Phased-array antenna calibration method and device, computer equipment and phased-array antenna

Abstract

The invention relates to the field of antenna calibration, and discloses a phased-array antenna calibration method and device, computer equipment and a phased-array antenna, and the method comprises the steps: obtaining an array element clustering result associated with a to-be-calibrated phased-array antenna; dividing the phased-array antenna to be calibrated into a plurality of array element groups according to an array element clustering result; obtaining measurement data of each array element group; and calibrating the phased-array antenna to be calibrated according to the measurement data. Through a grouping calibration strategy, the calibration efficiency of the phased array antenna is greatly improved, and the method is particularly suitable for a phased array system in a large-scale, high-density and dynamic environment.

Description

Technical Field

[0001] The present invention relates to the field of antenna calibration, and particularly to a phased array antenna calibration method, device, computer device, and phased array antenna. Background Art

[0002] A phased array antenna is composed of multiple unit channels, and each channel integrates microwave devices such as radiation units, phase shifters, and attenuators. Due to manufacturing process and material differences, it is difficult to make the performance of each channel device exactly the same, resulting in amplitude and phase errors, which need to be compensated by calibration technology. Traditional calibration methods rely on external antennas or embedded calibration units, and the measured parameters include mutual coupling, scattering, etc., but there are significant limitations: the number of measurements reaches the kN order of magnitude (N is the number of array elements, k≥1). For large-scale phased arrays (such as thousands or tens of thousands of array elements), calibration takes several hours, which is difficult to meet the requirements of efficient production. There is an urgent need for a new calibration method to improve the calibration efficiency of phased array antennas. Summary of the Invention

[0003] Based on this, in view of the above technical problems, it is necessary to provide a phased array antenna calibration method, device, computer device, and phased array antenna to improve the calibration efficiency of phased array antennas.

[0004] A phased array antenna calibration method includes: Obtaining an array element clustering result associated with a phased array antenna to be calibrated; Dividing the phased array antenna to be calibrated into multiple array element groups according to the array element clustering result; Obtaining measurement data of each of the array element groups; Calibrating the phased array antenna to be calibrated according to the measurement data.

[0005] Optionally, before obtaining the array element clustering result associated with the phased array antenna to be calibrated, it further includes: Obtaining historical calibration data associated with the phased array antenna to be calibrated; the historical calibration data includes calibration data of multiple calibrated phased array antennas; Clustering the historical calibration data to obtain the array element clustering result.

[0006] Optionally, the clustering the historical calibration data to obtain the array element clustering result includes: Preprocessing the historical calibration data to obtain normalized data; Processing the normalized data through a hierarchical aggregation algorithm to obtain the array element clustering result.

[0007] Optionally, the preprocessing the historical calibration data to obtain normalized data includes: Perform bad point removal on the historical calibration data to obtain bad point removed data; Normalize the phase data in the bad point removed data to generate the normalized data.

[0008] Optionally, the performing bad point removal on the historical calibration data to obtain bad point removed data includes: Process the historical calibration data through a bad point evaluation rule to obtain the bad point removed data; the bad point evaluation rule includes: For any channel of the m-th set of antennas in the historical calibration data I if there exists then mark the data of channel I as a bad data point; where represents the amplitude of the k-th frequency point of channel I of the m-th set of antennas, represents the mean value of all channels at frequency point k of the m-th set of antennas; is a truncation value.

[0009] Optionally, the truncation value is 3 times the variance of the amplitude distribution at frequency point k.

[0010] Optionally, the processing the normalized data through a hierarchical aggregation algorithm to obtain the array element clustering result includes: Regard the calibration data of each channel in the normalized data as an independent cluster; Calculate the distances between all clusters; Merge the two clusters with the smallest distance to form a cluster set; Repeat the process of calculating cluster distances and merging clusters until the phase error in the cluster set is greater than the phase control threshold, and determine the clustering result with the smallest number of cluster groups whose phase error is not greater than the phase control threshold as the array element clustering result.

[0011] A phased array antenna calibration device, comprising: A clustering result acquisition module, configured to acquire an array element clustering result associated with a phased array antenna to be calibrated; An array element grouping division module, configured to divide the phased array antenna to be calibrated into multiple array element groups according to the array element clustering result; A measurement data acquisition module, configured to acquire measurement data of each of the array element groups; A calibration module, configured to calibrate the phased array antenna to be calibrated according to the measurement data.

[0012] A computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, where when the processor executes the computer-readable instructions, the above phased array antenna calibration method is implemented.

[0013] A phased array antenna, and the phased array antenna is calibrated by any one of the above phased array antenna calibration methods.

[0014] The above phased array antenna calibration method, device, computer device and storage medium utilize the similarity between calibration data for grouped calibration. The number of groups is much smaller than the number of array channels, and the test volume can be reduced by an order of magnitude, realizing sparse calibration of the phased array antenna, greatly improving the calibration efficiency of the antenna. At the same time, the number of element groups is related to the phase deviation, and different element groups can be obtained under different phase deviation requirements, which is conducive to the flexible adjustment of the calibration method. Through the grouped calibration strategy, the present invention is significantly superior to the traditional global calibration method in terms of efficiency, accuracy, resource utilization and system robustness, and is particularly suitable for phased array systems in large-scale, high-density and dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0016] Figure 1 It is a schematic flowchart of a phased array antenna calibration method in an embodiment of the present invention; Figure 2 It is the phase distribution of the m-th set of phased array antennas divided into 150 element groups in an embodiment of the present invention; Figure 3 It is the phase distribution of the m-th set of phased array antennas divided into 75 element groups in an embodiment of the present invention; Figure 4 It is the amplitude distribution of the m-th set of phased array antennas divided into 150 element groups in an embodiment of the present invention; Figure 5 It is the amplitude distribution of the m-th set of phased array antennas divided into 75 element groups in an embodiment of the present invention; Figure 6 It is a clustering cluster number-distance relationship diagram of phased array antenna phase data clustering in an embodiment of the present invention; Figure 7 It is a schematic diagram of solving unknowns using an improved rotation vector method in an embodiment of the present invention Figure 1 ( Values are 0°, 90°, 180°); Figure 8 It is a schematic diagram of solving unknowns using an improved rotation vector method in an embodiment of the present invention Figure 2 ( Values are 0°, 90°, 270°); Figure 9 It is a schematic structural diagram of a phased array antenna calibration device in an embodiment of the present invention; Figure 10 It is a schematic diagram of a computer device in an embodiment of the present invention. Specific embodiments

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] In one embodiment, as Figure 1 shown, a phased array antenna calibration method is provided, including the following steps S10 to S40.

[0019] S10. Obtain the array element clustering result associated with the phased array antenna to be calibrated; S20. Divide the phased array antenna to be calibrated into multiple array element groups according to the array element clustering result; S30. Obtain the measurement data of each of the array element groups; S40. Calibrate the phased array antenna to be calibrated according to the measurement data.

[0020] It can be understood that the links between the channels of the phased array antenna have similarity, and the calibration data shows aggregation. In this embodiment, the phased array antenna to be calibrated is calibrated by the measurement data of the array element groups, and fast calibration far less than the number of channels can be achieved.

[0021] Specifically, the array element clustering result associated with the phased array antenna to be calibrated can be obtained. Clustering can be performed based on historical calibration data to form the array element clustering result. Dividing the array element groups based on the array element clustering result can achieve the purpose of compressing the number of measurement points while ensuring the calibration accuracy. In one example, the phased array antenna to be calibrated has N radiation units, and the number of calibration frequency points is K. The array element clustering result can be represented by the array element numbers indicating that all the radiation units of the phased array antenna to be calibrated are grouped according to the array element clustering result. The rth array element group can be represented by , where r is the group number, the array element clustering result includes G, is the number of array elements in the th array element group, , and there are a total of array elements.

[0022] The measurement data of each array element group can be measured to obtain the phase And amplitude . That is to say, through G measurements, the calibration of N radiation units can be achieved.

[0023] In an application example, there are 528 phased array antennas to be calibrated. Each phased array antenna to be calibrated contains 752 radiation units. The measurement frequency is 10.7 GHz to 12.7 GHz, with a total of 12 frequency points. The test field uses a compact range. The measurement data (the microwave signal is generated and measured using a vector network analyzer). The calibration method uses an improved rotation vector method. Using the above grouping method for calibration, the calibration data of the m-th phased array antenna can be obtained: amplitude data , phase data , where m is the antenna number, (m = 0, 1,..., 527); n is the radiation unit serial number (same as the channel serial number), (n = 0, 1,..., 751); k is the frequency point number, (K = 0, 1,..., 11). The amplitude and phase errors are respectively: .

[0024] As Figures 2 to 5 shown, Figure 2 is the phase distribution of the m-th phased array antenna divided into 150 element groups, Figure 3 is the phase distribution of the m-th phased array antenna divided into 75 element groups, Figure 4 is the amplitude distribution of the m-th phased array antenna divided into 150 element groups, Figure 5 is the amplitude distribution of the m-th phased array antenna divided into 75 element groups, Figures 2 to 5 The ordinate of all is the frequency. According to Figures 2 to 5 it can be known that when the number of element groups is 75, the phase variance is 7.9°, and the amplitude variance is 1.34 dB. When the number of element groups is 150, the phase variance is 5.05°, and the amplitude variance is 1.19 dB.

[0025] This embodiment utilizes the similarity between calibration data for grouped calibration. The number of groups is much smaller than the number of array channels, and the test volume can be reduced by an order of magnitude, achieving sparse calibration of the phased array antenna, greatly improving the calibration efficiency of the antenna. At the same time, the number of element groups is related to the phase deviation. Different element groups can be obtained under different phase deviation requirements, which is beneficial to the flexible adjustment of the calibration method. Through the grouped calibration strategy, this embodiment is significantly superior to the traditional global calibration method in terms of efficiency, accuracy, resource utilization, and system robustness, and is especially suitable for phased array systems in large-scale, high-density, and dynamic environments.

[0026] Optionally, before step S10, that is, before obtaining the element clustering result associated with the phased array antenna to be calibrated, it further includes: S11. Obtain historical calibration data associated with the phased array antenna to be calibrated; the historical calibration data includes calibration data of multiple sets of calibrated phased array antennas. S12. Cluster the historical calibration data to obtain the array element clustering result.

[0027] Understandably, historical calibration data associated with the phased array antenna to be calibrated can be obtained. The historical calibration data includes calibration data of multiple sets of calibrated phased array antennas. Here, the phased array antenna to be calibrated and the calibrated phased array antennas belong to phased array antennas of the same specification. Clustering the historical calibration data requires a large amount of calibration data. In some examples, the historical calibration data contains more than 100 sets of calibration data.

[0028] By clustering the historical calibration data, the array element clustering result can be obtained. An appropriate clustering algorithm can be selected according to actual needs. For example, the hierarchical clustering method can be used. The hierarchical clustering method divides the data set at different levels, thereby forming a tree-shaped clustering structure. In some examples, the AGNE algorithm (Agglomerative Hierarchical Clustering Algorithm) can be used to cluster the historical calibration data. The AGNE algorithm is a bottom-up hierarchical clustering method. At the beginning, each sample (array element) is regarded as a separate cluster, and then the nearest clusters are gradually merged until a certain termination condition is met, such as reaching the preset number of clusters or all data points are merged into one cluster. The obtained array element clustering result can be used to group each radiation unit (i.e., the array element) of the phased array antenna to form an array element grouping.

[0029] In this embodiment, by analyzing the historical calibration data, the behavior of the phased array antenna to be calibrated can be better understood and predicted, thereby improving the calibration efficiency and accuracy of the phased array antenna.

[0030] Optionally, step S12, that is, clustering the historical calibration data to obtain the array element clustering result, includes: S121. Preprocess the historical calibration data to obtain normalized data. S122. Process the normalized data through the hierarchical aggregation algorithm to obtain the array element clustering result.

[0031] Understandably, the collected historical calibration data can be preprocessed. Preprocessing is a key step in data analysis, which ensures the effectiveness and accuracy of subsequent analysis. Preprocessing includes but is not limited to outlier detection and processing, and phase normalization. After preprocessing, normalized data can be obtained, which can improve the quality of clustering analysis and obtain a better array element clustering result.

[0032] After preprocessing, the hierarchical agglomeration algorithm can be applied to process this normalized data. The hierarchical agglomeration method is a method for constructing a hierarchical structure of clusters, such as agglomerative hierarchical clustering. Agglomerative Hierarchical Clustering starts with each sample as a separate cluster and then gradually merges the most similar clusters.

[0033] Specifically, using the hierarchical agglomeration algorithm, we can start with a single array element as an independent cluster and then gradually merge the closest clusters according to the Chebyshev distance. This process can be represented by a dendrogram, and finally, the final cluster division can be determined according to specific stopping conditions (such as the set maximum number of clusters or threshold distance), so as to obtain the array element clustering result. For example, arbitrarily select the K0th frequency point and perform hierarchical agglomeration on the data for hierarchical agglomeration. . Among them, K0 is the selected fixed frequency point index, n is the antenna array element index (the value range is from 0 to N - 1, corresponding to N array elements), is the calibration data of the K0th frequency point of the ith radiation element of the mth phased array antenna. The clustering cluster distance metric function , where it means using the Chebyshev distance, and are the clustering clusters of the phased array antenna calibration data. Among them, e belongs to the calibration data points in, and f belongs to the calibration data points in.

[0034] In the embodiment, processing the normalized data based on the hierarchical agglomeration algorithm helps to discover the internal connections between array elements and can help identify groups of array elements with similar performance characteristics, which is very useful for optimizing the calibration of phased array antennas.

[0035] Optionally, step S121, that is, preprocessing the historical calibration data to obtain normalized data, includes: S1211. Remove the bad points from the historical calibration data to obtain the data with bad points removed; S1212. Normalize the phase data in the data with bad points removed to generate the normalized data.

[0036] Understandably, bad points refer to the outliers or error data points in the historical calibration data. Bad points may be caused by measurement errors, equipment failures, or other abnormal operating conditions resulting in data deviations. The historical calibration data can be processed to remove bad points to obtain the data with bad points removed. Obtaining the data with bad points removed helps to improve the accuracy and reliability of subsequent data clustering.

[0037] After obtaining the data for removing bad points, the phase data therein can be further normalized to generate normalized data. Through normalization, all phase data can be made to fall within the range of [0, 360). For example, the phase data can be normalized by the following formula: .

[0038] It can be defined that , is the phase data before normalization, is the phase data after normalization.

[0039] In this embodiment, noise in the historical calibration data can be cleared by removing bad points; through phase normalization, more stable and easily analyzable normalized data can be obtained, improving the quality of clustering analysis and obtaining better array element clustering results.

[0040] Optionally, step S1211, that is, removing bad points from the historical calibration data to obtain data for removing bad points, includes: S12111. Process the historical calibration data through a bad point evaluation rule to obtain the data for removing bad points; the bad point evaluation rule includes: For any channel I of the m-th set of antennas in the historical calibration data, if there exists , then the data of channel I is marked as a data bad point; where represents the amplitude of the k-th frequency point of channel I of the m-th set of antennas, represents the mean value of all channels at frequency point k of the m-th set of antennas; is the truncation value.

[0041] Understandably, for any channel I of the m-th set of antennas in the historical calibration data, if there exists , then the data of channel I is marked as a data bad point. Where represents the amplitude of the k-th frequency point of channel I of the m-th set of antennas, , K is the number of calibration frequency points, represents the mean value of all channels at frequency point k of the m-th set of antennas; is the truncation value. The data bad point can be represented by , and the data of channel I is recorded as not processed, . The truncation value can be set according to actual needs.

[0042] In this embodiment, by removing amplitude values with too small a difference from the mean value, the clustering efficiency of the historical calibration data is greatly improved.

[0043] Optionally, the truncation value is three times the variance of the amplitude distribution at frequency point k.

[0044] Understandably, the truncation value can be set to three times the variance of the amplitude distribution at frequency point k. Since the amount of historical calibration data is large, most of the amplitude values at frequency points are near the mean. By selecting a reasonable truncation value, the calibration data participating in clustering can be greatly reduced, improving the clustering accuracy and efficiency.

[0045] Optionally, step S122, that is, processing the normalized data through the hierarchical aggregation algorithm to obtain the array element clustering result, includes: S1221. Regarding the calibration data of each channel in the normalized data as an independent cluster; S1222. Calculating all cluster distances; S1223. Merging the two clusters with the smallest distance to form a cluster set; S1224. Repeating the process of calculating cluster distances and merging clusters until the phase error in the cluster set is greater than the phase control threshold, and determining the clustering result with the smallest number of cluster groups whose phase error is not greater than the phase control threshold as the array element clustering result.

[0046] Understandably, the normalized data can be processed through the hierarchical aggregation algorithm to obtain the array element clustering result. Here, the hierarchical aggregation algorithm can be the AGNE algorithm. The calibration data of each channel in the normalized data can be regarded as independent clusters, and then all cluster distances (the cluster distance refers to the distance between two clusters) are calculated. The two clusters with the smallest distance are merged to form a cluster set. The process of calculating cluster distances and merging clusters is repeated until the phase error in the cluster set is greater than the phase control threshold, and the clustering result with the smallest number of cluster groups whose phase error is not greater than the phase control threshold is determined as the array element clustering result. Here, the phase control threshold can be set according to the actual phase deviation requirement. For example, the phase control threshold . As Figure 6 shown, Figure 6 is the relationship diagram of the number of clustering clusters (i.e., the number of cluster groups) - distance for the phase data clustering of the phased array antenna. The phase control threshold determines the cluster distance. It can be seen from Figure 6 that there is a negative correlation between the cluster distance and the number of cluster groups.

[0047] In this embodiment, through the hierarchical merging strategy, with the phase error threshold as the termination condition, it is ensured that the channel phase error within each cluster is minimized, improving the calibration accuracy. Based on the phase control threshold, the number of cluster groups is controlled, balancing the grouping fineness and system complexity.

[0048] In some application examples, the historical calibration data in step S11 can be obtained based on the improved rotation vector method. The calibration of the phased array antenna essentially involves obtaining the relative amplitude values between array elements Relative value of sum phase (with the full array signal as the reference object), an accurate amplitude-phase distribution is formed to optimize the beam performance. That is, the unknowns to be solved are the relative value of amplitude and the relative value of phase . In order to obtain this unknown through the changing cosine curve, the traditional rotating vector method has far more than 2 sampling points, so there are many redundant measurements in the rotating vector method.

[0049] In the improved rotating vector method, the relative value of amplitude and the relative value of phase and can be solved by designing special channel phase differences ( Figures 7 to 8 value). As shown in n , in the complex plane coordinate system (real part Re as the horizontal axis and imaginary part Im as the vertical axis), the relative value of amplitude and the relative value of phase can be determined by arbitrarily changing three angles of the channel under test. Generally, the value is selected as 0°, 90°, 180° ( Figure 7 ) and 0°, 90°, 270° ( Figure 8 ). Taking Figure 7 as an example, by changing the phase of the channel under test, the measured energy is: ; where E is the measured energy value; E 0 is the amplitude of the full array signal, used as a reference benchmark, is the amplitude of the independent signal of the channel under test n, reflecting the gain characteristic of this channel. is the initial phase of the full array signal (usually taking the average value or specifying the phase of the reference channel), is the initial phase of the channel under test n, including manufacturing errors and temperature drift effects. is the basic term, representing the energy of the original channel under test, is the interference cancellation term, used to represent the phase reverse compensation for interference signals (such as noise or reflected waves), is the phase adjustment term, used to represent the new state after applying a phase shift Δ to the interference signal. Represents the adjusted combined energy. Represents the adjusted combined energy.

[0050] Take Δ as 0°, 90° and 180° respectively, and measure the corresponding energy values and .

[0051] The equation can be obtained: ; where, ; A is an orthogonal basis matrix based on the Δ value, which is used to simplify the equation solving.

[0052] The vector P represents the power value measured under three - phase switching , reflecting the energy change of the synthesized signal. Calibration needs to determine the amplitude ratio of each channel relative to the entire array , compensating for manufacturing tolerances and mutual coupling effects. By determining the channel phase deviation, which is used for phase shifter compensation.

[0053] Solving the above - mentioned equation can obtain and , and by analogy, the normalized amplitudes and phases of each channel of the entire array surface can be obtained. By this method, the entire antenna array needs to be measured 2N + 1 times to complete the calibration of all channels. In an example, if calibrating the k - th frequency point of the m - th antenna, then .

[0054] It should be understood that the magnitudes of the sequence numbers of the steps in the above - mentioned embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0055] In an embodiment, a phased - array antenna calibration device is provided, and this phased - array antenna calibration device corresponds one - to - one with the phased - array antenna calibration method in the above - mentioned embodiment. As Figure 9 shown, this phased - array antenna calibration device includes: A clustering result acquisition module 10, which is used to acquire the array element clustering result associated with the phased - array antenna to be calibrated; An array element grouping division module 20, which is used to divide the phased - array antenna to be calibrated into multiple array element groups according to the array element clustering result; A measurement data acquisition module 30, which is used to acquire the measurement data of each of the array element groups; A calibration module 40, which is used to calibrate the phased - array antenna to be calibrated according to the measurement data.

[0056] Optionally, the clustering result acquisition module 10 further includes: A historical data acquisition unit, which is used to acquire the historical calibration data associated with the phased - array antenna to be calibrated; the historical calibration data includes the calibration data of multiple calibrated phased - array antennas; A clustering unit, which is used to cluster the historical calibration data to obtain the array element clustering result.

[0057] Optionally, the clustering unit includes: A pre - processing unit, which is used to pre - process the historical calibration data to obtain normalized data; A hierarchical clustering unit for processing the normalized data through a hierarchical aggregation algorithm to obtain the array element clustering result.

[0058] Optionally, the preprocessing unit includes: A bad point removal unit for removing bad points from the historical calibration data to obtain bad point removed data; A normalization unit for normalizing the phase data in the bad point removed data to generate the normalized data.

[0059] Optionally, the bad point removal unit is further configured to: Process the historical calibration data through a bad point evaluation rule to obtain the bad point removed data; the bad point evaluation rule includes: For any channel of the m-th set of antennas in the historical calibration data I , if there exists , then mark the data of channel I as a data bad point; where represents the amplitude of the k-th frequency point of channel I of the m-th set of antennas, represents the mean value of all channels at frequency point k of the m-th set of antennas; is a truncation value.

[0060] Optionally, the truncation value is 3 times the variance of the amplitude distribution at frequency point k.

[0061] Optionally, the hierarchical clustering unit includes: An independent cluster determination unit for treating the calibration data of each channel in the normalized data as an independent cluster; A cluster distance calculation unit for calculating all cluster distances; A merging unit for merging the two clusters with the smallest distance to form a cluster set; A clustering result determination unit for repeating the processes of calculating cluster distances and merging clusters until the phase error in the cluster set is greater than the phase control threshold, and determining the clustering result with the smallest number of cluster groups whose phase error is not greater than the phase control threshold as the array element clustering result.

[0062] For the specific limitations of the phased array antenna calibration device, reference can be made to the limitations of the phased array antenna calibration method in the above text, which will not be elaborated here. Each module in the above phased array antenna calibration device can be implemented in whole or in part through software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0063] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be asFigure 10 As shown. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium and an internal memory. The readable storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The database of the computer device is used to store the data involved in the phased array antenna calibration method. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer-readable instructions are executed by the processor, a phased array antenna calibration method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0064] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor. When the processor executes the computer-readable instructions, the following steps are implemented: Obtain an array element clustering result associated with the phased array antenna to be calibrated; Divide the phased array antenna to be calibrated into multiple array element groups according to the array element clustering result; Obtain the measurement data of each of the array element groups; Calibrate the phased array antenna to be calibrated according to the measurement data.

[0065] In one embodiment, a phased array antenna is provided, and the phased array antenna is calibrated by any of the above phased array antenna calibration methods.

[0066] In one embodiment, one or more computer-readable storage media storing computer-readable instructions are provided. The readable storage media provided in this embodiment include a non-volatile readable storage medium and a volatile readable storage medium. Computer-readable instructions are stored on the readable storage medium. When the computer-readable instructions are executed by one or more processors, the following steps are implemented: Obtain an array element clustering result associated with the phased array antenna to be calibrated; Divide the phased array antenna to be calibrated into multiple array element groups according to the array element clustering result; Obtain the measurement data of each of the array element groups; Calibrate the phased array antenna to be calibrated according to the measurement data.

[0067] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. 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 an 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 (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0068] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example for illustration. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0069] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A phased array antenna calibration method, characterized in that: include: Obtaining array element clustering results associated with the phased array antenna to be calibrated; Dividing the phased array antenna to be calibrated into a plurality of array element groups according to the array element clustering result; Acquiring measurement data of each of the array element groups; The phased array antenna to be calibrated is calibrated according to the measurement data.

2. The phased array antenna calibration method according to claim 1, characterized in that: Before obtaining the array element clustering result associated with the phased array antenna to be calibrated, the method further includes: Acquire historical calibration data associated with the phased array antenna to be calibrated; the historical calibration data includes calibration data of multiple sets of calibrated phased array antennas; The historical calibration data is clustered to obtain the array element clustering result.

3. The phased array antenna calibration method according to claim 2, characterized in that: The clustering of the historical calibration data to obtain the array element clustering result includes: Preprocessing the historical calibration data to obtain normalized data; The normalized data is processed by a hierarchical aggregation algorithm to obtain the array element clustering result.

4. The phased array antenna calibration method according to claim 3, characterized in that: The preprocessing of the historical calibration data to obtain normalized data includes: Remove bad points from the historical calibration data to obtain bad point-removed data; The phase data in the bad pixel removal data is normalized to generate the normalized data.

5. The phased array antenna calibration method according to claim 4, characterized in that: The step of removing bad points from the historical calibration data to obtain bad point-removed data includes: The historical calibration data is processed by a bad pixel evaluation rule to obtain the bad pixel removal data; the bad pixel evaluation rule includes: For any channel of the mth set of antennas in the historical calibration data I , if exists , then the data of the channel I is marked as a bad data point; wherein, represents the amplitude of the kth frequency point of channel I of the mth set of antennas, Represents the mean value of all channels at the frequency k of the mth set of antennas; is the cutoff value.

6. The phased array antenna calibration method according to claim 5, characterized in that: The cutoff value is three times the variance of the amplitude distribution at frequency point k.

7. The phased array antenna calibration method according to claim 3, characterized in that: The step of processing the normalized data by a hierarchical aggregation algorithm to obtain the array element clustering result includes: treating the calibration data of each channel in the normalized data as an independent cluster; Calculate all cluster distances; Merge the two clusters with the smallest distance to form a cluster set; The process of calculating cluster distance and merging clusters is repeated until the phase error in the cluster set is greater than the phase control threshold, and the clustering result with the smallest number of cluster groups whose phase error is not greater than the phase control threshold is determined as the array element clustering result.

8. A phased array antenna calibration device, characterized in that: include: A clustering result acquisition module is used to acquire the array element clustering result associated with the phased array antenna to be calibrated; An array element grouping module is used to divide the phased array antenna to be calibrated into a plurality of array element groups according to the array element clustering result; A measurement data acquisition module, used to acquire measurement data of each array element group; A calibration module is used to calibrate the phased array antenna to be calibrated according to the measurement data.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executed on the processor, characterized in that: When the processor executes the computer-readable instructions, the phased array antenna calibration method according to any one of claims 1 to 7 is implemented.

10. A phased array antenna, characterized in that: The phased array antenna is calibrated by the phased array antenna calibration method according to any one of claims 1 to 7.

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