Low-coherence Doppler lidar technique for satellite remote wind sensing

1997 ◽  
Author(s):  
Kenneth W. Fischer
Keyword(s):  
Doppler Lidar ◽  
Low Coherence ◽  
Remote Wind Sensing

Amplified reference pulse storage for low-coherence pulsed Doppler lidar

2006 ◽  
Vol 45 (32) ◽  
pp. 8346 ◽  
Author(s):  
Jyi-Lai Shen ◽  
Rainer Künnemeyer
Keyword(s):  
Doppler Lidar ◽  
Pulsed Doppler ◽  
Reference Pulse ◽  
Low Coherence

Low-coherence doppler lidar with multiple time coherence of reference and probe waves

Laser Physics ◽  
2007 ◽  
Vol 17 (11) ◽  
pp. 1327-1332 ◽  
Author(s):  
G. G. Matvienko ◽  
S. N. Polyakov ◽  
V. K. Oshlakov
Keyword(s):  
Multiple Time ◽  
Doppler Lidar ◽  
Low Coherence

Reference-beam storage for long-range low-coherence pulsed Doppler lidar

2001 ◽  
Vol 40 (18) ◽  
pp. 3076 ◽  
Author(s):  
Adrian A. Dorrington ◽  
Rainer Künnemeyer ◽  
Paul M. Danehy
Keyword(s):  
Long Range ◽  
Reference Beam ◽  
Doppler Lidar ◽  
Pulsed Doppler ◽  
Low Coherence

All Fiber Coherent Doppler LIDAR for Wind Sensing

2008 ◽  
Vol 1076 ◽  
Author(s):  
Toshiyuki Ando ◽  
Shumpei Kameyama ◽  
Kimio Asaka ◽  
Yoshihito Hirano ◽  
Hisamichi Tanaka ◽  
...  
Keyword(s):  
Time Signal ◽  
High Peak Power ◽  
Doppler Lidar ◽  
Optical Components ◽  
Optical Amplification ◽  
Power Amplification ◽  
Coherent Doppler Lidar ◽  
Fiber Optical ◽  
Performance Results ◽  
Remote Wind Sensing

ABSTRACTAn 1.5 micron pulsed Coherent Doppler LIDAR system using all fiber optical components has attracted attention for remote wind sensing application because of its eye-safety, reliability and easy deployment. We report on our key technologies such as fiber based MOPA (Master Oscillator Power Amplification) transmitter, high peak power optical amplification and a real-time signal processing. Some performance results and applications are also provided.

Heterodyne processing scheme for low coherence interferometric sensor systems

1991 ◽  
Vol 138 (6) ◽  
pp. 393
Author(s):  
B.T. Meggitt ◽  
W.J.O. Boyle ◽  
K.T.V. Grattan ◽  
A.E. Baruch ◽  
A.W. Palmer
Keyword(s):  
Sensor Systems ◽  
Processing Scheme ◽  
Low Coherence ◽  
Interferometric Sensor

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.

scholarly journals A multi-channel $$\varvec{H}_\infty $$ preview control approach to load alleviation design for flexible aircraft

2021 ◽  
Author(s):  
Ahmed Khalil ◽  
Nicolas Fezans
Keyword(s):  
Design Methodology ◽  
Control Design ◽  
Doppler Lidar ◽  
Preview Control ◽  
Reduced Order ◽  
Control Approach ◽  
Structural Fatigue ◽  
Trade Offs ◽  
Gust Load Alleviation ◽  
Near Future

AbstractGust load alleviation functions are mainly designed for two objectives: first, alleviating the structural loads resulting from turbulence or gust encounter, and hence reducing the structural fatigue and/or weight; and second, enhancing the ride qualities, and hence the passengers’ comfort. Whilst load alleviation functions can improve both aspects, the designer will still need to make design trade-offs between these two objectives and also between various types and locations of the structural loads. The possible emergence of affordable and reliable remote wind sensor techniques (e.g., Doppler LIDAR) in the future leads to considering new types of load alleviation functions as these sensors would permit anticipating the near future gusts and other types of turbulence. In this paper, we propose a preview control design methodology for the design of a load alleviation function with such anticipation capabilities, based on recent advancements on discrete-time reduced-order multi-channel $$H_\infty $$ H ∞ techniques. The methodology is illustrated on the DLR Discus-2c flexible sailplane model.

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