A method, apparatus, device and medium for phase adjustment of an antenna

Abstract

The application discloses an antenna phase adjustment method, device, equipment and medium, and is applied to the technical field of antennas. The antenna phase adjustment method comprises the following steps: generating an initial population set, the position matrix of each individual in the population set corresponds to an antenna in an antenna array in a one-to-one manner, the numerical value of one position in the position matrix is the numerical value of one gene in the individual, and the gene corresponds to an antenna unit in the antenna in a one-to-one manner; performing iterative operation on the initial population set according to a particle swarm algorithm and a genetic algorithm to obtain the position matrix of the optimal individual in the population set; and adjusting the phase of the antenna according to the position matrix of the optimal individual. Through the scheme of the application, the problems of difficult antenna calibration, low accuracy, time-consuming and the like in the related technologies about antenna calibration are solved, the time cost of antenna calibration is reduced, and the accuracy of antenna calibration and the antenna performance are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of antennas, and in particular to a phase adjustment method, device, equipment and medium of an antenna. BACKGROUND

[0002] In the field of low-orbit satellite communication, due to the high speed of satellite movement, the time-varying position and the large number of satellites, the antenna design of the ground communication terminal has high requirements. Compared with the traditional mechanical scanning form of the antenna, the antenna scanning has no inertia, the beam can be flexibly changed, and the communication requirements of the low-orbit satellite can be met. The liquid crystal antenna is an array antenna based on a liquid crystal phase shifter, and the basic principle is to use the dielectric anisotropy change of the liquid crystal when different bias voltages are added to achieve the purpose of phase shifting. However, the liquid crystal antenna is a passive device, and there is a problem of array calibration. When the liquid crystal antenna is calibrated, a single probe is generally used to collect the voltage-phase (Voltage-Phase, referred to as V_Phase) relationship curve, and then the array is matched in phase to realize the optimization of the antenna performance. However, the liquid crystal phase shifter cannot be switched alone, so the measurement result obtained by the above method is the superposition result of the mutual coupling of each unit, and then the antenna performance after the phase matching by using the V_Phase relationship curve is greatly different from the theoretical value. In addition, the measurement of the V_Phase relationship curve needs to be measured by voltage and channel, and for a phased array with a large number of channels, the labor cost is large. SUMMARY

[0003] The present application aims to at least solve one of the technical problems existing in the related art of antenna calibration, and proposes a phase adjustment method, device, equipment and medium of an antenna.

[0004] An aspect of the present application provides a phase adjustment method of an antenna, which specifically includes the following steps:

[0005] An initial population set is generated, each individual in the population set corresponds to an antenna in an antenna array in one-to-one, and the value of a position in the position matrix of the individual is the value of a gene in the individual, the gene corresponds to an antenna element in the antenna in one-to-one; the number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2;

[0006] An iterative operation is performed on the initial population set to obtain the position matrix of the optimal individual in the population set; wherein the step of performing the iterative operation on the initial population set to obtain the position matrix of the optimal individual in the population set includes:

[0007] updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm to obtain a first population set; performing cross calculation on the first population set based on the genetic algorithm to obtain a second population set; performing mutation calculation on the second population set based on the genetic algorithm to obtain a third population set;

[0008] determining the fitness value of the X individuals in the current iteration operation according to the position matrix of the X individuals in the third population set, and determining an optimal individual based on the fitness value of the X individuals;

[0009] determining whether the current iteration operation reaches a termination condition; if the termination condition is reached, terminating the iteration and outputting the position matrix corresponding to the optimal individual in the third population set; if the termination condition is not reached, returning to the step of updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm after each iteration of the initial population set is completed; the termination condition includes that the number of iterations reaches a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value;

[0010] adjusting the phase of the antenna according to the position matrix of the optimal individual.

[0011] In some embodiments, after the step of generating the initial population set, the method further comprises:

[0012] loading voltage to the antenna array based on the position matrix of the X individuals in the initial population set to obtain an initial fitness value set; one value in the fitness value set corresponds to the electrical performance of one antenna, and the electrical performance includes the level.

[0013] In some embodiments, the step of updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm comprises:

[0014] for the jth individual in the population, obtaining the velocity matrix of the k+1th iteration operation based on the velocity matrix and the position matrix of the jth individual obtained in the kth iteration operation, the optimal position matrix in the position matrix of the jth individual obtained in the 1st to kth iteration operation, and the optimal position matrix in the position matrix of the population obtained in the 1st to kth iteration operation, and obtaining the position matrix of the k+1th iteration operation according to the velocity matrix of the k+1th iteration operation and the position matrix obtained in the kth iteration operation; j is 1 to X.

[0015] In some embodiments, the step of obtaining the velocity matrix of the k+1th iteration operation and obtaining the position matrix of the k+1th iteration operation according to the velocity matrix of the k+1th iteration operation and the position matrix obtained in the kth iteration operation comprises:

[0016] updating the velocity matrix obtained in the kth iteration operation of the jth individual according to the velocity updating algorithm in the particle swarm algorithm to obtain the velocity matrix of the k+1th iteration operation of the individual, wherein the velocity updating algorithm is:

[0017] v(j k+1 ) = w * v(j k ) + c1 * rand1 * (selfbest - pop(j k )) + c2 * rand2 * (popbest - pop(j k ));

[0018] wherein v(j k+1 ) represents the velocity matrix of the k+1th iteration operation of the jth individual, v(j k ) represents the velocity matrix obtained in the kth iteration operation of the jth individual, pop(j k ) represents the position matrix obtained in the kth iteration operation of the jth individual, rand1 and rand2 represent two random numbers between 0 and 1, selfbest represents the optimal position matrix of the jth individual in the historical iteration operation, and popbest represents the optimal position matrix of the population in the historical iteration operation.

[0019] updating the position matrix obtained in the kth iteration operation of the jth individual according to the position updating algorithm of the particle swarm algorithm to obtain the position matrix of the k+1th iteration operation of the jth individual, wherein the position updating algorithm is:

[0020] pop(j k+1 ) = pop(j k ) + v(j k+1 );

[0021] wherein pop(j k+1 ) represents the position matrix of the k+1th iteration operation of the jth individual.

[0022] In some embodiments, the step of performing the crossover calculation on the first population set according to the genetic algorithm comprises:

[0023] selecting a plurality of individuals requiring crossover from the population; for the i-th individual requiring crossover, performing crossover calculation on the position matrix of the i-th individual requiring crossover in the first population set according to the position matrix of the i-th individual requiring crossover in the first population set and the position matrix of the (i+1)-th individual requiring crossover in the first population set, wherein i takes 1 to Y, Y is greater than or equal to 2, and Y≤X.

[0024] In some embodiments, the step of performing crossover calculation on the position matrix of the i-th individual requiring crossover in the first population set according to the position matrix of the i-th individual requiring crossover in the first population set and the position matrix of the (i+1)-th individual requiring crossover in the first population set comprises:

[0025] performing crossover calculation on the position matrix of the i-th individual requiring crossover in the first population set according to the position matrix of the i-th individual requiring crossover in the first population set and the position matrix of the (i+1)-th individual requiring crossover in the first population set according to a crossover algorithm in the genetic algorithm, to obtain the position matrix of the i-th individual requiring crossover in the second population set, wherein the crossover algorithm is:

[0026] pop'(i) = rand3*pop(i) + (1-rand3)*pop(i+1), if i+1>popsize, i+1 is replaced by 1;

[0027] wherein pop'(i) represents the position matrix of the i-th individual requiring crossover in the second population set, pop(i) represents the position matrix of the i-th individual requiring crossover in the first population set, pop(i+1) represents the position matrix of the (i+1)-th individual requiring crossover in the first population set, rand3 represents a random number between 0 and 1, and popsize represents the size of the population.

[0028] In some embodiments, the step of selecting a plurality of individuals requiring crossover from the population comprises:

[0029] determining the number of individuals requiring crossover in the population according to a preset crossover probability, and randomly selecting a plurality of individuals requiring crossover from the population according to the number of individuals requiring crossover.

[0030] In some embodiments, the step of performing mutation calculation on the second population set based on the genetic algorithm comprises:

[0031] selecting several genes requiring mutation from each of the individuals in the population; for the mth gene requiring mutation of the hth individual, performing mutation calculation on the mth gene requiring mutation of the hth individual according to a preset gene threshold, where m is greater than or equal to 1 and h is 1 to X.

[0032] In some embodiments, the step of performing mutation calculation on the mth gene requiring mutation of the hth individual according to a preset gene threshold comprises:

[0033] performing mutation calculation on the mth gene requiring mutation of the hth individual according to a mutation algorithm in a genetic algorithm, where the mutation algorithm is:

[0034] pop(h, m) = rand4 * popmax;

[0035] where pop(h, m) represents the mth gene of the hth individual, rand4 represents a random number between 0 and 1, popmax is a preset gene threshold, represents the maximum value of each gene, and h is 1 to X.

[0036] In some embodiments, the step of selecting several genes requiring mutation from each of the individuals in the population comprises:

[0037] determining the number of genes requiring mutation in each of the individuals according to a preset mutation probability, and selecting several genes requiring mutation from the individual according to the number of genes requiring mutation.

[0038] In some embodiments, the step of determining the fitness value of the X individuals in the third population set in the current iteration operation according to the position matrix of the X individuals in the third population set comprises:

[0039] loading a voltage to the individual according to the position matrix of the individual in the third population set, to obtain the fitness value of the individual in the current iteration operation.

[0040] In some embodiments, it is determined whether the current iteration operation reaches a termination condition; if the termination condition is reached, the iteration is terminated, and the position matrix corresponding to the optimal individual in the third population set is output; if the termination condition is not reached, the step of updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm is returned, which comprises:

[0041] determining whether the fitness value of the optimal individual in the current iteration operation reaches the preset maximum fitness value;

[0042] If the fitness value corresponding to the optimal individual reaches the preset maximum fitness value, iteration is terminated, and a position matrix corresponding to an optimal individual in the third population set is output.

[0043] If the fitness value corresponding to the optimal individual does not reach the preset maximum fitness value, it is determined whether the number of iterations reaches a preset maximum number of iterations.

[0044] If the number of iterations reaches the preset maximum number of iterations, iteration is terminated, and a position matrix corresponding to an optimal individual in the third population set is output.

[0045] If the number of iterations does not reach the preset maximum number of iterations, the step of updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm is returned to the third population set obtained after each iteration of the initial population set.

[0046] Another aspect of the embodiments of the present disclosure also provides an antenna phase adjustment device, comprising:

[0047] A generating module is configured to generate an initial population set, a position matrix of each individual in the population set corresponds to an antenna in an antenna array one by one, a value of one position in the position matrix is a value of one gene in the individual, the gene corresponds to an antenna element in the antenna one by one; the number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2.

[0048] An iterative operation module is configured to perform iterative operation on the initial population set to obtain a position matrix of an optimal individual in the population set; wherein the iterative operation module comprises an operation module, a judgment module and a driving module,

[0049] The operation module is configured to, for a third population set obtained after each iteration of the initial population set, update a position matrix of X individuals in the third population set based on a particle swarm algorithm to obtain a first population set; perform cross calculation on the first population set based on a genetic algorithm to obtain a second population set; and perform mutation calculation on the second population set based on the genetic algorithm to obtain a third population set.

[0050] The driving module is configured to determine a fitness value of the X individuals in the current iterative operation according to the position matrix of the X individuals in the third population set, and determine an optimal individual based on the fitness values of the X individuals.

[0051] The judgment module is configured to judge whether the current iterative operation meets the termination condition; if the termination condition is met, the iteration is terminated and the position matrix corresponding to the optimal individual in the third population set is output; if the termination condition is not met, the step of returning to the third population set obtained after each iteration of the initial population set is completed, and updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm; the termination condition includes the number of iterations reaching a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaching a preset maximum fitness value;

[0052] The phase adjustment module is configured to adjust the phase of the antenna according to the position matrix of the optimal individual.

[0053] According to another aspect of the embodiments of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory, wherein the memory stores a computer program that can be run on the processor, and the computer program implements the steps of the above method when executed by the processor.

[0054] According to another aspect of the embodiments of the present disclosure, a storage medium is provided, which stores a computer program that implements the above method steps when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or related technologies regarding antenna calibration, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without any creative work.

[0056] Figure 1 A schematic diagram of applying power to a liquid crystal phased array antenna to achieve phase change;

[0057] Figure 2 A system diagram of antenna phase calibration provided by an embodiment of the present disclosure;

[0058] Figure 3 A flow chart of a method for adjusting the phase of an antenna provided in an embodiment of the present disclosure;

[0059] Figure 4 A flow chart of a population iterative operation method provided in an embodiment of the present disclosure;

[0060] Figure 5 A flowchart of a method for determining whether an iteration is terminated during a population iterative operation according to an embodiment of the present disclosure;

[0061] Figure 6A structural diagram of a phase adjustment device for an antenna provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0062] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present disclosure are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0063] It should be noted that all expressions using "first" and "second" in the embodiments of the present disclosure are for the purpose of distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present disclosure. Subsequent embodiments will not explain this one by one.

[0064] The embodiments of the present disclosure may be applied to a liquid crystal phased array antenna, which is an antenna based on a liquid crystal phase shifter. Figure 1 The figure is a schematic diagram of applying power to a liquid crystal phased array antenna to achieve phase change. Figure 1 As shown in Figure 1, the basic principle of liquid crystal phased array antennas is to achieve phase shifting by utilizing the changes in dielectric anisotropy produced by liquid crystals when different bias voltages are applied. However, liquid crystal phased array antennas are passive devices, making array calibration difficult. Measuring V_Phase channel by channel is labor-intensive and prone to errors, making it difficult to maximize antenna performance.

[0065] The present invention combines genetic algorithm and particle swarm algorithm to perform phase-only adjustment on the liquid crystal phase shifter and optimize the antenna pattern performance, so as to solve the problem that mechanical calibration of the antenna array requires a lot of manpower and the calibration efficiency and accuracy cannot effectively meet the calibration requirements.

[0066] According to a first aspect of the embodiments of the present disclosure, a method for adjusting the phase of an antenna is proposed.

[0067] Figure 2 A schematic diagram of antenna phase calibration provided in an embodiment of the present disclosure; Figure 3 A flow chart of a method for adjusting the phase of an antenna provided in an embodiment of the present disclosure; Figure 4 A flow chart of a population iterative operation method provided in an embodiment of the present disclosure; Figure 5 A flow chart of a method for determining whether an iteration is terminated during a population iteration operation provided by an embodiment of the present disclosure. Figures 2 to 5 , the method comprises the following steps:

[0068] S10, generate an initial population set, the position matrix of each individual in the population set corresponds to an antenna in the antenna array, and the value of a position in the position matrix is the value of a gene in the individual, and the gene corresponds to an antenna element in the antenna; the number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2.

[0069] This step S10 is performed by an optimizer (Optimizer). Specifically, before generating the initial population set, an iteration parameter can be set first. The iteration parameter can include: general parameters and antenna parameters. Among them, the general parameters include population size popsize, the maximum value popmax of each gene of each individual in the population, the maximum iteration number epoch_th, and the iteration performance threshold F_th. The antenna parameters include: frequency f, unit horizontal spacing dx, unit vertical spacing dy, horizontal unit number M, and vertical unit number N. After setting the iteration parameter, population initialization is performed, that is, an initial population set with a size of popsize is generated, which is uniformly distributed in (0, popmax). The initial population set contains the position matrix of the randomly generated X individuals, and the position matrix is also the energized matrix. The value of a position in the position matrix corresponds to the energized voltage of an antenna. The voltage can be loaded on an individual according to a position matrix, so as to obtain the electrical performance of the individual under the position matrix. The electrical performance includes but is not limited to level, signal strength, gain, bandwidth, etc. In the embodiment of the present disclosure, only the level is used to represent the electrical performance of the antenna.

[0070] S20, iteratively operating on the initial population set to obtain the position matrix of the optimal individual in the population set.

[0071] Specifically, the initial population set can be iteratively operated by an intelligent optimization algorithm, and the energized voltage of each antenna element of each antenna in the antenna array is continuously adjusted and updated, so as to finally obtain the optimal solution of the antenna performance, thereby avoiding measuring the V_Phase curve, saving a large amount of manpower cost, and improving the accuracy of the phase pattern debugging of the antenna, and further improving the electrical performance of the antenna.

[0072] This step S20 is performed by an optimizer (Optimizer), a driver (Driver), and an antenna pattern acquisition module (Antenna Pattern Acquisition Module). Among them, the antenna pattern acquisition module includes a probe (Probe) and a collector (VNA). The embodiment of the present disclosure will be described below by taking one iteration operation as an example. It should be understood that the examples described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0073] In the optimizer performs the iterative operation, and outputs the position matrix of each individual in the population output by the iterative operation to the driver. After the driver receives the position matrix, the driver loads the power-on voltage to the antenna according to the position matrix. The value of each position in the position matrix corresponds to the voltage value of the antenna element in the antenna. After the antenna receives the power-on voltage of each antenna element, the antenna outputs the beam to the receiver. The receiver obtains the electrical performance of the antenna according to the beam, and sends the electrical level to the optimizer. In the example of the disclosure, the electrical performance is the electrical level, the receiver receives the beam through the probe, sends the electrical level to the optimizer, and the optimizer takes the electrical level as the fitness value of the individual obtained by the current iteration operation. In the example of the disclosure, only one antenna is given for receiving the power-on voltage of the driver. In the actual process, a corresponding number of antennas can be set based on the antenna scale, so that the fitness values of all individuals in the cluster can be obtained at one time. Of course, a number of antennas less than the antenna scale can also be set to obtain the fitness values of all individuals in the cluster in multiple times. In the example of the embodiment of the disclosure, the antenna is a liquid crystal phased array antenna (Liquid Crystal Phased Antenna). The liquid crystal phased array antenna includes MxN antenna elements, M represents the number of horizontal elements, and N represents the number of vertical elements. Both M and N are greater than or equal to 1.

[0074] In some examples, when the liquid crystal phased array antenna is applied with the power-on voltage to obtain the electrical performance of the liquid crystal phased array antenna, the liquid crystal phased array antenna, the probe and the collector are all in the microwave darkroom. In the microwave darkroom, the electrical performance of the liquid crystal phased array antenna is measured. Specifically, the far-field method, the approach method and the compact field method can be used to measure the electrical performance of the antenna in the microwave darkroom. The compact field reduces the test site occupation area and avoids the error caused by the near-far field conversion by using a lens, a reflector antenna and the like, and the stability of the test process is higher. In the example of the disclosure, the combination algorithm of the particle swarm algorithm and the genetic algorithm is combined with the microwave darkroom, and the software and hardware complementary fusion is realized to optimize the performance of the liquid crystal phased array antenna, especially the performance of the reflector phased array antenna.

[0075] In some examples, the intelligent optimization algorithm can include one or more of the intelligent optimization algorithms such as the particle swarm algorithm, the genetic algorithm and the bee colony algorithm. In the example of the embodiment of the disclosure, only the particle swarm algorithm and the genetic algorithm are combined to perform the iterative operation on the initial population set, but the disclosure is not limited thereto.

[0076] In some examples, step S20 specifically includes steps S21-S23.

[0077] S21, for the third population set obtained after each iteration of the initial population set is completed, the position matrix of the X individuals in the third population set is updated based on the particle swarm algorithm to obtain a first population set; the first population set is subjected to crossover calculation based on the genetic algorithm to obtain a second population set; the second population set is subjected to mutation calculation based on the genetic algorithm to obtain a third population set.

[0078] This step S21 is performed by the optimizer. Specifically, the genetic algorithm draws on the natural selection and genetic evolution mechanism in nature, and obtains individuals with excellent shapes through the hybridization and mutation behavior of individuals. The particle swarm algorithm draws on the foraging behavior of bird flocks, and the birds change the speed and flight direction in the foraging process, record their own optimal positions and learn the optimal position of the population, and iteratively fly to find the optimal solution. By combining the particle swarm algorithm and the genetic algorithm, the performance of the liquid crystal phased array antenna can be converged to the preset state at a faster speed through the optimization of the antenna pattern.

[0079] The particle swarm algorithm enables faster optimization of the antenna performance to a good level due to the mutual learning among particles, but there is a problem that the motion state of the particles tends to be consistent in the later optimization period, which may fall into local optimization. The genetic algorithm preserves the diversity of the population in the process of crossover and mutation, and has a higher upper limit of the optimal solution when solving the optimization problem, but the number of iterations is large. By combining the two algorithms, the particle swarm algorithm is used to guide the evolution direction, and the genetic algorithm is used to enhance the local search capability and preserve a certain population diversity, thereby avoiding the problem that a single algorithm falls into local optimization or cannot converge for a long time in the later optimization period.

[0080] In some examples, for the third population set obtained after each iteration of the initial population set is completed, the position matrix of the X individuals in the third population set is updated based on the particle swarm algorithm to obtain a first population set. Specifically, the speed matrix obtained by the jth individual in the kth iteration operation is updated according to the speed update algorithm in the particle swarm algorithm to obtain the speed matrix of the individual in the k+1th iteration operation, wherein the speed update algorithm is:

[0081] v(j k+1 )=w*v(j k )+c1*rand1*(selfbest-pop(j k ))+c2*rand2*(popbest-pop(j k ));

[0082] wherein v(j k+1 ) represents the speed matrix of the jth individual in the k+1th iteration operation, v(j k ) represents the speed matrix obtained by the jth individual in the kth iteration operation, pop(jk ) represents the position matrix of the jth individual in the kth iteration operation, rand1 and rand2 represent two random numbers between 0 and 1, selfbest represents the optimal position matrix of the jth individual in the historical iteration operation, and popbest represents the optimal position matrix of the population in the historical iteration operation.

[0083] The position matrix of the jth individual in the kth iteration operation is updated according to the position updating algorithm of the particle swarm algorithm, to obtain the position matrix of the jth individual in the k+1th iteration operation, and the position updating algorithm is:

[0084] pop(j k+1 ) = pop(j k ) + v(j k+1 );

[0085] pop(j k+1 ) represents the position matrix of the jth individual in the k+1th iteration operation.

[0086] According to the embodiments of the present disclosure, the position matrix of the X individuals in the population obtained after the kth iteration operation is calculated at the k+1th iteration operation, to obtain the position matrix of the X individuals in the population in the k+1th iteration operation, thereby forming the first population set in the k+1th iteration operation.

[0087] In some examples, after obtaining the first population set in the k+1th iteration operation, a plurality of individuals that need to be crossed are selected from the population, and for the ith individual that needs to be crossed, the position matrix of the ith individual that needs to be crossed in the first population set is crossed and calculated according to the position matrix of the ith individual that needs to be crossed in the first population set and the position matrix of the (i+1)th individual that needs to be crossed in the first population set, where i is 1 to Y, Y is greater than or equal to 2, and Y≤X. Specifically, the position matrix of the ith individual that needs to be crossed in the first population set and the position matrix of the (i+1)th individual that needs to be crossed in the first population set are crossed and calculated according to the crossover algorithm in the genetic algorithm, to obtain the position matrix of the ith individual that needs to be crossed in the second population set, where the crossover algorithm is:

[0088] pop'(i) = rand3*pop(i) + (1-rand3)*pop(i+1), if i+1>popsize, i+1 is replaced by 1;

[0089] Wherein, pop'(i) represents the position matrix of the ith individual requiring crossover in the second population set, pop(i) represents the position matrix of the ith individual requiring crossover in the first population set, pop(i+1) represents the position matrix of the (i+1)th individual requiring crossover in the first population set, rand3 represents a random number between 0 and 1, and popsize represents the size of the population.

[0090] In some examples, the number of individuals requiring crossover in the population can be determined according to a preset crossover probability, and a number of individuals requiring crossover can be randomly selected from the population according to the number of individuals requiring crossover. Specifically, for example, the preset crossover probability is 0.8, and the population size is 200, so that 160 individuals requiring crossover are obtained based on 0.8*200, 160 individuals are randomly selected from the 200 individuals, the position matrix of the 160 individuals in the first population set is subjected to crossover calculation, and the position matrix of the remaining 40 individuals is not subjected to crossover calculation, thereby obtaining the second population set. It should be understood that the examples described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0091] In some examples, all individuals in the population can be randomly numbered at each iteration operation, that is, the serial number of the same individual at each iteration operation can be the same or different. When selecting individuals requiring crossover, a number of individuals with different serial numbers are randomly selected from the population for crossover calculation, thereby preserving the diversity of the population.

[0092] In some examples, after obtaining the second population set at the (k+1)th iteration operation according to the first population set at the (k+1)th iteration operation, a number of genes requiring mutation are selected from each individual in the population; for the mth gene requiring mutation of the hth individual, the mth gene requiring mutation of the hth individual is subjected to mutation calculation according to a preset gene threshold, m is greater than or equal to 1, and h takes 1 to X. Specifically, the mth gene requiring mutation of the hth individual is subjected to mutation calculation according to a mutation algorithm in the genetic algorithm, wherein the mutation algorithm is:

[0093] pop(h,m) = rand4*popmax;

[0094] Wherein, pop(h,m) represents the mth gene of the hth individual, rand4 represents a random number between 0 and 1, popmax is a preset gene threshold, represents the maximum value of each gene, and h takes 1 to X.

[0095] In some examples, the number of genes that need to be mutated in each individual can be determined according to a preset mutation probability, and a number of genes that need to be mutated can be selected from each individual according to the number of genes that need to be mutated. For example, the preset mutation probability is 0.2, the population size is 200, and the number of genes in each individual in the population is 100. Based on 0.2x100, 20 genes in each individual need to be mutated. 20 genes are randomly selected from each individual, and mutation calculation is performed on the 20 genes of each individual in the second population set. No calculation is performed on the remaining 80 genes of the individual. Thus, a new position matrix of each individual is obtained, and a third population set is obtained.

[0096] In some examples, when performing mutation calculation on individuals in the population, a number of individuals can also be randomly selected from the population. For example, the individuals that need to be crossed can be directly determined as the individuals that need to be mutated. Mutation calculation is performed on the genes of the individuals that need to be mutated. The process of the crossover calculation is the same as the above example, and will not be repeated here.

[0097] In some examples, after obtaining the first population set, selection calculation can be performed on the first population set before performing crossover calculation on the first population set. Specifically, through the selection operation of the genetic algorithm, individuals in the first population set whose fitness values are higher than a first fitness value are selected to obtain a fourth population set, or individuals in the first population set whose fitness values are lower than a second fitness value are selected to obtain the fourth population set, where the first fitness value is greater than the second fitness value. Then, crossover calculation and mutation calculation are performed on the fourth population set. The processes of the crossover calculation and the mutation calculation are the same as the above example, and will not be repeated here. Through the example of the present disclosure, the convergence speed of the iterative operation is improved.

[0098] S22, determining the fitness values of the X individuals in the third population set according to the position matrices of the X individuals in the third population set, and determining an optimal individual based on the fitness values of the X individuals.

[0099] The step S22 is performed by the optimizer, the driver, the collector of the antenna pattern acquisition module and the probe. Specifically, the driver loads the corresponding driving voltage to the antenna according to the position matrix of the X individuals in the third population set, and the probe of the antenna pattern acquisition module collects the beam output by the antenna and transmits it to the collector. The collector generates the phase pattern of the antenna according to the beam, and obtains the electrical performance of the antenna according to the phase pattern of the antenna, and sends it to the optimizer. The optimizer obtains the fitness value of the X individuals according to the electrical performance of each antenna, and selects the maximum fitness value from them, and takes the individual corresponding to the maximum fitness value as the optimal individual. The optimizer records the optimal individual and the corresponding fitness value, and the historical optimal position of the X individuals in the population. For the historical optimal position of the i-th individual in the X individuals in the population, assuming that the population has been iterated for 8 times including this iteration operation, the historical optimal position of the i-th individual in the population is the position matrix of the third population set obtained by the i-th individual in the iteration operation process in which the fitness value is the maximum in the 8 iterations.

[0100] S23, judging whether the current iteration operation reaches a termination condition; if yes, terminating the iteration and outputting the position matrix corresponding to the optimal individual in the third population set; if no, returning to step S21; the termination condition includes that the iteration number reaches a preset maximum iteration number or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value.

[0101] The step S23 is performed by the optimizer. Specifically, the optimizer sets the iteration termination condition in advance before the iteration operation starts, and judges whether the iteration is terminated according to the iteration termination condition in each iteration process. When the iteration operation reaches the iteration termination condition, the iteration can be terminated, and the position matrix corresponding to the optimal individual is output. In the examples of the present disclosure, the iteration termination condition can include that the iteration number reaches a preset maximum iteration number or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value, so that the antenna with the optimal main lobe electrical performance can be obtained. In some examples, different termination conditions can be set according to different uses of the algorithm. For example, a minimum fitness value can be set, and when the fitness value of the optimal individual is less than the minimum fitness value, the iteration is terminated, so as to control the sidelobe electrical performance; a minimum fitness value can also be set, and the average fitness value of all individuals in the population is calculated, and when the average fitness value is less than the minimum fitness value, the iteration is terminated, so as to determine the antenna null.

[0102] In some examples, the step S23 specifically includes:

[0103] S231, judging whether the fitness value corresponding to the optimal individual of the current iteration operation reaches a preset maximum fitness value; if the fitness value corresponding to the optimal individual reaches the preset maximum fitness value, executing step S233; if the fitness value corresponding to the optimal individual does not reach the preset maximum fitness value, executing step S232.

[0104] S232, if the fitness value corresponding to the optimal individual does not reach the preset maximum fitness value, judging whether the iteration number reaches a maximum iteration number; if the iteration number reaches the maximum iteration number, executing step S233; if the iteration number does not reach the maximum iteration number, returning to the step of updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm after each iteration of the initial population set is completed.

[0105] S233, terminating the iteration and outputting the position matrix corresponding to the optimal individual in the third population set.

[0106] Specifically, in each iteration operation of the population set, after obtaining the third population set, it is first judged whether the fitness value of the optimal individual in the population set reaches a preset maximum fitness value. If the fitness value of the optimal individual reaches the preset maximum fitness value, the iteration is terminated in advance when the iteration number does not reach a maximum iteration number, thereby improving the convergence speed of the iteration operation.

[0107] S30, adjusting the phase of the antenna according to the position matrix of the optimal individual.

[0108] Specifically, according to the value at each position in the determined position matrix of the optimal individual, a power-on voltage is loaded to each antenna unit in the antenna to adjust the phase of the antenna, to obtain a phase pattern of the antenna under the power-on voltage, and to obtain the electrical performance of the antenna under the power-on voltage according to the phase pattern. Since the position matrix of the optimal individual is an optimal solution of the antenna performance generated by continuously iterating the population individuals, the performance of the antenna obtained by this method is optimal.

[0109] It should be noted that the phase pattern of the antenna refers to a pattern in which the relative field strength of the radiation field changes with the direction at a certain distance from the antenna. From the phase pattern of the antenna, various parameters of the antenna can be observed to measure the good or bad performance of the antenna. In the antenna production process, the phase pattern of the antenna is generally optimized to maximize the performance of the antenna.

[0110] The embodiment of the present disclosure simulates the power-on numerical change of the antenna elements in the antenna by setting a population of a certain scale, uses the electrical performance of the antenna in a specified direction as algorithm feedback, iterates the individuals in the population according to the fitness value, and finally obtains the optimal solution of the antenna performance, thereby solving the problems of difficult antenna calibration, low accuracy, and time-consuming in the related art, reducing the time overhead of antenna calibration, and improving the antenna performance.

[0111] In some embodiments, the method not only includes steps S10, S20 and S30 as described above, but also includes the following step S15 between steps S10 and S20:

[0112] S15, load the voltage into the antenna array based on the position matrix of the X individuals in the initial population set, to obtain an initial fitness value set; one numerical value in the fitness value set corresponds to the electrical performance of one antenna, and the electrical performance includes the level.

[0113] The embodiment of the present disclosure differs from the above-mentioned disclosed embodiments in that the initial fitness set obtained from the initial population set during the first iteration operation is also included in the historical fitness set, so that during each iteration operation in the future, the fitness value of the individual in the initial fitness set is also included in the selection range when determining the historical optimal position of each individual in the population, thereby further improving the antenna performance.

[0114] Based on the same inventive concept, the second aspect of the embodiment of the present disclosure proposes an antenna phase adjustment device. Figure 6 A structural diagram of an antenna phase adjustment device provided by the embodiment of the present disclosure is shown in FIG. 1. Figure 6 As shown in the embodiment shown in FIG. 1, the device specifically includes a generation module 110, an iteration operation module 120, and a phase adjustment module 130. Among them,

[0115] The generation module 110 is configured to generate an initial population set, and the position matrix of each individual in the population set corresponds to one-to-one to the antenna in the antenna array. The numerical value of one position in the position matrix is the numerical value of one gene in the individual, and the gene corresponds to one-to-one to the antenna element in the antenna. The number of antennas in the antenna array and the number of individuals in the population are both X, and X is greater than or equal to 2.

[0116] The iterative operation module 120 is configured to perform iterative operation on the initial population set to obtain a position matrix of an optimal individual in the population set. Specifically, the iterative operation module 120 can include an operation module 121, a judgment module 122, and a driving module 123. The operation module 121 is configured to, for a third population set obtained after each iteration of the initial population set is completed, update position matrices of X individuals in the third population set based on a particle swarm algorithm to obtain a first population set; perform crossover calculation on the first population set based on a genetic algorithm to obtain a second population set; and perform mutation calculation on the second population set based on the genetic algorithm to obtain the third population set. The driving module 123 is configured to determine fitness values of the X individuals in the current iteration operation according to the position matrices of the X individuals in the third population set, and determine an optimal individual based on the fitness values of the X individuals. The judgment module 122 is configured to determine whether the current iteration operation reaches a termination condition; if the termination condition is reached, terminate the iteration, and output a position matrix corresponding to the optimal individual in the third population set; if the termination condition is not reached, return to the step of updating the position matrices of the X individuals in the third population set based on the particle swarm algorithm for the third population set obtained after each iteration of the initial population set is completed. The termination condition includes that the number of iterations reaches a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaches a preset maximum fitness value.

[0117] The phase adjustment module 130 is configured to adjust the phase of the antenna according to the position matrix of the optimal individual.

[0118] In the embodiments of the present disclosure, a population of a certain scale is set to simulate the change of the energized number of the antenna elements in the antenna, the electrical performance of the antenna in a specified direction is used as the algorithm feedback, the population individuals are iterated according to the fitness value, and the optimal solution of the antenna performance is finally obtained by using the characteristics of the particle swarm and the genetic algorithm. Thus, the problems of difficult antenna calibration, low accuracy, and time-consuming in the related technologies of antenna calibration are solved, the time cost of antenna calibration is reduced, and the antenna performance is improved.

[0119] Based on the same inventive concept, according to a third aspect of the present disclosure, embodiments of the present disclosure further provide an electronic device, which includes a processor and a memory, the memory stores a computer program capable of running on the processor, and the processor executes the steps of the method as above when executing the program.

[0120] The memory, as a non-volatile storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the program instructions / modules corresponding to the antenna phase adjustment method in the embodiments of the present application. The processor executes various function applications and data processing of the system by running the non-volatile software programs, instructions and modules stored in the memory, that is, implements the antenna phase adjustment method of the above method embodiments.

[0121] The memory can include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function; the data storage area can store data created according to the use of the system, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged with respect to the processor, and these remote memories can be connected to the local module through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0122] Based on the same inventive concept, according to a fourth aspect of the present application, embodiments of the present application also provide a storage medium, which stores a computer program executed by a processor to perform the above method.

[0123] The storage medium (e.g., memory) of the embodiments of the present disclosure can be a volatile memory or a non-volatile memory, or can include both volatile memory and non-volatile memory. As an example and not a limitation, the non-volatile memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory can include a random access memory (RAM), which can serve as an external cache memory. As an example and not a limitation, the RAM can be obtained in various forms, such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The storage device of the disclosed aspects is intended to include but not limited to these and other suitable types of memory.

[0124] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure embodiments.

[0125] The above are exemplary embodiments disclosed by the present disclosure, but it should be noted that various changes and modifications can be made without departing from the scope of the embodiments disclosed by the present disclosure defined by the claims. The functions, steps and / or actions of the method claims in accordance with the embodiments described herein need not be performed in any particular order. The above-described sequence of the embodiments disclosed by the present disclosure is merely for description and does not represent the advantages and disadvantages of the embodiments. Furthermore, although the elements of the embodiments disclosed by the present disclosure can be described or claimed in individual form, they can also be understood as plural unless explicitly limited to singular.

Claims

1. A method for adjusting the phase of an antenna, characterized in that: include: An initial population set is generated, wherein a position matrix of each individual in the population set corresponds one-to-one to an antenna in an antenna array, a value of a position in the position matrix corresponds to a value of a gene in the individual, and the gene corresponds one-to-one to an antenna element in the antenna; the number of antennas in the antenna array and the number of individuals in the population are both X, where X is greater than or equal to 2; Performing an iterative operation on the initial population set to obtain a position matrix of the optimal individual in the population set; wherein the step of performing an iterative operation on the initial population set to obtain the position matrix of the optimal individual in the population set includes: For a third population set obtained after each iteration of the initial population set, updating the position matrix of X individuals in the third population set based on the particle swarm algorithm to obtain a first population set; performing a crossover calculation on the first population set based on the genetic algorithm to obtain a second population set; performing a mutation calculation on the second population set based on the genetic algorithm to obtain a third population set; Determining fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set, and determining an optimal individual based on the fitness values ​​of the X individuals; Determine whether this iterative operation meets the termination condition; if the termination condition is met, terminate the iteration and output the position matrix corresponding to the optimal individual in the third population set; if the termination condition is not met, return to the third population set obtained after each iteration of the initial population set, and update the position matrix of the X individuals in the third population set based on the particle swarm algorithm; the termination condition includes the number of iterations reaching a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaching a preset maximum fitness value; The phase of the antenna is adjusted according to the position matrix of the optimal individual.

2. The method according to claim 1, characterized in that After the step of generating the initial population set, the method further includes: A voltage is applied to the antenna array based on the position matrix of the X individuals in the initial population set to obtain an initial fitness value set; a value in the fitness value set corresponds to an electrical property of the antenna, and the electrical property includes a level.

3. The method according to claim 1 or 2, characterized in that The step of updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm includes: For the jth individual in the population, based on the velocity matrix and position matrix obtained by the kth iterative operation of the jth individual, the optimal position matrix of the jth individual in the position matrices obtained by the 1st to kth iterative operations, and the optimal position matrix of the population in the position matrices obtained by the 1st to kth iterative operations, the velocity matrix of the k+1th iterative operation is obtained, and based on the velocity matrix of the k+1th iterative operation and the position matrix obtained by the kth iterative operation, the position matrix of the k+1th iterative operation is obtained; j is 1 to X.

4. The method according to claim 3, characterized in that The step of obtaining a velocity matrix for a k+1th iterative operation based on the velocity matrix and position matrix obtained by the kth iterative operation of the jth individual, the optimal position matrix of the jth individual in the position matrices obtained by the 1st to kth iterative operations, and the optimal position matrix of the population in the position matrices obtained by the 1st to kth iterative operations, and obtaining a position matrix for a k+1th iterative operation based on the velocity matrix for the k+1th iterative operation and the position matrix obtained by the kth iterative operation includes: The velocity matrix of the j-th individual obtained by the k-th iteration operation is updated according to the velocity update algorithm in the particle swarm algorithm to obtain the velocity matrix of the individual in the k+1-th iteration operation. The velocity update algorithm is: in(j k+1 )=w*v(j k )+c1*rand1*(selfbest-pop(j k ))+c2*rand2*(popbest-pop(j k )); Among them, v(j k+1 ) represents the velocity matrix of the jth individual in the k+1th iteration operation, v(j k ) represents the velocity matrix obtained by the k-th iteration of the j-th individual, pop(j k ) represents the position matrix of the j-th individual obtained by the k-th iteration operation, rand1 and rand2 represent two random numbers between 0 and 1, selfbest represents the optimal position matrix of the j-th individual in the historical iteration operation, and popbest represents the optimal position matrix of the population in the historical iteration operation; The position matrix obtained by the k-th iteration operation of the j-th individual is updated according to the position update algorithm of the particle swarm algorithm to obtain the position matrix of the k+1-th iteration operation of the j-th individual. The position update algorithm is: pop(j k+1 )=pop(j k )+v(j k+1 ); Among them, pop(j k+1 ) represents the position matrix of the jth individual in the k+1th iteration operation.

5. The method according to claim 1, wherein The step of performing crossover calculation on the first population set based on a genetic algorithm includes: Select a number of individuals that need to be crossed from the population; for the i-th individual among the several individuals that need to be crossed, perform a crossover calculation on the position matrix of the i-th individual that needs to be crossed in the first population set based on the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set, where i is 1 to Y, Y is greater than or equal to 2, and Y≤X.

6. The method according to claim 5, characterized in that The step of performing crossover calculation on the position matrix of the i-th individual that needs to be crossed in the first population set based on the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set includes: A crossover algorithm in a genetic algorithm is used to perform a crossover calculation on the position matrix of the i-th individual that needs to be crossed in the first population set and the position matrix of the i+1-th individual that needs to be crossed in the first population set to obtain the position matrix of the i-th individual that needs to be crossed in the second population set. The crossover algorithm is: pop′(i)=rand3*pop(i)+(1-rand3)*pop(i+1), if i+1>popsize, then i+1 is replaced by 1; Among them, pop′(i) represents the position matrix of the i-th individual that needs to be crossed in the second population set, pop(i) represents the position matrix of the i-th individual that needs to be crossed in the first population set, pop(i+1) represents the position matrix of the i+1-th individual that needs to be crossed in the first population set, rand3 represents a random number between 0 and 1, and popsize represents the size of the population.

7. The method according to claim 5, characterized in that The step of selecting a number of individuals to be crossed from the population includes: The number of individuals in the population that need to be crossovered is determined according to a preset crossover probability, and a number of individuals that need to be crossovered are randomly selected from the population according to the number of individuals that need to be crossovered.

8. The method according to claim 1, characterized in that The step of performing mutation calculation on the second population set based on the genetic algorithm includes: A number of genes that need to be mutated are selected from each individual in the population; for the mth gene among the genes that need to be mutated of the hth individual, a mutation calculation is performed on the mth gene among the genes that need to be mutated of the hth individual according to a preset gene threshold, where m is greater than or equal to 1 and h is 1 to X.

9. The method according to claim 8, characterized in that The step of performing mutation calculation on the mth gene among the genes that need to be mutated of the hth individual according to a preset gene threshold comprises: The mutation algorithm in the genetic algorithm is used to perform mutation calculation on the mth gene that needs to be mutated in the hth individual. The mutation algorithm is: pop(h,m)=rand4*popmax; Where pop(h,m) represents the mth gene of the hth individual, rand4 represents a random number between 0 and 1, and popmax is the preset gene threshold, which represents the maximum value of each gene.

10. The method according to claim 8, characterized in that The step of selecting a plurality of genes to be mutated from each individual in the population comprises: The number of genes that need to be mutated in each individual is determined according to a preset mutation probability, and a number of genes that need to be mutated are selected from the individual according to the number of genes that need to be mutated.

11. The method according to claim 1, wherein The step of determining the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set includes: Voltage is applied to the individual according to the position matrix of the individual in the third population set to obtain the fitness value of the individual in this iterative operation.

12. The method according to claim 1, characterized in that The step of determining whether the current iterative operation meets the termination condition; if the termination condition is met, terminating the iteration and outputting the position matrix corresponding to the optimal individual in the third population set; if the termination condition is not met, returning to the third population set obtained after each iteration of the initial population set, and updating the position matrix of X individuals in the third population set based on the particle swarm algorithm, includes: Determine whether the fitness value corresponding to the optimal individual in this iterative calculation reaches the preset maximum fitness value; If the fitness value corresponding to the optimal individual reaches the preset maximum fitness value, the iteration is terminated, and the position matrix corresponding to the optimal individual in the third population set is output; If the fitness value corresponding to the optimal individual does not reach the preset maximum fitness value, determining whether the number of iterations reaches the preset maximum number of iterations; If the number of iterations reaches a preset maximum number of iterations, the iteration is terminated, and a position matrix corresponding to the optimal individual in the third population set is output; If the number of iterations does not reach the preset maximum number of iterations, returning to the step of updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm, after completing each iteration of the initial population set, the third population set is obtained.

13. A phase adjustment device for an antenna, characterized in that: include: a generation module configured to generate an initial population set, wherein a position matrix of each individual in the population set corresponds one-to-one to an antenna in an antenna array, a value of a position in the position matrix corresponds to a value of a gene in the individual, and the gene corresponds one-to-one to an antenna element in the antenna; the number of antennas in the antenna array and the number of individuals in the population are both X, where X is greater than or equal to 2; An iterative operation module is configured to perform iterative operations on the initial population set to obtain a position matrix of the optimal individual in the population set; wherein the iterative operation module includes a calculation module, a judgment module and a driving module, The operation module is configured to update the position matrix of X individuals in the third population set obtained after each iteration of the initial population set based on the particle swarm algorithm to obtain a first population set; perform a crossover calculation on the first population set based on the genetic algorithm to obtain a second population set; and perform a mutation calculation on the second population set based on the genetic algorithm to obtain a third population set; The driving module is configured to determine the fitness values ​​of the X individuals in this iterative operation according to the position matrix of the X individuals in the third population set, and determine the optimal individual based on the fitness values ​​of the X individuals; The judgment module is configured to judge whether the current iterative operation meets the termination condition; if the termination condition is met, the iteration is terminated and the position matrix corresponding to the optimal individual in the third population set is output; if the termination condition is not met, the step of returning to the third population set obtained after each iteration of the initial population set is completed, and updating the position matrix of the X individuals in the third population set based on the particle swarm algorithm; the termination condition includes the number of iterations reaching a preset maximum number of iterations or the fitness value corresponding to the optimal individual reaching a preset maximum fitness value; The phase adjustment module is configured to adjust the phase of the antenna according to the position matrix of the optimal individual.

14. An electronic device comprising: at least one processor; as well as A memory storing a computer program that can be run on the processor, wherein the processor performs the steps of the method according to any one of claims 1 to 12 when executing the program.

15. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are performed.

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