4.7 Article

A Robust Adaptive Unscented Kalman Filter for Floating Doppler Wind-LiDAR Motion Correction

Journal

REMOTE SENSING
Volume 13, Issue 20, Pages -

Publisher

MDPI
DOI: 10.3390/rs13204167

Keywords

floating Doppler Wind Lidar; apparent turbulence; motion compensation; adaptive filtering; Kalman Filter; Unscented Kalman Filter; six degrees of freedom

Funding

  1. Ministerio de Ciencia e Investigacion (MCIN)/Agencia Estatal de Investigacion (AEI) [PGC2018-094132-B-I00, MDM-2016-0600]
  2. Generalitat de Catalunya-AGAUR [2020 FISDU 00455]
  3. European Commission [GA-871115, GA-101008004]

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A new method for correcting motion-induced error in Doppler Wind-LiDAR data was presented using a Robust Adaptive Unscented Kalman Filter. After correction, motion-induced turbulence was reduced, and statistical indicators showed overall improvement.
This study presents a new method for correcting the six degrees of freedom motion-induced error in ZephIR 300 floating Doppler Wind-LiDAR-derived data, based on a Robust Adaptive Unscented Kalman Filter. The filter takes advantage of the known floating Doppler Wind-LiDAR (FDWL) dynamics, a velocity-azimuth display algorithm, and a wind model describing the LiDAR-retrieved wind vector without motion influence. The filter estimates the corrected wind vector by adapting itself to different atmospheric and motion scenarios, and by estimating the covariance matrices of related noise processes. The measured turbulence intensity by the FDWL (with and without correction) was compared against a reference fixed LiDAR over a 25-day period at El Pont del Petroli , Barcelona. After correction, the apparent motion-induced turbulence was greatly reduced, and the statistical indicators showed overall improvement. Thus, the Mean Difference improved from -1.70% (uncorrected) to 0.36% (corrected), the Root Mean Square Error (RMSE) improved from 2.01% to 0.86%, and coefficient of determination improved from 0.85 to 0.93.

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