Bayesian approach for spatial super-resolution of heterodyne wind lidars
Wind speed measurements using heterodyne lidars are limited in spatial resolution because of the current signal processing methods. This limit is equal to c $\tau$ ( c is the speed of light and $\tau$ is the laser pulse duration) corresponding to the length of the atmosphere contributing to the wind speed measurement at one distance. To go beyond this limit, we use an inverse problem approach based on a model of the spectrogram (concatenation of periodograms of each range) and prior distributions on our unknowns: backscattering amplitude and wind speed at each range. We apply our inversion method to simulated and experimental spectrograms, demonstrating a gain in resolution by a factor of 2 to 2.5 depending on the signal-to-noise ratio.
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