A Robust Adaptive Unscented Kalman Filter for floating Doppler Wind-LiDAR motion correction

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–...

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Detalles Bibliográficos
Autores: Salcedo Bosch, Andreu|||0000-0001-7398-925X, Rocadenbosch Burillo, Francisco|||0000-0001-8614-4408, Sospedra Iglesias, Joaquim|||0000-0003-4207-7922
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/355110
Acceso en línea:https://hdl.handle.net/2117/355110
https://dx.doi.org/10.3390/rs13204167
Access Level:acceso abierto
Palabra clave:Atmospheric turbulence
Environmental monitoring
Floating Doppler Wind LiDAR
Apparent turbulence
Motion compensation
Adaptive filtering
Kalman Filter
Unscented Kalman Filter
Six degrees of freedom
Turbulència atmosfèrica
Seguiment ambiental
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
Descripción
Sumario: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.