CN115511011A - Radar data correction method and system based on countermeasure generation network model - Google Patents
Radar data correction method and system based on countermeasure generation network model Download PDFInfo
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- CN115511011A CN115511011A CN202211454168.0A CN202211454168A CN115511011A CN 115511011 A CN115511011 A CN 115511011A CN 202211454168 A CN202211454168 A CN 202211454168A CN 115511011 A CN115511011 A CN 115511011A
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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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
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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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
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Abstract
The invention discloses a radar data correction method and a system based on an confrontation generation network model, which belong to the field of radar detection, and the radar data correction method based on the confrontation generation network model comprises the following steps: inputting radar data to be corrected, wherein the radar data to be corrected is a radial wind speed field measured by a laser wind measuring radar; reconstructing the data to be corrected by adopting a gray level conversion mode; adopting a shielding detection program to carry out shielding judgment on the reconstructed data, and carrying out shielding mask processing on the data to be corrected according to the obtained shielding judgment result to obtain a shielded image and a mask; judging whether the shielding degree of the mask is within a preset threshold range; inputting the shielded image and the mask to a confrontation generation network model obtained by training; the invention can combine the correction model to output the corrected radar data, and accurately provide various meteorological factors for atmospheric science research in time.
Description
Technical Field
The invention relates to the field of radar detection, in particular to a radar data correction method and system based on a countermeasure generation network model.
Background
Due to the propagation characteristics of electromagnetic waves and the blockage of hard targets such as tall buildings and the like near the radar, the radar often has the phenomenon of beam blockage in actual detection, so that the radar data quality is not high. Particularly, in the radar arranged in the urban area, the echo data is more susceptible to the influence of beam blocking, and even if the echo data is blocked to a slight degree, the emitted electromagnetic waves cannot be completely transmitted forwards, so that the echo is weak or completely blocked, and the accuracy of radar data is influenced. The radar detection technology is developed to the present, is one of the most important remote sensing technologies, is indispensable in the atmospheric science field, and can obtain the space-time distribution condition of various meteorological elements by means of the radar detection technology, thereby making a great contribution to solving the problems in the atmospheric science field. How to solve the wave beam blocking phenomenon in the radar detection process is a key problem for improving the radar detection technology.
Currently, the correction for radar beam blocking mainly depends on three modes, namely a Digital Elevation Model (DEM), an identification algorithm based on echo probability characteristics and a beam blocking identification algorithm based on spatial correlation. The inventor of the invention finds out through research that: the digital elevation model can perform echo correction on an area where beam blocking occurs according to actual mapping data, but the method has certain limitation because new buildings continuously appear along with social and economic development and urban construction, beam blocking may be caused, and the mapping data is difficult to update in real time. Therefore, the beam blocking correction scheme based on the digital elevation model has difficulty in ensuring the accuracy of correction. The beam blocking identification algorithm based on the spatial correlation does not need digital high-range data, is not influenced by terrain and atmospheric refraction conditions, can better identify and correct beam blocking, but cannot utilize adjacent echo signals when the condition of large-range beam blocking occurs, because the strong correlation of a radar echo large-span space cannot be ensured, and the condition of over-high correction amount can be caused when a zero-degree layer bright band appears and is used for correcting.
Disclosure of Invention
1. Technical problem to be solved
Aiming at the problems in the prior art, the invention aims to provide a radar data correction method and a system based on a countermeasure generation network model, which can output corrected radar data by combining a correction model according to the radar data and accurately provide various meteorological elements for atmospheric science research in time.
2. Technical scheme
In order to solve the above problems, the present invention adopts the following technical solutions.
A radar data correction method based on an confrontation generation network model comprises the following steps:
detecting a shielded portion based on the radar data;
training the confrontation according to radar data to generate a network model; the countermeasure generation network model corrects radar data;
selecting preset parameters to classify radar data and establishing a multi-radar data correction model;
acquiring real-time radar data;
matching the radar data with the established multi-radar data correction system to obtain the matched correction system; the correction system is used to identify and correct the beam blocking of the real-time radar data.
And (5) inverting according to the corrected radar data to obtain and output real-time meteorological elements.
Further, detecting the masked portion based on the radar data includes: converting radar data into a gray image, judging whether the value of each pixel point is within a preset threshold range, if the value is out of the threshold range, determining the pixel point as a shielding area, and outputting the shielding area as a mask; if the value is within the threshold range, the point is a valid region and is output as a broken image.
Further, training the confrontation generation network model according to the radar data, and the method comprises the following steps: selecting a certain amount of radar data to extract masks in batches, judging whether the shielding degree of the masks is within a preset threshold range, selecting the masks within the threshold range, and classifying the masks according to different shielding degrees of the masks to be used as a mask set. Selecting a certain amount of radar data, judging whether the shielding degree of the mask is within a preset threshold range, and selecting the radar data within the threshold range as a background set. The mask set and the background set are divided into a training set, a verification set and a test set of the mask and a training set, a verification set and a test set of the background according to a preset proportion. And generating a network model by using the data training confrontation, wherein the trained model comprises an edge reconstruction network and an image restoration network. The edge reconstruction network reconstructs edge information of the missing area according to the input radar data and the mask information, and the image restoration network reconstructs an image of the missing area under the adjustment of the edge information to obtain a restored image.
Further, selecting predetermined parameters to classify radar data, and establishing a multi-radar data correction model, including: training correction models suitable for different data according to different radar detection data; inputting the radar data to be corrected into the correction system, classifying and judging the radar data to be corrected by adopting a probability algorithm, and selecting a proper correction model.
Further, acquiring real-time radar data comprises: and establishing communication with the output end of the radar system, and receiving the radar data output from the output end in real time.
Further, matching the radar data with the established multi-radar data correction system to obtain the matched correction system; the correction system is used to identify and correct the beam blocking of the real-time radar data.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that: the corrected radar data can be output by combining the correction model according to the radar data, and various meteorological elements can be accurately provided for atmospheric scientific research in time.
Drawings
FIG. 1 is a flow chart of a method for correcting radar data based on a countermeasure generation network model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for establishing multi-radar data correction according to an embodiment of the present invention;
FIG. 3 is a flow chart of the training of the countermeasure generation network model according to an embodiment of the present invention;
FIG. 4 is a flowchart of a countermeasure-generation-based network model radar data correction system according to an embodiment of the present invention;
FIG. 5 is a radial wind velocity field image to be corrected according to an embodiment of the present invention;
fig. 6 is a radial wind velocity field image reconstructed after the gray scale conversion provided by the embodiment of the present invention;
FIG. 7 is an image after masking and masking processing provided by an embodiment of the present invention;
fig. 8 is a corrected radial wind velocity field image according to an embodiment of the present invention.
Detailed Description
The drawings in the embodiments of the invention will be incorporated below; the technical scheme in the embodiment of the invention is clearly and completely described; obviously; the described embodiments are only some of the embodiments of the invention; but not all embodiments, are based on the embodiments of the invention; all other embodiments obtained by a person of ordinary skill in the art without making any creative effort; all fall within the scope of protection of the present invention.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top/bottom", etc. indicate orientations or positional relationships based on orientations or positional relationships shown in the drawings, which are merely for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "sleeved/connected," "connected," and the like are to be construed broadly, e.g., "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1:
referring to fig. 1, a radar data correction method based on a countermeasure generation network model includes the following steps:
s1, inputting radar data to be corrected;
in this embodiment, the radar data to be corrected refers to the radial wind speed field measured by the laser wind radar, and the input is a 256 × 256 × 3 RGB color radial wind speed field to be corrected.
S2, reconstructing the data to be corrected by adopting a gray scale conversion mode, wherein a radial wind speed field image obtained by reconstruction after gray scale conversion is shown in a figure 6;
the method specifically comprises the following steps: converting the radial wind speed field image into a gray image, then sequentially judging whether the value of each pixel point is within a preset threshold range, if the value is out of the threshold range, regarding that the point has beam blockage, and setting the pixel value of the point to be 255, so as to be a shielding area; if the value is within the threshold range, the spot is not beam-blocked and the spot pixel value is set to 0, which is the active area.
S3, adopting a shielding detection program to perform shielding judgment on the reconstructed data, and performing shielding mask processing on the data to be corrected according to the obtained shielding judgment result to obtain a shielded image and a mask, wherein the shielded image and the mask are shown in FIG. 7;
in one embodiment, the reconstructed gray image in S2 is used for occlusion detection, and if the pixel value of a certain point is 255, the point is marked as a mask; otherwise, marking the image as an effective area, and respectively generating a mask and outputting the masked image after completing the masking detection.
S4, judging whether the shielding degree of the mask is within a preset threshold range;
calculating a shielding degree (a ratio of the size of the mask to the size of the radial wind speed field to be corrected) by using the mask generated in the step S3, wherein an image of the radial wind speed field to be corrected is shown in FIG. 5, and if the shielding degree is within a predetermined threshold range, the shielding degree is input into the step S5 according to the availability of the data to be corrected input this time; otherwise, the correction is finished.
S5, inputting the shielded image and the mask to a confrontation generation network model obtained through training;
in one embodiment, the mask generated in S4 and the masked image are input into a trained confrontation generating network model, and the confrontation generating network model includes an edge reconstruction network and an image restoration network. The edge reconstruction network can reconstruct the edge of the missing part of the image according to the input edge information of the shielded image. And then the image repairing network repairs the image of the missing area under the regulation of the edge information, so that the repairing effect is better.
S6, performing radar data correction process and outputting the correction result, as shown in FIG. 8.
In one embodiment, the confrontation generated network model is utilized to repair the radial wind speed field shielded by the mask, so that a reconstructed radial wind speed field is obtained, and the radar data correction is completed.
The countermeasure generation network model is mainly a technology for restoring a missing part in an image based on the input existing information of a radial wind speed field to be corrected. Therefore, after the confrontation generation network model obtains the masked and shielded image, the confrontation generation network model can be used for repairing the image, and the reconstructed radial wind speed field can be obtained.
As shown in fig. 4, a radar data correction system based on a countermeasure generation network model includes a gray level conversion module, a mask detection module, a probability algorithm module, a correction model selection module, and a radar data correction module.
As shown in fig. 3, the training process of the countermeasure generation network model is as follows:
s1, extracting a corresponding mask by using radar data to generate a mask set, and selecting relatively complete radar data as a background field to generate a background set;
in this embodiment, the radar data refers to a radial wind speed field measured by the lidar. And detecting the mask by using a shielding detection module and extracting a corresponding mask to generate a mask set. The shielding detection module screens radar data according to a preset threshold value, and if the radar data are within the threshold value range, the radar data are selected as a background field and a background set is generated.
S2, inputting the mask set and the background field into a countermeasure generation network model, carrying out shielding treatment on the background field by using the mask set by the model to generate a damaged image and a missing region image, wherein the missing region image is a shielding part of the mask in the background, and then obtaining a repaired image by the model;
s3, determining a loss value of the confrontation generation network model according to the background, the repaired image, the mask and the missing region image;
and S4, if the loss value is higher than a preset threshold value, updating the model.
Example 2:
referring to fig. 2, a method for correcting radar data based on a countermeasure generation network model includes the following steps:
s1, inputting radar data to be corrected, wherein the radar data to be corrected is a radial wind speed field measured by a laser wind measuring radar;
s2, classifying and judging the radar data to be corrected by adopting a maximum probability algorithm, and selecting a proper correction model;
s3, reconstructing the data to be corrected by adopting a gray level conversion mode;
s4, adopting a shielding detection program to perform shielding judgment on the reconstructed data, and performing shielding mask processing on the data to be corrected according to the obtained shielding judgment result to obtain a shielded image and a mask;
s5, judging whether the shielding degree of the mask is within a preset threshold range;
s6, inputting the shielded image and the mask to a confrontation generation network model obtained through training;
s7, performing radar data correction process and outputting the correction result.
The above; but are merely preferred embodiments of the invention; the scope of the invention is not limited thereto; any person skilled in the art is within the technical scope of the present disclosure; the technical scheme and the improved concept of the invention are equally replaced or changed; are intended to be covered by the scope of the present invention.
Claims (9)
1. A radar data correction method based on an confrontation generation network model is characterized in that: the method comprises the following steps:
s1, inputting radar data to be corrected, wherein the radar data to be corrected is a radial wind speed field measured by a laser wind measuring radar;
s2, reconstructing the data to be corrected by adopting a gray level conversion mode;
s3, adopting a shielding detection program to perform shielding judgment on the reconstructed data, and performing shielding mask processing on the data to be corrected according to the obtained shielding judgment result to obtain a shielded image and a mask;
s4, judging whether the shielding degree of the mask is within a preset threshold range;
s5, inputting the shielded image and the mask to a confrontation generation network model obtained through training;
s6, performing radar data correction process and outputting the correction result.
2. The method of claim 1, wherein the radar data correction method based on the countermeasure generation network model comprises: in S1, the radar data to be corrected includes an RGB color radial wind velocity field to be corrected, which is input as 256 × 256 × 3.
3. The method of claim 2, wherein the radar data correction based on the countermeasure generation network model comprises: the specific method of S2 is as follows: converting the radial wind speed field image into a gray image, then sequentially judging whether the value of each pixel point is within a preset threshold range, and if the value is out of the threshold range, considering that the point has beam blockage and setting the pixel value of the point to be 255, wherein the pixel value is a shielding area; when the value is within the threshold range, no beam blocking occurs at the point and the pixel value of the point is set to 0, which is an effective area.
4. The method of claim 3, wherein the radar data correction method based on the countermeasure generation network model comprises: in S3, carrying out shielding detection by adopting the gray level image reconstructed in S2, and marking a certain point as a mask when the pixel value of the point is 255; otherwise, marking the image as an effective area, and respectively generating a mask and outputting the masked image after completing the masking detection.
5. The method of claim 4, wherein the radar data correction method based on the countermeasure generation network model comprises: in S4, the masking film generated in S3 is adopted to calculate the shielding degree, and when the shielding degree is in a preset threshold range, the data to be corrected input this time is input into S5 according to the availability of the data to be corrected; otherwise, the correction is finished.
6. The method of claim 5, wherein the radar data correction method based on the countermeasure generation network model comprises: the confrontation generation network model comprises an edge reconstruction network and an image restoration network, the edge reconstruction network reconstructs the edge of the missing part of the image according to the input edge information of the shielded image, and the image restoration network restores the image of the missing area under the regulation of the edge information.
7. The method of claim 6, wherein the radar data correction method based on the countermeasure generation network model comprises: the training process of the countermeasure generation network model comprises the following steps:
s1, extracting a corresponding mask by using radar data to generate a mask set, selecting complete radar data as a background field and generating a background set;
s2, inputting a mask set and a background field into a countermeasure generation network model, carrying out shielding treatment on the background field by using the mask set by the model to generate a damaged image and a missing region image, wherein the missing region image is a shielding part of the mask in the background, and then obtaining a repaired image by the model;
s3, determining a loss value of the confrontation generation network model according to the background, the repaired image, the mask and the missing region image;
and S4, if the loss value is higher than a preset threshold value, updating the model.
8. The method of claim 1, wherein the radar data correction method based on the countermeasure generation network model comprises: before the data to be corrected is reconstructed by adopting a gray level conversion mode, classification judgment is carried out on the radar data to be corrected by adopting a maximum probability algorithm, and a proper correction model is selected.
9. A radar data correction system based on an countermeasure generation network model, characterized in that: the system comprises a gray level conversion module, a shielding detection module, a probability algorithm module, a correction model selection module and a radar data correction module.
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| US20120206292A1 (en) * | 2011-02-11 | 2012-08-16 | Boufounos Petros T | Synthetic Aperture Radar Image Formation System and Method |
| CN111861901A (en) * | 2020-06-05 | 2020-10-30 | 西安工程大学 | Edge generation image restoration method based on GAN network |
| CN113311436A (en) * | 2021-04-30 | 2021-08-27 | 中国人民解放军国防科技大学 | Method for correcting wind measurement of motion attitude of laser wind measuring radar on mobile platform |
| CN115358151A (en) * | 2022-08-25 | 2022-11-18 | 兰州大学 | Correction method for near-stratum wind speed product of numerical weather forecast |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120206292A1 (en) * | 2011-02-11 | 2012-08-16 | Boufounos Petros T | Synthetic Aperture Radar Image Formation System and Method |
| CN111861901A (en) * | 2020-06-05 | 2020-10-30 | 西安工程大学 | Edge generation image restoration method based on GAN network |
| CN113311436A (en) * | 2021-04-30 | 2021-08-27 | 中国人民解放军国防科技大学 | Method for correcting wind measurement of motion attitude of laser wind measuring radar on mobile platform |
| CN115358151A (en) * | 2022-08-25 | 2022-11-18 | 兰州大学 | Correction method for near-stratum wind speed product of numerical weather forecast |
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