Design and performance of a fiber array coupled multi-channel photon counting, 3D imaging, airborne lidar system

2014 ◽  
Author(s):  
Genghua Huang ◽  
Rong Shu ◽  
Libing Hou ◽  
Ming Li
Keyword(s):  
3D Imaging ◽  
Photon Counting ◽  
Airborne Lidar ◽  
Lidar System ◽  
Fiber Array ◽  
And Performance

Design and performance of a 3D imaging photon-counting microlaser altimeter operating from aircraft cruise altitudes under day or night conditions

2002 ◽  
Author(s):  
John J. Degnan ◽  
Jan F. McGarry ◽  
Thomas W. Zagwodzki ◽  
Phillip W. Dabney ◽  
Jennifer Geiger ◽  
...  
Keyword(s):  
3D Imaging ◽  
Photon Counting ◽  
And Performance

scholarly journals Noise and Breakdown Characterization of SPAD Detectors with Time-Gated Photon-Counting Operation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5287
Author(s):  
Hiwa Mahmoudi ◽  
Michael Hofbauer ◽  
Bernhard Goll ◽  
Horst Zimmermann
Keyword(s):  
Breakdown Voltage ◽  
Single Photon ◽  
Photon Counting ◽  
Low Noise ◽  
Dark Count ◽  
Fully Integrated ◽  
Trade Offs ◽  
And Performance ◽  
Noise Models

Being ready-to-detect over a certain portion of time makes the time-gated single-photon avalanche diode (SPAD) an attractive candidate for low-noise photon-counting applications. A careful SPAD noise and performance characterization, however, is critical to avoid time-consuming experimental optimization and redesign iterations for such applications. Here, we present an extensive empirical study of the breakdown voltage, as well as the dark-count and afterpulsing noise mechanisms for a fully integrated time-gated SPAD detector in 0.35-μm CMOS based on experimental data acquired in a dark condition. An “effective” SPAD breakdown voltage is introduced to enable efficient characterization and modeling of the dark-count and afterpulsing probabilities with respect to the excess bias voltage and the gating duration time. The presented breakdown and noise models will allow for accurate modeling and optimization of SPAD-based detector designs, where the SPAD noise can impose severe trade-offs with speed and sensitivity as is shown via an example.

Boresight Calibration of Airborne LiDAR System Without Ground Control Points

2012 ◽  
Vol 9 (1) ◽  
pp. 85-89 ◽  
Author(s):  
Chen Siying ◽  
Ma Hongchao ◽  
Zhang Yinchao ◽  
Zhong Liang ◽  
Xu Jixian ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Ground Control ◽  
Control Points ◽  
Lidar System ◽  
Ground Control Points

Construction and performance of a scanning, photon‐counting spectrofluorometer

1976 ◽  
Vol 47 (9) ◽  
pp. 1034-1038 ◽  
Author(s):  
D. M. Jameson ◽  
R. D. Spencer ◽  
G. Weber
Keyword(s):  
Photon Counting ◽  
And Performance

scholarly journals LIDAR BATHYMETRIC EVALUATION BASED ON SCATTERING CLASSIFICATION ALGORITHM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 97-103
Author(s):  
J. Gao ◽  
G. Q. Zhou ◽  
H. Y. Wang ◽  
X. Zhou ◽  
Y. X. Mu ◽  
...  
Keyword(s):  
Attenuation Coefficient ◽  
Classification Algorithm ◽  
Airborne Lidar ◽  
Discrimination Factor ◽  
Actual Situation ◽  
Particle Scattering ◽  
Lidar System ◽  
Scattering Intensity ◽  
Diffuse Attenuation Coefficient ◽  
532 Nm

Abstract. The evaluation of the bathymetric capability of traditional airborne lidar system is mostly based on the formula of bathymetric capability by evaluating the diffuse attenuation coefficient (Kd). This method is derived form the assumption that the reflectance of sediment is fixed. In this study ,however,the reflectance of sediment is not fixed. Therefore, this study improves the ability of bathymetric formula, and proposes a particle scattering classification algorithm to obtain the transmissivity value. The algorithm filters the scattering modes of particles by scattering discrimination factor (q), and obtains the transmissivity values by using the scattering intensity formulas. Experiments show that, when the transmissivity is in the range of 0–1 and the average values of Kd(532 nm) are 0.1150 m−1, 0.0894 m−1 and 0.0903 m−1 in January, June and October respectively, accordingly, the bathymetric capabilities are 0–44 m, 0–61.5 m and 0–52.5 m, respectively. Compared with the original bathymetric method, these results show that the maximum bathymetric value has measured by the improved bathymetric capability formula and scattering classification algorithm has decreased under the influence of the change of sediment reflectance, and the result is more consistent with the actual situation and more accurate.

Time-Correlated Raman and Fluorescence Spectroscopy Based on a Silicon Photomultiplier and Time-Correlated Single Photon Counting Technique

2013 ◽  
Vol 67 (2) ◽  
pp. 136-140 ◽  
Author(s):  
Chunling Zhang ◽  
Liying Zhang ◽  
Ru Yang ◽  
Kun Liang ◽  
Dejun Han
Keyword(s):  
Single Photon ◽  
Photon Counting ◽  
Light Level ◽  
Sers Spectrum ◽  
Silicon Photomultiplier ◽  
Temporal Separation ◽  
Single Photon Counting ◽  
Surface Enhanced Raman ◽  
And Performance ◽  
High Fluorescence

We report a time-correlated Raman spectroscopy technique based on a silicon photomultiplier (SiPM) and a time-correlated single photon counting (TCSPC) technique to exploit the natural temporal separation between Raman and fluorescence phenomena to alleviate the high fluorescence background with conventional Raman detection. The TCSPC technique employed can greatly reduce the effect of high dark count rate (DCR) and crosstalk of SiPM that seriously hinder its application in low light level detection. The operating principle and performance of the 400 ps time resolution system are discussed along with the improvement of the peak-to-background ratio (PBR) for bulk trinitrotoluene (TNT) Raman spectrum relative to a commercial Raman spectrometer with charge coupled device (CCD). The fluorescence lifetime for solid TNT and Surface Enhanced Raman Scattering (SERS) spectrum for 10−6 mol/L trace TNT have also been obtained by this system, showing excellent versatility and convenience in spectroscopy measurement.

scholarly journals Airborne Waveform Lidar Simulator Using the Radiative Transfer of a Laser Pulse

2019 ◽  
Vol 9 (12) ◽  
pp. 2452 ◽  
Author(s):  
Minsu Kim
Keyword(s):  
Radiative Transfer ◽  
Laser Pulse ◽  
Accuracy Assessment ◽  
Three Dimensional ◽  
Finite Size ◽  
Airborne Lidar ◽  
Beam Divergence ◽  
System Response ◽  
Lidar System ◽  
Lidar Equation

An airborne lidar simulator creates a lidar point cloud from a simulated lidar system, flight parameters, and the terrain digital elevation model (DEM). At the basic level, the lidar simulator computes the range from a lidar system to the surface of a terrain using the geomatics lidar equation. The simple computation effectively assumes that the beam divergence is zero. If the beam spot is meaningfully large due to the large beam divergence combined with high sensor altitude, then the beam plane with a finite size interacts with a ground target in a realistic and complex manner. The irradiance distribution of a delta-pulse beam plane is defined based on laser pulse radiative transfer. The airborne lidar simulator in this research simulates the interaction between the delta-pulse and a three-dimensional (3D) object and results in a waveform. The waveform will be convoluted using a system response function. The lidar simulator also computes the total propagated uncertainty (TPU). All sources of the uncertainties associated with the position of the lidar point and the detailed geomatics equations to compute TPU are described. The boresighting error analysis and the 3D accuracy assessment are provided as examples of the application using the simulator.

Fabrication and performance of a 128-element crossed-electrode relaxor array, for a novel 3D imaging approach

2017 ◽  
Author(s):  
Katherine Latham ◽  
Christopher Samson ◽  
Christopher Ceroici ◽  
Roger J. Zemp ◽  
Jeremy A. Brown
Keyword(s):  
3D Imaging ◽  
Imaging Approach ◽  
And Performance

scholarly journals Improved cloud-phase determination of low-level liquid and mixed-phase clouds by enhanced polarimetric lidar

2018 ◽  
Vol 11 (2) ◽  
pp. 835-859 ◽  
Author(s):  
Robert A. Stillwell ◽  
Ryan R. Neely III ◽  
Jeffrey P. Thayer ◽  
Matthew D. Shupe ◽  
David D. Turner
Keyword(s):  
Dynamic Range ◽  
Photon Counting ◽  
Energy Budgets ◽  
Orthogonal Polarization ◽  
Systematic Bias ◽  
Radiative Effects ◽  
Lidar System ◽  
Phase Determination ◽  
Polarization Measurements ◽  
Cloud Properties

Abstract. The unambiguous retrieval of cloud phase from polarimetric lidar observations is dependent on the assumption that only cloud scattering processes affect polarization measurements. A systematic bias of the traditional lidar depolarization ratio can occur due to a lidar system's inability to accurately measure the entire backscattered signal dynamic range, and these biases are not always identifiable in traditional polarimetric lidar systems. This results in a misidentification of liquid water in clouds as ice, which has broad implications on evaluating surface energy budgets. The Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland employs multiple planes of linear polarization, and photon counting and analog detection schemes, to self evaluate, correct, and optimize signal combinations to improve cloud classification. Using novel measurements of diattenuation that are sensitive to both horizontally oriented ice crystals and counting system nonlinear effects, unambiguous measurements are possible by over constraining polarization measurements. This overdetermined capability for cloud-phase determination allows for system errors to be identified and quantified in terms of their impact on cloud properties. It is shown that lidar system dynamic range effects can cause errors in cloud-phase fractional occurrence estimates on the order of 30 % causing errors in attribution of cloud radiative effects on the order of 10–30 %. This paper presents a method to identify and remove lidar system effects from atmospheric polarization measurements and uses co-located sensors at Summit to evaluate this method. Enhanced measurements are achieved in this work with non-orthogonal polarization retrievals as well as analog and photon counting detection facilitating a more complete attribution of radiative effects linked to cloud properties.

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