CN109459753A - Weather radar data coordinate converts Fast Interpolation method - Google Patents
Weather radar data coordinate converts Fast Interpolation method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The present invention provides a kind of weather radar data coordinates to convert Fast Interpolation method, and being counted according to preset interpolation kernel and quantifying displacement precalculates sinc interpolation kernel table；Calculate it is to be converted to geographic coordinate system uniform three dimensional grid in each nexus mesh point coordinate value corresponding to coordinate under polar coordinate system centered on current radar itself, calculate its location of in uniform polar coordinates discrete grid block；According to the integer part of positional value, a three-dimensional matrice data block is extracted from the weather radar volumetric scan data that given discretization saves；It is inquired from sinc interpolation kernel table according to the fractional part of positional value and obtains three row elements in table to separately constitute column vectorWithUsing three-dimensional matrice data block andObtain dimensional matrix data block；Using dimensional matrix data block andObtain a dimensional vector；Using a dimensional vector withObtain the interpolation result of current grid value.The present invention improves interpolation speed.
Description
Technical Field
The invention relates to the field of weather radar signal and data processing, in particular to a quick interpolation method for weather radar data coordinate conversion.
Background
Weather radar derived data products are typically in polar coordinates centered on the radar itself, including the range, azimuth, and elevation coordinates of the meteorological target. For the application of networking observation of multiple weather radars, a unified coordinate system needs to be constructed, for example, a geographic coordinate system consisting of longitude, latitude and height is used as a reference, and data products obtained by each radar are transformed from a polar coordinate system to a new coordinate system through an interpolation method, so that the networking jigsaw of weather radar data is facilitated. Because most meteorological targets, such as clouds, are spatially continuous and have many fine-scale structures, it is desirable that the interpolated scattering power field be spatially continuous, while preserving the original echo structure characteristics present in the radar data to the maximum extent during the interpolation process. Common interpolation methods include: nearest neighbor, linear interpolation, and sinc kernel interpolation. Although the nearest neighbor method is high in speed, the precision is too low, and the scattering rate of the radar after interpolation is discontinuous in space; the linear interpolation method introduces larger smoothness to the data before the original interpolation, so that the original scattering rate fine-scale structural characteristics are easily lost. The sinc kernel interpolation method is a method for performing interpolation according to the nyquist sampling theorem, and has the highest precision, but generally has higher computational complexity.
Disclosure of Invention
In view of the above technical problem, the present invention provides a fast interpolation method for coordinate transformation of weather radar data.
The invention discloses a weather radar data coordinate conversion fast interpolation method, which comprises the following steps: step 1, pre-calculating a sinc interpolation kernel table according to a preset interpolation kernel number and a quantitative displacement; step 2, calculating coordinates under a polar coordinate system taking the current radar as the center, which correspond to grid point coordinate values of each network point in a uniform three-dimensional grid of the geographic coordinate system to be converted through the mapping relation; step 3, calculating the position of a certain specific distance, pitch and azimuth coordinates obtained by calculation in the step 2 in a uniform polar coordinate discrete grid of the volume scanning data of the weather radar; step 4, extracting a three-dimensional matrix data block from the given discretized stored weather radar volume scanning data according to the integer part of the position value of the position obtained in the step 3; step 5, respectively inquiring and obtaining three rows of elements in the table from the sinc interpolation kernel table according to the decimal part of the position value of the position obtained in the step 3 to respectively obtainForm a column vectorAndstep 6, utilizing the three-dimensional matrix data block obtained in the step 4 and the column vector obtained in the step 5Performing weighting operation to obtain a two-dimensional matrix data block; step 7, using the two-dimensional matrix data block obtained in step 6 and the column vector obtained in step 5Performing weighting operation to obtain a one-dimensional column vector; step 8, using the one-dimensional column vector obtained in step 7 and the column vector obtained in step 5And performing weighting operation to obtain an interpolation result of the current grid value.
Preferably, in the step 1, a typical value of the interpolation kernel number P is any even number between 6 and 16, and a typical value of L in quantization displacement 1/L is an even number greater than or equal to 10;
the sinc interpolation kernel table is a numerical table with L +1 rows and P columns;
element w of ith row and jth column of the sinc interpolation kernel tablei,jIs calculated by the following formula
Where i 1,2,. L +1, j 1,2,. P, sin c (x) ═ sin (pi x)/(pi x) denotes a sinc function.
Preferably, in step 2, the grid point coordinate value (x) of the geographic coordinate system is usedlat,m′,ylon,n′,hk′) The expression of the mapping relationship to the polar coordinate system coordinate centered on the current radar itself is:
wherein (x)r,yr,hr) Is the longitude and latitude height coordinate of the weather radar self position, R represents the earth radius, s is R multiplied by arccos [ sin (x)lat,m′)sin(xr)+cos(xlat,m′)cos(xr)cos(ylon,n′-yr)]xlat,m′,ylon,n′,hk′Respectively, the mth 'latitude, the nth' longitude and the kth altitude coordinate represented by the grid.
Preferably, in the step 3, a specific distance, pitch and azimuth coordinate is selectedComputing its uniform polar discrete grid in weather radar volume scan data
The method of the position (x1, x2, x3) is
Preferably, the step 4 comprises:
step 41, rounding x1, x2 and x3 respectively to obtain WhereinRepresenting a rounding operator;
step S42, storing weather radar volume scan data at given discretizationWhere M is 1,2,. M, N is 1,2,. N, K is 1,2,. K, K is indexed in a first dimension from N1-(P/2-1)、n1-P/2、n1-P/2+1、…、n1+ P/2, index of the second dimension from n2-(P/2-1)、n2-P/2、n2-P/2+1、…、n2+ P/2, index of the third dimension from n3-(P/2-1)、n3-P/2、n3-P/2+1、…、n3+ P/2, a three-dimensional matrix data block s (i, j, l) of dimensions P × P is extracted, i, j, l being 1, 2.
Preferably, the step 5 comprises:
calculate the fractional part of x1Divide it by the value of the quantized displacement 1/L and round it down to an integerWhereinExpressing an operator, inquiring a sinc interpolation table, selecting the elements of the L +1-m1 th lines in the interpolation table as weighted values to form a line vectorround () represents the rounding operator;
calculate the fractional part of x2Divide it by the value of the quantized displacement 1/L and round it down to an integerInquiring the sinc interpolation table and selecting the elements of the L +1-m2 th line in the interpolation table as the weighted value to form a line vector
Calculate the fractional part of x3Divide it by the value of the quantized displacement 1/L and round it down to an integerInquiring the sinc interpolation table and selecting the elements of the L +1-m3 th line in the interpolation table as the weighted value to form a line vector
Preferably, the step 6 comprises:
in a three-dimensional matrix data block sP×P×PFor a fixed index pair (j, l), the three-dimensional matrix data block sP×P×PP data elements s (1, j, l), s (2, j, l),.. s (P, j, l) form a column vectorThen, the obtained value is obtainedThe vector and the column vectorInner product of (2)As a P × P two-dimensional matrix data block s'P×P×PThe jth row of (a), the l column of data elements, where the superscript T represents a matrix or vector transpose. P is traversed for all index pairs (j, l), j, l ═ 1, 2.. until a P × P two-dimensional matrix data block s 'is computed'P×P×P。
Preferably, the expression of the one-dimensional column vector in step 7 is:
wherein,is one dimensional column vector, s'P×P×PIn the form of a two-dimensional matrix of data blocks,for a column vector, T represents a matrix or vector transpose.
Preferably, in step 8, the interpolation result of the current grid value is:
where T represents a matrix or vector transpose.
According to the technical scheme, the invention has the following beneficial effects:
(1) in the technical scheme of the invention, sinc kernel interpolation is used for data coordinate transformation of the weather radar, the interpolation precision is higher, and the fine-scale structural characteristics of the scattering rate of the weather target can be kept;
(2) the interpolation in the technical scheme of the invention is realized by inquiring the pre-calculated sinc interpolation kernel table to obtain the weighted value, thereby avoiding the process of calculating the sinc function with higher complexity in real time and improving the interpolation speed.
Drawings
FIG. 1 is a schematic view of a weather radar performing volume scanning according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a sine kernel interpolation principle used in the embodiment of the present invention.
Fig. 3 is a flowchart of a fast interpolation method for coordinate transformation of weather radar data according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
In the technical scheme of the invention, sinc kernel interpolation is used for data coordinate transformation of the weather radar, the interpolation precision is higher, and the fine-scale structural characteristics of the scattering rate of the weather target can be kept; the interpolation is realized by inquiring a precomputed sinc interpolation kernel table to obtain a weighted value, so that the process of calculating a sinc function with higher complexity in real time is avoided, and the interpolation speed is increased.
Embodiments of the invention are directed to transforming weather radar data from its own polar coordinate data to other coordinate systems, such as a geographic coordinate system represented by longitude and latitude heights. Before describing in detail the details of embodiments of the present invention, the principle of coordinate transformation using data interpolation is briefly described first.
Referring to FIG. 1, in general, a weather radar performs a volume scan to obtain an extreme weather targetDistance r, azimuth angle under coordinate systemFundamental data products of radar scattering power factor of pitch angle theta coordinate, which are expressed asLet Z' (x) be the data after the data performs the coordinate transformation to the geographic coordinate systemlat,ylonH) in which xlatRepresenting a longitudinal coordinate, ylonRepresenting the dimensional coordinate and h the height coordinate. For a particular coordinate value (x)lat,ylonH) weather object, coordinate-transformed data Z' (x)lat,ylonH) is prepared fromObtained by coordinate mapping:
wherein, r (x)lat,ylon,h),θ(xlat,ylon,h),The weather radar can be obtained by the theory of great circle geometry, and the longitude and latitude high coordinate of the position of the weather radar is set as (x)r,yr,hr) Then, then
Wherein R represents the radius of the earth, and s is expressed as
s＝R×arccos[sin(xlat)sin(xr)+cos(xlat)cos(xr)cos(ylat-yr)]
In the usual case of the use of a magnetic tape,data obtained by scanning weather radar bodyIs data which is uniformly distributed in a grid under a polar coordinate system and is stored in a discretization way1,2,. M, N, 1,2,. N, K, 1,2,. K. And M, N and K respectively represent data dimensions of the weather radar data in the distance direction, the pitching direction and the azimuth direction on the polar coordinate system before interpolation. A specific coordinate value (x) of the geographical coordinate systemlat,ylonH) the polar coordinate r (x) of the corresponding mappinglat,ylon,h),θ(xlat,ylon,h),It is not necessarily exactly located at the integer grid points on the polar coordinate system, and it is necessary to obtain the transformed data Z '(x') by interpolation using the data at the peripheral grid pointslat,ylon,h)。
Common interpolation methods include: nearest neighbor, linear interpolation, and sinc kernel interpolation. Although the nearest neighbor method is high in speed, the precision is too low, and the scattering rate of the radar after interpolation is discontinuous in space; the linear interpolation method introduces larger smoothness to the data before the original interpolation, so that the original scattering rate fine-scale structural characteristics are easily lost. The sinc kernel interpolation method is a method for performing interpolation according to the nyquist sampling theorem, and has the highest precision, but generally has higher computational complexity.
The interpolation in the embodiment of the invention is based on sinc kernel interpolation. Before the details of embodiments of the present invention are presented in detail, a brief description of some concepts and principles of sinc kernel interpolation will be presented.
Generally, a discrete sequence s [ i ], i is 1,2, on the premise that a signal band limit and a sampling rate greater than a nyquist sampling frequency are satisfied, an interpolation result s' (x) with high precision at any non-integer sampling point x can be obtained through sinc kernel interpolation, and is expressed as follows:
the interpolation formula indicates that the signal value at any non-integer sampling point x can be obtained by weighted sum of discrete sequences s [ i ], and the weight of each discrete sequence sampling point is the sinc interpolation kernel.
Fig. 2 is a schematic diagram of a sine core interpolation principle used in the embodiment of the present invention, generally, a sine core weight value does not need to be calculated for all samples, but sample values of a discrete sequence s [ i ] of 8 to 16 points around a non-integer sampling point x are used to participate in an interpolation operation, for example, in fig. 2, a value at the non-integer sampling point x needs to be interpolated, and then 4 integer sample values (sample values represented by 4 "o" symbols at the left and right of the x) at the left and right of the sample point x and a corresponding sine function weight value (a sine function sample value represented by "□") are used to perform weighted summation. When the sinc kernel interpolation formula is calculated, the process of calculating the value of the interpolation kernel sinc (x) according to the input value of x relates to trigonometric function operation, the calculation complexity is high, and in order to overcome the defect, the invention stores the pre-calculated interpolation kernel in a table mode to avoid real-time calculation, thereby improving the interpolation speed.
Fig. 1 is a flowchart of a fast interpolation method for coordinate transformation of weather radar data according to an embodiment of the present invention. Referring to fig. 1, in an embodiment of the present invention, a fast interpolation method for coordinate transformation of weather radar data is provided, and the method may include:
step S1, pre-calculating a sinc interpolation kernel table according to the preset interpolation kernel number P and the quantization displacement 1/L, as shown in Table 1;
the typical value of the interpolation kernel number P is any even number between 6 and 16, and the typical value of L in the quantization displacement 1/L is an even number which is more than or equal to 10;
the sinc interpolation kernel table is a numerical table with L +1 rows and P columns;
element w of ith row and jth column of the sinc interpolation kernel tablei,jIs calculated by the following formula
Where i ═ 1,2,. L +1, j ═ 1,2,. P, sinc (x) ═ sin (pi x)/(pi x) denotes a sinc function.
Table 1 is an interpolation kernel table of 13 rows and 8 columns when P is 8 and L is 12
| 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| -0.021 | 0.028 | -0.043 | 0.090 | 0.990 | -0.076 | 0.040 | -0.027 |
| -0.042 | 0.057 | -0.087 | 0.192 | 0.961 | -0.137 | 0.074 | -0.051 |
| -0.061 | 0.083 | -0.130 | 0.304 | 0.912 | -0.182 | 0.101 | -0.070 |
| -0.077 | 0.105 | -0.169 | 0.422 | 0.843 | -0.211 | 0.120 | -0.084 |
| -0.088 | 0.122 | -0.199 | 0.540 | 0.756 | -0.222 | 0.130 | -0.092 |
| -0.093 | 0.131 | -0.218 | 0.653 | 0.653 | -0.218 | 0.131 | -0.093 |
| -0.092 | 0.130 | -0.222 | 0.756 | 0.540 | -0.199 | 0.122 | -0.088 |
| -0.084 | 0.120 | -0.211 | 0.843 | 0.422 | -0.169 | 0.105 | -0.077 |
| -0.0717-1059 | 0.101 | -0.182 | 0.912 | 0.304 | -0.130 | 0.083 | -0.061 |
| -0.051 | 0.074 | -0.137 | 0.961 | 0.192 | -0.087 | 0.057 | -0.042 |
| -0.027 | 0.040 | -0.076 | 0.990 | 0.090 | -0.043 | 0.028 | -0.021 |
| 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
In step S2, let Ω { (x) be the uniform three-dimensional grid of the geographic coordinate system to be converted tolat,m′,ylon,n′,hk′) 1,2,. M ', N ', 1,2,. N ', K ' 1,2,. K ' }, where x is equal to 1,2lat,m′,ylon,n′,hk′Respectively representing the m ' th latitude, the n ' th longitude and the k ' th height represented by the gridThe degree coordinates, M, N and K, respectively, represent the dimension of the grid, with the coordinate value (x) for each specific grid point of all grid pointslat,m′,ylon,n′,hk′) Firstly, the coordinate under the corresponding polar coordinate system taking the current radar as the center is calculated through the mapping relation fThen, the following steps S3 to S8 are repeatedly performed, wherein r ', theta',respectively representing the distance, the pitch angle and the azimuth angle coordinate under the mapped polar coordinate system;
the grid point coordinate value (x) of the geographic coordinate systemlat,m′,ylon,n′,hk′) The expression of the mapping relationship f to the polar coordinate system coordinate centered on the current radar itself is:
wherein (x)r,yr,hr) Is the longitude and latitude height coordinate of the self position of the weather radar, R represents the radius of the earth, and the expression of s is
s＝R×arccos[sin(xlat,m′)sin(xr)+cos(xlat,m′)cos(xr)cos(ylon,n′-yr)]
Step S3, calculating the specific distance, pitch and azimuth coordinates obtained in step S2Computing its uniform polar discrete grid in weather radar volume scan dataPosition (x1, x2, x3) representing a distance coordinater' is located at rmThe x1 th sample point in the sequence, M ═ 1, 2.. M; the pitch coordinate theta' being located at thetanThe x2 th sample point in the sequence, N ═ 1, 2.. N; orientation coordinateIs located atThe x3 th sample point in the sequence, K ═ 1,2,. K; x1, x2 and x3 are integers or non-integers not less than 1;
the said coordinate is composed of a specific distance, pitch and azimuthComputing its uniform polar discrete grid in weather radar volume scan dataThe method of the position (x1, x2, x3) is as follows:
step S4, storing weather radar volume scan data from given discretization according to integer part of x1, x2, x3 position value obtained in step S3M1, 2,. M, N1, 2,. N, K1, 2,. K, a three-dimensional matrix data block s (i, j, l) of dimension P × P is extracted, i, j, l 1,2,. P, where the integer P is the step s1The number of preset interpolation kernels shown, P;
the integer part of the position values according to x1, x2 and x3 is used for storing the volume scanning data of the weather radar from the given discretizationIn the case of M1, 2,. M, N1, 2,. N, K1, 2,. K, a three-dimensional matrix data block s (i, j, l) of dimension P × P is extracted, and the steps of i, j, l 1,2,. P may further include:
substep S41 of rounding x1, x2 and x3 to obtain WhereinRepresenting a rounding operator;
substep S42-weather Radar volume Scan data saved for given discretizationM1, 2,. M, N1, 2,. N, K1, 2,. K, where K is indexed in the first dimension from N1-(P/2-1)、n1-P/2、n1-P/2+1、…、n1+ P/2, index of the second dimension from n2-(P/2-1)、n2-P/2、n2-P/2+1、…、n2+ P/2, index of the third dimension from n3-(P/2-1)、n3-P/2、n3-P/2+1、…、n3+ P/2, extracting a three-dimensional matrix data block s (i, j, l) with dimensions of P × P, where i, j, l is 1,2,. P;
step S5: inquiring and obtaining three rows of elements in the table from the sinc interpolation kernel table according to the decimal parts of the x1, x2 and x3 position values obtained in the step S3 respectively to form column vectorsAnd
the three rows of elements in the table are inquired and obtained from the sinc interpolation kernel table according to the decimal part of the position values of x1, x2 and x3 to respectively form column vectorsAndthe method comprises the following steps:
calculate the fractional part of x1Divide it by the value of the quantized displacement 1/L and round it down to an integerWhereinExpressing an operator, inquiring a sinc interpolation table, selecting the elements of the L +1-m1 th lines in the interpolation table as weighted values to form a line vectorround () represents the rounding operator;
calculate the fractional part of x2Divide it by the value of the quantized displacement 1/L and round it down to an integerInquiring the sinc interpolation table and selecting the elements of the L +1-m2 th line in the interpolation table as the weighted value to form a line vector
Calculate the fractional part of x3Divide it by the value of the quantized displacement 1/L and round it down to an integerInquiring the sinc interpolation table and selecting the elements of the L +1-m3 th line in the interpolation table as the weighted value to form a line vector
Step S6, using the three-dimensional matrix data block S obtained in step S4P×P×PAnd the column vector obtained in step S5Carrying out weighting calculation to obtain a P multiplied by P two-dimensional matrix data block s'P×P×P；
The data block s using the three-dimensional matrixP×P×PSum column vectorThe process of performing the weighting operation is:
in a three-dimensional matrix data block sP×P×PFor a fixed index pair (j, l), the three-dimensional matrix data block sP×P×PP data elements s (1, j, l), s (2, j, l),.. s (P, j, l) form a column vectorThen, the vector and the column vector are obtainedInner product of (2)As a P × P two-dimensional matrix data block s'P×P×PThe jth row of (a), the l column of data elements, where the superscript T represents a matrix or vector transpose. P is traversed for all index pairs (j, l), j, l ═ 1, 2.. until a P × P two-dimensional matrix data block s 'is computed'P×P×P；
In step S7, the two-dimensional matrix data block S 'obtained in step S6 is used'P×P×PAnd the column vector obtained in step S5Performing weighting operation to obtain a one-dimensional column vector containing P elements
The data is obtained by using a two-dimensional matrix data block s'P×P×PSum column vectorAnd performing weighting operation by using multiplication of vectors and matrixes, wherein the expression is as follows:
obtaining a one-dimensional column vector containing P elementsWherein, superscript T represents matrix or vector transposition;
step S8, using the one-dimensional vector containing P elements obtained in step S7And the column vector obtained in step S5Performing weighting operation to obtain the interpolation result of the current grid value, wherein the expression is
Where superscript T represents a matrix or vector transpose.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should also be considered as falling within the scope of the present invention.
Claims (9)
1. A weather radar data coordinate conversion fast interpolation method is characterized by comprising the following steps:
step 1, pre-calculating a sinc interpolation kernel table according to a preset interpolation kernel number and a quantitative displacement;
step 2, calculating coordinates under a polar coordinate system taking the current radar as the center, which correspond to grid point coordinate values of each network point in a uniform three-dimensional grid of the geographic coordinate system to be converted through the mapping relation;
step 3, calculating the position of a certain specific distance, pitch and azimuth coordinates obtained by calculation in the step 2 in a uniform polar coordinate discrete grid of the volume scanning data of the weather radar;
step 4, extracting a three-dimensional matrix data block from the given discretized stored weather radar volume scanning data according to the integer part of the position value of the position obtained in the step 3;
step 5, respectively inquiring and obtaining three rows of elements in the table from the sinc interpolation kernel table according to the decimal part of the position value of the position obtained in the step 3 to respectively form column vectors And
step 6, utilizing the three-dimensional matrix data block obtained in the step 4 and the column vector obtained in the step 5Performing weighting operation to obtain a two-dimensional matrix data block;
step 7, using the two-dimensional matrix data block obtained in step 6 and the column vector obtained in step 5Performing weighting operation to obtain a one-dimensional column vector;
step 8, using the one-dimensional column vector obtained in step 7 and the column vector obtained in step 5And performing weighting operation to obtain an interpolation result of the current grid value.
2. The method according to claim 1, wherein in the step 1, a typical value of the number P of the interpolation kernels is any even number between 6 and 16, and a typical value of L in quantization displacement 1/L is an even number greater than or equal to 10;
the sinc interpolation kernel table is a numerical table with L +1 rows and P columns;
element w of ith row and jth column of the sinc interpolation kernel tablei,jIs calculated by the following formula
Where i ═ 1,2,. L +1, j ═ 1,2,. P, sinc (x) ═ sin (pi x)/(pi x) denotes a sinc function.
3. Method according to claim 2, characterized in that in step 2 the grid point coordinate values (x) of the geographical coordinate system are determined from the grid point coordinate values (x) of the geographical coordinate systemlat,m′,ylon,n′,hk′) The expression of the mapping relationship to the polar coordinate system coordinate centered on the current radar itself is:
wherein (x)r,yr,hr) Is the longitude and latitude height coordinate of the weather radar self position, R represents the earth radius, s is R multiplied by arccos [ sin (x)lat,m′)sin(xr)+cos(xlat,m′)cos(xr)cos(ylon,n′-yr)]xlat,m′,ylon,n′,hk′Respectively, the mth 'latitude, the nth' longitude and the kth altitude coordinate represented by the grid.
4. The method of claim 3, wherein in step 3, the distance, pitch and azimuth are specified by a certain valueCoordinates of the objectComputing its uniform polar discrete grid in weather radar volume scan dataThe method of the position (x1, x2, x3) is
5. The method of claim 4, wherein the step 4 comprises:
step 41, rounding x1, x2 and x3 respectively to obtain WhereinRepresenting a rounding operator;
step S42, storing weather radar volume scan data at given discretizationWhere M is 1,2,. M, N is 1,2,. N, K is 1,2,. K, K is indexed in a first dimension from N1-(P/2-1)、n1-P/2、n1-P/2+1、…、n1+ P/2, index of the second dimension from n2-(P/2-1)、n2-P/2、n2-P/2+1、…、n2+ P/2, index of the third dimension from n3-(P/2-1)、n3-P/2、n3-P/2+1、…、n3+ P/2, a three-dimensional matrix data block s (i, j, l) of dimensions P × P is extracted, i, j, l being 1, 2.
6. The method of claim 5, wherein the step 5 comprises:
calculate the fractional part of x1Divide it by the value of the quantized displacement 1/L and round it down to an integerWhereinExpressing an operator, inquiring a sinc interpolation table, selecting the elements of the L +1-m1 th lines in the interpolation table as weighted values to form a line vectorround () represents the rounding operator;
calculate the fractional part of x2Divide it by the value of the quantized displacement 1/L and round it down to an integerInquiring the sinc interpolation table and selecting the elements of the L +1-m2 th line in the interpolation table as the weighted value to form a line vector
Calculate the fractional part of x3Divide it by the value of the quantized displacement 1/L and round it down to an integerInquiring the sinc interpolation table and selecting the elements of the L +1-m3 th line in the interpolation table as the weighted value to form a line vector
7. The method of claim 6, wherein the step 6 comprises:
in a three-dimensional matrix data block sP×P×PFor a fixed index pair (j, l), the three-dimensional matrix data block sP×P×PP data elements s (1, j, l), s (2, j, l),.. s (P, j, l) form a column vectorThen, the vector and the column vector are obtainedInner product of (2)As a P × P two-dimensional matrix data block s'P×P×PThe jth row of (a), the ith column of data elements, where superscript T represents a matrix or vector transpose; p is traversed for all index pairs (j, l), j, l ═ 1, 2.. until a P × P two-dimensional matrix data block s 'is computed'P×P×P。
8. The method of claim 7, wherein the expression of the one-dimensional column vector in step 7 is:
wherein,is one dimensional column vector, s'P×P×PIn the form of a two-dimensional matrix of data blocks,for a column vector, T represents a matrix or vector transpose.
9. The method according to claim 8, wherein in step 8, the interpolation result of the current grid value is:
where T represents a matrix or vector transpose.
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