Azimuth calibration method and device for rotating phased array radar based on ground object echo correlation

Abstract

The present application relates to a method and apparatus for azimuth calibration of a rotating phased array radar based on ground object echo correlation. The method utilizes a fuzzy logic algorithm to process the current round data obtained by scanning a rotating phased array radar through one rotation, extracting ground object echo sample points from each azimuth region. Based on the ground object echo sample points, the method calculates the correlation between each azimuth region and multiple adjacent azimuth regions in the previous round data for each azimuth region. The azimuth angle corresponding to the azimuth region in the previous round data with the maximum correlation is then used to calibrate the azimuth angle of the current round azimuth region. This method can effectively improve the accuracy of azimuth calibration of a rotating phased array radar.

Description

Technical Field

[0001] The present application relates to the technical field of calibration of meteorological observation radars, and in particular to a method and device for calibrating the azimuth of a rotating phased array radar based on ground object echo correlation. Background Art

[0002] As high-precision, long-range sensing equipment, weather radar has broad application value in analyzing precipitation dynamics, monitoring cloud microphysical properties, and tracking biological migration trajectories. As weather radar technology has evolved from analog to digital, Doppler, polarimetric, and other information processing technologies, the introduction of rotating phased array technology has significantly improved the spatial and temporal resolution of meteorological observations. Rotating phased array radar is a cost-effective radar architecture widely used for high-precision, timely meteorological observations. It typically uses mechanical rotational scanning in the azimuth dimension and electronic scanning in the elevation dimension.

[0003] However, due to the influence of azimuth pointing deviation during the scanning process of the rotating phased array radar system, it poses a huge challenge to data accuracy. Summary of the Invention

[0004] Based on this, it is necessary to provide a rotating phased array radar azimuth calibration method and device based on ground object echo correlation that can achieve high-precision azimuth calibration in response to the above technical problems.

[0005] A method for aligning a rotating phased array radar based on ground object echo correlation, the method comprising:

[0006] Obtain the PPI base data obtained by the current scan, where the PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is referred to as the current circle data;

[0007] The current circle data is processed using a fuzzy logic algorithm to extract ground object echo sample points in various azimuth angle regions;

[0008] Based on the ground object echo sample points, for each azimuth area, the correlation between the azimuth area and multiple adjacent azimuth areas in the previous round of data is calculated;

[0009] The azimuth angle of the azimuth angle area of ​​the current round is calibrated using the azimuth angle corresponding to the azimuth angle area in the previous round data with the maximum correlation.

[0010] In one embodiment, when calculating the correlation between two azimuth angle regions between two adjacent scanning passes, the calculation is performed based on the Pearson correlation coefficient.

[0011] In one embodiment, when calculating the correlation, the calculation is performed based on the echo reflectivity of all ground object echo sample points in two azimuth regions.

[0012] In one embodiment, the correlation is calculated using the following formula:

[0013]

[0014] In the above formula, ρ(Z n-1 ,Z n ) represents the correlation between the two azimuth areas of the nth round, i.e., the current round, and the n-1th round, i.e., the previous round. n (i, r) represents the echo reflectivity of the ground object echo sample point at a distance of r in the i-th azimuth area of ​​the n-th circle, Z n Represents the ground object echo sampling point sequence in a certain azimuth area, R represents the maximum value of r, cov(Z n-1 ,Z n ) represents Z n-1 and Z n The covariance of , the superscript * indicates conjugate, σ indicates variance, and μ indicates mean.

[0015] In one embodiment, when calculating the correlation between the azimuth area and multiple adjacent azimuth areas in the previous lap data, a correction range is set with the current azimuth area as the center, and the correlation between each azimuth area in the previous lap data within the correction range and the current azimuth area is calculated.

[0016] In one embodiment, the correction range is centered on the current azimuth area, three adjacent azimuth areas in the counterclockwise direction, and three adjacent azimuth areas in the clockwise direction, wherein the correction range includes calibration values ​​corresponding to each azimuth area.

[0017] In one embodiment, if the calculated maximum correlation is less than a preset threshold:

[0018] Calculate the weight of the maximum correlation;

[0019] Performing a smoothing convolution operation according to the weight and the calibration value corresponding to the maximum correlation to obtain an updated calibration value;

[0020] In the previous lap data, the corresponding azimuth angle is extracted according to the updated calibration value based on the correction range, and the azimuth angle of the current lap azimuth angle area is calibrated using the azimuth angle.

[0021] In one embodiment, the weight of the maximum correlation is calculated using the following formula:

[0022]

[0023] in,

[0024] In the above formula, represents the maximum correlation, W n,i represents the weight, and T represents the preset correlation threshold.

[0025] In one embodiment, a smoothing convolution operation is performed based on the weights and the calibration values ​​corresponding to the maximum correlation, and the process is expressed as follows:

[0026]

[0027] In the above formula, L n,i represents the calibration value corresponding to the maximum correlation, represents the updated calibration value, K represents the convolution kernel, represents the convolution operation, and round[·] represents the rounding operation.

[0028] The present application also provides a rotating phased array radar azimuth calibration device based on ground object echo correlation, the device comprising:

[0029] A real-time base data acquisition module is used to obtain the PPI base data obtained by the current scan. The PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is called the current circle data;

[0030] A ground object echo sample point extraction module is used to process the current round data using a fuzzy logic algorithm to extract ground object echo sample points in various azimuth angle areas;

[0031] A correlation calculation module based on ground object echoes is used to calculate the correlation between each azimuth angle region and multiple adjacent azimuth angle regions in the previous round of data based on ground object echo sample points;

[0032] The azimuth calibration module is used to calibrate the azimuth angle of the current round azimuth angle area using the azimuth angle corresponding to the azimuth angle area in the previous round data with maximum correlation.

[0033] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] Obtain the PPI base data obtained by the current scan, where the PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is referred to as the current circle data;

[0035] The current circle data is processed using a fuzzy logic algorithm to extract ground object echo sample points in various azimuth angle regions;

[0036] Based on the ground object echo sample points, for each azimuth area, the correlation between the azimuth area and multiple adjacent azimuth areas in the previous round of data is calculated;

[0037] The azimuth angle of the azimuth angle area of ​​the current round is calibrated using the azimuth angle corresponding to the azimuth angle area in the previous round data with the maximum correlation.

[0038] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0039] Obtain the PPI base data obtained by the current scan, where the PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is referred to as the current circle data;

[0040] The current circle data is processed using a fuzzy logic algorithm to extract ground object echo sample points in various azimuth angle regions;

[0041] Based on the ground object echo sample points, for each azimuth area, the correlation between the azimuth area and multiple adjacent azimuth areas in the previous round of data is calculated;

[0042] The azimuth angle of the azimuth angle area of ​​the current round is calibrated using the azimuth angle corresponding to the azimuth angle area in the previous round data with the maximum correlation.

[0043] The above-mentioned rotating phased array radar azimuth calibration method and device based on ground object echo correlation uses a fuzzy logic algorithm to process the current rotation data obtained by the rotating phased array radar during one rotation scan. It extracts ground object echo sample points in each azimuth angle region. Based on the ground object echo sample points, the correlation between each azimuth angle region and multiple adjacent azimuth angle regions in the previous rotation data is calculated. The azimuth angle corresponding to the azimuth angle region in the previous rotation data with the maximum correlation is then used to calibrate the azimuth angle of the azimuth angle region in the current rotation. This method can effectively improve the accuracy of the rotating phased array radar azimuth calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 1 is a flow chart of a method for azimuth calibration of a rotating phased array radar based on ground object echo correlation in one embodiment;

[0045] Figure 2 1 is a distribution diagram of ground object echoes in two consecutive scans in one embodiment;

[0046] Figure 3This is the distribution map of ground object echoes after calibration based on the radar reading method in an experiment;

[0047] Figure 4 This is the distribution map of ground object echoes after calibration based on this method in an experiment;

[0048] Figure 5 1 is a structural block diagram of a rotating phased array radar azimuth calibration device based on ground object echo correlation in one embodiment;

[0049] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0051] The sources of azimuth pointing deviation of rotating phased array radar system include mechanical transmission system error, environmental factors, electronic sensor error and beam tailing effect.

[0052] Mechanical transmission system error refers to the fact that the antenna of a rotating phased array radar typically achieves azimuth scanning through mechanical rotation. However, due to design flaws in the mechanical transmission (such as gear backlash and bearing wear), the antenna rotation angle may deviate from the preset azimuth angle. Due to the inaccuracy of the transmission, the azimuth deviation can reach as high as 0.2°, and even greater deviations can occur in high wind conditions. This mechanical deviation becomes increasingly significant over long-term use, especially due to factors such as gear wear, drive system lag, and reduced angle sensor accuracy, which can cause the azimuth angle to gradually deviate from the predetermined trajectory.

[0053] Environmental factors are also a significant cause of azimuth pointing deviation. Rotating phased array radars operate in open, natural environments and are subject to external factors such as wind and temperature fluctuations. For example, in strong winds, the radar tower may tilt, causing the antenna installation angle to shift, leading to azimuth errors. Research shows that in high wind speeds or uneven wind conditions, the tower tilt angle can exceed 0.1°, further causing scanning azimuth deviation. Furthermore, temperature fluctuations can cause thermal expansion or contraction of the equipment, resulting in physical deformation of the bracket and mechanical system.

[0054] Electronic sensor error refers to the fact that the accuracy of azimuth angles depends on high-precision angle sensors. However, during rotation, angle sensors are susceptible to electromagnetic interference and mechanical vibration, resulting in pointing errors. Some radars experience significant azimuth errors during observations due to sensor failures and signal drift. Especially in harsh environments, the radar's pointing deviation can reach as much as 1°.

[0055] Beam smearing occurs when a rotating phased array radar maintains beam consistency in azimuth during rotation, affecting data accuracy. This occurs when the antenna's beam cannot perfectly point in the intended direction during rotation. Especially at high rotation speeds, beam pointing accuracy deteriorates, causing a discrepancy between the actual scan angle and the target's azimuth. This effect accumulates during continuous scanning, resulting in dynamic deviations in azimuth.

[0056] In order to solve the above problems, various calibration methods, including the use of drone-assisted measurement and the improvement of angle sensor accuracy, have the disadvantages of being time-consuming and costly. In this application, Figure 1 As shown, a rotating phased array radar azimuth calibration method based on ground object echo correlation is provided, which specifically includes the following steps:

[0057] Step S100: Acquire the PPI base data obtained by the current scan. The PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is called the current circle data.

[0058] Step S110 , using a fuzzy logic algorithm to process the current round data, and extracting ground object echo sample points in each azimuth angle area.

[0059] Step S120 , based on the ground object echo sample points, for each azimuth angle region, calculate the correlation between the azimuth angle region and a plurality of adjacent azimuth angle regions in the previous round of data.

[0060] Step S130, calibrating the azimuth angle of the current round azimuth angle area using the azimuth angle corresponding to the azimuth angle area in the previous round data with the maximum correlation.

[0061] This application proposes an efficient solution for azimuth calibration based on ground object echo correlation. This method, based on the correlation of ground object echoes from two adjacent scans, can correct for accumulated azimuth deviations during continuous scanning. Compared to traditional methods that rely on mechanical sensors, this calibration method avoids the accumulation of sensor and mechanical errors, deriving and correcting azimuth deviations in a very short time, and effectively reducing azimuth deviations during rotation.

[0062] In step S100, the PPI base data to be processed is obtained by scanning with a rotating phased array radar. The radar antenna of a rotating phased array radar mechanically rotates 360 degrees at a constant speed, achieving full coverage on the horizontal plane. In this method, the PPI base data obtained after one rotation of the radar antenna, namely the current rotation data, is processed in real time, and the angles corresponding to each azimuth zone are corrected. The azimuth zones are divided according to preset angles, and the angles of each azimuth zone are equal.

[0063] In step S110, azimuth correction is performed using the ground object echo data in the PPI base data. Ground object echoes typically include clutter generated by buildings, terrain features, and other factors at low elevation angles. This clutter is fixed, and azimuth correction is achieved using these fixed echoes.

[0064] In this embodiment, a fuzzy logic algorithm is used based on PPI basic data, such as horizontal polarization reflectivity Zh, differential reflectivity ZDR and correlation coefficient CC, which can effectively distinguish between ground clutter and meteorological targets.

[0065] In this embodiment, the optimal number of ground object echo sample points is adaptively selected for each azimuth region in the current pass data by analyzing the signal-to-noise ratio (CNR) of the ground object echo to ensure high correlation of the ground object echoes. Optimizing the number of sample points can reduce computational costs and, to a certain extent, minimize noise interference, thereby improving calibration accuracy.

[0066] Specifically, the number of ground object echo sample points is dynamically adjusted according to the amount of clutter and noise in the echo area, ensuring that azimuth calibration is performed in an area with sufficient ground object echoes.

[0067] In step S120, when correcting the angle of a certain azimuth area in the current round data, the correction is performed through the corrected angles corresponding to the azimuth area in the previous round data and its adjacent multiple azimuth areas. Furthermore, by calculating the correlation between the azimuth areas, the corrected angle corresponding to the azimuth area in the previous round that has the greatest correlation with the current azimuth area to be corrected is selected for correction.

[0068] Specifically, when correcting the azimuth angle of the current azimuth angle area to be corrected, the azimuth angle is adjusted to the azimuth angle corresponding to the maximum correlation.

[0069] In this embodiment, when calculating the correlation between two azimuth angle regions between two adjacent scanning passes, the calculation is performed based on the Pearson correlation coefficient. It is conceivable that the calculation of the correlation may also adopt correlation algorithms including the Spearman rank correlation coefficient, the Kendall correlation coefficient, or the mutual information.

[0070] In this embodiment, when calculating the correlation, the calculation is performed based on the echo reflectivity of all ground object echo sample points in the two azimuth regions.

[0071] Specifically, the following formula is used to calculate the correlation:

[0072]

[0073] In formula (1), ρ(Z n-1 ,Z n ) represents the correlation between the two azimuth areas of the nth round, i.e., the current round, and the n-1th round, i.e., the previous round. n (i, r) represents the echo reflectivity of the ground object echo sample point at a distance of r in the i-th azimuth area of ​​the n-th circle, Z n Represents the ground object echo sampling point sequence in a certain azimuth area, R represents the maximum value of r, cov(Z n-1 ,Z n ) represents Z n-1 and Z n The covariance of , the superscript * indicates conjugate, σ indicates variance, and μ indicates mean.

[0074] Further,

[0075]

[0076] In this embodiment, when calculating the correlation between the azimuth area and multiple adjacent azimuth areas in the previous round of data, a correction range is set with the current azimuth area as the center, and the correlation between each azimuth area in the previous round of data within the correction range and the current azimuth area is calculated.

[0077] In one embodiment, the correction range is the three adjacent azimuth angle areas in the counterclockwise direction and the three adjacent azimuth angle areas in the clockwise direction with the current azimuth angle area as the center. That is, if the current azimuth angle area to be corrected is represented by A n i , where n represents the number of scans, and i represents the ith azimuth area. n i The azimuth area for correlation calculation is A n-1 i 、A n-1 i-1 、A n-1 i-2 、A n-1 i-3 、A n-1 i+1 、A n-1 i+2 and An-1 i+3 7 azimuth zones.

[0078] Specifically, the correction range includes calibration values ​​corresponding to each azimuth region. When calculating the correlation between each azimuth region and obtaining the maximum correlation, the process is to find a calibration value corresponding to the maximum correlation, and then use the calibration value to obtain the corresponding azimuth region. The process can be expressed as:

[0079] C n,i (l)=ρ(Z n (i),cirshift[Z n-1 (i),l]) (2)

[0080] In formula (2), cirshift[,] represents the cyclic shift function. When the calibration value l is 0, C n,i (l) represents Z n (i) With Z n-1 (i) When the calibration value l is 1, then C n,i (l) represents Z n (i) With Z n-1 (i+1), when the calibration value l is -1, then C n,i (l) represents Z n (i) With Z n-1 (i-1), and so on.

[0081] Further, we can get the following formula And the corresponding calibration value l:

[0082]

[0083] In formula (3), the range of the calibration value l is taken as [-3, 3] as an example for explanation.

[0084] Then, in step S130, The corresponding calibration value l finds the azimuth angle corresponding to the corresponding azimuth area, and uses this angle as a reference to adjust the azimuth angle currently to be calibrated.

[0085] The azimuth angle calibration can be achieved by calibrating the azimuth angles corresponding to all azimuth angle areas in the current round in sequence according to the above calibration method.

[0086] Furthermore, considering that there are fewer ground object echoes or stronger noise in some azimuth areas, in order to avoid azimuth deviation caused by sudden changes in calibration values, the maximum correlation weighted smoothing method is used to find another calibration value l for azimuth calibration.

[0087] In this embodiment, if the calculated maximum correlation is less than the preset threshold: the weight of the maximum correlation is calculated, and a smoothing convolution operation is performed based on the weight and the calibration value corresponding to the maximum correlation to obtain an updated calibration value. In the previous round data, the corresponding azimuth angle is extracted based on the updated calibration value based on the correction range, and the azimuth angle of the current round azimuth angle area is calibrated using this azimuth angle.

[0088] Specifically, the weight of the maximum correlation is calculated using the following formula:

[0089]

[0090] in,

[0091] In formula (4), represents the maximum correlation, W n,i represents the weight, and T represents the preset correlation threshold. Preferably, T can be set to 0.6.

[0092] It should be noted here that, in fact, when judging the maximum correlation, we use Among them, T is the preset threshold.

[0093] Specifically, a smooth convolution operation is performed based on the calibration values ​​corresponding to the weights and maximum correlations, and the process is expressed as follows:

[0094]

[0095] In formula (5), L n,i represents the calibration value corresponding to the maximum correlation, represents the updated calibration value, K represents the convolution kernel, represents the convolution operation, and round[·] represents the rounding operation.

[0096] In one embodiment, the convolution kernel is selected as K = [1, 1, 1, 1, 1]

[0097] The weighted smoothing process ensures the continuity of azimuth calibration and avoids calibration errors caused by insufficient ground object echoes.

[0098] In this application, a specific algorithm is also proposed to implement the rotating phased array radar azimuth calibration method proposed in this article, as shown below:

[0099] Input: n: total number of scans; Z n (i):PPI reflectivity

[0100] Output: Correction number When p=2;p<n,p++

[0101] When l∈[-3,3], calculate C n,i (l)=ρ(Z n (i),cirshift[Z n-1 (i),l]).

[0102] pass Get the l value and

[0103] Perform calculations in

[0104] Perform calculations,

[0105] Get the final correction number

[0106] End the loop

[0107] End the loop

[0108] This article also provides experimental data that proves the effectiveness of this method. Since the position of the ground object echo is fixed, if the two scan echoes are aligned, that is, the radar measures the same azimuth, then they should cancel each other out after subtraction. However, if one of the scans is not aligned, the clutter at the location measured by the radar is not in the same radial direction, and the subtraction of the two scans will still leave clutter. Figure 2 As shown in Figure 1, the distribution of ground object echoes in two consecutive scans shows the azimuth deviation before calibration.

[0109] like Figure 3 The figure shows the distribution of ground object echoes after calibration based on the radar reading method.

[0110] like Figure 4 The figure shows the distribution of ground object echoes after calibration using this method. Figure 3 It can be seen that there are a lot of clutter. In contrast, after using this method, the amount of remaining clutter is greatly reduced. Construct the residual factor F to evaluate it:

[0111] use The remaining clutter is evaluated. The results are shown in the table below. Among them, the proposed method has the best performance.

[0112]

[0113]

[0114] The OMGCC method involves calculating the Pearson correlation coefficient between two scans. As the sequence length increases, the computational complexity also increases. The time complexity of calculating the average value in Equation 1 and Equation 1 is O(n) and O(1), respectively. Assuming that there are m azimuths per scan, the time complexity of OMGCC is O(m×n), which confirms the importance of optimizing the radial length of each azimuth of the clutter to minimize unnecessary calculations. Then, the computational performance of the OMGC algorithm was evaluated. On a laptop equipped with a 2.3GHz CPU, the time complexity of the OMGCC algorithm is O(n). Figure 2 The average execution time of the algorithm for the medium scenario is about 4.6 seconds. Although the OMGCC algorithm involves multiple complex processes, including adaptive clutter region selection and correlation-based azimuth calibration adjustment, its computational efficiency remains high due to the application of optimization techniques.

[0115] Compared to traditional azimuth calibration methods that mostly rely on mechanical sensors or external calibration equipment, the above-mentioned rotating phased array radar azimuth calibration method based on ground object echo correlation utilizes the high correlation characteristics of ground object echoes. By analyzing the consistency of ground object echoes in continuous scanning data, it calculates and optimizes the azimuth calibration value. This calibration method based on echo correlation is highly adaptable and real-time, making it suitable for rapid calibration of rotating polarization phased arrays in dynamic environments. Furthermore, the method automatically identifies ground object echo regions through a fuzzy logic algorithm, selects valid sample points, and dynamically adjusts the number of sample points based on echo correlation, thereby reducing the amount of computation while ensuring calibration accuracy. The adaptive optimization of the number of sample points can effectively reduce noise interference and improve computational efficiency, enabling the method to maintain good calibration performance even in high-noise environments.

[0116] This method uses a correlation-based weighted smoothing algorithm to address the lack of ground object echoes in certain directions. By setting a maximum correlation weight and performing smoothing convolution on the calibration values, this method ensures that correlation-weighted smoothing is used in directions lacking ground object echoes. This reduces the impact of irrelevant noise on the calibration results, ensuring that the azimuth calibration results remain continuous and stable.

[0117] This method can be directly applied to existing rotating phased array radar systems without requiring additional hardware modifications, making it highly feasible for practical deployment. The algorithm's design is simple and easy to implement, making it easily integrated into radar software systems. It is suitable for calibration and maintenance of various operational radars, and exhibits good compatibility with existing radar systems and is easily implemented.

[0118] The azimuth calibration method based on ground object echo correlation has many advantages such as high accuracy, excellent efficiency, and strong adaptability. It is suitable for the high-precision azimuth calibration needs of rotating phased array radar in complex meteorological observations.

[0119] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0120] In one embodiment, Figure 5 As shown, a rotating phased array radar azimuth calibration device based on ground object echo correlation is provided, comprising: a real-time base data acquisition module 200, a ground object echo sample point extraction module 210, a ground object echo-based correlation calculation module 220, and an azimuth calibration module 230, wherein:

[0121] The real-time base data acquisition module 200 is used to obtain the PPI base data obtained by the current scan. The PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is called the current circle data.

[0122] The ground object echo sample point extraction module 210 is used to process the current round data using a fuzzy logic algorithm to extract ground object echo sample points in various azimuth angle regions.

[0123] The ground object echo-based correlation calculation module 220 is used to calculate the correlation between each azimuth area and multiple adjacent azimuth areas in the previous round data based on the ground object echo sample points.

[0124] The azimuth calibration module 230 is used to calibrate the azimuth angle of the current round azimuth angle area using the azimuth angle corresponding to the azimuth angle area in the previous round data with the maximum correlation.

[0125] Regarding the specific limitations of the rotating phased array radar azimuth calibration device based on ground object echo correlation, please refer to the limitations of the rotating phased array radar azimuth calibration method based on ground object echo correlation above, and will not be repeated here. The various modules in the above-mentioned rotating phased array radar azimuth calibration device based on ground object echo correlation can be implemented in whole or in part through software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0126] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for aligning the azimuth of a rotating phased array radar based on ground object echo correlation is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0127] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0128] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0129] Obtain the PPI base data obtained by the current scan, where the PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is referred to as the current circle data;

[0130] The current circle data is processed using a fuzzy logic algorithm to extract ground object echo sample points in various azimuth angle regions;

[0131] Based on the ground object echo sample points, for each azimuth area, the correlation between the azimuth area and multiple adjacent azimuth areas in the previous round of data is calculated;

[0132] The azimuth angle of the azimuth angle area of ​​the current round is calibrated using the azimuth angle corresponding to the azimuth angle area in the previous round data with the maximum correlation.

[0133] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0134] Obtain the PPI base data obtained by the current scan, where the PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is referred to as the current circle data;

[0135] The current circle data is processed using a fuzzy logic algorithm to extract ground object echo sample points in various azimuth angle regions;

[0136] Based on the ground object echo sample points, for each azimuth area, the correlation between the azimuth area and multiple adjacent azimuth areas in the previous round of data is calculated;

[0137] The azimuth angle of the azimuth angle area of ​​the current round is calibrated using the azimuth angle corresponding to the azimuth angle area in the previous round data with the maximum correlation.

[0138] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0139] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for azimuth calibration of a rotating phased array radar based on ground object echo correlation, characterized in that: The method comprises: Obtain the PPI base data obtained by the current scan, where the PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is referred to as the current circle data; The current circle data is processed using a fuzzy logic algorithm to extract ground object echo sample points in various azimuth angle regions; Based on the ground object echo sample points, for each azimuth area, the correlation between the azimuth area and multiple adjacent azimuth areas in the previous round of data is calculated; The azimuth angle of the azimuth angle area of ​​the current round is calibrated using the azimuth angle corresponding to the azimuth angle area in the previous round data with the maximum correlation.

2. The method for azimuth calibration of a rotating phased array radar according to claim 1, wherein: When calculating the correlation between two azimuth angle regions between two adjacent scanning passes, the calculation is performed based on the Pearson correlation coefficient.

3. The method for azimuth calibration of a rotating phased array radar according to claim 2, wherein: When calculating the correlation, the calculation is performed based on the echo reflectivity of all ground object echo sample points in the two azimuth areas.

4. The method for azimuth calibration of a rotating phased array radar according to claim 3, wherein: The following formula is used to calculate the correlation: In the above formula, ρ(Z n-1 ,Z n ) represents the correlation between the two azimuth areas of the nth round, i.e., the current round, and the n-1th round, i.e., the previous round. n (i, r) represents the echo reflectivity of the ground object echo sample point at a distance of r in the i-th azimuth area of ​​the n-th circle, Z n Represents the ground object echo sampling point sequence in a certain azimuth area, R represents the maximum value of r, cov(Z n-1 ,Z n ) represents Z n-1 and Z n The covariance of , the superscript * indicates conjugate, σ indicates variance, and μ indicates mean.

5. The method for azimuth calibration of a rotating phased array radar according to any one of claims 1 to 4, characterized in that: When calculating the correlation between the azimuth area and multiple adjacent azimuth areas in the previous round of data, a correction range is set with the current azimuth area as the center, and the correlation between each azimuth area in the previous round of data within the correction range and the current azimuth area is calculated.

6. The method for azimuth calibration of a rotating phased array radar according to claim 5, characterized in that: The correction range is centered on the current azimuth area, three adjacent azimuth areas in the counterclockwise direction, and three adjacent azimuth areas in the clockwise direction, wherein the correction range includes calibration values ​​corresponding to each azimuth area.

7. The method for azimuth calibration of a rotating phased array radar according to claim 6, wherein: If the calculated maximum correlation is less than the preset threshold: Calculate the weight of the maximum correlation; Performing a smoothing convolution operation according to the weight and the calibration value corresponding to the maximum correlation to obtain an updated calibration value; In the previous lap data, the corresponding azimuth angle is extracted according to the updated calibration value based on the correction range, and the azimuth angle of the current lap azimuth angle area is calibrated using the azimuth angle.

8. The method for azimuth calibration of a rotating phased array radar according to claim 7, wherein: The weight of the maximum correlation is calculated using the following formula: in, In the above formula, represents the maximum correlation, W n,i represents the weight, and T represents the preset correlation threshold.

9. The method for azimuth calibration of a rotating phased array radar according to claim 8, wherein: A smooth convolution operation is performed based on the weights and the calibration values ​​corresponding to the maximum correlation, and the process is expressed as follows: In the above formula, L n,i represents the calibration value corresponding to the maximum correlation, represents the updated calibration value, K represents the convolution kernel, represents the convolution operation, and round[·] represents the rounding operation.

10. A rotating phased array radar azimuth calibration device based on ground object echo correlation, characterized in that: The device comprises: A real-time base data acquisition module is used to obtain the PPI base data obtained by the current scan. The PPI base data of the current scan is obtained by scanning the rotating phased array radar in one circle, and is called the current circle data; A ground object echo sample point extraction module is used to process the current round data using a fuzzy logic algorithm to extract ground object echo sample points in various azimuth angle areas; A correlation calculation module based on ground object echoes is used to calculate the correlation between each azimuth angle region and multiple adjacent azimuth angle regions in the previous round of data based on ground object echo sample points; The azimuth calibration module is used to calibrate the azimuth angle of the current round azimuth angle area using the azimuth angle corresponding to the azimuth angle area in the previous round data with maximum correlation.

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