A method for separating wind-generated waves from swell based on two-dimensional wave spectrum of radar image

Abstract

The application discloses a method for separating wind waves and swell waves based on a two-dimensional sea wave spectrum of a radar image, and belongs to the technical field of marine remote sensing.The method solves the problem that the existing method cannot separate wind waves and swell waves when it is difficult to obtain wind speed and wind direction parameters, and the problem of poor accuracy of the existing method in separating wind waves and swell waves.The position information of a wave system is obtained through a calibrated spectrum segmentation technology, data smoothing and wave peak merging are performed, and the two-dimensional sea wave spectrum of an effective wave system is obtained, so that the traditional wave system division method can be reduced due to the existence of many false wave peak values in the sea wave spectrum, and the wave system of the frequency direction spectrum is excessively segmented;the determination of wind wave components and swell wave components is realized in the two-dimensional sea wave spectrum field, compared with the traditional wind wave and swell wave separation method, the wind wave spectrum and the swell wave spectrum can be effectively separated, the wind speed and wind direction information is not required, and the accuracy of wind wave and swell wave separation is improved.The application can be applied to the separation of wind waves and swell waves.

Description

Technical Field

[0001] This invention belongs to the field of marine remote sensing technology, specifically relating to a method for separating wind swell waves based on the two-dimensional wave spectrum of radar images. Background Technology

[0002] With the deepening of marine research, its diverse untapped resources provide an alternative to the depletion of terrestrial energy. Waves are one of the forms of ocean water movement, and their changes are highly random (Zhang Shun. Research on wind and swell separation algorithm based on wavenumber spectrum [D]. Heilongjiang: Harbin Engineering University, 2018.). Waves usually manifest as wind waves and swells, which combine to form complex mixed waves. The effects of wind waves and swells on ships are different, and it is very necessary to separate wind waves and swells in complex sea conditions (Yin Lu, Li Yanbin, Ma Jingang. Review of the current status of marine observation technology [J]. Ship Electronic Engineering, 2013, 33(11): 4-7.).

[0003] Currently, the main methods for separating wind-surge wave systems include waveform separation, wave element method, and spectral analysis. Waveform separation provides the theoretical basis for wind-surge separation, but it suffers from accuracy limitations. Wave element method, on the other hand, considers parameters such as wind speed, wave age, wave steepness, wave height, and period to determine surge components that meet certain conditions. However, this method lacks clarity in its ability to separate the application range of surges from that of waves. In describing the complex and diverse properties of ocean waves, wave spectra can link the characteristics of ocean waves obtained from actual measurements at sea with idealized ocean waves. Through wave spectra, we can obtain statistical characteristics very close to those observed on the sea surface; therefore, spectral analysis methods have wider applications. Currently, wind-surge separation methods based on two-dimensional frequency and directional spectra—that is, separating wind-wave components from surge components in the frequency and directional domains—are a research hotspot in wind-surge separation. In existing, relatively mature research methods, wind speed and direction are indispensable parameters in the process of separating wind-surge systems. This leads to the inability to successfully separate wind-surge waves when this information is difficult to obtain. Furthermore, when performing preprocessing on ocean waves to remove noise and spurious systems, most existing methods set different thresholds based on different systems. This results in a high degree of subjectivity in the filtering process. Selecting an inappropriate threshold can produce spurious wave systems, thereby reducing the accuracy of subsequent ocean wave system separation.

[0004] In summary, existing methods cannot achieve wind surge wave separation when wind speed and direction parameters are difficult to obtain, and the accuracy of existing methods for wind surge separation is poor. Therefore, it is necessary to propose a new method for separating wind surge wave systems. Summary of the Invention

[0005] The purpose of this invention is to solve the problems that existing methods cannot achieve wind surge wave separation when it is difficult to obtain wind speed and wind direction parameters, and that existing methods have poor accuracy in wind surge separation. Therefore, this invention proposes a wind surge wave separation method based on the two-dimensional wave spectrum of radar images.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0007] A method for separating wind-surge waves based on two-dimensional wave spectra from radar images, the method specifically includes the following steps:

[0008] Step 1: Obtain the radar image sequence of the wave spectrum in the polar coordinate system of the wave monitoring area, and preprocess the radar image sequence in the polar coordinate system to obtain the radar image sequence in the rectangular coordinate system.

[0009] Then, a three-dimensional Fourier transform is performed on the radar image sequence in the rectangular coordinate system to obtain a three-dimensional image spectrum; a bandpass filter is applied to the three-dimensional image spectrum, and then the frequencies in the filtered three-dimensional image spectrum are integrated to obtain a two-dimensional image spectrum;

[0010] The two-dimensional image spectrum is modulated to obtain the two-dimensional wave spectrum in the rectangular coordinate system, and then the two-dimensional wave spectrum in the rectangular coordinate system is converted into the frequency direction spectrum in the polar coordinate system.

[0011] Step 2: Integrate the direction of the frequency spectrum obtained in Step 1 to obtain a one-dimensional frequency spectrum; after identifying the number and location of wave systems in the one-dimensional frequency spectrum, divide the one-dimensional frequency spectrum into partitions according to the number and location of wave systems to obtain partitioning results; and after filtering out partitions with energy less than the energy threshold, obtain the number of remaining partitions.

[0012] The frequency direction spectrum obtained in step 1 is processed using an iterative smoothing method to obtain a wave system with the same number of remaining partitions. Then, wave systems belonging to the same surge source are merged based on the wave crests of the wave system.

[0013] Step 3: Separate the wind wave system and the swell system based on the peak amplitude of each wave system obtained after merging.

[0014] Furthermore, the specific process of step 1 is as follows:

[0015] Step 1.1: Obtain the radar image sequence of the wave spectrum of the wave monitoring area in the polar coordinate system, and perform near-point interpolation on the radar image sequence in the polar coordinate system to convert the radar image sequence in the polar coordinate system into a radar image sequence in the rectangular coordinate system.

[0016] Step 1.2: Denote the radar image sequence in the rectangular coordinate system as... A three-dimensional Fourier transform is performed on the radar image sequence in the rectangular coordinate system to obtain the three-dimensional image spectrum. :

[0017]

[0018] In the formula, and These are radar images along the Cartesian coordinate system. and The range of directions It is the total time corresponding to the radar image sequence. It is the imaginary unit. and They are respectively and Wave number in direction, For frequency, For three-dimensional image spectrum, It is time;

[0019] Step 1.3: Filter the 3D image spectrum using a bandpass filter, and then process the filtered 3D image spectrum. frequency in Integrating, we obtain the two-dimensional image spectrum. :

[0020]

[0021] in, It is the filtered three-dimensional image spectrum;

[0022] Step 1.4: Modulate the two-dimensional image spectrum using the modulation transfer function, that is, multiply the two-dimensional image spectrum with the modulation transfer function to obtain the two-dimensional wave spectrum in rectangular coordinates:

[0023]

[0024] in, It is a two-dimensional wave spectrum in a rectangular coordinate system. The modulation transfer function;

[0025] Step 1.5: Calculate the two-dimensional wave spectrum in a rectangular coordinate system. Transformation into wavenumber-direction wave spectrum in polar coordinates :

[0026]

[0027] in, As direction, ;

[0028] Based on wave number-direction wave spectrum Determine the frequency direction spectrum of the ocean waves :

[0029] .

[0030] Further, the direction of the frequency directional spectrum obtained in step 1 is integrated to obtain a one-dimensional frequency spectrum; specifically...

[0031]

[0032] in, As direction, It is a one-dimensional frequency spectrum.

[0033] Furthermore, the identification of the number and location of wave systems in the one-dimensional frequency spectrum specifically involves:

[0034] Using kernel functions to analyze one-dimensional frequency spectra Perform data smoothing:

[0035]

[0036] in, The template length is kernel function, It is the frequency spectrum after data smoothing;

[0037] Frequency spectrum The number of wave crests in the wave system is the number of wave systems. The frequency corresponding to the wave crest is the position information of the wave system on the frequency spectrum. .

[0038] Furthermore, the energy threshold is , It is the total energy of the one-dimensional frequency spectrum.

[0039] Furthermore, the frequency direction spectrum obtained in step 1 is processed using an iterative smoothing method to obtain wave systems with the same number of remaining partitions. Then, wave systems belonging to the same surge source are merged based on the wave crests of the wave systems. Specifically:

[0040] Step 1: Analyze the frequency spectrum of ocean waves. Perform 2D discrete convolution:

[0041]

[0042] in, It is the frequency spectrum after convolution. Represents the convolution operation. For convolution kernel, and It is the dimension of the convolution kernel;

[0043] Step 2: Judgment Is the number of wave systems greater than the number of remaining partitions?

[0044] like If the number of wave systems in the interval is greater than the number of remaining partitions, then let = Then return to step one;

[0045] like If the number of wave systems in the system is not greater than the number of remaining partitions, then proceed to step three.

[0046] Step 3, according to The wave system is divided into partitions based on the number of wave systems, and then the partitions are merged.

[0047] The specific process of step three is as follows:

[0048] Step 31: Calculate the first... The wave crest of the first wave system and the first The distance between the crests of a wave system:

[0049]

[0050]

[0051]

[0052] In the formula, For the first The frequency corresponding to the wave crest of a wave system. For the first The frequency corresponding to the wave crest of a wave system. For the first The location information of the wave crests of a wave system in the frequency direction spectrum. For the first The location information of the wave crests of a wave system in the frequency direction spectrum. and They are respectively along and Components in direction, and They are respectively along and Components in direction, For the first The wave crest of the first wave system and the first The square of the distance between the crests of a wave system;

[0053] Step 3.2, Calculate the first... The propagation of the wave crest of the first wave system in any direction and the second wave system in the third wave system. The wave crest of a wave system can propagate in any direction.

[0054] Step 33, Determine the first The wave system and the first The criterion for determining whether a wave system undergoes crest merging is:

[0055]

[0056] in, To minimize the merging parameters, It propagates in any direction from the crest;

[0057] If the first The wave crest of the first wave system propagates in any direction or the second wave system propagates in any direction. If the propagation of the crest of a wave system in any direction satisfies the discriminant, then the th wave system... The wave system and the first The wave system is based on the same swell source, and the first wave system is the second wave system. The wave system and the first Wave crest merging is performed on each wave system; otherwise, wave crest merging is not performed.

[0058] The process continues until all wave systems no longer meet the conditions for wave crest merging.

[0059] Furthermore, the minimum merging parameter The selection method is as follows:

[0060] By comparing the peak energies of the two wave systems, the smaller value is obtained, and this smaller value is denoted as... Minimum merging parameter satisfy .

[0061] Furthermore, the propagation of the wave crest of the calculated wave system in any direction is specifically as follows:

[0062] With the first Taking a wave system as an example:

[0063]

[0064] in: It is the first The wave crest of a wave system can propagate in any direction.

[0065]

[0066]

[0067]

[0068]

[0069] In the formula, In the frequency directional spectrum obtained in step 1, except for the first... Other azimuth angles besides the wave crest location of the wave system It is the total energy of the wave spectrum;

[0070] .

[0071] Furthermore, the specific process of step 3 is as follows:

[0072] Step 31: Find the troughs between the crests of adjacent wave systems and use the troughs as the dividing line between adjacent wave systems.

[0073] Step 32: Calculate the PM spectrum :

[0074]

[0075] In the formula, For the effective wave height, The peak frequency of the PM spectrum;

[0076] Step 33: For a peak frequency of Calculate the peak amplitude of the wave system. With PM spectrum at peak frequency The ratio of the amplitudes below:

[0077]

[0078] in, It's a ratio. The PM spectrum at its peak frequency The amplitude below;

[0079] If the calculated ratio Greater than the threshold If the wave pattern is positive, then the wave system is a wind wave system; otherwise, the wave system is a swell system.

[0080] Furthermore, the method for calculating the effective wave height is as follows:

[0081]

[0082]

[0083] .

[0084] The beneficial effects of this invention are:

[0085] (1) The position information of the wave system is obtained by calibrated spectrum segmentation technology, and data smoothing and wave peak merging are performed to obtain the two-dimensional wave spectrum of the effective wave system. This can reduce the excessive segmentation of the frequency direction spectrum wave system caused by the presence of many false wave peaks in the wave spectrum in the traditional wave system segmentation method. By setting a noise energy threshold threshold for noise filtering, the subjective uncertainty of the traditional noise energy threshold detection that relies too much on the threshold setting can be reduced.

[0086] (2) In the field of two-dimensional wave spectrum, the determination of wind wave component and swell component has been realized. Compared with the traditional wind and swell separation method, it can not only effectively separate the wind wave spectrum and swell spectrum, but also does not require wind speed and wind direction information, and improves the accuracy of wind and swell separation. Attached Figure Description

[0087] Figure 1 This is a flowchart of a wind surge wave separation method based on a two-dimensional wave spectrum of radar images according to the present invention;

[0088] Figure 2 A schematic diagram of the Cartesian frame for the wave monitoring area in a marine radar image;

[0089] Figure 3 The frequency spectrum before noise filtering;

[0090] Figure 4 This is the frequency spectrum after noise filtering;

[0091] Figure 5 To smooth the frequency direction spectrum of the iterative data after one convolution;

[0092] Figure 6 To smooth the frequency direction spectrum of the iterative data after two convolutions;

[0093] Figure 7 To smooth the frequency direction spectrum of the iterative data after 3 convolutions;

[0094] Figure 8 To smooth the frequency direction spectrum of the iterative data after 4 convolutions;

[0095] Figure 9 The frequency direction spectrum after peak merging;

[0096] Figure 10 The initial frequency directional spectrum was collected at 1:00 AM on October 24, 2017.

[0097] Figure 11 The frequency direction spectrum after convolution smoothing;

[0098] Figure 12 The one-dimensional frequency spectrum corresponding to time-wave system 1;

[0099] Figure 13 The one-dimensional frequency spectrum corresponding to time-wave system 2 is shown in Figure 1.

[0100] Figure 14 The results are the regression results of wind and wave parameters from the supersonic method. Detailed Implementation

[0101] Specific Implementation Method 1: Combination Figure 1 This embodiment describes a method for separating wind-surge waves based on a two-dimensional wave spectrum from radar images. The method specifically includes the following steps:

[0102] Step 1: Obtain the radar image sequence of the wave spectrum in the polar coordinate system of the wave monitoring area, and preprocess the radar image sequence in the polar coordinate system to obtain the radar image sequence in the rectangular coordinate system.

[0103] Then, a three-dimensional Fourier transform is performed on the radar image sequence in the rectangular coordinate system to obtain a three-dimensional image spectrum; a bandpass filter is applied to the three-dimensional image spectrum, and then the frequencies in the filtered three-dimensional image spectrum are integrated to obtain a two-dimensional image spectrum;

[0104] The two-dimensional image spectrum is modulated to obtain the two-dimensional wave spectrum in the rectangular coordinate system, and then the two-dimensional wave spectrum in the rectangular coordinate system is converted into the frequency direction spectrum in the polar coordinate system.

[0105] Step 2: Integrate the direction of the frequency direction spectrum obtained in Step 1 to obtain a one-dimensional frequency spectrum; after identifying the number and location of wave systems in the one-dimensional frequency spectrum based on the calibrated spectrum segmentation technique, divide the one-dimensional frequency spectrum into partitions according to the number and location of wave systems to obtain the partitioning results; and after filtering out partitions with energy less than the energy threshold, obtain the number of remaining partitions.

[0106] The frequency direction spectrum obtained in step 1 is processed using an iterative smoothing method to obtain a wave system with the same number of remaining partitions. Then, wave systems belonging to the same surge source are merged based on the wave crests of the wave system.

[0107] Step 3: Separate the wind wave system and the swell system based on the peak amplitude of each wave system obtained after merging.

[0108] Based on the practical needs of wind-surge separation, this invention proposes an improved wave system segmentation method to obtain the main wave system that conforms to the actual physical meaning. It also introduces the "overshoot method" which only requires wave spectrum information and does not require wind speed and direction information to determine the wind-wave and surge system, thereby improving the ability of two-dimensional wave spectrum wind-surge separation based on radar images.

[0109] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that the specific process of step 1 is as follows:

[0110] Step 1.1: Obtain the radar image sequence of the wave spectrum of the wave monitoring area in the polar coordinate system, and perform near-point interpolation on the radar image sequence in the polar coordinate system to convert the radar image sequence in the polar coordinate system into a radar image sequence in the rectangular coordinate system.

[0111] Step 1.2: Denote the radar image sequence in the rectangular coordinate system as... A three-dimensional Fourier transform is performed on the radar image sequence in the rectangular coordinate system to obtain the three-dimensional image spectrum. :

[0112]

[0113] In the formula, and These are radar images along the Cartesian coordinate system. and The range of directions It is the total time corresponding to the radar image sequence. It is the imaginary unit. and They are respectively and Wave number in direction, For frequency, For three-dimensional image spectrum, It is time;

[0114] Step 1.3: Filter the 3D image spectrum using a bandpass filter, and then process the filtered 3D image spectrum. frequency in Integrating, we obtain the two-dimensional image spectrum. :

[0115]

[0116] in, It is the filtered three-dimensional image spectrum;

[0117] Step 1.4: Modulate the two-dimensional image spectrum using the modulation transfer function, that is, multiply the two-dimensional image spectrum with the modulation transfer function to obtain the two-dimensional wave spectrum in rectangular coordinates:

[0118]

[0119] in, It is a two-dimensional wave spectrum in a rectangular coordinate system. The modulation transfer function;

[0120] Step 1.5: Calculate the two-dimensional wave spectrum in a rectangular coordinate system. Transformation into wavenumber-direction wave spectrum in polar coordinates :

[0121]

[0122] in, As direction, ;

[0123] Based on wave number-direction wave spectrum Determine the frequency direction spectrum of the ocean waves That is, the frequency directional spectrum in polar coordinates:

[0124]

[0125] The other steps and parameters are the same as in Specific Implementation Method 1.

[0126] Specific Implementation Method Three: This implementation method differs from Specific Implementation Method One or Two in that it integrates the directions of the frequency direction spectrum obtained in step 1 to obtain a one-dimensional frequency spectrum; specifically...

[0127]

[0128] in, As direction, It is a one-dimensional frequency spectrum.

[0129] Other steps and parameters are the same as in specific implementation method one or two.

[0130] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that the identification of the number and location of wave systems in the one-dimensional frequency spectrum specifically involves:

[0131] Using kernel functions to analyze one-dimensional frequency spectra Perform data smoothing:

[0132]

[0133] in, The template length is kernel function, It is the frequency spectrum after data smoothing;

[0134] Frequency spectrum The number of wave crests in the wave system is the number of wave systems. The frequency corresponding to the wave crest is the position information of the wave system on the frequency spectrum. .

[0135] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0136] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One to Four in that the energy threshold is... , It is the total energy of the one-dimensional frequency spectrum.

[0137] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0138] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One to Five in that it uses an iterative smoothing method to process the frequency direction spectrum obtained in step 1 to obtain a wave system with the same number of remaining partitions. Then, based on the wave crests of the wave systems, wave systems belonging to the same surge source are merged. Specifically:

[0139] Step 1: Analyze the frequency spectrum of ocean waves. Perform 2D discrete convolution:

[0140]

[0141] in, It is the frequency spectrum after convolution. Represents the convolution operation. For convolution kernel, and It is the dimension of the convolution kernel;

[0142] Step 2: Judgment Is the number of wave systems greater than the number of remaining partitions?

[0143] like If the number of wave systems in the interval is greater than the number of remaining partitions, then let = Then return to step one;

[0144] like If the number of wave systems in the system is not greater than the number of remaining partitions, then proceed to step three.

[0145] Step 3, according to The wave system is divided into partitions based on the number of wave systems, and then the partitions are merged.

[0146] The specific process of step three is as follows:

[0147] Step 31: Calculate the first... The wave crest of the first wave system and the first The distance between the crests of a wave system:

[0148]

[0149]

[0150]

[0151] In the formula, For the first The frequency corresponding to the wave crest of a wave system. For the first The frequency corresponding to the wave crest of a wave system. For the first The location information of the wave crests of a wave system in the frequency direction spectrum. For the first The location information of the wave crests of a wave system in the frequency direction spectrum. and They are respectively along and Components in direction, and They are respectively along and Components in direction, For the first The wave crest of the first wave system and the first The square of the distance between the crests of a wave system;

[0152] Step 3.2, Calculate the first... The propagation of the wave crest of the first wave system in any direction and the second wave system in the third wave system. The wave crest of a wave system can propagate in any direction.

[0153] Step 33, Determine the first The wave system and the first The criterion for determining whether a wave system undergoes crest merging is:

[0154]

[0155] in, To minimize the merging parameters, It propagates in any direction from the crest;

[0156] If the first The wave crest of the first wave system propagates in any direction or the second wave system propagates in any direction. If the propagation of the crest of a wave system in any direction satisfies the discriminant (at least one of the two conditions must be met), then the th wave system... The wave system and the first The wave system is based on the same swell source, and the first wave system is the second wave system. The wave system and the first Wave crest merging is performed on each wave system; otherwise, wave crest merging is not performed.

[0157] The process continues until all wave systems no longer meet the conditions for wave crest merging.

[0158] The other steps and parameters are the same as those in one of the specific implementation methods one to five.

[0159] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Methods One through Six in that the minimum merging parameter... The selection method is as follows:

[0160] By comparing the peak energies of the two wave systems, the smaller value is obtained, and this smaller value is denoted as... Minimum merging parameter satisfy .

[0161] The other steps and parameters are the same as those in one of the specific implementation methods one to six.

[0162] Specific Implementation Method Eight: This implementation method differs from Specific Implementation Methods One to Seven in that the wave crest of the calculated wave system propagates in any direction, specifically as follows:

[0163] With the first Taking a wave system as an example:

[0164]

[0165] in: It is the first The wave crest of a wave system can propagate in any direction.

[0166]

[0167]

[0168]

[0169]

[0170] In the formula, In the frequency directional spectrum obtained in step 1, except for the first... Other azimuth angles besides the wave crest location of the wave system It is the total energy of the wave spectrum;

[0171]

[0172] The other steps and parameters are the same as those in any of the specific implementation methods one to seven.

[0173] This implementation method uses the first... Taking a wave system as an example, calculations are performed to determine the propagation direction in any direction. The calculation method for the propagation of wave crests in any direction is the same for all wave systems.

[0174] Specific Implementation Method Nine: This implementation method differs from Specific Implementation Methods One to Eight in that the specific process of step 3 is as follows:

[0175] Step 31: Find the troughs between the crests of adjacent wave systems and use the troughs as the dividing line between adjacent wave systems.

[0176] Step 32: Calculate the PM spectrum :

[0177]

[0178] In the formula, For the effective wave height, The peak frequency of the PM spectrum;

[0179] Step 33: For a peak frequency of Calculate the peak amplitude of the wave system. With PM spectrum at peak frequency The ratio of the amplitudes below:

[0180]

[0181] in, It's a ratio. The PM spectrum at its peak frequency The amplitude below;

[0182] If the calculated ratio Greater than the threshold (Threshold in this invention) If the value of is 1, then the wave system is a wind wave system; otherwise, the wave system is a swell system.

[0183] The other steps and parameters are the same as those in one of the specific implementation methods one to eight.

[0184] Specific Implementation Method Ten: This implementation method differs from Specific Implementation Methods One to Nine in that the method for calculating the effective wave height is as follows:

[0185]

[0186]

[0187]

[0188] The other steps and parameters are the same as those in any of the specific implementation methods one to nine.

[0189] Example

[0190] The radar data used in this invention is based on a standard X-band marine synaptic radar, and its performance specifications are shown in Table 1. The radar antenna rotation period is 2.5 s, and the pulse repetition frequency is 2 kHz. The experimental data selected for this invention consists of 32 images per radar image sequence, representing an 80-second period. The radar is connected to a wave monitoring device, which digitizes the radar backscattering information and stores it as a 14-bit grayscale image sequence. In practical engineering, the grayscale values ​​are converted into voltage intensity values ​​ranging from 0 to 2.5V.

[0191] The main technical parameters of the marine radar are shown in Table 1:

[0192] Table 1 Performance Indicators of X-band Marine Radar

[0193]

[0194] Combination Figure 1-13 The specific implementation steps of this invention are as follows:

[0195] The first step is radar image preprocessing. Taking the measured wave spectrum of X-band marine radar on the COA maritime surveillance vessel (3:00 AM on October 24, 2017) as an example, an offline observation experiment was conducted, using software to read the radar raw files and draw the images. A Cartesian frame area of ​​960m × 960m with a radial distance of 600m to 1600m at a 135-degree azimuth was selected as the fixed wave monitoring area for the experiment. The above data processing method is explained, including the following steps:

[0196] Step 1.1: Convert the image of the target to be processed, which is in polar coordinates, into a Cartesian coordinate image using near-point interpolation. A diagram illustrating the selection of the Cartesian bounding box in this region is shown below. Figure 2 As shown, the fan-shaped area is a schematic diagram of the selected wave monitoring area, and the rectangular area is a schematic diagram of the Cartesian frame. Find the point in the fan-shaped area that is closest to each point in the Cartesian frame, and interpolate the points in the Cartesian frame one by one. The Cartesian frame echo intensity matrix can be obtained through interpolation.

[0197] Step 1.2, the radar image sequence obtained after coordinate system transformation is denoted as... Then, a three-dimensional Fourier transform is performed to obtain the three-dimensional image spectrum. The transformation formula is:

[0198]

[0199] In the above formula, , The Cartesian frame of the selected study and The range of directions The total time for 32 images, and They are respectively and Wave number in direction, For frequency, This is a sequence of radar images in a Cartesian coordinate system. This is a three-dimensional image spectrum.

[0200] The formula for calculating resolution is:

[0201] , ,

[0202] In the above formula, for Wavenumber resolution in the direction, for Wavenumber resolution in the direction, This refers to frequency resolution. In this invention... , Both are 960m, T=80s.

[0203] Step 1.3: Filter the 3D image spectrum using a bandpass filter. Then, process the filtered 3D image spectrum... frequency in Integration yields the two-dimensional image spectrum. The integral formula is:

[0204]

[0205] Step 1.4: Multiply the two-dimensional image spectrum by the empirical modulation transfer function to achieve the conversion between the two-dimensional image spectrum and the wave spectrum. The formula is:

[0206]

[0207] In the formula , These are two-dimensional wave spectra and two-dimensional image spectra, respectively. This is the modulation transfer function.

[0208] The empirical modulation transfer function is obtained by fitting a large amount of experimental data. Its form is as follows:

[0209]

[0210] In the formula For empirical coefficients, the widely used empirical coefficients for modulation transfer functions are chosen to be 1.2.

[0211] Step 1.5, finally, the wave spectrum obtained in the rectangular coordinate system can be obtained using the following formula. Transformation into wavenumber-direction wave spectrum in polar coordinates :

[0212]

[0213] in,

[0214]

[0215] Wave number-direction spectrum The frequency spectrum of ocean waves can be obtained. :

[0216]

[0217] The second step is to design a model and extract the wave system's frequency directional spectrum. This includes the following steps:

[0218] Step 2.1.1: Integrate the obtained frequency directional spectrum to obtain the corresponding one-dimensional frequency spectrum, as shown in the following formula:

[0219]

[0220] in For frequency direction spectrum, As direction, It is a one-dimensional frequency spectrum.

[0221] Step 2.1.2 employs a convolutional data smoothing method to roughly identify the number of major wave systems in the frequency spectrum. Based on the target sea state, a kernel function with a suitable template length is found and convolved with the frequency spectrum. This invention selects a kernel function with a template length of 3 for the one-dimensional frequency spectrum. Perform data smoothing. The formula is as follows:

[0222]

[0223] Based on the frequency spectrum after convolution Number of peaks in With peak frequency Obtain the number of wave systems Position information of the wave system in the frequency direction spectrum In this invention, if the number of peaks in the frequency spectrum after convolution is 1, then the number of wave systems is... It is also 1.

[0224] Step 2.1.3, based on the known number of expected wave systems Location information By analyzing the frequency directional spectrum A sliding window traversal is performed to determine the steepest data ascent path. Based on the determined number and frequency location information of the target wave system, the corresponding wave system partition in the frequency direction spectrum is found. This invention sets... The matrix is ​​traversed using a sliding window matrix of the same size.

[0225] Step 2.2 involves noise filtering. A noise threshold is directly set, utilizing an energy level of 1% to 2% of the total energy of the frequency spectrum to remove zones where the noise energy to total energy ratio is very small. In this invention, the noise energy filtering threshold is set to 2% of the total energy, which enhances the effectiveness of wave system segmentation after filtering. Figure 3 , Figure 4 These are the frequency direction spectra before and after noise filtering, respectively.

[0226] Step 2.3.1: Process the pseudo-wave system and adjacent wave systems in the wave system partition. Use an iterative smoothing method to continuously sort out adjacent peaks and remove pseudo-peaks. This method involves a 2D discrete convolution operation with an equally weighted convolution kernel.

[0227] The 2D discrete convolution operation method is shown in the following equation:

[0228]

[0229] In the formula: The frequency direction spectrum after convolution. For convolution kernel, , The dimension of the convolution kernel. This represents the original frequency direction spectrum. After testing, the convolution kernel selected in this invention is shown in the following equation:

[0230]

[0231] Step 2.3.2: If the number of wave systems exceeds a preset value, repeat the discrete convolution operation described above until the number of wave systems equals the preset value. Therefore, in this invention, a total of 4 2D discrete convolution operations are performed. Figure 5 , Figure 6 , Figure 7 , Figure 8 These are the frequency direction spectra corresponding to convolutions 1, 2, 3, and 4 times, respectively.

[0232] Step 2.4: Merge wave system partitions belonging to the same surge source. The wave crest merging discriminant is:

[0233] in, To minimize the merging parameters, If the product of the two peaks is not greater than the minimum energy between the two peaks, then select... The value is the minimum merging parameter; however, data varies depending on sea conditions in different regions. The value needs to be adjusted according to the specific sea conditions. This invention selects... . Figure 9 This is the frequency directional spectrum after peak merging.

[0234] As can be seen from the figure, the frequency directional spectrum obtained after processing removes false partitions and pseudo-peaks from the spectrum, and the wave system in the spectrum is obtained, which proves the effectiveness of the model.

[0235] The third step is the determination of the wind surge component. Taking X-band marine radar data collected at 1:00 AM on October 24, 2017 as an example, the initial frequency directional spectrum was obtained after preprocessing, such as... Figure 10 As shown. Substituting this into the model proposed in the second step, and after filtering and smoothing the wave system through two-dimensional discrete convolution, the frequency direction spectrum containing only the wave system is obtained, as shown. Figure 11 As shown, the third step involves separating the wind surge components. This includes the following steps:

[0236] Step 2.1: For the processed frequency spectrum, find the "trough" between two adjacent peaks and identify it as the dividing line between the two wave systems. In this invention, the frequency value corresponding to the trough is 0.1 Hz. Figure 11 The spectrum was divided into upper and lower parts at a frequency of 0.1 Hz.

[0237] Step 2.2: Based on the concept of overshoot characteristics in wind waves, i.e., the peak amplitude of the wind wave spectrum is greater than the amplitude of the PM spectrum at the same peak frequency, while the peak amplitude of the swell spectrum is less than the amplitude of the PM spectrum at the same peak frequency, the two wave systems are identified as the wind wave system and the swell system, respectively.

[0238] The formula for calculating PM spectrum is as follows:

[0239]

[0240] In the formula, For the effective wave height, This is the peak frequency.

[0241] The method for calculating the significant wave height is as follows:

[0242]

[0243]

[0244]

[0245] Use a ratio The relationship between the peak amplitude of a zone and the peak amplitude of the PM wind and wave spectrum is used to determine whether the zone is a wind and wave zone or a swell zone. The expression is as follows:

[0246]

[0247] in, The frequency of the peak value corresponding to a single partition in the wave spectrum; Represents a single section of the wave spectrum at peak frequency The magnitude of the spectral peak at that location; Represents the peak frequency The magnitude of the peak value in the PM spectrum at that location. If Above a threshold ( ),but If the wave zone satisfies the overshoot phenomenon of wind waves, then the wave zone, i.e., the wave system, is determined to be a wind wave system; otherwise, if the overshoot phenomenon is not satisfied, then the wave system is determined to be a swell system.

[0248] Figure 12 and Figure 13 The image shows two wave systems at time 1. The solid line represents the one-dimensional wave spectrum of the wave system at this time, and the dashed line represents the PM spectrum (which has the same peak frequency as the one-dimensional wave spectrum) of the corresponding wave system. From the image, we can see that for the two wave systems in the frequency direction spectrum at time 1, wave system 1 has a peak frequency... The peak amplitude is 0.122Hz. The peak amplitude of the PM spectrum is 1.355 at the same peak frequency. The value is 0.447, calculated as follows: , The value is greater than 1, therefore wave system 1 should be a wind-wave system; the peak frequency of wave system 2... The peak amplitude is 0.089Hz. The peak amplitude of the PM spectrum is 0.198 at the same peak frequency. The value is 0.726, calculated as follows: , The value is less than 1, therefore wave system 2 should be identified as a surge system.

[0249] The wave age method is a commonly used method for determining wind-driven wave systems. Its determination criteria are as follows:

[0250]

[0251] In the above formula, For the separation frequency, It is the direction sequence of the frequency direction spectrum. The wind speed is at 10m above sea level. Based on the anemometer readings, the wind speed at 1 hour was 10.3 m / s, and the wind direction was 57.9968°. Let gravitational acceleration be 9.8. , Determining an important empirical parameter for this identification method is crucial. Typically, researchers can adopt their own threshold based on certain physical characteristics of wind and waves; however, choosing the "optimal" threshold is a rather subjective process. Here, we will... Setting the value to 1.5, the wind-surge separation discrimination curve is obtained. The included portion is identified as the wind-wave system, and the remaining portion as the surge system. The separation curve obtained by the wave age method (WA method) is shown below. Figure 11 As shown. By Figure 11 It can be seen that the wind-driven wave system divided according to the "overshoot method" is consistent with the separation results of the traditional wave age method.

[0252] For the components of the wind-wave system in ocean waves, there is a certain relationship between wind speed, wave height, and wave period. Through the analysis of a large amount of experimental data, the wind-wave growth function was obtained, as shown in the following formula:

[0253]

[0254] in Let gravitational acceleration be 9.8. , The wind speed at a height of 10 meters above sea level. The effective wave height of wind and waves, This refers to the peak period of the wind and waves.

[0255] The method for calculating the significant wave height is as follows:

[0256]

[0257]

[0258] Taking the logarithm of both sides of the wave growth function, we can see that and The relationship is a linear function. Experiments were conducted using marine radar data from 12:00 AM to 7:30 AM on October 24, 2017. The wind and wave parameters obtained by the overshoot method were regressed as a linear function, and the results are as follows: Figure 14 As shown, the calculation yields and The relationship is as follows:

[0259]

[0260] The results show that the exponent obtained by regression calculation of the wind and wave parameters obtained by the overshoot method is 0.63, which is consistent with the exponential law value of 2 / 3 given by the wind and wave growth function. This also shows that the wind and wave parameters obtained by the overshoot method are consistent with the growth law of the wind and wave system, which proves from the side that the overshoot method can also obtain the correct wind and surge separation results in the wave spectrum inverted by X-band marine radar.

[0261] The two-dimensional wave spectrum wind surge wave system separation and determination technology based on the supersonic method proposed in this invention can not only obtain a more effective two-dimensional wave spectrum of the wave system, but also eliminate the need for prior information such as wind direction and wind speed when using the supersonic method to separate and determine the wind surge component in the later stage, so as to ensure that the correct separation results can be obtained. This makes it more practical in engineering applications.

[0262] The above examples of the present invention are merely illustrative of the computational model and process of the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is impossible to exhaustively list all possible implementations here. Any obvious variations or modifications derived from the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for separating wind-surge waves based on two-dimensional wave spectra from radar images, characterized in that, The method specifically includes the following steps: Step 1: Obtain the radar image sequence of the wave spectrum in the polar coordinate system of the wave monitoring area, and preprocess the radar image sequence in the polar coordinate system to obtain the radar image sequence in the rectangular coordinate system. Then, a three-dimensional Fourier transform is performed on the radar image sequence in the rectangular coordinate system to obtain a three-dimensional image spectrum; a bandpass filter is applied to the three-dimensional image spectrum, and then the frequencies in the filtered three-dimensional image spectrum are integrated to obtain a two-dimensional image spectrum; The two-dimensional image spectrum is modulated to obtain the two-dimensional wave spectrum in the rectangular coordinate system, and then the two-dimensional wave spectrum in the rectangular coordinate system is converted into the frequency direction spectrum in the polar coordinate system. Step 2: Integrate the direction of the frequency spectrum obtained in Step 1 to obtain a one-dimensional frequency spectrum; after identifying the number and location of wave systems in the one-dimensional frequency spectrum, divide the one-dimensional frequency spectrum into partitions according to the number and location of wave systems to obtain partitioning results; and after filtering out partitions with energy less than the energy threshold, obtain the number of remaining partitions. The frequency direction spectrum obtained in step 1 is processed using an iterative smoothing method to obtain a wave system with the same number of remaining partitions. Then, wave systems belonging to the same surge source are merged based on the wave crests of the wave system. The frequency direction spectrum obtained in step 1 is processed using an iterative smoothing method to obtain wave systems with an equal number of remaining partitions. Then, wave systems belonging to the same surge source are merged based on their wave crests. Specifically: Step 1: Analyze the frequency spectrum of ocean waves. Perform 2D discrete convolution: in, It is the frequency spectrum after convolution. Represents the convolution operation. For convolution kernel, and It is the dimension of the convolution kernel; Step 2: Judgment Is the number of wave systems greater than the number of remaining partitions? like If the number of wave systems in the middle is greater than the number of remaining partitions, then let = Then return to step one; like If the number of wave systems in the system is not greater than the number of remaining partitions, then proceed to step three. Step 3, according to The wave system is divided into partitions based on the number of wave systems, and then the partitions are merged. The specific process of step three is as follows: Step 31: Calculate the first... The wave crest of the first wave system and the first The distance between the crests of a wave system: In the formula, For the first The frequency corresponding to the wave crest of a wave system. For the first The frequency corresponding to the wave crest of a wave system. For the first The location information of the wave crests of a wave system in the frequency direction spectrum. For the first The location information of the wave crests of a wave system in the frequency direction spectrum. and They are respectively along and Components in direction, and They are respectively along and Components in direction, For the first The wave crest of the first wave system and the first The square of the distance between the crests of a wave system; Step 3.2, Calculate the first... The propagation of the wave crest of the first wave system in any direction and the second wave system in the third wave system. The wave crest of a wave system can propagate in any direction. Step 33, Determine the first The wave system and the first The criterion for determining whether a wave system undergoes crest merging is: in, To minimize the merging parameters, It propagates in any direction from the crest; If the first The wave crest of the first wave system propagates in any direction or the second wave system propagates in any direction. If the propagation of the crest of a wave system in any direction satisfies the discriminant, then the th wave system... The wave system and the first The wave system is based on the same swell source, and the first wave system is the second wave system. The wave system and the first Wave crest merging is performed on each wave system; otherwise, wave crest merging is not performed. Stop when the wave system no longer meets the conditions for wave crest merging; Step 3: Separate the wind wave system and the swell system based on the peak amplitude of each wave system obtained after merging.

2. The method for separating wind-surge waves based on two-dimensional wave spectra of radar images according to claim 1, characterized in that, The specific process of step 1 is as follows: Step 1.1: Obtain the radar image sequence of the wave spectrum of the wave monitoring area in the polar coordinate system, and perform near-point interpolation on the radar image sequence in the polar coordinate system to convert the radar image sequence in the polar coordinate system into a radar image sequence in the rectangular coordinate system. Step 1.2: Denote the radar image sequence in the rectangular coordinate system as... A three-dimensional Fourier transform is performed on the radar image sequence in the rectangular coordinate system to obtain the three-dimensional image spectrum. : In the formula, and These are radar images along the Cartesian coordinate system. and The range of directions It is the total time corresponding to the radar image sequence. It is the imaginary unit. and They are respectively and Wave number in direction, For frequency, For three-dimensional image spectrum, It is time; Step 1.3: Filter the 3D image spectrum using a bandpass filter, and then process the filtered 3D image spectrum. frequency in Integrating, we obtain the two-dimensional image spectrum. : in, It is the filtered three-dimensional image spectrum; Step 1.4: Modulate the two-dimensional image spectrum using the modulation transfer function, that is, multiply the two-dimensional image spectrum with the modulation transfer function to obtain the two-dimensional wave spectrum in rectangular coordinates: in, It is a two-dimensional wave spectrum in a rectangular coordinate system. The modulation transfer function; Step 1.5: Calculate the two-dimensional wave spectrum in a rectangular coordinate system. Transformation into wavenumber-direction wave spectrum in polar coordinates : in, As direction, ; Based on wave number-direction wave spectrum Determine the frequency direction spectrum of the ocean waves : 。 3. The method for separating wind-surge waves based on two-dimensional wave spectra of radar images according to claim 2, characterized in that, The frequency spectrum obtained in step 1 is integrated along its direction to obtain a one-dimensional frequency spectrum; specifically... in, As direction, It is a one-dimensional frequency spectrum.

4. The method for separating wind-surge waves based on two-dimensional wave spectra of radar images according to claim 3, characterized in that, The identification of the number and location of wave systems in a one-dimensional frequency spectrum specifically involves: Using kernel functions to analyze one-dimensional frequency spectra Perform data smoothing: in, The template length is kernel function, It is the frequency spectrum after data smoothing; Frequency spectrum The number of wave crests in the wave system is the number of wave systems. The frequency corresponding to the wave crest is the position information of the wave system on the frequency spectrum. .

5. The method for separating wind-surge waves based on two-dimensional wave spectra of radar images according to claim 4, characterized in that, The energy threshold is , It is the total energy of the one-dimensional frequency spectrum.

6. The method for separating wind-surge waves based on two-dimensional wave spectra of radar images according to claim 5, characterized in that, The minimum merging parameter The selection method is as follows: By comparing the peak energies of the two wave systems, the smaller value is obtained, and this smaller value is denoted as... Minimum merging parameter satisfy .

7. The method for separating wind-surge waves based on two-dimensional wave spectra of radar images according to claim 6, characterized in that, The calculation of the wave crest propagation in any direction of the wave system is specifically as follows: in: It is the first The wave crest of a wave system can propagate in any direction. In the formula, In the frequency directional spectrum obtained in step 1, except for the first... Other azimuth angles besides the wave crest location of the wave system It is the total energy of the wave spectrum; 。 8. The method for separating wind-surge waves based on two-dimensional wave spectra of radar images according to claim 7, characterized in that, The specific process of step 3 is as follows: Step 31: Find the troughs between the crests of adjacent wave systems and use the troughs as the dividing line between adjacent wave systems. Step 32: Calculate the PM spectrum : In the formula, For the effective wave height, The peak frequency of the PM spectrum; Step 33: For a peak frequency of Calculate the peak amplitude of the wave system. With PM spectrum at peak frequency The ratio of the amplitudes below: in, It's a ratio. The PM spectrum at its peak frequency The amplitude below; If the calculated ratio Greater than the threshold If the wave pattern is positive, then the wave system is a wind wave system; otherwise, the wave system is a swell system.

9. The method for separating wind-surge waves based on two-dimensional wave spectra of radar images according to claim 8, characterized in that, The method for calculating the effective wave height is as follows: 。

Images