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Mittlere Wellenperioden-Inversion des kohärenten X-Band-Radars durch Integration von Random Forest und Linearer Regression

  • role： First author第一作者
  • Affiliation：

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China

    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

  • Email：liuh@ctgu.edu.cn
  • Introduction：刘含， 研究方向为无线电海洋遥感。E-mail： liuh@ctgu.edu.cn
LIU Han,  
  • Affiliation：

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China

    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

WANG Suyue,  
  • Affiliation：

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China

    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

ZHANG Qinghe,  
  • Affiliation：

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China

    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

SHEN Zhaoyang

Resümee

Aufgrund des Wellenbruchs und ihrer Entwicklung treten im Wellenzahl-Frequenz-Spektrum Gruppenlinien und höhere Harmonische auf, die mit einer Verringerung der Hauptwellenenergie einhergehen. Gruppenlinien werden oft als Hauptursache für die Überschätzung der Wellenperiode bei kohärentem Mikrowellenradar angesehen. Frühere Studien haben versucht, einen Teil oder die gesamte Gruppenlinienenergie zu entfernen, um die Genauigkeit der Wellenperiodenschätzung zu verbessern. Aufgrund der Unsicherheit in der Energieverteilung im Wellenzahl-Frequenz-Spektrum besteht jedoch immer eine gewisse Verzerrung bei der Inversion der Wellenperiode aus dem Spektrum. Daher wird in diesem Artikel eine Methode zur Schätzung der mittleren Wellenperiode vorgestellt, die ein Ensemble-Modell aus Random Forest und linearer Regression auf Basis eines kohärenten X-Band-Radars verwendet. Diese Methode schätzt die Wellenperiode direkt aus der raumzeitlichen Geschwindigkeitssequenz der Wellenbewegung; zunächst wird aus dem zeitlichen Dopplerspektrum, das vom Radar erhalten wurde, die raumzeitliche Sequenz der Echosgeschwindigkeit abgeleitet; anschließend werden Merkmale aus der raumzeitlichen Geschwindigkeitssequenz extrahiert und mit ECMWF-Daten ein Modell zur Vorhersage des minimalen Peak-Abstands aufgebaut, um die Positionen von Wellenbergen und Wellentälern zu finden; schließlich wird die mittlere Wellenperiode mit Hilfe der Beziehung zwischen Wellenlänge und Periode invers berechnet. Die Wirksamkeit der Methode wurde durch Simulation validiert. Darüber hinaus wurde eine Analyse mit einem fast 3-tägigen Datensatz durchgeführt, der mit einem kohärenten X-Band-Radar gesammelt wurde, das an der Küste der chinesischen Provinz Shandong installiert ist. Der quadratische Mittelwertfehler für vertikale (VV) und horizontale (HH) Polarisation betrug jeweils 0,15 und 0,22 Sekunden. Die Ergebnisse zeigen, dass die Methode eine Echtzeitabschätzung der Wellenparameter ermöglicht.

Schlüsselwort

Kohärenter X-Band-Radar; Mittlere Wellenperiode; Raumzeitliche Geschwindigkeitssequenz; Vertikale Polarisation (VV); Horizontale Polarisation (HH); Random Forest; Echtzeitschätzung

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