Significant wave height extraction using a low‐frequency HFSWR system under low sea state

Li, Xiaodong; Yu, Changjun; Su, Fulin; Quan, Taifan; Chu, Xiaoliang; Wang, Shuyao

doi:10.1049/iet-rsn.2018.0044

Cited by 6 publications

(3 citation statements)

References 29 publications

(38 reference statements)

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“…A neural network method [12,16] was also used for wind field inversion. For the research of radar measurement from ocean surface at different radar frequencies and sea states, Li et al [17] presented some work on the wave measurements at low-frequency radar under low sea state. Halverson et al [18] collected HF radar data over one year, containing different sea states, tides, and so on.…”

Section: Introductionmentioning

confidence: 99%

Wind Direction Inversion from Narrow-Beam HF Radar Backscatter Signals in Low and High Wind Conditions at Different Radar Frequencies

Shen

Gurgel

2018

Remote Sensing

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Land-based, high-frequency (HF) surface wave radar has the unique capability of monitoring coastal surface parameters, such as current, waves, and wind, up to 200 km off the coast. The Doppler spectrum of the backscattered radar signal is characterized by two strong peaks that are caused by the Bragg-resonant scattering from the ocean surface. The wavelength of Bragg resonant waves is exactly half the radio wavelength (grazing incidence), and these waves are located at the higher frequency part of the wave spectral distribution. When HF radar operates at higher frequencies, the resonant waves are relatively shorter waves, which are more sensitive to a change in wind direction, and they rapidly respond to local wind excitation and a change in wind direction. When the radar operates at lower frequencies, the corresponding resonant waves are relatively longer and take longer time to respond to a change in wind direction due to the progress of wave growth from short waves to long waves. For the wind inversion from HF radar backscatter signals, the accuracy of wind measurement is also relevant to radar frequency. In this paper, a pattern-fitting method for extracting wind direction by estimating the wave spreading parameter is presented, and a comparison of the pattern-fitting method and a conventional method is given as well, which concludes that the pattern-fitting method presents better results than the conventional method. In order to analyze the wind direction inversion from radar backscatter signals under different wind conditions and at different radar frequencies, two radar experiments accomplished in Norway and Italy are introduced, and the results of wind direction inversion are presented. In the two experiments, the radar worked at 27.68 MHz and 12 MHz, respectively, and the wind conditions at the sea surface were quite different. In the experiment in Norway, 67.4% of the wind records were higher than 5 m/s, while, in the experiment in Italy, only 18.9% of the wind records were higher than 5 m/s. All these factors affect the accuracy of wind direction inversion. The paper analyzes the radar data and draws a conclusion on the influencing factor of wind direction inversion.

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Section: Introductionmentioning

confidence: 99%

Wind Direction Inversion from Narrow-Beam HF Radar Backscatter Signals in Low and High Wind Conditions at Different Radar Frequencies

Shen

Gurgel

2018

Remote Sensing

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“…High-frequency surface wave radar (HFSWR) plays an important role in maritime targets detection and wide-range ocean remote sensing over the horizon, working at 3-30 MHz. Based on the theoretical basis of Barrick's first-and second-order cross-sectional equations, the ocean dynamics parameters can be effectively extracted using the highfrequency (HF) waves backscattered from the surface of the sea, such as the speed and direction of current and wind and wave [1,2]. However, in the HF band, the performance of the HFSWR system is seriously affected by external transient interferences, e.g., lightning, meteor echoes, man-made impulse bursts, and so on [3].…”

Section: Introductionmentioning

confidence: 99%

Space-Time Cascaded Processing-Based Adaptive Transient Interference Mitigation for Compact HFSWR

2023

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In high-frequency (HF) radar systems, transient interference is a common phenomenon that dramatically degrades the performance of target detection and remote sensing. Up until now, various suppression algorithms of transient interference have been proposed. They mainly concentrate on the skywave over-the-horizon radar on the basis of the assumption that the interference is sparse in a coherent processing interval (CPI). However, HF surface wave radar (HFSWR) often faces more complex transient interference due to various extreme types of weather, such as thunderstorm and typhoon, etc. The above algorithms usually suffer dramatic performance loss when transient interference contaminates the enormously continuous parts of a CPI. Especially for the compact HFSWR, which suffers from severe beam broadening and fewer array degrees of freedom. In order to solve the above problem, this study developed a two-dimensional interference suppression algorithm based on space-time cascaded processing. First, according to the spatial correlation of the compact array, the statistical samples of the main-lobe transient interference are estimated using a rotating spatial beam method. Next, an adaptive selection strategy is developed to obtain the optimal secondary samples based on information geometry distance. Finally, based on a quadratic constraint approximation, a precise estimation method of the optimal weight is developed when the interference covariance matrix is singular. The experimental results of simulation and measured data demonstrate that the proposed approach provides far superior suppression performance.

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“…However, given the complex and mutable nature of marine environments, coupled with errors inherent in wave sensors and the influence of external environmental noise, the wave data we obtain often contain significant errors [ 4 ]. As a result, extracting true wave information amidst the noise presents a substantial challenge in contemporary marine engineering and oceanographic research.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Buoy Wave Data Processing through the Application of the Sage–Husa Adaptive Kalman Filtering Algorithm

Jiang,

Chen,

Liu

2023

Sensors

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In this paper, we propose a combined filtering method rooted in the application of the Sage–Husa Adaptive Kalman filtering, designed specifically to process wave sensor data. This methodology aims to boost the measurement precision and real-time performance of wave parameters. (1) This study delineates the basic principles of the Kalman filter. (2) We discuss in detail the methodology for analyzing wave parameters from the collected wave acceleration data, and deeply study the key issues that may arise during this process. (3) To evaluate the efficacy of the Kalman filter, we have designed a simulation comparison encompassing various filtering algorithms. The results show that the Sage–Husa Adaptive Kalman Composite filter demonstrates superior performance in processing wave sensor data. (4) Additionally, in Chapter 5, we designed a turntable experiment capable of simulating the sinusoidal motion of waves and carried out a detailed errors analysis associated with the Kalman filter, to facilitate a deep understanding of potential problems that may be encountered in practical application, and their solutions. (5) Finally, the results reveal that the Sage–Husa Adaptive Kalman Composite filter improved the accuracy of effective wave height by 48.72% and the precision of effective wave period by 23.33% compared to traditional bandpass filter results.

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Significant wave height extraction using a low‐frequency HFSWR system under low sea state

Cited by 6 publications

References 29 publications

Wind Direction Inversion from Narrow-Beam HF Radar Backscatter Signals in Low and High Wind Conditions at Different Radar Frequencies

Wind Direction Inversion from Narrow-Beam HF Radar Backscatter Signals in Low and High Wind Conditions at Different Radar Frequencies

Space-Time Cascaded Processing-Based Adaptive Transient Interference Mitigation for Compact HFSWR

Advancements in Buoy Wave Data Processing through the Application of the Sage–Husa Adaptive Kalman Filtering Algorithm

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