种新型的光谱滤波技术,极大地提升了单光子激光雷达在白天的探测性能。同时,还提出了一种普适性的评价模型,能够极为直观地对各种激光雷达系统的探测性能进行评价。Abstract: Single photon lidar (also known as photon counting lidar) has detection sensitivity of single photon magnitude. Compared with traditional linear detection lidar, it can obtain longer detection distance, and it has become the frontier and development trend of lidar technology. However, the extremely high detection sensitivity also makes the single photon lidar highly susceptible to the interference of background noise photons in detection, which greatly reduces its performance in daytime and greatly limits its application scope. Based on the detection principle of single photon lidar, this paper briefly reviews its technical development, analyzes the requirements of all-time work for single photon lidar detection system, and a new spectral filtering technique is adopted to greatly improve the detection performance of single photon lidar in daylight. At the same time, this paper also proposed a general evaluation model, which can be very intuitive to evaluate the detection performance of various lidar systems.
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表 1 单光子探测器的典型参数(基于主流商用探测器参数,来源厂商包括Hamamatsu、Excelitas、PicoQuant、Laser Components、ID Quantique、Aurea、Scontel、Becker & Hickl 等)
Table 1. Typical parameters of single-photon detectors (Based on mainstream commercial detector parameters, source vendors include Hamamatsu, Excelitas, PicoQuant, Laser Components, ID Quantique, Aurea, Scontel, Becker & Hickl, etc.)
Parameter PMT Si APD InGaAs APD SNSPD Quantum efficiency@532 nm 40%-60% 40%-60% - 60%-80% Quantum efficiency@1064 nm - 2%-3% 10%-15% 60%-90% Quantum efficiency@1550 nm - - 20%-25% 60%-90% Dark count/s−1 50-600 100-1000 800-5000 10-50 Dead time/ns - 25-50 200-10000 10-30 表 2 对比分析用雷达参数表
Table 2. Lidar parameters for comparative analysis
Wavelength & Detector 532 nm
Si APD1064 nm Si APD 1064 nm InGaAs APD 1550 nm InGaAs APD Receiving aperture/mm 100 Transmitting efficiency 0.8 Pulse energy/μJ 10 Receiving efficiency 0.6 Receiving FOV/mrad 1 Time bin width/ns 1 Target reflectivity 0.3 Atmospheric visibility/km 12 Target distance/km 10 Solar zenith angle/(°) 30 Detector quantum efficiency 60% 3% 15% 25% Detector dark count/s−1 200 200 2 000 2 000 Detector dead time/ns 40 40 200 200 Solar constant/W·m–2·μm–1 1880 642 642 274 表 3 不同激光雷达系统ROI指数对比
Table 3. Comparison of ROI index of different lidar systems
System Institute Receiving aperture/m Pulse energy/J Single pixel detection time/s Distance/km ROI index 1 HWU (Night) 0.21 8×10−10 0.3 10.5 1.06×1014 2 NASA (Day) 0.8 4.8×10−5 1×10−4 500 1.18×1014 3 CSTU (Night) 0.279 1×10−4 0.19 201 5.89×1012 4 CSTU (Day) 0.279 1.2×10−6 2.2×10−2 21.6 2.9×1012 5 IOE (Day) 0.025 2×10−5 6×10−3 7.7 6.03×1014 -
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