Abstract
Multi-classification using a convolutional neural network (CNN) is proposed as a denoising method for coherent Doppler wind lidar (CDWL) data. The method is intended to enhance the usable range of a CDWL beyond the atmospheric boundary layer (ABL). The method is implemented and tested in an all-fiber pulsed CWDL system operating at 1550 nm wavelength with 20 kHz repetition rate, 300 ns pulse length and 180 µJ of laser energy. Real-time pre-processing using a field programmable gate array (FPGA) is implemented producing averaged lidar spectrograms. Real-world measurement data is labeled using conventional frequency estimators and mixed with simulated spectrograms for training of the CNN. First results of this methods show that the CNN can outperform conventional frequency estimations substantially in terms of maximum range and delivers reasonable output in very low signal-to-noise (SNR) situations while still delivering accurate results in the high-SNR regime. Comparing the CNN output with radiosonde data shows the feasibility of the proposed method.
Highlights
The remote sensing of wind velocity using lidar systems is an established technique with applications in atmospheric research [1], wind energy systems [2] and airport turbulence and wind shear monitoring [3]
Wind lidar systems are either based on a direct detection of photons scattered from the atmosphere using a spectral analyzer for Doppler shift discrimination or on coherent detection where the received photons are mixed with a local oscillator and the resulting beat signal is analyzed
The dependence on aerosol concentration is challenging as the signal quality and maximum range is highly variable with local meteorologic conditions
Summary
The remote sensing of wind velocity using lidar systems is an established technique with applications in atmospheric research [1], wind energy systems [2] and airport turbulence and wind shear monitoring [3]. Wind lidar systems are either based on a direct detection of photons scattered from the atmosphere using a spectral analyzer for Doppler shift discrimination or on coherent detection where the received photons are mixed with a local oscillator and the resulting beat signal is analyzed. In this work we suggest using a convolutional neural network trained on real and simulated coherent lidar signals in order to achieve a robust estimate of the Doppler frequency shift
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