A weather radar noise threshold adaptive monitoring method and system

Abstract

The application discloses a weather radar noise threshold self-adaptive monitoring method, comprising the following steps: obtaining radar base data; performing normalization processing on the radar base data; extracting the current front 2 and rear 2 points of the distance library, i.e. 5*5 grids; calculating the TVEL of the velocity field of any grid; if the number of effective data points in the grid is greater than 5 and the TVEL is less than 5, marking the effective data points as echo points, otherwise, marking as noise; counting the number of effective data points corresponding to the noise of all grids; if the number of noise points is less than 5000, it is a meteorological echo; extracting 31*31 grids, if the number of effective data points is the same as the total number of grid points, there is a large area of meteorological echo; obtaining the average intensity value of the marked noise points outside 50km from the location of the distance Doppler weather radar; according to the average intensity value, using the signal-to-noise ratio formula and the noise floor, the required increased LOG threshold value is obtained.

Description

Technical Field

[0001] This invention relates to the field of airport Doppler weather radar noise processing technology, and in particular to an adaptive monitoring method and system for weather radar noise threshold. Background Technology

[0002] As is well known, radar echo signals may contain both target echo signals and interference signals such as noise and clutter. Therefore, the detection of radar target echo signals is an optimal binary signal detection problem under noise and clutter backgrounds. Noise sources are multifaceted, including resistors, amplifiers, and mixers in circuits. Given that precipitation echoes and noise echoes have different signal characteristics, quality control of the acquired raw radar data (time-domain I / Q data) typically involves introducing noise threshold parameters, such as the signal-to-noise ratio (LOG), signal quality index (SQI), clutter correction (CCOR), and weather signal power (SIG). Since the LOG threshold significantly affects reflectivity before and after clutter suppression, this technique primarily employs a LOG threshold adjustment method to address noise echoes.

[0003] Currently, there are numerous Doppler weather radars widely distributed in existing technology. During operation, noise echoes may appear in the received radar data due to factors such as performance degradation of high-frequency components like the receiving subsystem. This should be eliminated by adjusting relevant parameters in the radar signal processor. However, due to varying operator skill levels, incorrect radar parameter settings, or untimely parameter adjustments, the radar data quality is low, resulting in low data utilization.

[0004] Therefore, there is an urgent need to propose a simple, accurate, and reliable adaptive monitoring method and system for weather radar noise threshold. Summary of the Invention

[0005] To address the above problems, the present invention aims to provide a weather radar noise threshold adaptive monitoring method and system. The technical solution adopted by the present invention is as follows:

[0006] The first part of this technology provides an adaptive monitoring method for weather radar noise threshold, which includes the following steps:

[0007] Acquire radar base data collected by Doppler weather radar;

[0008] The radar base data is normalized.

[0009] Extracting the current azimuth from the normalized radar base data One and after Each point, current distance from the front of the database One and after Points, and obtain One grid; the b, , , , All are integers greater than 0;

[0010] Calculate the TVEL of the velocity field for any grid.

[0011] If the number of valid data points within the grid is greater than M and TVEL is less than Q, then the valid data point is marked as an echo point; otherwise, it is marked as noise. M and Q are both integers greater than 0.

[0012] Count the number of valid data points corresponding to noise in all grids; if the number of noise points is less than K, it is a meteorological echo; where K is an integer greater than 0.

[0013] extract If the number of valid data points in a grid is the same as the total number of grid points, then there is a large area of ​​meteorological echo.

[0014] Calculate the average intensity value of the marked noise points located S km away from the Doppler weather radar location;

[0015] Based on the average intensity value, and using the signal-to-noise ratio formula and noise floor, the required increase in the LOG threshold value is determined.

[0016] The second part of this technology provides a device for adaptive monitoring of weather radar noise threshold, which includes:

[0017] The radar-based data acquisition module acquires radar-based data collected by the Doppler weather radar;

[0018] A normalization processing module is connected to the radar-based data acquisition module and performs normalization processing on the radar-based data.

[0019] The grid generation module, connected to the normalization module, extracts the current azimuth from the normalized radar base data. One and after Each point, current distance from the front of the database One and after Points, and obtain One grid; the b, , , and All are integers greater than 0;

[0020] The TVEL calculation module is connected to the mesh generation module to calculate the TVEL of the velocity field for any mesh.

[0021] The marking and judgment module is connected to the TVEL calculation module. If the number of valid data points in the grid is greater than M and the TVEL is less than Q, then the valid data point is marked as an echo point; otherwise, it is marked as noise. M and Q are both integers greater than 0.

[0022] The statistics module, connected to the labeling and judgment module, counts the number of valid data points corresponding to noise in all grids; if the number of noise points is less than K, it is a meteorological echo; where K is an integer greater than 0.

[0023] The large-area meteorological echo determination module is connected to the statistics module to extract... If the number of valid data points in a grid is the same as the total number of grid points, then there is a large area of ​​meteorological echo.

[0024] The average intensity value calculation module is connected to the large-area meteorological echo determination module to obtain the average intensity value of the marked noise points located 5 km away from the Doppler weather radar location.

[0025] The LOG threshold calculation module is connected to the average intensity calculation module. Based on the average intensity value, and using the signal-to-noise ratio formula and the noise floor, it calculates the required increase in the LOG threshold value.

[0026] Thirdly, this technology provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a weather radar noise threshold adaptive monitoring method.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] (1) The present invention monitors the radar PPI scanning base data file in real time. When the radar collects the PPI scanning base data file, it uses the noise threshold adaptive algorithm. When the algorithm detects and identifies noise echoes, it loads the new LOG parameters into the radar to complete the radar noise threshold detection process, thereby achieving the purpose of filtering out noise echoes and improving the data quality of subsequent radar data.

[0029] (2) This invention cleverly extracts the two points before and two points after the current azimuth, and performs TVEL analysis of the velocity field and determination of valid data points. It distinguishes between noise echoes and meteorological echoes through TVEL texture description parameters. For meteorological echo points, the velocity field is more uniform and the value is smaller, while the echo value of noise points is larger. The number of noise points is counted and marked to distinguish between noise echoes and meteorological echoes. By distinguishing between meteorological echoes and noise echoes, the influence of meteorological echoes on the subsequent calculation of the noise threshold is eliminated.

[0030] (3) The present invention cleverly extracts a 31×31 grid. If the number of valid data points is the same as the total number of grid points, it is determined that there is a large area of ​​meteorological echo. This distinguishes between large area meteorological echo and large area noise echo, and eliminates the interference of large area meteorological echo on the calculation of noise threshold.

[0031] (4) This invention obtains the average intensity value of the marked noise point 50km away from the location of the Doppler weather radar, and calculates the LOG threshold value that needs to be increased using the signal-to-noise ratio formula and the noise floor. When noise echo is identified, the LOG threshold value is automatically given and the noise threshold is modified to achieve the purpose of filtering out noise echo and improving the data quality of subsequent radar data.

[0032] In summary, this invention has the advantages of simple logic and high accuracy and reliability, and has high practical and promotional value in the field of airport Doppler weather radar noise processing technology. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope of protection. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a logic flowchart of the present invention.

[0035] Figure 2 This is a diagram showing the distribution of radar data before noise reduction in this invention.

[0036] Figure 3 This is a distribution diagram of the radar data after noise reduction in this invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the present invention will be further described below with reference to the accompanying drawings and embodiments. The embodiments of the present invention include, but are not limited to, the following embodiments. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0038] In this embodiment, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0039] The terms "first" and "second," etc., used in the specification and claims of this embodiment are used to distinguish different objects, not to describe a specific order of objects. For example, "first target object" and "second target object," etc., are used to distinguish different target objects, not to describe a specific order of target objects.

[0040] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0041] In the description of the embodiments in this application, unless otherwise stated, "multiple" means two or more. For example, multiple processing units means two or more processing units; multiple systems means two or more systems.

[0042] like Figures 1 to 3 As shown, this embodiment provides an adaptive monitoring method for noise threshold in weather radar. During weather radar signal processing, echo signals are estimated using spectral moments to obtain parameters reflecting meteorological target information, such as average echo power, average radial velocity, and velocity spectral width. However, the processed echo data not only contains useful echo data but also retains interference echoes such as ground clutter, system noise, and distorted echo data, which severely affect radar data quality. However, interference echoes and precipitation echoes have significant differences in signal characteristics, such as signal strength and signal coherence. Based on these characteristic parameters, interference echo data can be effectively identified. Parameters with significant signal characteristic differences are selected for study, and quality control is performed on the baseline data obtained from spectral moment estimation by setting a threshold. Various measures can be taken to address noise echoes, including increasing the number of accumulated pulses and adjusting the noise threshold. This project primarily uses the LOG threshold adjustment method to address noise echoes.

[0043] The implementation steps of this embodiment are as follows:

[0044] Step 1, Data Acquisition Process:

[0045] Acquire radar base data collected by Doppler weather radar.

[0046] The second step is to normalize the radar base data:

[0047] Because the data formats of single-polarization Doppler radar and dual-polarization Doppler radar are different, in order to meet the greater adaptability of the noise threshold adaptive algorithm, the basic data of the two types of radar are read, and then the format is transformed and normalized, and saved into a unified format before subsequent algorithm work.

[0048] Step 3, preliminary judgment using the grid method:

[0049] (1) Take a 5×5 grid, and calculate the TVEL (texture description parameter of velocity field) of the velocity field, which is the current distance from the two points before and the two points after the current distance. If the number of valid data points is greater than 5 and the TVEL is less than 5, then mark the valid data point as an echo point; otherwise, record it as noise. The expression for TVEL is as follows:

[0050]

[0051] in, Indicates the calculation range defined in the orientation direction; This indicates the calculation range defined in the distance direction; This represents the echo velocity of the valid data point in the i-th row and j-th column; This represents the echo velocity of the valid data point in the i-th row and j+1-th column.

[0052] (2) The number of noise points recorded in step (1) is used to determine the number of noise points. If the number of noise points is less than 5000, it is a meteorological echo; if it is greater than 5000, it is a noise echo.

[0053] (3) If a large area of ​​meteorological echoes appears, and there are also large areas of noise echoes, by taking a 31×31 grid, the 15 points before and after the current distance to the database are used to determine whether the number of valid data points is the same as the total number of grid points. If they are the same, it is determined that there are large areas of meteorological echoes; otherwise, it is recorded as large areas of noise echoes.

[0054] Step 4: Calculate the signal-to-noise ratio (SNR) LOG threshold:

[0055] (1) Based on the noise points recorded in the third step, calculate the average intensity value of the marked noise points that appear 50km away. The reason for selecting 50km away is to exclude the influence of nearby blind spots and other ground clutter.

[0056] (2) Based on the average intensity value, the required increase in the LOG threshold is calculated using the signal-to-noise ratio formula and the noise floor, to provide an estimated total signal-to-noise ratio as a threshold for reflectivity. The LOG calculation formula is as follows, with units in dB;

[0057]

[0058] in, represents the autocorrelation coefficient without suppression of reflectivity factor; N represents the noise floor power.

[0059] In this embodiment, taking the intensity of a Doppler weather radar at an airport as an example, a large amount of noise echo appeared at 17:50:36 on April 26, 2022. After the calculation steps in this embodiment, the returned LOG value is 1.18dB. The next PPI scan generates new data at 17:52:36, at which point the large-area noise echo is eliminated. Therefore, noise data can be suppressed by adjusting the noise level LOG value in the receiver parameters, achieving noise self-adaptation.

[0060] By applying the above solution to existing Doppler weather radars, the fully automated operation of Doppler weather radars can be greatly improved. This can significantly reduce the workload of existing radar operators, reduce the number of weather radar operators, and solve the problem of erroneous data such as noise echoes caused by the uneven technical level of radar operators. This can improve the data quality of Doppler weather radars and achieve good economic benefits and practical significance.

[0061] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any changes made based on the design principles of the present invention, or any non-creative modifications made thereon, shall fall within the scope of protection of the present invention.

Claims

1. A weather radar noise threshold adaptive monitoring method, characterized in that, Includes the following steps: Acquire radar base data collected by Doppler weather radar; The radar base data is normalized. Extracting the current azimuth from the normalized radar base data One and after Each point, current distance from the front of the database One and after Points, and obtain One grid; the b, , , and All are integers greater than 0; The TVEL of the velocity field for any grid is calculated using the following expression: in, Indicates the calculation range defined in the orientation direction; This indicates the calculation range defined in the distance direction; This represents the echo velocity of the valid data point in the i-th row and j-th column; This represents the echo velocity of the valid data point in the i-th row and j+1-th column; If the number of valid data points within the grid is greater than M and TVEL is less than Q, then the valid data point is marked as an echo point; otherwise, it is marked as noise. M and Q are both integers greater than 0. Count the number of valid data points corresponding to noise in all grids; if the number of noise points is less than K, it is a meteorological echo; where K is an integer greater than 0. extract If the number of valid data points in a grid is the same as the total number of grid points, then there is a large area of ​​meteorological echo. Calculate the average intensity value of the marked noise points located S km away from the Doppler weather radar location; Based on the average intensity value, and using the signal-to-noise ratio formula and noise floor, the required increase in the LOG threshold value is calculated, and its expression is as follows: in, represents the autocorrelation coefficient without suppression of reflectivity factor; N represents the noise floor power.

2. The weather radar noise threshold adaptive monitoring method according to claim 1, characterized in that, The and , , The value of b is 2; the value of b is 5.

3. The weather radar noise threshold adaptive monitoring method according to claim 2, characterized in that, The values ​​of M and Q are 5.

4. The weather radar noise threshold adaptive monitoring method according to claim 3, characterized in that, The value of K is 5000.

5. The weather radar noise threshold adaptive monitoring method according to claim 4, characterized in that, The The value is 31.

6. The weather radar noise threshold adaptive monitoring method according to claim 5, characterized in that, The value of S is 50.

7. A device for adaptive monitoring of weather radar noise threshold, characterized in that, include: The radar-based data acquisition module acquires radar-based data collected by the Doppler weather radar; A normalization processing module is connected to the radar-based data acquisition module and performs normalization processing on the radar-based data. The grid generation module, connected to the normalization module, extracts the current azimuth from the normalized radar base data. One and after Each point, current distance from the front of the database One and after Points, and obtain One grid; the b, , , and All are integers greater than 0; The TVEL calculation module, connected to the mesh generation module, calculates the TVEL of the velocity field for any given mesh. Its expression is: in, Indicates the calculation range defined in the orientation direction; This indicates the calculation range defined in the distance direction; This represents the echo velocity of the valid data point in the i-th row and j-th column; This represents the echo velocity of the valid data point in the i-th row and j+1-th column; The marking and judgment module is connected to the TVEL calculation module. If the number of valid data points in the grid is greater than M and the TVEL is less than Q, then the valid data point is marked as an echo point; otherwise, it is marked as noise. M and Q are both integers greater than 0. The statistics module, connected to the labeling and judgment module, counts the number of valid data points corresponding to noise in all grids; if the number of noise points is less than K, it is a meteorological echo; where K is an integer greater than 0. The large-area meteorological echo determination module is connected to the statistics module to extract... If the number of valid data points in a grid is the same as the total number of grid points, then there is a large area of ​​meteorological echo. The average intensity value calculation module is connected to the large-area meteorological echo determination module to obtain the average intensity value of the marked noise points located 5 km away from the Doppler weather radar location. The LOG threshold calculation module is connected to the average intensity calculation module. Based on the average intensity value, and using the signal-to-noise ratio formula and noise floor, it calculates the required increase in the LOG threshold value. Its expression is as follows: in, represents the autocorrelation coefficient without suppression of reflectivity factor; N represents the noise floor power.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the weather radar noise threshold adaptive monitoring method according to any one of claims 1 to 6.

Images