CN115456083A - Data processing method under multi-radar networking scene - Google Patents
Data processing method under multi-radar networking scene Download PDFInfo
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- CN115456083A CN115456083A CN202211129306.8A CN202211129306A CN115456083A CN 115456083 A CN115456083 A CN 115456083A CN 202211129306 A CN202211129306 A CN 202211129306A CN 115456083 A CN115456083 A CN 115456083A
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous 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
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention provides a data processing method under a multi-radar networking scene, which comprises the following steps: preprocessing radar data; performing sliding correlation processing on adjacent radar data, searching for a position with the maximum correlation coefficient difference value, judging according to a preset threshold value, comparing the value with the maximum correlation coefficient difference value with the threshold value, and determining whether the correlation coefficient difference value is related to the threshold value; according to the sampling interval of the radar data and the distance interval between the two radars, converting the radar data with the maximum difference value of the relative numbers into data on the physical space positions of the two radars to realize time and space conversion; performing sliding correlation processing on other adjacent radar data, and calculating to obtain spatial position radar sampling data between adjacent radars; and obtaining cloud layer information in the deployed radar area through linear interpolation. The method and the device solve the problem that the vertical pointing radar in the prior art can only obtain the cloud layer information of one point and cannot obtain the cloud layer information in the area range.
Description
Technical Field
The invention relates to the technical field of meteorological radar systems, in particular to a data processing method in a multi-radar networking scene.
Background
The existing radar is a scanning type radar with a servo mechanism and a vertical directional type radar without a servo mechanism, and the two radars have advantages and disadvantages respectively.
The vertical directional radar without the servo can only obtain the vertical cloud layer information over a certain point, but cannot obtain the information of a certain area. The vertical directional radar has the advantages of no mechanical structure, high reliability, low cost and more accurate measurement data of the vertical cloud layer.
The cloud detection radar with the servo mechanism can obtain the three-dimensional cloud layer information of a certain area through body scanning. Disadvantages of radar with servomechanism: one is high cost, and the other is because with the servo mechanism, the long term operation reliability is not as good as that of the vertical directional radar, and secondly, the servo radar is easily affected by the environment, such as the surrounding of higher buildings, or mountainous areas, etc., is easily shielded, the deployment environment is limited, and is easily interfered by the environment. Still another key factor is that the millimeter wave radar has a short wavelength and limited cloud penetration, so the horizontal detection range is not as far as that of the C/X band radar, and is usually only twenty-three kilometers.
Regardless of the servo radar or the vertical-direction radar, if fusion processing of multiple radar data is not performed, only the cloud layer information of a radar deployment place and a small range can be obtained, but the cloud layer information of a slightly distant position cannot be obtained, and if the cloud layer information of a larger range or more detailed cloud layer information in an area is to be obtained, the deployment number of the radars needs to be increased, which results in very high deployment cost.
Disclosure of Invention
The invention provides a data processing method in a multi-radar networking scene, which aims to solve the problem that in the prior art, a vertical directional radar can only obtain cloud layer information of one point and cannot obtain the cloud layer information in an area range.
In order to solve the technical problem, the invention provides a data processing method under a multi-radar networking scene, which comprises the following steps:
step (1): preprocessing radar data;
step (2): performing sliding correlation processing on adjacent radar data, searching for a position with the largest correlation coefficient difference value, judging according to a preset threshold value, comparing the value with the largest correlation coefficient difference value with the threshold value, and determining whether the correlation coefficient difference value is related to the threshold value;
and (3): according to the sampling interval of the radar data and the distance interval between the two radars, converting the radar data with the maximum difference value of the relative numbers into data on the physical space positions of the two radars to realize time and space conversion;
and (4): performing sliding correlation processing on other adjacent radar data by adopting the methods from the step (1) to the step (3), and calculating to obtain radar sampling data of spatial positions between adjacent radars;
and (5): and obtaining cloud layer information in the deployed radar area through linear interpolation.
Performing data preprocessing on radar data in the step (1), wherein the data preprocessing comprises smoothing radar intensity factor data through a K neighborhood algorithm; clear sky clutter filtering algorithm processing; filtering and processing the side lobe signals; and (5) cloud layer judgment algorithm processing.
The step (2) specifically comprises the following steps:
step (2.1): forming a data sequence a (1:n) by the n-point radar data cloud layer thickness information between two vertical dotted lines on the first radar intensity factor data map;
step (2.2): taking an n-point radar data cloud layer thickness data sequence b (1:n) of a second radar in the same time period as the step (2.1), and carrying out correlation calculation on the n-point radar data cloud layer thickness data sequence b and the first radar data to obtain a correlation coefficient k = Cov (a, b)/√ D (a) ] √ D (b) ];
step (2.3): taking an n-point radar data cloud layer thickness data sequence b '(1:n) of the second radar with m points after the time period delay of the step (2.2), and carrying out correlation calculation on the n-point radar data cloud layer thickness data sequence and the first radar data to obtain a correlation coefficient k' = Cov (a, b ')/√ D (a) ]/√ D (b') ];
step (2.4): comparing the sizes of k and k ', if k' is larger than k, continuing to take the data of m points after the time period is delayed in the step (2.3) to perform correlation calculation, and otherwise, taking the data of m points before the time period in the step (2.2) to perform correlation calculation;
step (2.5): in the same step (2.4), after delaying or in advance for a period of time, carrying out correlation calculation on the data, and searching for a point with the maximum correlation coefficient difference;
step (2.6): and judging whether the detection data of the two radar stations with the largest correlation coefficient difference value are correlated or not through threshold judgment.
The invention has the following beneficial effects: according to the data processing method in the multi-radar networking scene, based on the multi-vertical-direction radar networking with lower deployment cost, the multi-point radar data are subjected to fusion processing to obtain the correlation among the radar data, so that the time-space conversion of the radar data is realized, the time continuous information of a certain point obtained by a single radar is expanded to the cloud layer information of a certain space area, the cloud layer information of a larger area and more detailed cloud layer information is obtained through calculation processing, and the weather forecasting capacity is improved.
Drawings
FIG. 1 is a flow chart of a data processing method in a multi-radar networking scenario of the present invention;
FIG. 2 is a diagram of a radar, central server arrangement of the present invention;
FIG. 3 is a first radar cloud thickness data plot;
FIG. 4 is a second radar cloud thickness data plot;
FIG. 5 is a correlation value diagram of a sliding correlation process;
fig. 6 is a diagram of detection data of millimeter wave radars of two radar stations.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Example one
As shown in fig. 2, in an important city or area, millimeter wave cloud-measuring radars are arranged at certain intervals. And the cloud measuring radar data is sent to a central server through a wired or wireless communication network for receiving, storing and processing.
The central server receives each radar data and carries out relevant fusion processing, and the processing process is as follows:
and performing sliding correlation processing on adjacent radar data, searching for a position with the maximum correlation coefficient difference value, judging with a preset threshold value, and determining whether the two are correlated.
The cloud layer thickness information data of a certain radar for a period of time is taken, as shown in fig. 3, and the other radar data shown in fig. 4 is subjected to sliding correlation processing, and a point with the largest correlation coefficient difference is searched, as shown in fig. 5. It can be seen from fig. 5 that the correlation coefficient difference between the 202 point positions is maximized.
And converting the radar data which are shown in the figure 4 and correspond to the radar difference of 202 points into data on the physical space positions of the two radars according to the sampling interval time of the two radar data and the distance interval between the two radars. Temporal and spatial conversion is implemented.
By adopting the same method, other adjacent radar data can be subjected to sliding correlation processing, and spatial position radar sampling data between adjacent radars can be obtained through calculation.
And obtaining cloud layer information in the deployed radar area through linear interpolation.
Example two
And selecting the detection data of two millimeter wave radars of spatially adjacent sites. As shown in fig. 6, the intensity factor data of the first radar and the second radar are plotted, respectively, with the abscissa as the time axis. Shown is the change in cloud cover over the first radar and the second radar over time over the same period of time.
And respectively carrying out data preprocessing on the data of the first radar and the data of the second radar. Firstly, smoothing is carried out on radar intensity factor data through a K neighborhood algorithm, and data noise is reduced. And secondly, reducing the influence of low-altitude clutter through a clear-sky clutter filtering algorithm. And thirdly, eliminating false echo signals caused by the pulse pressure of the wide pulse signals by a side lobe signal filtering method, and improving the accuracy of the signal correlation processing result. And finally, calculating cloud layer signals through a cloud layer judgment algorithm, and performing height accumulation on effective cloud layer data to obtain cloud layer thickness information.
And (3) taking the upper graph of the figure 6, carrying out sliding correlation processing on cloud layer thickness data of a section of radar data between two dotted lines of the first radar and the cloud layer thickness data of the second radar data in the lower graph of the figure 6. The position of the point where the correlation coefficient difference is largest is obtained. The method comprises the following steps:
1) And forming a data sequence a (1.
2) And taking a 1024-point radar data cloud layer thickness data sequence b (1) of a second radar in the same time period as the step 1), and carrying out correlation calculation on the data sequence b and the first radar data to obtain a correlation coefficient k = Cov (a, b)/√ D (a) ] √ D (b) ].
3) And taking a cloud layer thickness data sequence b '(1) 1024) of 1024-point radar data of the second radar with 64 points after the time period is delayed in the step 2), and carrying out correlation calculation on the cloud layer thickness data sequence and the first radar data to obtain a correlation coefficient k' = Cov (a, b ')/√ D (a) ]/√ D (b') ].
4) And comparing the sizes of k and k ', if k' is larger than k, continuing to take 64 point data delayed by the time period in the step 3) for correlation calculation, and otherwise, taking 64 point data before the time period in the step 2) for correlation calculation.
5) And 4), a synchronization step, namely, carrying out correlation calculation on the data after delay or in advance for a period of time, and searching for a point with the maximum correlation coefficient difference.
6) And judging whether the detection data of the two radar sites with the maximum correlation coefficient difference are correlated or not through threshold judgment.
According to the maximum correlation time point with the maximum correlation coefficient difference value, a section of radar original data between the current time point and the maximum correlation time point is subjected to linear interpolation according to the requirement of spatial resolution to form a section of radar data sequence, and the linear interpolation formula is as follows: y = ((X-X1) (Y2-Y1)/(X2-X1)) + Y1. The radar intensity data sequence is the cloud layer information between the current time and the two radars, so that the conversion between the radar data time and space is realized.
And (3) selecting related radar data:
the radar data related to the selected data is not suitable to be too long and not suitable to be too short. If the data is too long, the calculation amount of the correlation calculation is too large, and the calculation time is too long, so that the real-time performance of the calculation is influenced. If the data is too short, the accuracy of the correlation coefficient value is affected, and a large error is easily caused. The method adopts the cloud layer data with 1024 point length to carry out sliding correlation processing, and has shorter calculation time on the basis of obtaining more accurate correlation judgment.
Whether two adjacent radar data are related and the accuracy degree of the related positions are the key for determining the space-time conversion of the data. If the two radar data have no correlation, the effect of the space-time transformation is very limited. According to the characteristics of the cloud layer and the detection performance of the radar, the selection mode of the radar related data is the key of the success or failure of space-time conversion: 1. in the patent, the cloud layer thickness information subjected to preprocessing operations such as smoothing, noise reduction, sidelobe removal and the like is preferentially selected as data to be subjected to relevant processing. 2. And selecting cloud top height data of the cloud layer data which is preprocessed in continuous time for relevant processing. 3. And selecting cloud layer average height information data with the intensity factor ranging from-30 db to-20 db for correlation processing. One or three combined modes of the three correlation processing methods are selected to carry out correlation judgment, and accurate correlation judgment can be obtained on the premise of meeting the real-time performance of time.
Selecting the position distance of the radar station:
usually, the distance between radars is required to be less than 50km, otherwise, the radar interval is too large, the correlation of radar data is small, and the correlation processing of data cannot be performed before a plurality of radars. Accurate time-space conversion cannot be performed.
In summary, the data processing method in the multi-radar networking scene of the present invention is based on multi-vertical-direction radar networking with lower deployment cost, performs fusion processing on multi-point radar data, obtains correlation between each radar data, thereby implementing time-space conversion of radar data, expands time continuous information of a certain point obtained by a single radar to cloud layer information of a certain spatial area, obtains more detailed cloud layer information of a larger area through calculation processing, and improves weather forecast capability.
The above description is only an example of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (3)
1. A data processing method under a multi-radar networking scene is characterized by comprising the following steps: the method comprises the following steps:
step (1): preprocessing radar data;
step (2): performing sliding correlation processing on adjacent radar data, searching for a position with the largest correlation coefficient difference value, judging according to a preset threshold value, comparing the value with the largest correlation coefficient difference value with the threshold value, and determining whether the correlation coefficient difference value is related to the threshold value;
and (3): according to the sampling interval of the radar data and the distance interval between the two radars, converting the radar data with the maximum difference value of the relative numbers into data on the physical space positions of the two radars to realize time and space conversion;
and (4): performing sliding correlation processing on other adjacent radar data by adopting the methods from the step (1) to the step (3), and calculating to obtain radar sampling data of spatial positions between adjacent radars;
and (5): and obtaining cloud layer information in the deployed radar area through linear interpolation.
2. The data processing method in the multi-radar networking scenario of claim 1, wherein the radar data is pre-processed in step (1), including smoothing the radar intensity factor data by a K-neighborhood algorithm; clear sky clutter filtering algorithm processing; filtering and processing the side lobe signals; and (5) cloud layer judgment algorithm processing.
3. The data processing method under the multi-radar networking scenario as claimed in claim 1, wherein step (2) specifically comprises:
step (2.1): forming a data sequence a by the n-point radar data cloud layer thickness information between two vertical dotted lines on the first radar intensity factor data graph (1:n);
step (2.2): taking the n-point radar data cloud layer thickness data sequence b (1:n) of the second radar in the same time period as the step (2.1), and carrying out correlation calculation on the n-point radar data cloud layer thickness data sequence b and the first radar data to obtain a correlation coefficient k = Cov (a, b)/√ D (a) ] √ D (b);
step (2.3): taking an n-point radar data cloud layer thickness data sequence b '(1:n) of the second radar with m points after the time period delay of the step (2.2), and carrying out correlation calculation on the n-point radar data cloud layer thickness data sequence and the first radar data to obtain a correlation coefficient k' = Cov (a, b ')/√ D (a) ]/√ D (b') ];
step (2.4): comparing the sizes of k and k ', if k' is larger than k, continuing to take the data of m points after the time period is delayed in the step (2.3) to perform correlation calculation, and otherwise, taking the data of m points before the time period in the step (2.2) to perform correlation calculation;
step (2.5): in the same step (2.4), after delaying or in advance for a period of time, carrying out correlation calculation on the data, and searching for a point with the maximum correlation coefficient difference;
step (2.6): and judging whether the detection data of the two radar stations with the largest correlation coefficient difference value are correlated or not through threshold judgment.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116184342A (en) * | 2023-04-27 | 2023-05-30 | 无锡智鸿达电子科技有限公司 | Cloud testing radar data calibration method and system based on multi-radar networking |
| CN116208304A (en) * | 2023-04-28 | 2023-06-02 | 无锡智鸿达电子科技有限公司 | Method, device, medium and electronic equipment for checking signal quality of transceiver |
| CN116626682A (en) * | 2023-05-24 | 2023-08-22 | 无锡智鸿达电子科技有限公司 | Multi-radar networking space profile conversion method, system, medium and equipment |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116184342A (en) * | 2023-04-27 | 2023-05-30 | 无锡智鸿达电子科技有限公司 | Cloud testing radar data calibration method and system based on multi-radar networking |
| CN116208304A (en) * | 2023-04-28 | 2023-06-02 | 无锡智鸿达电子科技有限公司 | Method, device, medium and electronic equipment for checking signal quality of transceiver |
| CN116626682A (en) * | 2023-05-24 | 2023-08-22 | 无锡智鸿达电子科技有限公司 | Multi-radar networking space profile conversion method, system, medium and equipment |
| CN116626682B (en) * | 2023-05-24 | 2024-01-30 | 无锡智鸿达电子科技有限公司 | Multi-radar networking space profile conversion method, system, medium and equipment |
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