Pulse coherent Doppler lidar signal processing on FPGA: implementation of the method of accumulation of a real autocorrelation function

2021 ◽  
Author(s):  
Artem M. Sherstobitov ◽  
Ramdas Makhmanazarov
Keyword(s):  
Signal Processing ◽  
Autocorrelation Function ◽  
Fpga Implementation ◽  
Doppler Lidar ◽  
Lidar Signal ◽  
Coherent Doppler Lidar

scholarly journals Development of an All-Fiber Coherent Doppler Lidar in the IAO SB RAS

2020 ◽  
Vol 237 ◽  
pp. 06005
Author(s):  
Artem Sherstobitov ◽  
Viktor Banakh ◽  
Alexander Nadeev ◽  
Igor Razenkov ◽  
Igor Smalikho ◽  
...  
Keyword(s):  
Signal Processing ◽  
Radial Velocity ◽  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Stream Line ◽  
Doppler Lidar ◽  
Hardware System ◽  
Coherent Doppler Lidar

Paper presents a model of the all-fiber pulsed coherent Doppler lidar (IAO-lidar) build in the IAO SB RAS. Here is described lidar design, the algorithm for processing of lidar signals and the software-hardware system that implements signal processing in real time, created with the use of open source software. The results of joint measurements of the radial velocity by the IAO-lidar and the HALO Photonics (Stream Line) lidar are given.

Adaptive parametric algorithms for processing coherent Doppler-lidar signal

1999 ◽  
Vol 37 (6) ◽  
pp. 2678-2691 ◽  
Author(s):  
J.-L. Zarader ◽  
A. Dabas ◽  
P.H. Flamant ◽  
B. Gas ◽  
O. Adam
Keyword(s):  
Doppler Lidar ◽  
Lidar Signal ◽  
Coherent Doppler Lidar ◽  
Parametric Algorithms

A novel intrinsic time–scale decomposition–graph signal processing–based characteristic extraction method for rotor–stator rubbing of aeroengine

2021 ◽  
pp. 107754632098596
Author(s):  
Mingyue Yu
Keyword(s):  
Signal Processing ◽  
Autocorrelation Function ◽  
Time Scale ◽  
Energy Index ◽  
Signal Processing Algorithm ◽  
Intrinsic Time ◽  
Graph Signal Processing ◽  
Laplacian Energy ◽  
Rotational Components ◽  
Time Scale Decomposition

Intrinsic time-scale decomposition and graph signal processing are combined to effectively identify a rotor–stator rubbing fault. The vibration signal is decomposed into mutually independent rotational components, and then, the Laplacian energy index is obtained by the graph signal of the autocorrelation function of rotational components, and the signal is reconstructed by an autocorrelation function of each proper rotation (PR) component relative to smaller Laplacian energy index (less complexity). Finally, characteristics are extracted from rotor–stator rubbing faults in an aeroengine according to square demodulation spectrum of a reconstructed signal. To validate the effectiveness of the algorithm, a comparative analysis is made among traditional intrinsic time-scale decomposition algorithm, combination of intrinsic time-scale decomposition and autocorrelation function, and the proposed intrinsic time-scale decomposition–graph signal processing algorithm. Comparative result shows that the proposed intrinsic time-scale decomposition–graph signal processing algorithm is more precise and effective than the traditional intrinsic time-scale decomposition and intrinsic time-scale decomposition and autocorrelation function algorithms in extracting characteristic frequency and frequency multiplication of rotor–stator rubbing faults and can greatly reduce the number of noise components irrelevant to faults.

scholarly journals Development and assessment of a Wake Vortex characterization algorithm based on a hybrid LIDAR signal processing

2016 ◽  
Author(s):  
Alexandre Hallermeyer ◽  
Agnès Dolfi-Bouteyre ◽  
Matthieu Valla ◽  
Laurent Le Brusquet ◽  
Gilles Fleury ◽  
...  
Keyword(s):  
Signal Processing ◽  
Wake Vortex ◽  
Lidar Signal

Characterization of turbulent wake of wind turbine by coherent Doppler lidar

2014 ◽  
Author(s):  
Songhua Wu ◽  
Jiaping Yin ◽  
Bingyi Liu ◽  
Jintao Liu ◽  
Rongzhong Li ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbulent Wake ◽  
Doppler Lidar ◽  
Coherent Doppler Lidar

scholarly journals Low-Level Wind Shear Identification along the Glide Path at BCIA by the Pulsed Coherent Doppler Lidar

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 50
Author(s):  
Hongwei Zhang ◽  
Xiaoying Liu ◽  
Qichao Wang ◽  
Jianjun Zhang ◽  
Zhiqiang He ◽  
...  
Keyword(s):  
Wind Shear ◽  
Vertical Direction ◽  
Small Scale ◽  
Observation Data ◽  
Doppler Lidar ◽  
Low Level ◽  
Glide Path ◽  
Coherent Doppler Lidar ◽  
Scanning Strategies ◽  
Meteorological Phenomenon

Low-level wind shear is usually to be a rapidly changing meteorological phenomenon that cannot be ignored in aviation security service by affecting the air speed of landing and take-off aircrafts. The lidar team in Ocean University of China (OUC) carried out the long term particular researches on the low-level wind shear identification and regional wind shear inducement search at Beijing Capital International Airport (BCIA) from 2015 to 2020 by operating several pulsed coherent Doppler lidar (PCDL) systems. On account of the improved glide path scanning strategy and virtual multiple wind anemometers based on the rang height indicator (RHI) modes, the small-scale meteorological phenomenon along the glide path and/or runway center line direction can be captured. In this paper, the device configuration, scanning strategies, and results of the observation data are proposed. The algorithms to identify the low-level wind shear based on the reconstructed headwind profiles data have been tested and proved based on the lidar data obtained from December 2018 to January 2019. High spatial resolution observation data at vertical direction are utilized to study the regional wind shear inducement at the 36L end of BCIA under strong northwest wind conditions.

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