Ground return recognition at airborne cw Doppler lidar measurements

1997 ◽  
Author(s):  
Stephan Rahm
Keyword(s):  
Doppler Lidar ◽  
Lidar Measurements

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

Dual-Doppler Lidar Measurements for Improving Dispersion Models

2005 ◽  
Vol 86 (6) ◽  
pp. 825-838 ◽  
Author(s):  
Chris G. Collier ◽  
Fay Davies ◽  
Karen E. Bozier ◽  
Anthony R. Holt ◽  
Doug R. Middleton ◽  
...  
Keyword(s):  
Doppler Lidar ◽  
Dispersion Models ◽  
Lidar Measurements

Railway track irregularity and curvature estimation using doppler LIDAR fiber optics

2016 ◽  
Vol 232 (1) ◽  
pp. 63-72 ◽  
Author(s):  
Masood Taheri Andani ◽  
Andrew Peterson ◽  
Josh Munoz ◽  
Mehdi Ahmadian
Keyword(s):  
Fiber Optics ◽  
Field Testing ◽  
Railway Track ◽  
Vehicle Speed ◽  
Doppler Lidar ◽  
Close Match ◽  
Track Geometry ◽  
Lidar Measurements ◽  
Curvature Estimation ◽  
Geometry Measurement

The application of Doppler-based LIght Detection and Ranging (LIDAR) technology for determining track curvature and lateral irregularities, including alignment and gage variation, are investigated. The proposed method uses track measurements by two low-elevation, slightly tilted LIDAR sensors nominally pointed at the rail gage face on each track. The Doppler LIDAR lenses are installed with a slight forward angle to measure track speed in both longitudinal and lateral directions. The lateral speed measurements are processed for assessing the track gage and alignment variations, using a method that is based on the frequency bandwidth dissimilarities between the vehicle speed and track geometry irregularity. Using the results from an extensive series of tests with a body-mounted Doppler LIDAR system on-board a track geometry measurement railcar, the study indicates a close match between the LIDAR measurements and those made with existing sensors on-board the railcar. The field testing conducted during this study indicates that LIDAR sensors could provide a reliable, non-contact track monitoring instrument for field use in various weather and track conditions, potentially in a semi-autonomous or autonomous manner.

Application of LIDAR technology for rail surface monitoring and quality indexing

2017 ◽  
Vol 232 (5) ◽  
pp. 1398-1406 ◽  
Author(s):  
Masood Taheri Andani ◽  
Abdullah Mohammed ◽  
Ashish Jain ◽  
Mehdi Ahmadian
Keyword(s):  
Doppler Frequency ◽  
Light Detection And Ranging ◽  
Doppler Lidar ◽  
Frequency Range ◽  
Absorption Properties ◽  
Surface Conditions ◽  
Lidar Measurements ◽  
Rail Inspection ◽  
Surface Monitoring ◽  
Surface Figure

This paper investigates the application of Doppler Light Detection and Ranging (LIDAR) sensors for the assessment of the top of rail lubricity condition and layer material. Different top of rail conditions are distinguished by the system using a new pair of rail surface indices defined based on LIDAR measurements. These indices provide quantitative representations of the top of rail condition due to the fact that Doppler frequency range and spectral magnitude of a backscattered LIDAR beam are functions of the rail surface figure as well as the light absorption properties of the surface material. Laboratory tests are conducted to demonstrate the feasibility of the proposed top of rail indexing operation. The results indicate that LIDAR sensors are capable of detecting and distinguishing between different top of rail surface conditions. Instrumenting rail inspection vehicles with Doppler LIDAR systems reduces reliance on empirical top of rail lubricity and surface assessments (such as observing the sheen of the rail or tactilely sensing various residues on the rail), in favor of reliable and repeatable measurements.

scholarly journals Statistical Analysis of Sodium Doppler Wind–Temperature Lidar Measurements of Vertical Heat Flux

2008 ◽  
Vol 25 (3) ◽  
pp. 401-415 ◽  
Author(s):  
Liguo Su ◽  
Richard L. Collins ◽  
David A. Krueger ◽  
Chiao-Yao She
Keyword(s):  
Heat Flux ◽  
Photon Counting ◽  
Doppler Lidar ◽  
State University ◽  
Colorado State University ◽  
Lidar Measurements ◽  
Vertical Heat ◽  
Different Seasons ◽  
Flux Measurements ◽  
Vertical Heat Flux

Abstract A statistical study is presented of the errors in sodium Doppler lidar measurements of wind and temperature in the mesosphere that arise from the statistics of the photon-counting process that is inherent in the technique. The authors use data from the Colorado State University (CSU) sodium Doppler wind-temperature lidar, acquired at a midlatitude site, to define the statistics of the lidar measurements in different seasons under both daytime and nighttime conditions. The CSU lidar measurements are scaled, based on a 35-cm-diameter receiver telescope, to the use of large-aperture telescopes (i.e., 1-, 1.8-, and 3.5-m diameters). The expected biases in vertical heat flux measurements at a resolution of 480 m and 150 s are determined and compared to Gardner and Yang’s reported geophysical values of 2.3 K m s−1. A cross-correlation coefficient of 2%–7% between the lidar wind and temperature estimates is found. It is also found that the biases vary from −4 × 10−3 K m s−1 for wintertime measurements at night with a 3.5-m telescope to −61 K m s−1 for summertime measurements at midday with a 1-m telescope. During winter, at night, the three telescope systems yield biases in their heat flux measurements that are less than 10% of the reported value of the heat flux; and during summer, at night, the 1.8- and 3.5-m systems yield biases in their heat flux measurements that are less than 10% of the geophysical value. While during winter at midday the 3.5-m system yields biases in their heat flux measurements that are less than 10% of the geophysical value, during summer at midday all of the systems yield flux biases that are greater than the geophysical value of the heat flux. The results are discussed in terms of current lidar measurements and proposed measurements at high-latitude sites.

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