Ground clutter identification method, device, storage medium and product

Abstract

The application discloses a ground clutter recognition method and device, a storage medium and a product. The recognition method comprises the following steps: calculating a weather judgment index of a current azimuth; calculating a spectrum power discrimination factor and a phase structure function of each distance bin in the current azimuth; mapping the spectrum power discrimination factor and the phase structure function to the interval [0, 1]; performing weighted calculation on the membership of the spectrum power discrimination factor to ground objects and the membership of the phase structure function to ground objects; when the weighted calculation result is equal to 1 and the weather judgment index is less than a first set threshold, it is indicated that the distance bin contains ground clutter; when the weighted calculation result is equal to 1 and the weather judgment index is greater than or equal to the first set threshold, a spectrum width discrimination factor of the distance bin is calculated, and when the spectrum width discrimination factor is less than or equal to a second set threshold, it is indicated that the distance bin contains ground clutter. The application solves the problem that zero-velocity atmospheric echo is easily misrecognized as ground clutter.

Description

[0001] This application claims priority to Chinese Patent Application No. 2023115211324, filed on November 15, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention belongs to the field of weather radar technology, and particularly relates to a method, device, storage medium and product for identifying ground clutter based on the time-frequency characteristics of phased array weather radar IQ data. Background Technology

[0003] When the main lobe or side lobe of a radar beam illuminates the ground or objects on the ground, ground clutter is generated. Ground clutter can cause the radar to make deviations in its weather measurements. Therefore, accurately identifying and suppressing ground clutter is particularly important in the radar signal processing process.

[0004] Currently, commonly used clutter identification methods based on the time-frequency characteristics of weather radar IQ data include: Clutter Mitigation Decision (CMD) method and Spectrum Clutter Identification (SCI) method. The CMD method mainly defines three feature quantities based on the phase and reflectivity characteristics of ground clutter: Clutter Phase Alignment (CPA), Reflectivity Texture (TDBZ), and Spatial variability of reflectivity field (SPIN). These three feature quantities are then combined using fuzzy logic to identify ground clutter. The calculation methods for the three feature quantities are as follows (references: Ice RL, Rhoton RD, Krause JC, et al. Automaticclutter mitigation in the WSR-88D, design, evaluation, and implementation[C]. 34th Conference on Radar Meteorology, Williamsburg, VA. 2009, 93.;Hubbert JC, Dixon M, Ellis SM, et al. Weather radar ground clutter. Part I: Identification, modeling, and simulation[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(7): 1165-1180.; Hubbert JC, Dixon M, Ellis S M.Weather radar ground clutter. Part II: Real-time identification and filtering[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(7): 1181-1197.): (1) The phase array calibration value mainly measures the phase (I and Q timing) stability between a set of radar echo pulses. The calculation formula is: Where m represents the number of pulses.

[0005] (2) The reflectance texture calculation is based on the rate of change of reflectance between adjacent distances. The calculation formula is: in, Indicates reflectivity, L Indicates the radar pulse count. M Indicates the distance to the library. .

[0006] (3) The radial variation rate of reflectance reflects the change of reflectance with respect to the radial gradient, and the calculation formula is: in, , and This represents the reflectivity of three consecutive radial distances.

[0007] The drawback of the CMD method is that it cannot effectively distinguish ground clutter from meteorological echoes with a radial velocity of 0 m / s (zero-velocity weather echoes) when there is precipitation, resulting in low ground clutter identification accuracy.

[0008] The SCI method uses four features to determine the presence of ground clutter: Spectral Power Distribution (SPD), Spectral Phase Fluctuations (SPF), Power Texture (PT), and Spectrum Width Texture (SWT). Fuzzy logic is used to combine these four features to determine the presence of ground clutter. The four features are defined as follows (references: Li Y, Zhang G, Doviak R J. A new approach to detect the ground clutter mixed with weather echoes[C]. 2011 IEEE RadarCon(RADAR). IEEE, 2011: 622-626.;Li Y, Zhang G, Doviak RJ, et al. A new approach to detect ground clutter mixed with weather signals[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 51(4): 2373-2387.): (1) The spectral power discrimination factor measures the ratio of the energy within the zero bandwidth of the Doppler spectrum to the remaining energy of the spectrum. The calculation formula is as follows: in, Indicates the Nyquist velocity. Indicates the velocity magnitudes on both sides of the wide range of the zero spectrum. A ( v () represents the spectral amplitude.

[0009] (2) The echo power spatial texture reflects the degree of change in average power between distances, and the calculation formula is: in, i Indicates the pulse sequence number. j Indicates the distance from the library index. P Indicates echo power. M = 9, representing the number of distance databases calculated. std This indicates the calculation of the standard deviation. mean This indicates that the mean is calculated.

[0010] (3) The spectral phase fluctuation factor measures what Whether the phase change between them is linear, assuming that in Between, the spectral phase satisfies The formula for calculating SPF is: in, , , It is the spectral phase.

[0011] (4) The spatial texture of velocity spectral width reflects the degree of change in spectral width between ranges. The calculation method is the same as that of PT, except that the power term is replaced by the spectral width term. The specific calculation formula is as follows: in, i Indicates the pulse sequence number. j Indicates the distance from the library index. The value M represents the spectral width, and M = 9 represents the distance library number.

[0012] The SCI method is mainly used to detect ground clutter that exists in weather conditions. Even in the presence of weather, it can identify ground clutter mixed with weather echoes. However, when the clutter-to-signal ratio (CSR) is low, the SCI method cannot identify ground clutter, and it is also prone to misidentifying zero-velocity weather echoes as ground clutter, resulting in identification errors. Summary of the Invention

[0013] The purpose of this invention is to provide a ground clutter identification method, device, storage medium, and product to solve the problem that existing ground clutter identification methods easily misidentify zero-velocity weather echoes as ground clutter.

[0014] This invention solves the above-mentioned technical problems through the following technical solution: a ground clutter identification method, comprising the following steps: Step S1: Calculate the weather assessment index for the current azimuth angle; Step S2: Calculate the spectral power discrimination factor and phase structure function of the first range library in the current azimuth angle; Step S3: Map the spectral power discriminant factor and phase structure function obtained in step S2 to the [0,1] interval respectively to obtain the membership degree of the spectral power discriminant factor to the ground object and the membership degree of the phase structure function to the ground object; Step S4: Perform a weighted calculation on the membership degree of the spectral power discriminant factor to the ground object and the membership degree of the phase structure function to the ground object obtained in step S3; Step S5: When the weighted calculation result of step S4 is equal to 1 and the weather judgment index of the current azimuth angle is less than the first set threshold, ground clutter exists in the distance library. Repeat steps S2 to S5 for the next distance library in the current azimuth angle until ground clutter identification of all distance libraries in the current azimuth angle is completed. When the weighted calculation result of step S4 is equal to 1, the weather judgment index of the current azimuth angle is greater than or equal to the first set threshold, and the spectral width discrimination factor of the distance library is less than or equal to the second set threshold, the distance library contains ground clutter. Repeat steps S2 to S5 for the next distance library in the current azimuth angle until the ground clutter identification of all distance libraries in the current azimuth angle is completed. When the weighted calculation result of step S4 is equal to 1, the weather judgment index of the current azimuth angle is greater than or equal to the first set threshold, and the spectral width discrimination factor of the distance library is greater than the second set threshold, there is no ground clutter in the distance library. Repeat steps S2 to S5 for the next distance library in the current azimuth angle until the ground clutter identification of all distance libraries in the current azimuth angle is completed. When the weighted calculation result of step S4 is not equal to 1, repeat steps S2 to S5 for the next range library in the current azimuth angle until ground clutter identification of all range libraries in the current azimuth angle is completed. Step S6: For the next azimuth angle, repeat steps S1 to S5 until ground clutter identification for each range in each azimuth angle is completed.

[0015] Furthermore, in step S1, the calculation formula for the weather judgment index of the current azimuth angle is: ; ; ; in, For the first j Weather assessment indicators for distance storage, For the first j The number of pulse signals in each distance library, For the first j The distance library The spectral power of a pulse signal The number of distances in the current azimuth angle. This is a weather assessment indicator based on the current azimuth angle. This is the standard deviation function.

[0016] Furthermore, in step S2, the formula for calculating the spectral power discriminant factor of the distance library is: in, For the first j Spectral power discrimination factor of a distance library, For the first j The number of pulse signals in each distance library, For the first j The distance library The spectral power of a pulse signal This represents the number of spectral points corresponding to the zero frequency of the spectrum. Add the number of spectral points corresponding to the zero frequency to the total number of points before or after it, i.e. +2; The formula for calculating the phase structure function of the distance library is: in, For the first j Phase structure function of a distance library, For the first j The distance library +1 pulse signal phase, For the first j The distance library The phase of a pulse signal.

[0017] Furthermore, in step S3, the obtained spectral power discrimination factor and phase structure function are mapped to the [0,1] interval using a membership function. The specific mapping formula is as follows: in, For the first j Spectral power discrimination factor of a distance library, For the first j The membership degree of the spectral power discriminant factor of each distance library to ground objects. For the first j Phase structure function of a distance library, For the first j The membership degree of each distance library's phase structure function to ground features.

[0018] Furthermore, in step S4, the specific formula for the weighted calculation is as follows: =α× +β× α + β = 1 in, For the first j The probability of ground features at a distance from the database. For the first j The membership degree of the spectral power discriminant factor of each distance library to ground objects. For the first j The membership degree of each distance library's phase structure function to ground features, where α and β are weighting coefficients.

[0019] Furthermore, the weighting coefficient α is 0.625, and the weighting coefficient β is 0.375.

[0020] Furthermore, in step S5, the specific formula for calculating the spectral width discriminant factor of the distance library is as follows: in, for Maximum spectral power in the interval; For the first j The spectral power of a pulse signal in a distance library; s The location of maximum spectral power. ; For the first j The number of pulse signals in each distance library; For the first j The distance library The spectral power of a pulse signal; For the number of spectrum points, For the first j The spectral width discriminant factor of a distance library.

[0021] Based on the same concept, the present invention also provides a terminal device, including a memory, a processor, and a computer program / instructions stored in the memory, wherein the processor executes the computer program / instructions to implement the ground clutter identification method as described above.

[0022] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the ground clutter identification method as described above.

[0023] Based on the same concept, the present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the ground clutter identification method as described above.

[0024] Beneficial effects Compared with the prior art, the advantages of the present invention are as follows: This invention improves the calculation of the spectral power discriminant factor, phase structure function, and spectral width discriminant factor. The spectral power discriminant factor measures the ratio of the energy at zero frequency of the Doppler spectrum to the sum of the energy within a certain range around zero spectral width. When the weather intensity is high (i.e., the signal-to-clutter ratio is low), the sum of the remaining energies in the spectrum will increase, thus improving the problem of reduced ground clutter identification probability caused by the traditional spectral power discriminant factor. The spectral width discriminant factor of this invention not only focuses on the spectral width characteristics of the entire Doppler spectrum but also on the local spectral width characteristics, which can better distinguish between ground clutter and zero-velocity weather echoes, improve the accuracy of ground clutter identification, and solve the problem of easily misidentifying zero-velocity weather echoes as ground clutter.

[0025] This invention defines a spectral power discrimination factor, a phase structure function, and a spectral width discrimination factor based on the characteristics of ground clutter. It combines the defined spectral power discrimination factor, phase structure function, and spectral width discrimination factor to identify ground clutter, which can effectively distinguish between zero-velocity weather echoes and ground clutter. Attached Figure Description

[0026] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart of the ground clutter identification method in an embodiment of the present invention; Figure 2 This is a statistical distribution characteristic diagram of ground clutter and non-zero velocity weather echoes in an embodiment of the present invention; Figure 3 This is a statistical distribution characteristic diagram of ground clutter and zero-velocity weather echoes in an embodiment of the present invention; Figure 4 This is a statistical distribution characteristic diagram of PSF of ground clutter and non-zero velocity weather echo in an embodiment of the present invention; Figure 5 This is a statistical distribution characteristic diagram of PSF of ground clutter and zero-velocity weather echo in an embodiment of the present invention; Figure 6 This is a SPW statistical distribution characteristic diagram of ground clutter and non-zero velocity weather echoes in an embodiment of the present invention; Figure 7 This is a SPW statistical distribution characteristic diagram of ground clutter and zero-velocity weather echo in an embodiment of the present invention; Figure 8 This is a clutter identification map with an elevation angle of 0.75° in an embodiment of the present invention; wherein, the horizontal axis represents the distance from the radar in the east direction, the vertical axis represents the distance from the radar in the north direction, wave represents the wave position number, beam represents the beam number, and ele represents the elevation angle; Figure 9 This is a clutter identification map with an elevation angle of 2.25° in an embodiment of the present invention; Figure 10 These are radial velocity and clutter identification diagrams with an elevation angle of 17.25° in this embodiment of the invention; wherein, the left diagram is the radial velocity diagram with an elevation angle of 17.25°, and the right diagram is the clutter identification diagram with an elevation angle of 17.25°. Figure 11 These are radial velocity and clutter identification diagrams with an elevation angle of 21.75° in this embodiment of the invention; wherein, the left diagram is the radial velocity diagram with an elevation angle of 21.75°, and the right diagram is the clutter identification diagram with an elevation angle of 21.75°. Figure 12 This is a comparison of the intensity of clutter identification and suppression after using the SCI method and the method of the present invention for radar 1 at an elevation angle of 40.5° in the Hezhuang radar of this embodiment of the invention; wherein, the left figure is the intensity figure after clutter identification and suppression using the SCI method, and the right figure is the intensity figure after clutter identification and suppression using the method of the present invention. Figure 13This is a comparison diagram of the radial velocity after clutter identification and suppression using the SCI method and the method of the present invention for radar 1 at an elevation angle of 40.5° in the Hezhuang radar of this embodiment of the invention; wherein, the left diagram is the radial velocity diagram after clutter identification and suppression using the SCI method, and the right diagram is the radial velocity diagram after clutter identification and suppression using the method of the present invention. Figure 14 This is a comparison of the intensity of clutter identification and suppression after using the SCI method and the method of the present invention for clutter identification and suppression in the Guizhou radar with an elevation angle of 52.5° in this embodiment of the invention; the left image is the intensity image after clutter identification and suppression using the SCI method, and the right image is the intensity image after clutter identification and suppression using the method of the present invention. Figure 15 This is a comparison diagram of the radial velocity after clutter identification and suppression using the SCI method and the method of the present invention for radar 2 elevation angle 52.5° in Guizhou radar in this embodiment of the invention; wherein, the left figure is the radial velocity diagram after clutter identification and suppression using the SCI method, and the right figure is the radial velocity diagram after clutter identification and suppression using the method of the present invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. 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.

[0029] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0030] Example 1 like Figure 1 As shown, the ground clutter identification method provided by this embodiment of the invention includes the following steps: Step S1: Calculate the first... i Weather assessment indicators for each azimuth angle; Step S2: Calculate the first... i The azimuth angle of the ? n Spectral power discrimination factor and phase structure function of each distance library; Step S3: Map the spectral power discriminant factor and phase structure function obtained in step S2 to the [0,1] interval respectively to obtain the membership degree of the spectral power discriminant factor to the ground object and the membership degree of the phase structure function to the ground object; Step S4: Perform a weighted calculation on the membership degree of the spectral power discriminant factor to the ground object and the membership degree of the phase structure function to the ground object obtained in step S3; Step S5: Determine whether the weighted calculation result obtained in step S4 is equal to 1. If yes, proceed to step S6; otherwise, proceed to step S8. Step S6: Determine whether the weather judgment index obtained in step S1 is greater than or equal to the first set threshold. If so, calculate the... n The spectral width discriminant factor of the distance library is determined, and then the process proceeds to step S7; otherwise, it indicates that the spectral width discriminant factor of the distance library is determined. n If there is clutter in the storage at a distance, proceed to step S8; Step S7: Determine the result obtained in step S6. n If the spectral width discrimination factor of the distance library is greater than the second set threshold, proceed to step S8; otherwise, it indicates that the first... n If there is clutter in the storage at a distance, proceed to step S8; Step S8: Increment the distance to the library index by 1, i.e., n' = n + 1, and determine whether n' is greater than the distance to the library index. i Number of distances in each azimuth angle If so, it indicates that the first step is complete. i Ground clutter identification for all distances in the azimuth angle, for the ... i Repeat steps S1 to S8 for +1 azimuth angles until ground clutter identification for each range library in each azimuth angle is completed; otherwise, repeat steps S2 to S8 for the next range library (i.e., the n'th range library).

[0031] Weather Discriminant (WD): Based on the ratio of the sum of the energies on both sides of the zero frequency of the Doppler spectrum for each range at a given azimuth angle, this indicator determines whether velocity-bearing weather exists at that range. If velocity-bearing weather exists, higher energy will be present in the Doppler spectrum at non-zero frequencies. Then, the standard deviation of this ratio across all radial ranges is calculated to determine whether weather exists in that radial region. Therefore, the... i The formula for calculating the weather judgment index for each azimuth angle is: (1) (2) in, For the first j Weather assessment indicators for distance storage, , For the first i The number of distances in each azimuth angle. For the first j The number of pulse signals in each distance library, For the first j The distance library The spectral power of a pulse signal For the first i Weather assessment indicators for each azimuth angle This is the standard deviation function.

[0032] The typical characteristics of ground clutter are a power peak near the zero frequency of the Doppler spectrum, a radial velocity close to 0, and a small spectral width at the zero frequency. Although the spectral power peak of zero-velocity weather is also near the zero frequency, the spectral width at the zero frequency is larger than that of ground clutter. Based on the characteristics of ground clutter, three characteristic quantities are defined: spectral power discrimination factor, phase structure function, and spectral width discrimination factor. These three characteristic quantities are combined to identify ground clutter.

[0033] Spectral Power Distribution (SPD): Calculated in the frequency domain of echo data, this is the ratio of the energy within the zero-frequency region due to system parameters and windowing to the sum of the energies of two points before and after the zero-frequency region. It reflects the intensity of the zero-frequency region. Unlike the SPD in the SCI method, the Spectral Power Distribution (SPD) of this invention measures the ratio of the energy at zero frequency to the sum of the energies within a certain range around the zero-frequency region.

[0034] In step S2, the formula for calculating the spectral power discriminant factor of the distance library is: (3) and Adjustments are made based on the actual number of spectral points. After extensive calculations considering the zero-bandwidth due to system parameters and windowing, in this embodiment... and The value can be: when hour, (4) when hour, (5) in, For the first j Spectral power discrimination factor of a distance library, This represents the number of spectral points corresponding to the zero frequency of the spectrum. Add the number of spectral points corresponding to the zero frequency to the total number of points before or after it, i.e. +2.

[0035] Phase Structure Function (PSF): This calculates the sum of squares of the phase differences between adjacent pulses at the same distance. Therefore, the formula for calculating the PSF for each distance is: (6) in, For the first j Phase structure function of a distance library, For the firstj The distance library +1 pulse signal phase, For the first j The distance library The phase of a pulse signal.

[0036] Spectral Power Width (SPW): This is a surrogate value that measures zero spectral width. Therefore, the specific formula for calculating the spectral power width discriminant of the distance library is as follows: The specific formula for calculating the spectral width discriminant factor of the distance library is as follows: (7) (8) in, for Maximum spectral power in the interval; For the first j The spectral power of a pulse signal in a distance library; s The location of maximum spectral power. ; For the number of spectrum points, For the first j The spectral width discriminant factor of each distance library. In this embodiment, =1.

[0037] and s They are Select the maximum spectral power and its location in any interval.

[0038] In the calculation of any feature quantity, a nine-point smoothed value on the radial distance is used to judge any feature quantity.

[0039] The statistical distribution characteristics of three characteristic quantities of ground clutter and weather are as follows: Figures 2-7 As shown. Figures 2-7 In the diagram, the horizontal axis represents the magnitude of the characteristic quantity, the vertical axis represents the corresponding statistical quantity, GC refers to ground clutter, "weather" refers to non-zero velocity weather echo, and "0-V weather" refers to zero velocity weather echo.

[0040] Adjusting the thresholds of the three characteristic quantities—spectral power discriminant factor, phase structure function, and spectral width discriminant factor—increases the probability of identifying ground clutter. Since the spectral width discriminant factor is used to distinguish between zero-velocity weather and ground objects, it has little effect under clear skies, and statistically, it may also lead to some weak echo ground objects not being identified. Therefore, a weather judgment characteristic (i.e., weather judgment index) is added, and the spectral width discriminant factor is not used for clutter identification under clear skies, so that more ground objects can be identified.

[0041] In step S3, the obtained spectral power discriminant factor and phase structure function are mapped to the [0,1] interval using a membership function. The specific mapping formula is as follows: (9) (10) in, For the first j The membership degree of the spectral power discriminant factor of each distance library to ground objects. For the first j The membership degree of each distance library's phase structure function to ground features.

[0042] In step S4, the membership degrees of the spectral power discrimination factor and the phase structure function to the ground objects are combined through a weighted calculation to determine whether ground clutter exists in the distance pool. The specific formula for the weighted calculation is as follows: =α× +β× (11) α+β=1(12) in, For the first j The probability of ground features in each distance database (i.e., the weighted calculation result). For the first j The membership degree of the spectral power discriminant factor of each distance library to ground objects. For the first j The membership degree of the phase structure function of each distance library to ground features is calculated, where α and β are weighting coefficients. In this embodiment, through extensive statistical analysis and calculation, the weighting coefficient α = 0.625 and the weighting coefficient β = 0.375.

[0043] In this embodiment, the first set threshold is 0.16, and the second set threshold is -5.

[0044] If the first n The distance from the inventory is in the ground clutter and radial direction. i Weather forecasting indicators for each azimuth angle If the value is 0.16, then the spectral width discriminant factor is further calculated to determine the first... n If the distance database does not contain weather, then determine the first distance. n The distance to the storage contains ground clutter. If the radial first i Weather forecasting indicators for each azimuth angle If the value is 0.16, then directly determine the first... n A distance from the inventory in the ground clutter.

[0045] SPD is an improvement on the SPD in the SCI method. The SPD in this invention measures the ratio of the energy at zero frequency in the Doppler spectrum to the sum of the energy within a certain range around zero spectral width. The SPD in the SCI method measures the ratio of the energy at zero frequency to the sum of the remaining energies in the spectrum. When weather intensity is high, i.e., when CSR is low, the sum of the remaining energies in the spectrum increases, reducing the probability of ground clutter identification by the SPD in the SCI method. The SPD in this invention can effectively improve this situation. SPW is designed based on the energy spectral width characteristics of ground objects and zero-velocity weather at zero frequency in the Doppler spectrum. Previous studies often only focused on the spectral width characteristics of the entire Doppler spectrum, ignoring its local spectral width characteristics. This is the key reason why this invention can better distinguish ground clutter from zero-velocity weather echoes than other methods. The azimuth weather assessment index WD is a simple characteristic quantity for determining whether there is weather in the radial direction. When weather is present, the radial velocity fluctuates significantly. This is the basic principle of WD design. Conventional weather radars calculate the radial velocity by performing a correlation function on IQ data to determine the dual polarization quantity, which involves a large amount of computation. The azimuth weather assessment index WD is designed based on the signal processing flow and the spectral characteristics of weather, thus reducing the amount of computation.

[0046] Example 2 This embodiment uses ground clutter maps from clear-sky IQ data of the Hezhuang dual-polarization phased array radar on September 18, 2023, and from IQ data of layered precipitation in Hezhuang on July 28, 2023, to verify the clutter identification performance of the ground clutter identification method of the present invention.

[0047] Figure 8 and Figure 9 The clutter identification results are presented for elevation angles of 0.75° and 2.25°, respectively. Figure 8 and Figure 9 The green portion represents ground clutter identified by the method of this invention, marked as 1; the red portion represents ground clutter not identified by the method of this invention, marked as 2.

[0048] The recognition rate and false alarm rate of the method of this invention are shown in Table 1. The recognition rate is the ratio of the number of correctly identified ground clutter waves by the method of this invention to the total number of ground clutter waves in the ground clutter map; the false alarm rate is the ratio of the number of incorrectly identified ground clutter waves by the method of this invention to the total number of ground clutter waves identified by the method of this invention.

[0049] Table 1. Clutter identification results and false alarm rate at elevation angles of 0.75° and 2.25°. As shown in Table 1, the clutter identification rate of the method of the present invention reaches more than 90% in clear sky at low elevation angles.

[0050] One challenge in clutter identification is the difficulty in distinguishing ground clutter from zero-velocity weather echoes, and misidentification of zero-velocity weather echoes can easily lead to an excessively high false alarm rate. This invention uses stratiform precipitation IQ data to plot clutter maps and compares them with the locations of zero-velocity weather echoes. Since there is a higher elevation angle for comparison, a higher elevation angle is chosen due to the abundance of low-elevation ground clutter. Figure 10 and Figure 11 The text demonstrates the misidentification of zero-speed weather at elevation angles of 17.25° and 21.75°.

[0051] from Figure 10 and Figure 11 A comparison of the clutter map and the radial velocity map shows that the method of the present invention can also identify ground clutter mixed with weather in stratiform precipitation data, and has relatively few false positives for zero-velocity weather. The false positive rate for zero-velocity weather, obtained by statistically analyzing the probability of false positives using the method of the present invention, is shown in Table 2. The false positive rate for zero-velocity weather is defined as the number of falsely identified zero-velocity weather events by the method of the present invention divided by the total number of ground clutter events identified by the method of the present invention.

[0052] Table 2. Zero-velocity weather false recognition rates for clutter identification results at elevation angles of 17.25° and 21.75°. The statistical analysis in Table 2 shows that the method of this invention for radar has a very low false recognition rate for zero-speed weather.

[0053] Figures 12-15 The intensity and radial velocity PPI plane plots of layered precipitation data from the Hezhuang radar and the Guizhou radar are compared after applying the SCI method and the method of this invention.

[0054] Figures 12-15 The black box outlines the situation in the SCI method where the intensity of zero-velocity weather echoes is weakened and the radial velocity space is discontinuous because the zero-velocity weather is suppressed due to misidentification as ground clutter. The method of this invention has greatly improved the situation of misidentification of zero-velocity weather compared to the SCI method.

[0055] Example 3 This invention also provides a terminal device, which includes a memory, a processor, and a computer program / instructions stored in the memory. The processor executes the computer program / instructions to implement the ground clutter identification method described in Embodiment 1.

[0056] Although not shown, the terminal device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) or loaded from a storage portion into random access memory (RAM). The processor can be a multi-core processor or may contain multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more specialized coprocessors, such as a central processing unit, graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Various programs and data required for the operation of the terminal device are also stored in the RAM. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0057] The processor and memory described above are used together to execute programs / instructions stored in the memory. When the program / instructions are executed by the computer, they can implement the methods, steps, or functions described in the above embodiments.

[0058] Although not shown, embodiments of the present invention also provide a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the ground clutter identification method described in Embodiment 1.

[0059] Storage media in embodiments of the present invention include articles that are permanent or non-permanent, removable or non-removable, and can store information by any method or technology. Examples of storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information that can be accessed by a computing device.

[0060] Readable storage media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0061] Although not shown, embodiments of the present invention also provide a computer program product, including: a computer program / instructions that, when executed by a processor, implement the ground clutter identification method as described in Embodiment 1.

[0062] The above description only discloses specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or modifications that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying ground clutter, characterized in that, The identification method includes the following steps: Step S1: Calculate the weather assessment index for the current azimuth angle; Step S2: Calculate the spectral power discrimination factor and phase structure function of the first range library in the current azimuth angle; Step S3: Map the spectral power discriminant factor and phase structure function obtained in step S2 to the [0,1] interval respectively to obtain the membership degree of the spectral power discriminant factor to the ground object and the membership degree of the phase structure function to the ground object; Step S4: Perform a weighted calculation on the membership degree of the spectral power discriminant factor to the ground object and the membership degree of the phase structure function to the ground object obtained in step S3; Step S5: When the weighted calculation result of step S4 is equal to 1 and the weather judgment index of the current azimuth angle is less than the first set threshold, ground clutter exists in the distance library. Repeat steps S2 to S5 for the next distance library in the current azimuth angle until ground clutter identification of all distance libraries in the current azimuth angle is completed. When the weighted calculation result of step S4 is equal to 1, the weather judgment index of the current azimuth angle is greater than or equal to the first set threshold, and the spectral width discrimination factor of the distance library is less than or equal to the second set threshold, the distance library contains ground clutter. Repeat steps S2 to S5 for the next distance library in the current azimuth angle until the ground clutter identification of all distance libraries in the current azimuth angle is completed. When the weighted calculation result of step S4 is equal to 1, the weather judgment index of the current azimuth angle is greater than or equal to the first set threshold, and the spectral width discrimination factor of the distance library is greater than the second set threshold, there is no ground clutter in the distance library. Repeat steps S2 to S5 for the next distance library in the current azimuth angle until the ground clutter identification of all distance libraries in the current azimuth angle is completed. When the weighted calculation result of step S4 is not equal to 1, repeat steps S2 to S5 for the next range library in the current azimuth angle until ground clutter identification of all range libraries in the current azimuth angle is completed. Step S6: For the next azimuth angle, repeat steps S1 to S5 until ground clutter identification for each range in each azimuth angle is completed; The weather judgment index is a ratio of the sum of the energies on both sides of the zero frequency of the Doppler spectrum at each distance in a certain azimuth angle, used to determine whether there is weather with velocity in the distance. The spectral power discrimination factor is the ratio of the energy within the zero-spectral width calculated in the frequency domain of the echo data to the sum of the energies of two points before and after the zero-spectral width region. The phase structure function is obtained by calculating the sum of the squares of the phase differences between adjacent pulses in the same distance. The spectral width discrimination factor is obtained by calculating the substitution value that measures the zero-spectral width.

2. The ground clutter identification method according to claim 1, characterized in that, In step S1, the formula for calculating the weather judgment index of the current azimuth angle is: ； ； ； in, For the first j Weather assessment indicators for distance storage, For the first j The number of pulse signals in each distance library, For the first j The distance library The spectral power of a pulse signal The number of distances in the current azimuth angle. This is a weather assessment indicator based on the current azimuth angle. This is the standard deviation function.

3. The ground clutter identification method according to claim 1, characterized in that, In step S2, the formula for calculating the spectral power discrimination factor of the distance library is: in, For the first j Spectral power discrimination factor of a distance library, For the first j The number of pulse signals in each distance library, For the first j The distance library The spectral power of a pulse signal This represents the number of spectral points corresponding to the zero frequency of the spectrum. Add the number of spectral points corresponding to the zero frequency to the total number of points before or after it, i.e. +2; The formula for calculating the phase structure function of the distance library is: in, For the first j Phase structure function of a distance library, For the first j The distance library +1 pulse signal phase, For the first j The distance library The phase of a pulse signal.

4. The ground clutter identification method according to claim 1, characterized in that, In step S3, the obtained spectral power discrimination factor and phase structure function are mapped to the [0,1] interval using a membership function. The specific mapping formula is as follows: in, For the first j Spectral power discrimination factor of a distance library, For the first j The membership degree of the spectral power discriminant factor of each distance library to ground objects. For the first j Phase structure function of a distance library, For the first j The membership degree of each distance library's phase structure function to ground features.

5. The ground clutter identification method according to claim 1, characterized in that, In step S4, the specific formula for weighted calculation is as follows: ＝α× ＋β× α＋β＝1 in, For the first j The probability of ground features at a distance from the database. For the first j The membership degree of the spectral power discriminant factor of each distance library to ground objects. For the first j The membership degree of each distance library's phase structure function to ground features, where α and β are weighting coefficients.

6. The ground clutter identification method according to claim 5, characterized in that, The weighting coefficient α is 0.625, and the weighting coefficient β is 0.

375.

7. The ground clutter identification method according to any one of claims 1 to 6, characterized in that, In step S5, the specific formula for calculating the spectral width discriminant factor of the distance library is as follows: in, for Maximum spectral power in the interval; For the first j The spectral power of a pulse signal in a distance library; s The location of maximum spectral power. ; For the first j The number of pulse signals in each distance library; For the first j The distance library The spectral power of a pulse signal; For the number of spectrum points, For the first j The spectral width discriminant factor of a distance library.

8. A terminal device, comprising a memory, a processor, and a computer program / instructions stored in the memory, characterized in that, The processor executes the computer program / instructions to implement the ground clutter identification method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the ground clutter identification method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the ground clutter identification method according to any one of claims 1 to 7.

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