A Machine Learning-Based Correction Method for High-Frequency Surface Wave Radar Current Measurements

Yang, Yufan; Wei, Chunlei; Yang, Fan; Lu, Tianyi; Zhu, Langfeng; Wei, Jiangong

doi:10.3390/app122412980

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Figure 9 provides comparisons of the ADCP and radar EOF ellipses for the total velocities and residual currents. The EOF ellipse method was described in our previous work [38,46]. In Figure 9a, the ADCP ellipses at points R1 and R6 closely resemble the radar ellipses, both in size and phase.…”

Section: Total Velocitymentioning

confidence: 74%

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Performance Assessment of a High-Frequency Radar Network for Detecting Surface Currents in the Pearl River Estuary

Zhu,

Lu,

Yang

et al. 2024

Remote Sensing

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The performance of a high-frequency (HF) radar network situated within the Pearl River Estuary from 17 July to 13 August 2022 is described via a comparison with seven acoustic Doppler current profilers (ADCPs). The radar network consists of six OSMAR-S100 compact HF radars, with a transmitting frequency of 13–16 MHz and a direction-finding technique. Both the radial currents and vector velocities showed good agreement with the ADCP results (coefficient of determination r2: 0.42–0.78; RMS difference of radials: 11–21.6 cm s−1; bearing offset Δθ: −4.8°–16.1°; complex correlation coefficient γ: 0.62–0.96; and phase angle α: −24.3°–17.8°). For these radars, the Δθ values are not constant but vary with azimuthal angles. The relative positions between the HF radar and ADCPs, as well as factors such as the presence of island terrain obstructing the signal, significantly influence the errors. The results of spectral analysis also demonstrate a high level of consistency and the capability of HF radar to capture diurnal and semidiurnal tidal frequencies. The tidal characteristics and the Empirical Orthogonal Function (EOF) results measured by the HF radars also resemble the ADCPs and align with the characteristics of the estuarine current field.

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Section: Total Velocitymentioning

confidence: 74%

“…γ = 0.96, α = 5.8 • also demonstrate the consistency between the two vector sequences. In addition, the rotary power spectral analysis method [46] is used to compare the vector velocities of HF radar and ADCP. Similar to the radial velocity spectrum, the observed radar and ADCP data exhibit strong consistency.…”

Section: Total Velocitymentioning

confidence: 99%

Performance Assessment of a High-Frequency Radar Network for Detecting Surface Currents in the Pearl River Estuary

Zhu,

Lu,

Yang

et al. 2024

Remote Sensing

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“…In the prediction process, respective parameters were used to validate the correction effect on radar data. Prev experiments conducted by Zhu et al [31,32] have shown that the model's correctio radar data is a function of time. Therefore, this study also conducted sensitivity ex ments on the time of the input data in order to obtain the optimal tow route and tow time for correcting radar data.…”

Section: Empirical Orthogonal Function (Eof) Ellipsementioning

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

Applied Sciences

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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.

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“…We are dedicated to enhancing the quality of full-field radar data through machine learning methods to obtain a high-quality HF radar dataset. Yang et al [33] demonstrated the feasibility of a single-point correction using machine learning. However, due to limited in-situ data and the high costs of deploying and maintaining mooring ADCPs, it is imperative to develop a neural network-based method for cruising ADCP measurements.…”

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

Applied Sciences

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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.

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A Machine Learning-Based Correction Method for High-Frequency Surface Wave Radar Current Measurements

Cited by 4 publications

References 23 publications

Performance Assessment of a High-Frequency Radar Network for Detecting Surface Currents in the Pearl River Estuary

Performance Assessment of a High-Frequency Radar Network for Detecting Surface Currents in the Pearl River Estuary

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

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

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