A reflectivity factor weighting comparison method and system based on radar beam volume overlap ratio

Abstract

The application discloses a reflectivity factor weighting comparison method and system based on radar beam volume overlap rate, comprising the following steps: S1. According to the comparison distance library information, an elliptical table model for representing the comparison distance library is established in space, the distance resolution, the elevation angle and the azimuth angle of the beam are comprehensively considered, a beam volume matching model is established to obtain the beam space sampling volume of the distance library; S2. Based on Monte Carlo simulation, a large number of random points are generated, and different beam volume overlap rates are calculated according to the regional labeling method to quantify the matching degree of the radar beam space; S3. The reflectivity factor corresponding weight of each matching region is given through the volume overlap rate, the final weighted error, the weighted standard deviation and the weighted correlation coefficient are calculated by weighting, and the radar performance difference is evaluated; the application can accurately calculate the matching degree of different radar distance libraries, quantitatively evaluate the spatial matching degree and perform weighted calculation, improve the precision of radar consistency comparison, and calibrate the radar more accurately.

Description

Technical Field

[0001] This invention relates to the field of weather radar data processing and calibration technology, and more specifically to a reflectivity factor weighted comparison method and system based on radar beam volume overlap rate. Background Technology

[0002] Weather radar, as one of the pillars of the meteorological field, is widely used in weather forecasting, precipitation estimation and disaster monitoring. In order to ensure that the radar detection data has high accuracy and robustness, the radar must be calibrated regularly. Radar consistency comparison, as an important means of evaluating the performance of weather radar, mainly evaluates the radar reflectivity factor and has been widely used in operations.

[0003] Currently, existing radar consistency comparison algorithms mainly rely on spatial matching based on the center position of the beam volume in the range database, and judge the degree of spatial matching by the position difference of the center of different range databases. However, this matching method is relatively crude, which will cause different degrees of error in radar consistency comparison, and cannot intuitively reflect the degree of volume overlap of different radar beams in space; especially in the consistency of radars in different bands, the error caused by the difference in beam width and range resolution is even greater.

[0004] In addition, in radar comparisons across different bands, such as satellite-to-ground verification, although the volume matching method is used to calculate the weighted reflectivity factor, the weights are based on the size and distance of the range library sampling volume, and do not accurately consider the actual range library matching degree.

[0005] Therefore, how to accurately calculate the matching degree of different radar range databases and quantitatively evaluate their spatial matching degree in order to improve the accuracy of radar consistency comparison algorithms and to perform more accurate radar calibration is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a reflectivity factor weighted comparison method and system based on radar beam volume overlap rate to solve the technical problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A reflectivity factor-weighted comparison method based on radar beam volume overlap rate includes the following steps:

[0009] S1. Based on the comparison range database information stored in the radar base data, an elliptical frustum model representing the comparison range database is established in space. Taking into account the range resolution, the elevation angle and azimuth angle of the beam, a beam volume matching model is established to obtain the beam space sampling volume of the range database.

[0010] S2. Based on Monte Carlo simulation, a large number of random points are generated and the volume overlap rate of different beams is calculated according to the region labeling method, thereby quantifying the degree of matching of radar beams in space;

[0011] S3. Assign corresponding weights to the reflectivity factor of each matching region by volume overlap rate, and calculate the final weighted error, weighted standard deviation and weighted correlation coefficient in a weighted manner to accurately assess the differences in radar performance.

[0012] Preferably, step S1 includes the following:

[0013] S11. Extract target beam information from radar base data, including radar latitude and longitude, antenna feed height, radar beamwidth, range resolution, and the target beam's elevation angle, azimuth angle, and range corresponding to the radar.

[0014] S12. Calculate the major and minor axes of the upper and lower ellipses of the elliptic platform using the range resolution, beamwidth, and distance of the range center relative to the radar;

[0015] S13. Establish a station-centered coordinate system for each radar based on the antenna feed point, and represent the target beam center using station-centered coordinates;

[0016] S14. Convert the station-centered coordinates of the target beam B relative to radar B to geocentric coordinates, and then convert the geocentric coordinates to station-centered coordinates relative to radar A to achieve coordinate system unification. Calculate the elevation, azimuth, and range of the target beam B relative to the antenna feed of radar A to obtain the polar coordinates of the beam centers of all range libraries of radar A and radar B relative to radar A.

[0017] S15. Based on the endpoints of the major and minor axes of the ellipse and the height of the ellipse, construct an ellipse model centered on the feed point of radar antenna A, which includes the eight endpoints of the upper and lower ellipses of the ellipse.

[0018] S16. Construct a rotation matrix based on the elevation and azimuth angles of the target beam B center relative to radar A;

[0019] S17. Transform the directions of the eight endpoints representing the elliptical frustum model using a rotation matrix, and then translate its center to the station center coordinates of the corresponding beam relative to radar A, thereby realizing the modeling of the spatial positional relationship of different beams.

[0020] Preferably, in step S12, the major and minor axes of the two ellipses above and below the ellipse platform correspond to the larger and smaller values ​​of the horizontal and vertical beamwidths, respectively;

[0021]

[0022] Among them, Lup L represents the lengths of the major axes of the upper and lower ellipses on the elliptic frustum. low R represents the lengths of the minor axes of the upper and lower ellipses on the elliptic frustum. cen R is the distance between the range library and the corresponding radar antenna feed point. reso For range resolution, γ is the horizontal or vertical beamwidth;

[0023] Step S16, the constructed rotation matrix is:

[0024]

[0025] Where, θ A φ is the elevation angle of the beam center relative to the feed point of radar antenna A. A It is the azimuth angle of the beam center relative to the feed point of radar antenna A.

[0026] Preferably, step S2 includes the following:

[0027] S21. Model different radar beams using a beam volume matching model, and construct a beam bounding box based on the maximum and minimum values ​​of the coordinates of different elliptical frustums in the global coordinate system.

[0028] S22. Based on the range of the bounding box, randomly generate a series of scattered points within the beam bounding box;

[0029] S23. Subtract the center coordinates of beam A relative to radar A from the coordinates of all scattered points, and transform the global coordinates of all scattered points into local coordinates relative to radar A using the corresponding rotation matrix;

[0030] S24. In the local coordinate system, calculate the boundary of the truncated cone A, determine whether the scattered points are inside the truncated cone, and mark each point.

[0031] S25. Repeat steps S23 and S24 to determine whether the scattered points are inside the truncated ellipse B and mark them.

[0032] S26. Based on all the markers, obtain the number of scattered points contained in each of the different elliptic frustums and the number of scattered points in the common area;

[0033] S27. Calculate the volume overlap rate of different beams in space to obtain the degree of matching in space.

[0034] Preferably, the volume overlap rate is used to characterize the spatial matching degree between the comparison distance databases, specifically:

[0035]

[0036] Among them, V oveV1 and V2 represent the volume of the two overlapping ellipsoids, respectively, where V1 and V2 are the volumes of the two ellipsoids in space.

[0037] The number of scattered points contained in different beams is equivalent to the volume of the beam, and the number of scattered points within the common volume of different beams is equivalent to the beam overlap volume.

[0038] Preferably, step S3 includes the following:

[0039] S31. Use the traditional consistency comparison algorithm to obtain the well-matched regions as prior knowledge;

[0040] S32. Calculate the volume overlap rate of the comparison range database and determine the weighting strategy based on the characteristics of the radar itself;

[0041] S33. Calculate the volume overlap rate of the matched distance library as the weight of the region, and calculate the weighted evaluation parameters in a weighted manner, including the weighted average error, weighted standard deviation and weighted Pearson correlation coefficient.

[0042] The preferred weighted evaluation parameters are as follows:

[0043]

[0044]

[0045]

[0046]

[0047] Among them, Z ei Let ω be the difference between the two radars Z in the i-th matching region. i Err represents the volume overlap rate on the i-th matching region. w For the weighted average error, Std w For weighted standard deviation, Z Ai Let Z be the value of radar A in the i-th matching region. Let r be the weighted Z-mean of radar A. w This is the weighted Pearson correlation coefficient.

[0048] A reflectivity factor weighted comparison system based on radar beam volume overlap rate, based on the aforementioned reflectivity factor weighted comparison method based on radar beam volume overlap rate, includes: a beam volume matching module, a spatial matching degree calculation module, and a weighted reflectivity factor calculation module.

[0049] The beam volume matching module is used to establish an elliptic frustum model in space to represent the comparison range library based on the comparison range library information stored in the radar base data. Taking into account the range resolution and the elevation and azimuth angles of the beam, the beam volume matching model is established to obtain the beam spatial sampling volume of the range library.

[0050] The spatial matching degree calculation module is used to calculate the volume overlap rate of different beams in space by generating a large number of random points and according to the region labeling method based on Monte Carlo simulation.

[0051] The weighted reflectivity factor calculation module is used to assign corresponding weights to the reflectivity factor of each matching region based on the volume overlap rate, and to calculate the final weighted error, weighted standard deviation and weighted correlation coefficient in a weighted manner to accurately assess the differences in radar performance.

[0052] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned reflectivity factor weighted comparison method based on radar beam volume overlap rate.

[0053] A processing terminal includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the aforementioned reflectivity factor weighted comparison method based on radar beam volume overlap rate.

[0054] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a reflectivity factor weighted comparison method and system based on radar beam volume overlap rate, which can systematically solve the problem of insufficient spatial matching accuracy in traditional consistency comparison, and at the same time improve the comparison accuracy.

[0055] By establishing a beam volume matching model, the sampling volume of a certain range library of different radars in space is restored in real-world scenarios; based on Monte Carlo simulation, the volume overlap rate of the sampling volumes of different range libraries is accurately calculated; based on the volume overlap rate, the final consistency comparison result is obtained in a weighted manner.

[0056] This invention has good business applicability and scalability, helps to improve the consistency comparison accuracy of traditional radar, and is applicable to weather radar systems of different models and operating frequency bands. It can also be applied to satellite-to-ground calibration technology, and has broad business promotion value and application prospects. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0058] Figure 1 A schematic diagram of the reflectivity factor weighted comparison method based on radar beam volume overlap rate provided by the present invention;

[0059] Figure 2 A schematic diagram illustrating the consistency comparison of different weather radar range databases provided by this invention;

[0060] Figure 3 The following is a schematic diagram of the modeling of different weather radar beam illumination volumes provided by the present invention; wherein, (a) is an elliptical frustum model of the illumination volume of radar A beam; and (b) is an elliptical frustum model of the illumination volume of radar B beam.

[0061] Figure 4 This is a schematic diagram of the volume overlap rate calculation based on Monte Carlo simulation provided by the present invention; wherein, (a) is a schematic diagram of the overlap of the elliptic frustum, and (b) is a schematic diagram of the overlapping part of the elliptic frustum.

[0062] Figure 5 The diagram shows the radar consistency comparison results provided by the present invention; wherein, (a) is a diagram showing the correlation of radar reflectivity factors, and (b) is a diagram showing the weighted comparison results of reflectivity factors. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] This invention discloses a reflectivity factor weighted comparison method based on radar beam volume overlap rate, such as... Figure 1 This includes the following steps:

[0065] S1. Based on the comparison range database information stored in the radar base data, an elliptical frustum model representing the comparison range database is established in space. Taking into account the range resolution, the elevation angle and azimuth angle of the beam, a beam volume matching model is established to obtain the beam space sampling volume of the range database.

[0066] S2. Based on Monte Carlo simulation, a large number of random points are generated and the volume overlap rate of different beams is calculated according to the region labeling method, thereby quantifying the degree of matching of radar beams in space;

[0067] S3. Assign corresponding weights to the reflectivity factor of each matching region by volume overlap rate, and calculate the final weighted error, weighted standard deviation and weighted correlation coefficient in a weighted manner to accurately assess the differences in radar performance.

[0068] In radar data processing, information within a 3dB beamwidth is typically the focus. Assuming only a 3dB beamwidth is considered, the radar beam can be approximated as a pencil shape. If the focus is on information within a certain range of a weather radar, the shape of the radar beam illuminating that range in space can be approximated as an elliptical frustum. The major and minor axes of the upper elliptical surface of the frustum are determined by the beamwidth and the distance between the center of the range and the radar. The axis perpendicularly passing through the upper and lower elliptical surfaces of the frustum (the axis of the frustum) is determined by the elevation and azimuth angles of the transmitted electromagnetic wave signal relative to the radar. The volume of the frustum in space is the sampling volume of the range in space. In this embodiment, an elliptical frustum model is established in space to approximate the spatial sampling volume of a certain range.

[0069] To further implement the above technical solution, the specific content of step S1 includes:

[0070] S11. Extract target beam information from radar base data, including radar latitude and longitude, antenna feed height, radar beamwidth, range resolution, and the target beam's elevation angle, azimuth angle, and range corresponding to the radar.

[0071] S12. Calculate the major and minor axes of the upper and lower ellipses of the elliptic platform using the range resolution, beamwidth, and distance of the range center relative to the radar;

[0072] S13. Establish local coordinate systems separately: For each radar, establish a station-centered coordinate system based on the antenna feed point, and represent the target beam center in station-centered coordinates;

[0073] S14. Unify the local coordinate system: Convert the station-centered coordinates of the target beam B relative to radar B to geocentric coordinates, and then convert the geocentric coordinates to station-centered coordinates relative to radar A to achieve coordinate system unification. Calculate the elevation, azimuth, and range of the target beam B relative to the antenna feed of radar A to obtain the polar coordinates of the beam center of all range libraries of radar A and radar B relative to radar A.

[0074] In this embodiment, both beam A and beam B are target beams. Target beam B is emitted by radar B, and target beam A is emitted by radar A. Radar A has its own beam polar coordinate information, so no conversion is required.

[0075] S15. Construct an elliptical frustum in the local coordinate system: Based on the endpoints of the major and minor axes of the ellipse and the height of the elliptical frustum (range resolution), construct an elliptical frustum model centered at the feed point of radar antenna A, containing the eight endpoints of the upper and lower ellipses of the elliptical frustum.

[0076] In this embodiment, the elliptical frustum model in the local coordinate system does not consider the effects of radar elevation and azimuth. Therefore, the coordinates of the endpoints of the major axis of the ellipse on the elliptical frustum can be expressed as (±Lupa / 2,0,Rreso / 2), where Lupa represents the length of the major axis of the ellipse on the elliptical frustum. Similarly, the representation of the endpoints of the minor axis can be obtained.

[0077] S16. Obtain the representation of the elliptic frustum model in the global coordinate system: Construct a rotation matrix based on the elevation and azimuth angles of the target beam B center relative to radar A;

[0078] S17. Transform the directions of the eight endpoints representing the elliptical frustum model using a rotation matrix, and then translate its center to the station center coordinates of the corresponding beam relative to radar A, thereby realizing the modeling of the spatial positional relationship of different beams.

[0079] This modeling method can be extended to any point within an elliptic frustum, and is also applicable to beams of arbitrary width.

[0080] To further implement the above technical solution, in step S12, the major and minor axes of the two ellipses on the elliptical platform correspond to the larger and smaller values ​​of the horizontal and vertical beamwidths, respectively.

[0081]

[0082] Among them, L up L represents the lengths of the major axes of the upper and lower ellipses on the elliptic frustum. low R represents the lengths of the minor axes of the upper and lower ellipses on the elliptic frustum. cen R is the distance between the range library and the corresponding radar antenna feed point. reso For range resolution, γ is the horizontal or vertical beamwidth;

[0083] In this embodiment, although the lengths of the major and minor axes of the ellipse are approximated using the arc length formula, the error can be ignored;

[0084] Step S16, the constructed rotation matrix is:

[0085]

[0086] Where, θ A φ is the elevation angle of the beam center relative to the feed point of radar antenna A. A It is the azimuth angle of the beam center relative to the feed point of radar antenna A.

[0087] In practical applications, when different weather radars share a common illumination area, spatial matching of different range databases is used to find a range database with good overlap, such as... Figure 2 As shown, light yellow and light blue represent different emitted beams, yellow and blue represent the matched range banks, and green represents the common illumination volume. Traditional methods can only match the center of the range banks and cannot accurately calculate the volume overlap rate of the green portion. To accurately calculate the beam overlap volume, the beam shape is modeled in space using an elliptical frustum, as shown below. Figure 3 As shown, the spatial sampling volumes of different range databases of the two radars were modeled. First, a vertical elliptical frustum was established at the origin. Then, the elliptical frustum was rotated and translated according to the coordinates of the center of the range database and its elevation and azimuth angles to obtain the simulated real situation. This method can restore the beam illumination volume in real space to a certain extent.

[0088] To further implement the above technical solution, step S2 includes the following:

[0089] S21. Model different radar beams using a beam volume matching model, and construct a beam bounding box based on the maximum and minimum values ​​of the coordinates of different elliptical frustums in the global coordinate system.

[0090] S22. Based on the range of the bounding box, randomly generate a series of scattered points within the beam bounding box;

[0091] S23. Subtract the center coordinates of beam A relative to radar A from the coordinates of all scattered points, and transform the global coordinates of all scattered points into local coordinates relative to radar A using the corresponding rotation matrix;

[0092] S24. In the local coordinate system, calculate the boundary of the truncated cone A, determine whether the scattered points are inside the truncated cone, and mark each point.

[0093] S25. Repeat steps S23 and S24 to determine whether the scattered points are inside the truncated ellipse B and mark them.

[0094] S26. Based on all the markers, obtain the number of scattered points contained in each of the different elliptic frustums and the number of scattered points in the common area;

[0095] S27. Calculate the volume overlap rate of different beams in space to obtain the degree of matching in space.

[0096] To further implement the above technical solution, when two weather radars of the same specifications are very close to each other, if the spatial matching degree of the range database is very high, then the two elliptical frustums representing the sampling volume of the range database should be highly overlapping in space; conversely, if the spatial matching degree is low, then the two elliptical frustums have only a small portion of overlapping volume or even no overlapping volume.

[0097] The spatial matching degree between the comparison distance databases is characterized by the volume overlap rate, specifically:

[0098]

[0099] Among them, V ove V1 and V2 represent the volume of the two overlapping ellipsoids, respectively, where V1 and V2 are the volumes of the two ellipsoids in space.

[0100] In this embodiment, the volume overlap rate is 1 when the two beams completely overlap; the volume overlap rate is 0 when the two beams do not intersect in space.

[0101] The number of scattered points contained in different beams is equivalent to the volume of the beam, and the number of scattered points within the common volume of different beams is equivalent to the beam overlap volume.

[0102] In this embodiment, as Figure 4 As shown, using the Monte Carlo simulation method, a large number of three points are randomly generated within a given area, and the boundary of each elliptic frustum is given through the elliptic frustum model. By determining whether the scattered points are within the boundary, each scattered point is labeled, which can clearly show the overlap of the elliptic frustums and the shape of the overlapping part. The beam volume overlap rate can be calculated by the number of points in each elliptic frustum and the number of points in the common area.

[0103] To further implement the above technical solution, step S3 includes the following:

[0104] S31. Use the traditional consistency comparison algorithm to obtain the well-matched regions as prior knowledge;

[0105] S32. Calculate the volume overlap rate of the comparison range database and determine the weighting strategy based on the characteristics of the radar itself;

[0106] S33. Calculate the volume overlap rate of the matched distance library as the weight of the region, and calculate the weighted evaluation parameters in a weighted manner, including the weighted average error, weighted standard deviation and weighted Pearson correlation coefficient.

[0107] To further implement the above technical solution, the weighted evaluation parameters are as follows:

[0108]

[0109]

[0110]

[0111]

[0112] Among them, Z ei Let ω be the difference between the two radars Z in the i-th matching region. i Err represents the volume overlap rate on the i-th matching region. w For the weighted average error, Std w For weighted standard deviation, Z Ai Let Z be the value of radar A in the i-th matching region. Let r be the weighted Z-mean of radar A. w This is the weighted Pearson correlation coefficient.

[0113] In this embodiment, as Figure 5 As shown, after calculating the volume overlap rate of all matching points, the original matching results are weighted using the overlap rate as the weight to obtain the final weighted comparison result.

[0114] Since the weights can reflect the size of the beam overlap area in real space, they reduce the errors caused by different sampling spaces and ultimately obtain more accurate comparison results. They can be used not only for ground-based radar comparisons but also for comparisons between spaceborne and ground-based radars.

[0115] A reflectivity factor weighted comparison system based on radar beam volume overlap rate, which is based on a reflectivity factor weighted comparison method based on radar beam volume overlap rate, includes: a beam volume matching module, a spatial matching degree calculation module, and a weighted reflectivity factor calculation module.

[0116] The beam volume matching module is used to establish an elliptic frustum model in space to represent the comparison range library based on the comparison range library information stored in the radar base data. Taking into account the range resolution and the elevation and azimuth angles of the beam, the beam volume matching model is established to obtain the beam spatial sampling volume of the range library.

[0117] The spatial matching degree calculation module is used to calculate the volume overlap rate of different beams in space by generating a large number of random points and according to the region labeling method based on Monte Carlo simulation.

[0118] The weighted reflectivity factor calculation module is used to assign corresponding weights to the reflectivity factor of each matching region based on the volume overlap rate, and to calculate the final weighted error, weighted standard deviation and weighted correlation coefficient in a weighted manner to accurately assess the differences in radar performance.

[0119] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a reflectivity factor weighted comparison method based on radar beam volume overlap rate.

[0120] A processing terminal includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements a reflectivity factor weighted comparison method based on radar beam volume overlap rate.

[0121] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0122] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A reflectivity factor weighted comparison method based on radar beam volume overlap rate, characterized in that, Includes the following steps: S1. Based on the comparison range database information stored in the radar base data, establish an elliptic frustum model in space to represent the comparison range database; The shape of a radar beam illuminating a range bank in space is approximately an elliptical frustum. The major and minor axes of the upper elliptical surface of the elliptical frustum are determined by the beam width and the distance between the center of the range bank and the radar. The axes perpendicularly passing through the upper and lower elliptical surfaces of the elliptical frustum are determined by the elevation and azimuth angles of the transmitted electromagnetic wave signal relative to the radar. The volume of the elliptical frustum in space is the sampling volume of the range bank in space. By establishing an elliptical frustum model in space, the spatial sampling volume of the beam of a certain range bank can be approximated. S2. Using the Monte Carlo simulation method, a large number of scattered points are randomly generated within a given area, and the boundary of each elliptical frustum is given through the elliptical frustum model. By determining whether the scattered points are within the boundary, each scattered point is labeled to show the overlap of the elliptical frustums and the shape of the overlapping part. The beam volume overlap rate is calculated by the number of points in each elliptical frustum and the number of points in the common area. S3. Assign corresponding weights to the reflectivity factor of each matching region by volume overlap rate, and calculate the final weighted error, weighted standard deviation and weighted correlation coefficient in a weighted manner to accurately assess the differences in radar performance.

2. The reflectivity factor weighted comparison method based on radar beam volume overlap rate according to claim 1, characterized in that, The specific content of step S1 includes: S11. Extract target beam information from radar base data, including radar latitude and longitude, antenna feed height, radar beamwidth, range resolution, and the target beam's elevation angle, azimuth angle, and range corresponding to the radar. S12. Calculate the major and minor axes of the upper and lower ellipses of the elliptic platform using the range resolution, beamwidth, and distance of the range center relative to the radar; S13. Establish a station-centered coordinate system for each radar based on the antenna feed point, and represent the target beam center using station-centered coordinates; S14. Convert the station-centered coordinates of the target beam B relative to radar B to geocentric coordinates, and then convert the geocentric coordinates to station-centered coordinates relative to radar A to achieve coordinate system unification. Calculate the elevation, azimuth, and range of the target beam B center relative to the antenna feed of radar A to obtain the polar coordinates of the beam centers of all range libraries of radar A and radar B relative to radar A. Target beam B is emitted by radar B, and target beam A is emitted by radar A. S15. Based on the endpoints of the major and minor axes of the ellipse and the height of the ellipse, construct an ellipse model centered on the feed point of radar antenna A, which includes the eight endpoints of the upper and lower ellipses of the ellipse. S16. Construct a rotation matrix based on the elevation and azimuth angles of the target beam B center relative to radar A; S17. Transform the directions of the eight endpoints representing the elliptical frustum model using a rotation matrix, and then translate its center to the station center coordinates of the corresponding beam relative to radar A, thereby realizing the modeling of the spatial positional relationship of different beams.

3. The reflectivity factor weighted comparison method based on radar beam volume overlap rate according to claim 2, characterized in that, Step S12: The major and minor axes of the two ellipses above and below the ellipse platform correspond to the larger and smaller values ​​of the horizontal and vertical beamwidths, respectively. ； Among them, L up L represents the lengths of the major axes of the upper and lower ellipses on the elliptic frustum. low R represents the lengths of the minor axes of the upper and lower ellipses on the elliptic frustum. cen R is the distance between the range library and the corresponding radar antenna feed point. reso For range resolution, γ is the horizontal or vertical beamwidth; Step S16, the constructed rotation matrix is: ； Where, θ A φ is the elevation angle of the beam center relative to the feed point of radar antenna A. A It is the azimuth angle of the beam center relative to the feed point of radar antenna A.

4. The reflectivity factor weighted comparison method based on radar beam volume overlap rate according to claim 1, characterized in that, The specific content of step S2 includes: S21. Model different radar beams and construct a beam bounding box based on the maximum and minimum values ​​of the coordinates of different elliptical frustums in the global coordinate system. S22. Based on the range of the bounding box, randomly generate a series of scattered points within the beam bounding box; S23. Subtract the center coordinates of beam A relative to radar A from the coordinates of all scattered points, and transform the global coordinates of all scattered points into local coordinates relative to radar A using the corresponding rotation matrix; S24. In the local coordinate system, calculate the boundary of the truncated cone A, determine whether the scattered points are inside the truncated cone A, and mark each point. S25. Subtract the center coordinates of beam B relative to radar A from the coordinates of all scattered points, and transform the global coordinates of all scattered points into local coordinates relative to radar A using the corresponding rotation matrix; calculate the boundary of elliptical frustum B in the local coordinates, determine whether the scattered points are inside elliptical frustum B and mark them. S26. Based on all the markers, obtain the number of scattered points contained in each of the different elliptic frustums and the number of scattered points in the common area; S27. Based on the number of scattered points contained in each of the different elliptic frustums and the number of scattered points in the common area, calculate the volume overlap rate of different beams in space to obtain the degree of matching in space.

5. The reflectivity factor weighted comparison method based on radar beam volume overlap rate according to claim 4, characterized in that, Volume overlap rate is used to characterize the spatial matching degree between the comparison distance databases, specifically: ； Among them, V ove V1 and V2 represent the volume of the two overlapping ellipsoids, respectively, where V1 and V2 are the volumes of the two ellipsoids in space. The number of scattered points contained in different beams is equivalent to the volume of the beam, and the number of scattered points within the common volume of different beams is equivalent to the beam overlap volume.

6. The reflectivity factor weighted comparison method based on radar beam volume overlap rate according to claim 1, characterized in that, The specific content of step S3 includes: S31. Use the traditional consistency comparison algorithm to obtain the well-matched regions as prior knowledge; S32. Calculate the volume overlap rate of the comparison range database and determine the weighting strategy based on the characteristics of the radar itself; S33. Calculate the volume overlap rate of the matched distance library as the weight of the region, and calculate the weighted evaluation parameters in a weighted manner, including the weighted average error, weighted standard deviation and weighted Pearson correlation coefficient.

7. The reflectivity factor weighted comparison method based on radar beam volume overlap rate according to claim 6, characterized in that, The weighted evaluation parameters are as follows: ； ； ； ； Among them, Z ei Let ω be the difference between the two radars Z in the i-th matching region. i Err represents the volume overlap rate on the i-th matching region. w For the weighted average error, Std w For weighted standard deviation, Z Ai Let Z be the value of radar A in the i-th matching region. Let r be the weighted Z-mean of radar A. w Z is the weighted Pearson correlation coefficient, and Z is the reflectance factor. Bi Let Z be the value of radar B in the i-th matching region. This is the weighted Z-mean of radar B.

8. A reflectivity factor weighted comparison system based on radar beam volume overlap rate, characterized in that, A weighted comparison method for reflectivity factors based on radar beam volume overlap rate according to any one of claims 1-7 includes: a beam volume matching module, a spatial matching degree calculation module, and a weighted reflectivity factor calculation module. The beam volume matching module is used to establish an elliptic frustum model in space to represent the comparison range library based on the comparison range library information stored in the radar base data. The shape of the radar beam illuminating a certain range library in space is approximately an elliptic frustum. The major and minor axes of the upper elliptic surface of the elliptic frustum are determined by the beam width and the distance between the center of the range library and the radar. The axes perpendicularly passing through the upper and lower elliptic surfaces of the elliptic frustum are determined by the elevation and azimuth angles of the transmitted electromagnetic wave signal relative to the radar. The volume of the elliptic frustum in space is the sampling volume of the range library in space. By establishing an elliptic frustum model in space, the beam spatial sampling volume of a certain range library is approximated. The spatial matching degree calculation module is used to generate a large number of scattered points randomly in a given area using the Monte Carlo simulation method, and to give the boundary of each elliptical frustum model. By determining whether the scattered points are in the boundary, each scattered point is labeled to show the overlap of the elliptical frustum and the shape of the overlapping part. The beam volume overlap rate is calculated by the number of points in each elliptical frustum and the number of points in the common area. The weighted reflectivity factor calculation module is used to assign corresponding weights to the reflectivity factor of each matching region based on the volume overlap rate, and to calculate the final weighted error, weighted standard deviation and weighted correlation coefficient in a weighted manner to accurately assess the differences in radar performance.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a reflectivity factor weighted comparison method based on radar beam volume overlap rate as described in any one of claims 1-7.

10. A processing terminal, characterized in that, It includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements a reflectivity factor weighted comparison method based on radar beam volume overlap rate as described in any one of claims 1-7.

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