A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks

Abstract

We propose a quality control method based on machine learning neural networks to enhance the quality of high-frequency (HF) radar data. Unlike traditional quality control methods that rely on radar signals as indicators and involve extensive data manipulation in specialized software, our approach employs a Bi-LSTM neural network model. This method aims to improve data quality and streamline the quality control process. Through a series of analyses, we demonstrate the feasibility of using machine learning techniques to enhance radar data quality.

Keywords

• Bi-LSTM
• HF radar
• quality control
• anomaly detection
• F-score
• recall