CN107730582B - Ocean wave three-dimensional display method based on ocean remote sensing data - Google Patents
Ocean wave three-dimensional display method based on ocean remote sensing data Download PDFInfo
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Abstract
The invention discloses a sea wave three-dimensional display method based on ocean remote sensing data, which comprises the following steps of extracting second-order spectrum inversion sea wave parameters from a radar Doppler frequency spectrogram, filling grid nodes by using a Krigin space interpolation algorithm to simulate preliminary three-dimensional sea waves and smoothing the waves, and finally carrying out interpolation processing on a time sequence by using a cubic spline interpolation algorithm to ensure that time intervals are continuous so as to realize dynamic display, wherein the specific steps are as follows: extracting a second-order spectrum from a radar Doppler spectrogram; filling grid nodes by using a kriging spatial interpolation algorithm, simulating preliminary three-dimensional sea waves and smoothing the waves; and thirdly, carrying out interpolation processing on the time sequence by adopting a cubic spline interpolation algorithm to ensure that the time intervals are continuous to realize dynamic display. By adopting the technical scheme of the invention, the three-dimensional display of the sea waves can be verified by using the measured data, and the method is more suitable for monitoring and applying the marine environment.
Description
Technical Field
The invention relates to the technical field of radar remote sensing, in particular to a sea wave three-dimensional display method based on ocean remote sensing data.
Background
With the development of radar remote sensing technology, the large-scale real-time remote sensing monitoring of the ocean by using a shore-based high-frequency ground wave radar is realized. After the real-time data received by remote sensing are obtained, the motion state parameters and the distribution condition of the ocean surface can be obtained through data analysis and feature extraction, and the data comprise information such as the flow velocity and the flow direction of ocean current, the height and the intensity of ocean waves, the wind speed of the ocean surface and the like. Because of the wide range of ocean being monitored, the amount of information acquired at one time is large, and it is impractical if all are embodied or displayed in digital form.
At present, for the research of three-dimensional sea waves, the methods proposed in the past are all based on the simulation level, more importantly, the similarity of the sea wave forms is emphasized, and the measured data is not added for verification, so that the pure simulation is not enough to be applied to the monitoring of the marine environment.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a remote sensing monitoring method which can verify the three-dimensional display of sea waves by using measured data.
In order to solve the technical problems, the technical scheme adopted by the invention is a sea wave three-dimensional display method based on ocean remote sensing data, which comprises the following steps of extracting second-order spectrum inversion sea wave parameters from a radar Doppler spectrogram, filling grid nodes by using a kriging space interpolation algorithm to simulate preliminary three-dimensional sea waves and smoothing the waves, and finally carrying out interpolation processing on a time sequence by using a cubic spline interpolation algorithm to ensure that time intervals are continuous so as to realize dynamic display, wherein the method comprises the following specific steps:
the method comprises the following steps of (I) extracting a second-order spectrum from a radar Doppler spectrogram:
(1) selecting a Doppler frequency spectrogram of a certain distance element from a field of actually measured data, and estimating the position of a second-order spectrum according to the position of a first-order peak;
(2) selecting four areas in the second-order spectrum area, and simulating and searching extreme points through matlab programming, namely all the maximum values and the minimum values in the areas, wherein if no extreme point exists, the existence of the second-order spectrum is not shown, and if the extreme point exists, the minimum value point matching points on two sides of the first-order peak are continuously searched;
further, in order to accurately obtain the boundary of the second-order spectrum, the minimum value matching point region is screened, data processing is carried out through matlab, and the signal-to-noise ratio of the region contained in the minimum value point is calculated.
(3) And performing comprehensive analysis according to the selected sea states of different fields, setting a proper threshold value, and finishing second-order spectrum extraction when the signal-to-noise ratio is greater than the selected threshold value.
In general, the threshold value can be selected to be 5dB, and further, the threshold value of the signal-to-noise ratio is 15dB, so that the inversion accuracy of the wave spectrum can be improved.
Filling grid nodes by using a kriging spatial interpolation algorithm, simulating preliminary three-dimensional sea waves and smoothing the waves, wherein the method comprises the following specific steps:
(1) inputting an original stored array;
(2) determining the area range to be interpolated and the size of a grid according to the distribution of data, carrying out gridding processing on the area, and simulating a preliminary three-dimensional sea wave grid surface to ensure that each grid node is actually measured data;
(3) checking and analyzing the data, calculating the distance between the known data point sets, and if the distance value is small or the coordinates of two data points are close, rejecting the data points;
(4) knowing the spatial structure of the variable, and verifying whether the selected variation function is in accordance with the reality; at each actual measurement point, judging whether constraint exists between data points according to the minimum square average of the error between the estimated Kriging value calculated by the surrounding points and the actual measurement point value, and performing the next operation after the constraint exists;
(5) deriving the weighting factor λ from the system of kriging equationsi；
(6) The estimated point value Z (x) is obtained from the relation between the weighting coefficient and the sampling point0)；
(7) And (5) repeating the steps 3) to 6) to obtain an estimated point value, and interpolating to all grid points to complete the Krigin spatial interpolation.
The method of the Kriging spatial interpolation algorithm is as follows:
for the area variation Z (x), let it be at a series of sampling points xiAn observed value at (i ═ 1,2.., n) is Z (x)i) ( i 1,2.. times.n), then a certain mesh node X in the regionnAn estimated value of Z (x)0) Estimated from a linear combination, namely:
in the formula ofiIs a weighting coefficient, and Z (x) obeys the implication assumption as the premise, and has the following kriging equation set:
in the formula, gamma (x)i,xj) Is the sampling point xiAnd xjThe value of the variation function between, mu is the lagrange constant.
(III) carrying out interpolation processing on the time sequence by adopting a cubic spline interpolation algorithm to ensure that time intervals are continuous to realize dynamic display, and comprising the following steps:
(1) setting the definition of cubic spline function:
let f (x) be a continuous differentiable function over the interval [ a, b ], given a set of base points over the interval [ a, b ]:
a＝x0<x1<x2<...<xn＝b
the function s (x) satisfies the condition:
1) s (x) in each subinterval [ x ]i,xi+1](i-0, 1,2.., n-1) is a polynomial of degree not more than three;
2) s (x) has a second continuous derivative over the interval [ a, b ];
then s (x) is defined in [ a, b ]]Cubic spline interpolation function of (1), x0，x1，x2,...xnCalled spline nodes, where x1,...,xn-1Called inner node, x0,xnCalled boundary nodes;
(2) solving the cubic spline function:
setting cubic spline function S (X) in each subinterval [ X ]k-1,Xk]The above expression is:
S(x)＝Sk(x)＝akx3+bkx2+ckx+dk
X∈(Xk-1,Xk),k＝1,2...n；
wherein a isk,bk,ck,dkFor undetermined coefficients, the interpolation conditions are as follows:
1)S(Xl)＝f(Xl)l＝0,1,2...n；
2) (n-1) conditions of continuity and smoothness at internal nodes:
S(xj-0)＝S(xj+0),S’(xj-0)＝s(xj+0),S”(xj-0)＝S(xj+0)；j＝1,2...n；
for the undetermined coefficient ak,bk,ck,dkN, k is 1,2.. n; i.e. 4n unknown coefficients, and the interpolation conditions are 4n-2, but less two, so two conditions must be given, called boundary conditions, of the following three classes:
first class, known first derivative of both endpoints
Second, the known two-terminal second derivative
When M is0＝MnWhen 0 is a natural boundary condition
In the third category: periodic boundary condition
(3) The application of the interpolation of cubic spline functions:
in each sub-interval [ x ]j-1,xj]Determining the cubic polynomial S meeting the interpolation conditionk(x) Obtaining the result of the calculation of S (x),
more interpolation points are obtained between the data time, so that the time intervals are continuous, and the dynamic display of the three-dimensional sea waves is realized.
By adopting the technical scheme of the invention, the three-dimensional display of the sea waves can be verified by using the measured data, and the method is more suitable for monitoring and applying the marine environment.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of second spectral region division;
FIG. 3 is a method of second order spectrum extraction;
FIG. 4 is a three-dimensional sea wave diagram without an interpolation algorithm;
fig. 5 is a three-dimensional sea wave diagram after an interpolation algorithm is adopted.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings, but the present invention is not limited thereto.
FIG. 1 shows a sea wave three-dimensional display method based on ocean remote sensing data, which comprises the steps of extracting second-order spectrum inversion sea wave parameters from a radar Doppler frequency spectrogram, filling grid nodes by using a Krigin space interpolation algorithm to simulate preliminary three-dimensional sea waves and carry out wave smoothing, and finally carrying out interpolation processing on a time sequence by using a cubic spline interpolation algorithm to ensure that time intervals are continuous to realize dynamic display; the method comprises the following specific steps:
the method comprises the following steps of (I) extracting a second-order spectrum from a radar Doppler spectrogram, as shown in FIG. 3:
(1) selecting a Doppler frequency spectrogram of a certain distance element from a field of actually measured data, and estimating the position of a second-order spectrum according to the position of a first-order peak;
two peaks which are symmetrical about zero frequency and have obvious dominant amplitude exist in the spectrogram at the same time, namely a first-order peak, a region which is adjacent to the first-order peak is a second-order spectral region, and four approximate regions of a second-order spectrum are selected in the region, wherein the selection method is shown in fig. 2.
2) Selecting four areas in the second-order spectrum area, and simulating and searching extreme points through matlab programming, namely all the maximum values and the minimum values in the areas, wherein if no extreme point exists, the existence of the second-order spectrum is not shown, and if the extreme point exists, the minimum value point matching points on two sides of the first-order peak are continuously searched.
Further, in order to accurately obtain the boundary of the second-order spectrum, the minimum value matching point region is screened, data processing is carried out through matlab, and the signal-to-noise ratio of the region contained in the minimum value point is calculated.
3) And performing comprehensive analysis according to the selected sea states of different fields, setting a proper threshold value, and finishing second-order spectrum extraction when the signal-to-noise ratio is greater than the selected threshold value.
The threshold is typically, optionally, 5 dB; further, the threshold value of 15dB can improve the inversion accuracy of the wave spectrum.
Filling grid nodes by using a kriging spatial interpolation algorithm, simulating preliminary three-dimensional sea waves and smoothing the waves, wherein the method comprises the following specific steps:
(1) inputting an original stored array;
(2) determining the area range to be interpolated and the size of a grid according to the distribution of data, carrying out gridding processing on the area, and simulating a preliminary three-dimensional sea wave grid surface to ensure that each grid node is actually measured data;
(3) checking and analyzing the data, calculating the distance between the known data point sets, and if the distance value is small or the coordinates of two data points are close, rejecting the data points;
(4) knowing the spatial structure of the variable, and verifying whether the selected variation function is in accordance with the reality; at each actual measurement point, judging whether constraint exists between data points according to the minimum square average of the error between the estimated Kriging value calculated by the surrounding points and the actual measurement point value, and performing the next operation after the constraint exists;
(5) deriving the weighting factor λ from the system of kriging equationsi；
(6) The estimated point value Z (x) is obtained from the relation between the weighting coefficient and the sampling point0) Wherein n is the number of interpolation;
(7) and (5) repeating the steps 3) to 6) to obtain an estimated point value, and interpolating to all grid points to complete the Krigin spatial interpolation.
The method of the Kriging spatial interpolation algorithm is as follows:
for the area variation Z (x), let it be at a series of sampling points xiAn observed value at (i ═ 1,2.., n) is Z (x)i) ( i 1,2.. times.n), then a certain mesh node X in the regionnAn estimated value of Z (x)0) Estimated from a linear combination, namely:
in the formula ofiIs a weighting coefficient, and Z (x) obeys the implicit assumption as the premise, and has the following common Krigin equation set:
in the formula, gamma (x)i，xj) Is the sampling point xiAnd xjThe value of the variation function between, mu is the lagrange constant.
(III) carrying out interpolation processing on the time sequence by adopting a cubic spline interpolation algorithm to ensure that time intervals are continuous to realize dynamic display, and comprising the following steps:
(1) setting the definition of cubic spline function:
let f (x) be a continuous differentiable function over the interval [ a, b ], given a set of base points over the interval [ a, b ]:
a＝x0<x1<x2<...<xn＝b
the function s (x) satisfies the condition:
1) s (x) in each subinterval [ x ]i,xi+1](i-0, 1,2.., n-1) is a polynomial of degree not more than three;
2) s (x) has a second continuous derivative over the interval [ a, b ];
then s (x) is defined in [ a, b ]]Cubic spline interpolation function of (1), x0，x1，x2,...xnCalled spline nodes, where x1,...,xn-1Called inner node, x0,xnCalled boundary nodes;
(2) solving the cubic spline function:
setting cubic spline function S (X) in each subinterval [ X ]k-1,Xk]The above expression is:
S(x)＝Sk(x)＝akx3+bkx2+ckx+dk
X∈(Xk-1,Xk),k＝1,2...n；
wherein a isk,bk,ck,dkFor undetermined coefficients, the interpolation conditions are as follows:
1)S(Xl)＝f(Xl) l＝0,1,2...n；
2) (n-1) conditions of continuity and smoothness at internal nodes:
S(xj-0)＝S(xj+0),S’(xj-0)＝s(xj+0),S”(xj-0)＝S(xj+0)；j＝1,2...n；
for the undetermined coefficient ak,bk,ck,dkN, k is 1,2.. n; i.e. 4n unknown coefficients, and the interpolation conditions are 4n-2, but less two, so two conditions must be given, called boundary conditions, of the following three classes:
first class, known first derivative of both endpoints
Second, the known two-terminal second derivative
When M is0＝MnWhen 0 is a natural boundary condition
In the third category: periodic boundary condition
(3) The application of the interpolation of cubic spline functions:
in each sub-interval [ x ]j-1,xj]Determining the cubic polynomial S meeting the interpolation conditionk(x) Obtaining the result of the calculation of S (x),
more interpolation points are obtained between the data time, so that the time intervals are continuous, and the dynamic display of the three-dimensional sea waves is realized.
Fig. 4 shows a three-dimensional wave diagram without an interpolation algorithm, fig. 5 shows a three-dimensional wave diagram after an interpolation algorithm is adopted, and the roughness precision is low compared with the known three-dimensional wave diagram without an interpolation algorithm from fig. 4 and 5. The image after the interpolation algorithm becomes smooth, the dynamic sea wave image is more visual and is in close reality, and the visual effect is improved.
By adopting the technical scheme of the invention, the three-dimensional display of the sea waves can be verified by using the measured data, and the method is more suitable for monitoring and applying the marine environment.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention.
Claims (4)
1. A sea wave three-dimensional display method based on ocean remote sensing data is characterized by comprising the following steps: the method comprises the following steps of extracting a second-order spectrum inversion wave height parameter from a radar Doppler spectrogram, filling a grid node with a Kriging space interpolation algorithm to simulate preliminary three-dimensional sea waves and smoothing the waves, and finally performing interpolation processing on a time sequence by adopting a cubic spline interpolation algorithm to enable time intervals to be continuous so as to realize dynamic display, wherein the method comprises the following specific steps:
the method comprises the following steps of (I) extracting a second-order spectrum from a radar Doppler spectrogram:
(1) selecting a Doppler frequency spectrogram of a certain distance element from a field of actually measured data, and estimating the position of a second-order spectrum according to the position of a first-order peak;
(2) selecting four areas in the second-order spectrum area, and searching extreme points of each area through matlab programming simulation, namely all the maximum values and the minimum values in each area, wherein if no extreme point exists, the existence of the second-order spectrum is not shown, and if the extreme point exists, the minimum value point matching points on two sides of the first-order peak are continuously searched;
screening the minimum value matching point region, performing data processing through matlab, and calculating the signal-to-noise ratio of the region contained in the minimum value point;
(3) performing comprehensive analysis according to the selected sea conditions of different fields, setting a threshold value, and finishing second-order spectrum extraction when the signal-to-noise ratio is greater than the selected threshold value;
filling grid nodes by using a kriging spatial interpolation algorithm, simulating preliminary three-dimensional sea waves and smoothing the waves, wherein the method comprises the following specific steps:
(1) inputting an original stored array;
(2) determining the area range to be interpolated and the size of a grid according to the distribution of data, carrying out gridding processing on the area, and simulating a preliminary three-dimensional sea wave grid surface to ensure that each grid node is actually measured data;
(3) checking and analyzing the data, calculating the distance between the known data point sets, and if the distance value is small or the coordinates of two data points are close, rejecting the data points;
(4) knowing the spatial structure of the variable, and verifying whether the selected variation function is in accordance with the reality; at each actual measurement point, judging whether constraint exists between data points according to the minimum square average of the error between the estimated Kriging value calculated by the surrounding points and the actual measurement point value, and performing the next operation after the constraint exists;
(5) deriving the weighting factor λ from the system of kriging equationsi；
(6) The estimated point value Z (x) is obtained from the relation between the weighting coefficient and the sampling point0)；
(7) Repeating the steps 3) to 6) to obtain an estimated point value, and interpolating to all grid points to complete the Krigin spatial interpolation;
(III) carrying out interpolation processing on the time sequence by adopting a cubic spline interpolation algorithm to ensure that time intervals are continuous to realize dynamic display, and comprising the following steps:
(1) setting the definition of cubic spline function:
let f (x) be a continuous differentiable function over the interval [ a, b ], given a set of base points over the interval [ a, b ]:
a＝x0＜x1＜x2＜...＜xn＝b
the function s (x) satisfies the condition:
1) s (x) in each subinterval [ x ]i，xi+1](i-0, 1,2.., n-1) is a polynomial of degree not more than three;
2) s (x) has a second continuous derivative over the interval [ a, b ];
then s (x) is defined in [ a ]，b]Cubic spline interpolation function of (1), x0，x1，x2，...xnCalled spline nodes, where x1，...，xn-1Called inner node, x0，xnCalled boundary nodes;
(2) solving the cubic spline function:
setting cubic spline function S (X) in each subinterval [ X ]k-1，Xk]The above expression is:
X∈(Xk-1，Xk)，k＝1，2...n；
wherein a isk，bk，ck，dkFor undetermined coefficients, the interpolation conditions are as follows:
1)S(Xl)＝f(Xl)l＝0，1，2...n；
2) n-1 inner nodes are continuous and smooth:
S(xj-0)＝S(xj+0)，S′(xj-0)＝s(xj+0)，S″(xj-0)＝S(xj+0)；j＝1，2...n；
for the undetermined coefficient ak，bk，ck，dkN, k is 1,2.. n; i.e. 4n unknown coefficients, and the interpolation condition is 4n-2, thus giving two boundary conditions, the following three classes:
the first type: first derivative of known two endpoints
The second type: two-terminal second derivative is known
When M is0＝MnWhen 0 is a natural boundary condition
In the third category: periodic edgeBoundary conditions
(3) The application of the interpolation of cubic spline functions:
in each sub-interval [ x ]j-1，xj]Determining the cubic polynomial S meeting the interpolation conditionk(x) Obtaining the result of the calculation of S (x),
more interpolation points are obtained between the data time, so that the time intervals are continuous, and the dynamic display of the three-dimensional sea waves is realized.
2. A sea wave three-dimensional display method based on ocean remote sensing data as recited in claim 1, wherein: in the substep (3) in the step (one), the threshold value of the signal-to-noise ratio is 5 dB.
3. A sea wave three-dimensional display method based on ocean remote sensing data as recited in claim 2, wherein: in the step (3), the value of the signal-to-noise ratio threshold is 15 dB.
4. A sea wave three-dimensional display method based on ocean remote sensing data as recited in claim 1, wherein: the method of the kriging spatial interpolation algorithm in the step (two) is as follows:
for the area variation Z (x), let it be at a series of sampling points xiAn observed value at (i ═ 1,2.., n) is Z (x)i) (i 1,2.. times.n), then a certain mesh node X in the regionnAn estimated value of Z (x)0) Estimated from a linear combination, namely:
in the formula ofiThe weighting coefficient is, n is the interpolation number, and Z (x) obeys the implicit assumption as the premise, and has the following kriging equation set:
in the formula, gamma (x)i，xj) Is the sampling point xiAnd xjThe value of the variation function between, mu is the lagrange constant.
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