Estimation of Wave Period from Pitch and Roll of a Lidar Buoy

Salcedo-Bosch, Andreu; Rocadenbosch, Francesc; Gutierrez-Antunano, M. A.; Tiana-Alsina, Jordi

doi:10.3390/s21041310

Cited by 5 publications

(11 citation statements)

References 24 publications

(27 reference statements)

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“…The filter converged in most cases, achieving successful motion correction when compared to the reference fixed LiDAR. Divergent cases (accounting for less than 0.5% of the statistical sample) were attributable to strong wind shears, which motivated retuning of the measurement-noise variance settings in Equation (38).…”

Section: Ukf Resultsmentioning

confidence: 99%

“…) is computed. The window length chosen is the wave period over the 10 min series, which is estimated by means of the L-dB method [38] (other wave-period estimation methods in the literature [39,40] yielded virtually identical results). The wind component of the state-vector is initialized by retaining the first-time sample of the proxy wind, U U U proxy k…”

Section: Filter Initializationmentioning

confidence: 99%

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

2021

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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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Section: Ukf Resultsmentioning

confidence: 99%

Section: Filter Initializationmentioning

confidence: 99%

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

2021

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“…Model comparisons were carried out considering different motion scenarios clustered as a function of (i) mean WD, (ii) mean HWS and (iii) FDWL mean tilt. The mean tilt was computed from 10 min roll and pitch-tilt measurements [39]:…”

Section: Model Intercomparison Methodologymentioning

confidence: 99%

“…Provided correct measurement of the FDWL motion attitude by the IMUs (see Nyquist criterion and sampling requirements in Section 2.1), which was always the case, the filter was at all times able to compensate for the motion-corrupted wind. Therefore, rougher conditions such as those occurring in open seas (higher tilt amplitudes about 5 deg and wave periods longer than 2 s [39]) or the hydrodynamics associated with floating offshore wind turbines [53] are not expected to affect the filter performance.…”

Section: Methods Limitationsmentioning

confidence: 99%

Enhanced Dual Filter for Floating Wind Lidar Motion Correction: The Impact of Wind and Initial Scan Phase Models

2022

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An enhanced filter for floating Doppler wind lidar motion correction is presented. The filter relies on an unscented Kalman filter prototype for floating-lidar motion correction without access to the internal line-of-sight measurements of the lidar. In the present work, we implement a new architecture based on two cooperative estimation filters and study the impact of different wind and initial scan phase models on the filter performance in the coastal environment of Barcelona. Two model combinations are considered: (i) a basic random walk model for both the wind turbulence and the initial scan phase and (ii) an auto-regressive model for wind turbulence along with a uniform circular motion model for the scan phase. The filter motion-correction performance using each of the above models was evaluated with reference to a fixed lidar in different wind and motion scenarios (low- and high-frequency turbulence cases) recorded during a 25-day campaign at “Pont del Petroli”, Barcelona, by clustered statistical analysis. The auto-regressive wind model and the uniform circular motion phase model permitted the filter to overcome divergence in all wind and motion scenarios. The statistical indicators comparing both instruments showed overall improvement. The mean deviation increased from 1.62% (without motion correction) to −0.07% (with motion correction), while the root-mean-square error decreased from 1.87% to 0.58%, and the determination coefficient (R2) improved from 0.90 to 0.96.

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“…In addition, a FDWL can easily be redeployed to other locations and thus cover larger areas [12]. In the past few years, commercially available FDWLs have widely been deployed around the world and the reliability of their data has been proven, either as standalone instruments or collocated with masts to measure atmospheric-and sea-related parameters [13].…”

Section: Introductionmentioning

confidence: 99%

Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach

et al. 2022

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This work presents a parametric-solver algorithm for estimating atmospheric stability and friction velocity from floating Doppler wind lidar (FDWL) observations close to the mast of IJmuiden in the North Sea. The focus of the study was two-fold: (i) to examine the sensitivity of the computational algorithm to the retrieved variables and derived stability classes (the latter through confusion-matrix theory), and (ii) to present data screening procedures for FDWLs and fixed reference instrumentation. The performance of the stability estimation algorithm was assessed with reference to wind speed and temperature observations from the mast. A fixed-to-mast Doppler wind lidar (DWL) was also available, which provides a reference for wind-speed observations free from sea-motion perturbations. When comparing FDWL- and mast-derived mean wind speeds, the obtained determination coefficient was as high as that of the fixed-to-mast DWL against the mast (ρ2=0.996) with a root mean square error (RMSE) of 0.25 m/s. From the 82-day measurement campaign at IJmuiden (10,833 10 min records), the parametric algorithm showed that the atmosphere was neutral (31% of the cases), stable (28%), or near-neutral stable (19%) during most of the campaign. These figures satisfactorily agree with values estimated from the mast measurements (31%, 27%, and 19%, respectively).

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Estimation of Wave Period from Pitch and Roll of a Lidar Buoy

Cited by 5 publications

References 24 publications

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

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

Enhanced Dual Filter for Floating Wind Lidar Motion Correction: The Impact of Wind and Initial Scan Phase Models

Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach

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