Blind-time domain motion compensation of and significant-wave height extraction from high-frequency (HF) radar data acquired on a floating platform
Notice bibliographique
Résumé
High-frequency surface wave radar (HFSWR) is recognized as one of the essential tools for remote sensing of the ocean surface. It provides wide-area, all-weather, and near-real time surveillance. However, extracting useful information when the radar is mounted on a oating platform can be challenging since the platform motion may considerably affect and contaminate the high-frequency (HF) radar Doppler spectrum. The usual procedure for extracting ocean surface information from a high-frequency surface wave radar transmitting from a oating platform is to first compensate for the motion of the antenna in the acquired motion-contaminated Doppler spectrum and then extract the ocean wave parameters from the motion-compensated result. Two methods for motion compensation of HF radar signals for the case of a floating transmitter and fixed receiver are proposed when the motion parameters (including the amplitude and angular frequency of the motion) are not known a priori. This study assumes that the floating platform follows a single-frequency motion model. In the first method which is a time-domain technique, we estimate motion parameters from the autocorrelation function of the received electric field. The autocorrelation is related to the received radar cross section by application of an inverse temporal Fourier transform. The motion parameters are estimated by comparing the locations of the zeros of the autocorrelation function for the fixed antenna case with those for an antenna on a oating platform. Then, the zeros associated with the platform motion can be found. Alternatively, in the second method which is a frequency-domain approach, we aimed to estimate platform-motion parameters from the received motion-contaminated Doppler spectrum, which is proportional to the observed radar cross-section of the ocean surface from the oating platform. Motion parameters are determined from ii the relation between the locations and amplitudes of the Bragg peaks and motion- induced peaks, and the amplitude and angular frequency of the motion, respectively. While the results from both methods show that the motion parameters are estimated within 10% absolute error, the first method performs the motion compensation in the time domain and does not require frequency-domain data pre-processing, as well as demonstrates generally better results than the second method. The estimated motion parameters are then used to recover the motion-compensated Doppler spectrum from the Doppler spectrum of the antenna on a floating platform, and the results coincide well with the Doppler spectrum of the fixed antenna. In the next stage of the thesis, a new real-time method is proposed to estimate the significant wave height directly from the antenna's received electric field in the time- domain without requiring prior knowledge of the motion parameters or performing motion compensation. Based on the relation between the ocean surface displacement and the received electric field, this method calculates the significant wave height from the windowed variance of the upper envelope of the received electric field. This method is applied for up to second-order backscatter, and the results are compared with the case when only first-order backscatter is considered, and shows a considerable improvement. A preliminary calibration is required, which can be carried out either by the deployment of a wave buoy or by analyzing the data over a time period during which the sea state varies. The results from this simple proposed technique show that it may be used to estimate the signi�cant wave height with a root-mean-square error (RMSE) of less than 12 cm over a wide range of significant wave height values.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».