KR100950301B1 - Calibration Method of Significant Wave Height in Radar Type Wave Gauge System - Google Patents

Abstract

Even if Bragg resonance does not occur because there is almost no ripple, a significant wave height correction method is provided in a radar wave measurement system that can obtain a significant wave height similar to the real one by a radar wave height meter. Significant wave height correction method in a radar wave measurement system, the method comprising the steps of: a) collecting the radar reflection signal from the sea level and converting it into three-dimensional image data; b) calculating a three-dimensional image spectrum by performing three-dimensional FFT processing on the three-dimensional image data; c) removing noise by filtering the calculated three-dimensional image spectrum using a dispersion relation; d) calculating a three-dimensional wave spectrum with respect to the original wave; e) calculating a peak period Tp, an average period Tm, and a significant wave height Hs from the three-dimensional wave spectrum; f) regressing and formulating the correlation between the mean and peak periods; g) formulating by regression analysis the correlation between the significant wave height and the mean period; h) coalescing relational expressions to produce a relational expression between significant crests and peak periods; And i) substituting the measured average period into the relation between the significant crest and the mean period, or substituting the measured peak period into the relation between the significant crest and the peak period, and calculating the significant crest approximated with the actual. It includes.

Radar, significant wave height, peak period, average period, correction

Description

Significant wave height correction method in radar wave measurement system {Calibration Method of Significant Wave Height in Radar Type Wave Gauge System}

1 is a block diagram schematically showing a radar-type crestometer applied to an embodiment of the present invention.

2 is a flowchart illustrating a significant wave height correction method in a radar wave measurement system according to an exemplary embodiment of the present invention.

3 is a graph showing the correlation between the average period and the peak period by the linear regression analysis according to an embodiment of the present invention.

4 is a graph showing the correlation between the mean period and the significant wave by linear regression analysis according to an embodiment of the present invention.

5 is a graph showing the correlation between the mean period and the peak period by nonlinear regression analysis according to an embodiment of the present invention.

6 is a graph showing the correlation between the mean period and the significant peak by nonlinear regression analysis according to an embodiment of the present invention.

FIG. 7 is a graph comparing the significant peaks measured by a linear regression analysis and the significant peaks measured by a buoyancy wave height meter according to an embodiment of the present invention.

8 is a graph comparing significant waves measured by a nonlinear regression analysis and a significant wave measured by a buoyancy wave height meter according to an embodiment of the present invention.

9 is a graph comparing the significant peaks measured before correction with the significant peaks measured by the buoy-type crestometer.

Description of the Related Art

10: radar 20: signal processing device

30: personal computer

The present invention relates to a significant wave height correction method in a radar wave measurement system, and more specifically, to obtain a relationship by regression analysis of the correlation of wave data obtained by the radar wave height meter, using the relationship to approximate the actual and The present invention relates to a method of obtaining Significant Wave Height (hereinafter, referred to as 'Hs').

Representative examples of conventional crest systems include buoy type crest systems. The buoy type pagometer measures the wave information by the buoy floating on the sea as it moves along the waves. Such buoyancy-type crestometer can measure the crest relatively accurately, but is prone to loss due to typhoons or tsunamis and is difficult to maintain.

Therefore, in recent years, radar-type crestometers using marine radars, particularly X-band radars, have been used.

Conventional wave measuring methods using a radar crest meter are disclosed in Japanese Patent Laid-Open No. 2002-318115 and Japanese Patent Laid-Open No. 2003-307563. According to the conventional wave measuring method, first, image data obtained by a radar is converted into a three-dimensional image spectrum by three-dimensional Fourier transform. Next, a noise component is removed from the 3D image spectrum by using a pass-band filter (dispersion relational expression) and then converted into a 2D image spectrum. In addition, by applying an image transfer function (Image Transfer Function) to the two-dimensional image spectrum to convert it into a three-dimensional blue spectrum, from the three-dimensional blue spectrum (Hs), peak period (Peak Period: 'Tp' ), And mean data (Mean Period: 'Tm').

Specifically, the significant wave height Hs is obtained by Equation 1 below.

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Here, F (f, θ) means the blue spectrum.

As can be seen from Equation 1, the significant wave height (Hs) is a value obtained by integrating the whole wave energy and taking the square root. Means the average value of crest corresponding to.

In addition, the peak period (Tp) means the period of the crest of the largest energy in the wave spectrum. The average period Tm means an average value of the periods of the blue waves of various components.

On the other hand, the measurement of the wave using the radar is possible only when the Bragg resonance (Bragg resonance) occurs, this Bragg resonance phenomenon occurs when the wavelength of the blue wave is similar to the wavelength of the radar electromagnetic wave. The frequency range of the X-band radar used for wave measurement is 8 to 12 GHz, which is about 3 cm in terms of the wavelength of electromagnetic waves. Therefore, the ripple having a wavelength of about 3cm on the sea surface should be generated to properly observe the blue wave by the Bragg resonance phenomenon.

However, even when the sea wave height is large, the Bragg resonance phenomenon does not occur when the wind is calm and there is no ripple due to the wind, so the problem that the wave height is measured smaller than the actual problem occurs. That is, as shown in Figure 9, when there is little ripple due to the wind is calm there is a lot of difference between the significant wave height (Hs) measured with a buoy type crestometer and the significant wave height (Hs) measured with a radar crestometer, There was a problem that the reliability of the radar type crest meter is poor due to the error.

Therefore, in order to raise the accuracy of the wave height measurement by a radar-type crestometer, it is a subject to correct the error of the significant wave height (Hs) measurement which arises when a Bragg resonance phenomenon does not arise because there is no ripple.

An object of the present invention for solving the conventional problems as described above, even if there is little ripples caused by the wind Bragg resonance phenomenon, a significant wave height can be measured using a radar-type crestometer to measure relatively accurate The present invention aims to provide a significant wave height correction method in a radar wave measurement system that approximates actuality.

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Significant wave height correction method in the radar wave measurement system according to the present invention, in the wave height correction method of the radar wave measurement system using a radar wave height meter, a) the signal processing device is measured using a radar wave height meter Collecting radar reflected signals from the digital camera and converting the radar reflected signals into three-dimensional image data and transmitting the three-dimensional image data to a personal computer; b) the personal computer performing a three-dimensional fast Fourier transform (FFT) process on the three-dimensional image data to calculate a three-dimensional image spectrum; c) removing noise by filtering the calculated three-dimensional image spectrum using a dispersion relation; d) calculating a three-dimensional wave spectrum with respect to the original wave; e) calculating a peak period (Tp), a mean period (Tm), and a significant wave height (Hs) from the three-dimensional wave spectrum; f) regression analysis and formulating a co-relation between the mean period Tm and the peak period Tp; g) regressing and formulating a correlation between the significant wave height Hs and the mean period Tm; h) the relationship between the mean period Tm and the peak period Tp and the relationship between the significant peak height Hs and the mean period Tm are combined to form the significant peak height Hs and the peak period. Calculating a relationship between Tp; And i) substituting the average period measured in the relation between the significant peak Hs and the mean period Tm or the peak period measured in the relation between the significant peak Hs and the peak period Tp. Comprising a step, and calculating the significant height approximated to the actual, including the step, i) can measure the wave height even if the Bragg resonance phenomenon does not occur due to the significant height calibrated closely to the actual of step i) It is characterized by.

Here, the regression analysis for obtaining a relationship between the average period, the peak period and the significant wave height may be performed by linear regression analysis or nonlinear regression analysis.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram schematically showing a radar wave height meter according to an embodiment of the present invention. As shown in FIG. 1, the crest meter includes an X-band radar 10 and a personal computer 30 connected to the radar 10 through the signal processing device 20. The signal processing apparatus 20 transmits the video signal and the azimuth signal input through the radar 10 to the personal computer 30. The personal computer 30 displays the image representing the sleep state on the screen in real time using the video signal and the azimuth signal, and the blue information such as the peak period (Tp), the average period (Tm), and the significant wave height (Hs) is displayed. Calculate.

Hereinafter, a significant wave height correction method according to an embodiment of the present invention will be described with reference to FIG. 2.

First, the radar 1 reflection signal from the sea surface is converted into three-dimensional image data by the signal processing apparatus 20 and transmitted to the personal computer 30 (S1) . The personal computer 30 performs three-dimensional FFT processing on such three-dimensional image data to calculate a three-dimensional image spectrum I (k, ω) (S2) . Next, noise is removed by filtering the calculated three-dimensional image spectrum using a dispersion relation equation to calculate a three-dimensional blue spectrum F (f, θ) of the original wave (S3, S4) . Then, the peak period Tp, the average period Tm, and the significant wave height Hs are calculated from the thus obtained blue spectrum (S5) .

The peak period Tp, the average period Tm, and the significant wave height Hs are calculated, for example, in the form of time serise data at 1 hour intervals. Table 1 shows an example of data in which the peak period Tp, the average period Tm, and the significant wave height Hs are measured in time series at one hour intervals. The data in Table 1 were measured by the radar 10 at sea latitude 36 degrees 15 minutes 05.8 seconds and longitude 129 degrees 22 minutes 56.9 seconds.

Time Hs Tp Tm 0 One 10 7.3 One 1.08 9.5 7.3 2 1.1 9.1 7.3 3 1.01 9.1 7.4 4 1.1 9.1 7.5 5 0.96 9.1 6.9 6 1.09 9.5 7.4 7 1.05 9.1 7.5 8 1.05 9.5 7.1 9 0.99 9.5 6.9 10 1.05 9.1 7.1 11 0.95 9.1 6.8 12 0.87 9.1 7 13 0.89 9.1 6.9 14 0.87 9.1 6.6 15 0.86 8.7 7 ： ： ： ：

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Next, in order to correct the significant wave height Hs, the regression analysis shows the correlation between the peak period Tp and the mean period Tm, and the mean period. The correlation between Tm and significant wave height Hs is calculated, respectively (S6) .

Regression analysis to calculate the peak period (Tp), average period (Tm), and significant wave height (Hs) may be used for linear or nonlinear regression analysis.

First, an example of calculating the correlation between the peak period Tp, the average period Tm, and the significant wave height Hs by linear regression analysis will be described.

From the data in Table 1, the peak period Tp and the average period Tm correspond to one-to-one and are shown in a graph as shown in FIG. 3. And, through the linear regression analysis in the graph of Figure 3 to find the appropriate relationship (primary function) between the peak period (Tp) and the average period (Tm). The relation between the peak period Tp and the average period Tm thus found is Equation 2 below, which is represented by a straight line in the graph of FIG. 3.

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In addition, the average period (Tm) and the significant wave height (Hs) corresponding to the one-to-one correspondence from the data of Table 1 shown in the graph as shown in Figure 4, and then the average period (Tm) and significant in the graph of Figure 4 through a linear regression analysis Find the proper relationship between crests (Hs). The relation between the mean period Tm and the significant wave height Hs found in this way is represented by Equation 3 below, which is represented by a straight line in the graph of FIG. 4.

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In addition, when the equations (2) and (3) are combined, the relation between the significant wave height Hs and the peak period Tp can be obtained as in Equation 4 below.

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Significant wave height Hs may be corrected using Equation 3 or Equation 4 calculated as described above (S7) . That is, the corrected significant height Hs can be obtained by substituting the average period Tm measured by the radar 10 into Equation 3 or by substituting the peak period Tp in Equation 4.

Next, an example of the calculation of the correlation between the peak period Tp, the average period Tm, and the significant wave height Hs by nonlinear regression analysis will be described.

As in the case of the linear regression analysis, the time series data of the peak period (Tp) and the average period (Tm) are shown in a graph as shown in FIG. Then, a nonlinear regression analysis finds an appropriate relation (secondary function) between the peak period Tp and the average period Tm in the graph of FIG. 5. The relation between the peak period Tp and the average period Tm thus found is Equation 5 below, which is represented by a curve in the graph of FIG. 5.

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In addition, the data of the average period (Tm) and the significant wave height (Hs) is shown in the graph as shown in Figure 6 by one-to-one correspondence, and then the average period (Tm) and significant wave height (Hs) in the graph of Figure 6 through a nonlinear regression analysis Find the proper relationship between The relation between the mean period Tm and the significant wave height Hs is found in Equation 6 below, which is represented by a curve in the graph of FIG. 6.

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In addition, by combining Equations 5 and 6, the relationship between the significant wave height Hs and the peak period Tp can be obtained as shown in Equation 7 below.

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Significant wave height Hs may be corrected using Equation 6 or 7 calculated as described above. That is, the corrected significant height Hs can be obtained by substituting the average period Tm measured by the radar 10 into Equation 6 or by substituting the peak period Tp in Equation 7.

7 is a graph comparing the significant height Hs corrected by the relational equation calculated through the linear regression analysis with the significant height Hs measured by the buoyancy wave height meter. In addition, FIG. 8 is a graph comparing the significant height Hs corrected by the relational equation calculated by the linear regression analysis with the significant height Hs measured by the negative wave height meter.

When comparing the graphs of FIGS. 7 and 8 with the significant peak height Hs before correction and the significant peak height Hs measured by a buoy-type crestometer, the significant peaks corrected as shown in FIGS. 7 and 8 are compared. It can be seen that (Hs) is less significant error (Hs) and the error measured by the buoyancy crest meter compared to the significant height (Hs) before correction of FIG.

7 and 8, it can be seen that the corrected significant height Hs can be obtained more closely than the actual case corrected by nonlinear regression analysis.

As described above, according to the present invention, a significant wave height (Hs) corrected to approximate the actual state can be easily obtained by using a relational expression obtained by regression analysis of the correlation information of the wave information extracted from the radar wave height meter. Accordingly, there is an advantage in that the accuracy of the radar crest height can be increased even when Bragg resonance does not occur because there is almost no ripple.

The present invention is not limited to the above-described specific preferred embodiments, and various modifications can be made by any person having ordinary skill in the art without departing from the gist of the present invention claimed in the claims. Of course, such changes will fall within the scope of the claims.

Claims (4)

delete delete In a significant wave height correction method of a radar wave measurement system using a radar wave height meter, a) collecting, by the signal processing apparatus, radar reflection signals from the sea level measured using a radar clinometer, converting them into three-dimensional image data, and transferring the radar reflection signals to a personal computer; b) the personal computer performing a three-dimensional fast Fourier transform (FFT) process on the three-dimensional image data to calculate a three-dimensional image spectrum; c, d) removing the noise by filtering the calculated three-dimensional image spectrum using a dispersion relation to calculate a three-dimensional blue spectrum of a blue wave included in the radar reflected signal; e) calculating a peak period (Tp), a mean period (Tm), and a significant wave height (Hs) from the three-dimensional wave spectrum; f) regression analysis and formulating a co-relation between the mean period Tm and the peak period Tp; g) regressing and formulating a correlation between the significant wave height Hs and the mean period Tm; h) the relationship between the mean period Tm and the peak period Tp and the relationship between the significant peak height Hs and the mean period Tm are combined to form the significant peak height Hs and the peak period. Calculating a relationship between Tp; And i) Substituting the average period measured in the relation between the significant peak height Hs and the mean period Tm, or the peak period measured in the relation between the significant peak height Hs and the peak period Tp Substituting the algorithm, calculating the significant wave height corrected to the actual Including, Significant height correction method in the radar wave measurement system, characterized in that the wave height can be measured even if the Bragg resonance phenomenon does not occur due to the significant height corrected to the actual step of step i). The method of claim 3, Regression analysis to obtain a relationship between the average period, the peak period and the significant wave height correction method characterized in that the linear regression analysis or non-linear regression analysis.

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