Fully digital multi‐frequency compact high‐frequency radar system for sea surface remote sensing

This paper proposes an algorithm based on the long short-term memory (LSTM) network to improve the quality of high-frequency surface wave radar current measurements. In order to address the limitations of traditional high-frequency radar inversion algorithms, which solely rely on electromagnetic inversion and disregard physical oceanography, this study incorporates a bottom-mounted acoustic Doppler current profiler (ADCP) and towed ADCP into LSTM training. Additionally, wind and tidal oceanography data were included as inputs. This study compared high-frequency radar current data before and after calibration. The results indicated that both towed and bottom-mounted ADCP enhanced the quality of HF radar monitoring data. By comparing two methods of calibrating radar, we found that less towed ADCP data input is required for the same high-frequency radar data calibration effect. Furthermore, towed ADCP has a significant advantage in calibrating high-frequency radar data due to its low cost and wide calibration range. However, as the location of the calibrated high-frequency radar data moves further away from the towing position, the calibration error also increases. This article conducted sensitivity studies on the times and different positions of using towed ADCP to calibrate high-frequency radar data, providing reference for the optimal towing path and towing time for future corrections of high-frequency radar data.

Section: High-frequency Radarmentioning

confidence: 99%

High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration

Xiong,

Wei,

Yang

et al. 2024

“…The HF radars used in this study were OSMAR-S100 compact high-frequency surface wave radars [34] developed at Wuhan University. Compared to large-scale array-type ground wave radar, compact HF radar has advantages such as miniaturization of equipment, small-occupied area (antenna aperture less than hundreds of meters), low power consumption, and convenient antenna equipment installation and maintenance.…”

Section: High-frequency Radarmentioning

confidence: 99%

Designing Theoretical Shipborne ADCP Survey Trajectories for High-Frequency Radar Based on a Machine Learning Neural Network

Zhu

Yang²,

Yang

et al. 2023

A machine learning neural network-based design for shipborne ADCP navigation is proposed to improve the quality of high-frequency radar measurements. In traditional inversion algorithms for HF radars, sea surface velocity is directly extracted from electromagnetic echoes without constraints from oceanographic processes. Hence, we incorporated oceanographic information from observational data into seabed radar inversion results via an LSTM neural network model to enhance data accuracy. Through a series of numerical simulation experiments, we showed improved data accuracy and feasibility by incorporating both fixed-point and navigation observational data. The results indicate a significant reduction in (related) errors. This study has implications for guiding future navigation observations.

“…In our study, we employed the OSMAR-S100 high-frequency compact ground-wave radar system developed by Wuhan University [17]. The system was manufactured by Beijing Highlander Digital Technology Company Limited in Beijing, China.…”

Section: High-frequency Radarmentioning

confidence: 99%

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

Zhou,

Wei,

Yang

et al. 2023

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.