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–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.

[1]  Francesc Rocadenbosch,et al.  Estimation of Wave Period from Pitch and Roll of a Lidar Buoy , 2021, Sensors.

[2]  G. Taylor.  The Spectrum of Turbulence , 1938 .

[3]  Charlotte Bay Hasager,et al.  Remote sensing technologies for measuring offshore wind , 2016 .

[4]  Baoqing Li,et al.  A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance , 2018, Sensors.

[5]  A. H. Murphy,et al.  Time Series Models to Simulate and Forecast Wind Speed and Wind Power , 1984 .

[6]  G. Wolken-Möhlmann,et al.  Results and conclusions of a floating-lidar offshore test , 2014 .

[7]  Francisco Rocadenbosch Burillo,et al.  Performance evaluation of a floating lidar buoy in nearshore conditions , 2017 .

[8]  J. Peinke,et al.  Atmospheric turbulence and its influence on the alternating loads on wind turbines , 2011 .

[9]  J. Mann,et al.  A review of turbulence measurements using ground-based wind lidars , 2013 .

[10]  J. Mann.  Wind field simulation , 1998 .

[11]  Narjes Davari,et al.  An Asynchronous Adaptive Direct Kalman Filter Algorithm to Improve Underwater Navigation System Performance , 2017, IEEE Sensors Journal.

[12]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[13]  Francesc Rocadenbosch,et al.  Atmospheric Boundary Layer Height Estimation Using a Kalman Filter and a Frequency‐Modulated Continuous‐Wave Radar , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[14]  S. Larsen,et al.  Remote Sensing for Wind Energy , 2013 .

[15]  G. Wolken-Möhlmann,et al.  About offshore resource assessment with floating lidars with special respect to turbulence and extreme events , 2014 .

[16]  Jakob Mann,et al.  Taking the Motion out of Floating Lidar: Turbulence Intensity Estimates with a Continuous-Wave Wind Lidar , 2020, Remote. Sens..

[17]  Francesc Rocadenbosch,et al.  A wind-lidar buoy for offshore wind measurements: First commissioning test-phase results , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[18]  Matti Lehtonen,et al.  Wind speed modeling using a vector autoregressive process with a time-dependent intercept term , 2016 .

[19]  Miguel Ángel,et al.  Doppler wind LIDAR systems data processing and applications : an overview towards developing the new generation of wind remote-sensing sensors for off-shore wind farms , 2019 .

[20]  Michael Harris,et al.  Introduction to continuous-wave Doppler lidar , 2012 .

[21]  Torben Mikkelsen,et al.  Investigation of turbulence measurements with a continuous wave, conically scanning LiDAR , 2009 .

[22]  Rebecca J. Barthelmie,et al.  Meteorological aspects of offshore wind energy: Observations from the Vindeby wind farm , 1996 .

[23]  Francesc Rocadenbosch,et al.  Performance evaluation of a floating Doppler wind lidar buoy in mediterranean near-shore conditions , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[24]  Julia Gottschall,et al.  Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity , 2017 .

[25]  J. Mathisen,et al.  Measurement of wind profile with a buoy mounted lidar , 2013 .

[26]  Kishor C. Mehta,et al.  Investigation of stationary and nonstationary wind data using classical Box-Jenkins models , 1993 .

[27]  Junping Du,et al.  Robust unscented Kalman filter with adaptation of process and measurement noise covariances , 2016, Digit. Signal Process..

[28]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[29]  Francesc Rocadenbosch,et al.  Vertical Azimuth Display simulator for wind-Doppler lidar error assessment , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[30]  Jinling Wang,et al.  STOCHASTIC MODELING FOR REAL-TIME KINEMATIC GPS/GLONASS POSITIONING. , 1999 .

[31]  Frieder Schuon,et al.  KIC InnoEnergy Project Neptune: development of a floating LiDAR buoy for wind, wave and current measurements , 2012 .

[32]  Joaquim Sospedra Iglesias,et al.  Novel multipurpose buoy for offshore wind profile measurements EOLOS platform faces validation at ijmuiden offshore metmast , 2015 .

[33]  Halil Ersin Soken,et al.  Robust adaptive unscented Kalman filter for attitude estimation of pico satellites , 2014 .

[34]  F. Rocadenbosch,et al.  Motional Behavior Estimation Using Simple Spectral Estimation: Application to The Off-Shore Wind Lidar , 2020, IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium.

[35]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[36]  Francesc Rocadenbosch,et al.  Estimation of the Motion-Induced Horizontal-Wind-Speed Standard Deviation in an Offshore Doppler Lidar , 2018, Remote. Sens..

[37]  J. Mann,et al.  Conically scanning lidar error in complex terrain , 2009 .