Measuring Surface Wind Direction by Monostatic HF Ground-Wave Radar at the Eastern China Sea

Huang, Weimin; Gill, Eric W.; Wu, S.; Wen, Biyang; Yang, Zhijun; Hou, Jiechang

doi:10.1109/joe.2004.834175

Cited by 42 publications

(31 citation statements)

References 12 publications

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“…The β parameter obtained from the ACMB algorithms is different from the LSMB algorithm, which may be one reason for the results of the ACMB and LSMB are different obviously. The RMSE of 24.8° is low compared the results given by [6,10,11] and close the mean difference 20° by Huang et al [4]. Only considering from the aspects of sea state environment, there are two factors resulting in the low RMSE.…”

Section: Data Processing and Analysis With The Algorithmssupporting

confidence: 70%

“…Both theory simulations and experiments confirm that the ratio of spectral power density of the positive and negative Bragg peaks is highly sensitive to the wind direction and so this can be used to extract wind direction [1][2][3][4]. However, for the monostatic radar, there exists a directional ambiguity as the radar cannot tell if the wind is from the right of the radar beam or the left.…”

Section: Introductionmentioning

confidence: 82%

“…However, for the monostatic radar, there exists a directional ambiguity as the radar cannot tell if the wind is from the right of the radar beam or the left. To solve this problem, some methods are developed [2][3][4][5]. Heron & Rose [2] presented a multi-beam (MB) method which finds spreading parameter and wind directions from three radar beams.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Algorithms for Extracting Wind Direction from the Monostatic HF Radar Sea Echoes

Chu

Zhang²,

Wang³

et al. 2015

Proceedings of the 2015 International Conference on Electronic Science and Automation Control

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Abstract-In this paper, three algorithms are applied to extract the wind direction using a month HFSWR data in an experiment with a single radar system. The preliminary results show that the three algorithms can all eliminate the ambiguity of the wind direction. And the RMS error (RMSE) obtained by the angle comparison multi-beam (ACMB) algorithm is 24.80°, which is better than the others. We can obtain that the ACMB algorithm is best available for extracting wind direction using the data obtained by this experiment. However, for the other data with different conditions, whether this algorithm is the best needs further examination.

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Section: Data Processing and Analysis With The Algorithmssupporting

confidence: 70%

Section: Introductionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Algorithms for Extracting Wind Direction from the Monostatic HF Radar Sea Echoes

Chu

Zhang²,

Wang³

et al. 2015

Proceedings of the 2015 International Conference on Electronic Science and Automation Control

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“…4. The response of the first-order radar cross sections to the change of wind direction has been explained in, for example, [23]. The barge motion peaks behave similarly as the Bragg peaks.…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…Of course, the well-known Hasselmann term arising from a single scatter from second-order ocean waves may also be significant, and it will be considered in a related publication on the second-order spectrum. Having specified the time-varying scattering surface, the next step in deriving the desired cross section requires the determination of the an autocorrelation of the electric field (see [3] or [8] for example) whose form is given by (23) in which the receiving antenna area where is the gain of the receiving antenna, is the time shift between measurements, and indicates complex conjugation. Applying this to (20) gives (24) If we assume the motion of the antenna is independent of the sea state at the remote scattering "patch," then (25) A Fourier transform of (25) with respect to gives the received Doppler power density spectrum It will prove convenient to express the power spectral density for first-order gravity waves (see, e.g., [16]) in generalized form as…”

Section: E Pulse To Pulse Time Variation and The First-order Cross Smentioning

confidence: 99%

The First-Order High Frequency Radar Ocean Surface Cross Section for an Antenna on a Floating Platform

Walsh

Huang

Gill

2010

IEEE Trans. Antennas Propagat.

114

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The first-order high frequency surface wave radar (HFSWR) cross section of the ocean surface is derived for the case of the transmitting and receiving antenna being mounted on a floating, but otherwise fixed, ocean platform. It is assumed that the sway component of the platform or barge motion is responsible for observed differences in the cross section compared to that for the fixed antenna case. Based on earlier work, a general expression for the bistatically received first-order electric field, which consists of a two-dimensional spatial convolution, is presented and reduced to integral form. Then, it is assumed that the surface can be described by a Fourier series whose coefficients are zero-mean Gaussian random variables, and from there the analysis proceeds for the backscatter case. The integrals are taken to the time domain, with the source field being that of a barge-mounted omnidirectional vertically polarized pulsed dipole antenna. Subsequently, the first-order monostatic radar cross section is developed and found to consist of Bessel functions. Simulation results for the new cross section are also provided to show the effects of barge motion under different sea states and operating frequencies. It is seen that the results have important implications in the application of HFSWR technology to ocean remote sensing.

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HFSurface Wave Radar

Huang

Gill

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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HF surface wave radar (HFSWR) has been recognized and accepted as an important tool for ocean remote sensing for more than four decades. In this article, a comprehensive tutorial of such an ocean sensor is presented. It begins with the concept of HFSWR. Next, the history of HFSWR is briefly reviewed. Then, the classification of HFSWR in terms of waveform, array signal processing technique, the number of operating frequencies, and configuration geometry is described in detail. Subsequently, the state‐of‐the‐art applications of this type of ocean radar for sea surface current, and wave parameter measurements, wind mapping, and hard targets detection and tracking are presented. The working principles and data processing associated with each application are also explained. The remaining challenges and future trends are also discussed.

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Measuring Surface Wind Direction by Monostatic HF Ground-Wave Radar at the Eastern China Sea

Cited by 42 publications

References 12 publications

A Comparison of Algorithms for Extracting Wind Direction from the Monostatic HF Radar Sea Echoes

A Comparison of Algorithms for Extracting Wind Direction from the Monostatic HF Radar Sea Echoes

The First-Order High Frequency Radar Ocean Surface Cross Section for an Antenna on a Floating Platform

HFSurface Wave Radar

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