Array antenna sidelobe optimization method and system based on improved cross genetic algorithm

Abstract

The invention provides an array antenna sidelobe optimization method and system based on an improved cross genetic algorithm, and relates to the technical field of computer science and application, and the method comprises the steps: obtaining array antenna sidelobe design parameters, determining array antenna sidelobe optimization parameters, and obtaining an array antenna sidelobe directional diagram based on the parameters; performing feature extraction on the antenna sidelobe directional diagram, and performing analysis to obtain an anti-interference coefficient and a main and sidelobe difference coefficient of the directional diagram; constructing an array antenna sidelobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; and solving an optimal solution array antenna sidelobe optimization parameter through an improved genetic algorithm. Multiple performance indexes of the array antenna are effectively balanced, the comprehensive performance of the antenna is remarkably improved, the signal transmission capacity in the main lobe direction is guaranteed, the interference suppression capacity in the side lobe direction is enhanced, and adaptability is higher.

Description

Technical Field

[0001] The present invention relates to the field of computer science and application technology, and in particular to an array antenna sidelobe optimization method and system based on an improved crossover genetic algorithm. Background Art

[0002] Array antennas are widely used in fields such as communications, radar, and electronic countermeasures. Their performance depends to a large extent on the characteristics of the radiation pattern, especially the distribution of the main lobe and side lobes. Traditional array antenna sidelobe optimization methods mainly rely on precise mathematical models or analytical methods to optimize the radiation pattern by adjusting the array element phase and weights. However, these methods often lead to high computational complexity and unsatisfactory optimization effects when the array scale is large or the optimization requirements are more complex. In addition, traditional optimization algorithms are difficult to take into account multiple indicators of the antenna radiation pattern (such as main lobe gain, side lobe level, null depth, and half-power beamwidth), and cannot comprehensively improve the antenna's anti-interference performance and resolution.

[0003] In the prior art, the publication number CN115510733A discloses a method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm. The arrangement of the antenna array in the prior art selects a circular array and uses an improved genetic algorithm to optimize it. At the same time, the constraints of a fixed array radius and a constant total number of array elements are added. When the improved crossover genetic algorithm is used, the measurement process of the initial population and the method of selecting two different crossover modes according to the comparison set value to reduce the repetition rate are added. The optimal peak sidelobe level and the optimal radius of the sparse circular array are obtained by iteration, which can effectively reduce the peak sidelobe level compared with the traditional method. However, the prior art still has defects. The main focus of the prior art is to reduce the peak sidelobe level, without comprehensively considering other key directional diagram indicators, such as main lobe gain, main sidelobe ratio, null depth and half-power beam width. This single optimization target may not meet the higher requirements for the comprehensive performance of the array antenna, and it is difficult to ensure the signal transmission quality of the antenna in the target direction. At the same time, the anti-interference performance may be insufficient in a complex interference environment, affecting the adaptability and robustness of the array antenna.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide an array antenna sidelobe optimization method and system based on an improved crossover genetic algorithm to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: An array antenna sidelobe optimization method based on an improved crossover genetic algorithm, the specific steps comprising: Step 1: Obtain known array antenna side lobe design parameters and array antenna side lobe optimization parameters, and obtain the array antenna side lobe pattern according to the array antenna side lobe design parameters and the array antenna side lobe optimization parameters; Step 2: Extract the characteristics of the antenna side lobe pattern to obtain the characteristic data of the antenna side lobe pattern, analyze the characteristic data of the antenna side lobe pattern to obtain the anti-interference coefficient and the main-side difference coefficient of the pattern; construct the array antenna side lobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; Step 3: Combine the array antenna sidelobe optimization parameters into a chromosome vector and randomly generate The initial chromosome vectors are used as individuals in the initial population; the value of the array antenna sidelobe optimization function is used as the fitness, and the roulette wheel selection method is used to select individuals in the initial population for adaptive crossover and mutation operations; Step 4: Perform selection, crossover and mutation operations on the initial population, and place the two newly generated individual chromosomes obtained each time into the iterative population until the number of individual chromosomes in the iterative population reaches Arrange the initial population according to fitness and select the first Individuals constitute the initial selection population, and the iterative population is merged with the initial selection population to generate a new generation of initial population; Step 5: Repeat steps 3 and 4 until the maximum number of iterations is reached; the individual with the highest fitness in the last generation of initial population generated after the last iteration operation is completed is taken as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

[0007] Furthermore, a polar coordinate system is established with the center of the first array element as a reference point and the array extension direction as a reference direction; the origin of the polar coordinates is set as the observation reference point, and the observation angle is the polar angle of the polar coordinates; the coordinates of the array element center position are the coordinates of the array element center of each array element in the polar coordinates.

[0008] Furthermore, the array antenna sidelobe design parameters include the number of array elements, the operating frequency and the coordinates of the center position of the array element; the array antenna sidelobe optimization parameters include the array element phase and the array element weight; The specific logic for obtaining the side lobe pattern of the array antenna is: the number of array elements, the operating frequency, the center position of the array element, the array element phase, and the array element weight are analyzed to obtain the side lobe pattern of the array antenna; the specific formula for obtaining the side lobe pattern of the array antenna is: ; in, For the observation angle The observed sidelobe pattern of the array antenna is is the observation angle value, For the The element weight of the block element, is the array wave number For the The polar radius of the coordinates of the center position of the block array element, For the The element phase of the block element, is the total number of array elements, is the array element block number index, .

[0009] Further, the antenna sidelobe pattern characteristic data includes main lobe gain, maximum sidelobe level, main-sidelobe ratio, null depth and half-power beamwidth; The polar angles of the coordinates of the tail ends of the two array elements in the last section are , ,and , then the main lobe area is , the specific formula for calculating the main lobe gain is: ; in, is the main lobe gain; The specific formula based on which the maximum sidelobe level is calculated is: ; in, is the maximum sidelobe level; The specific formula for calculating the main-sidelobe ratio is: ; in, is the main lobe to side lobe ratio; The zero point depth is originally within the zero point area The minimum value of The zero depth is calculated by

[0010] The specific formula for calculating the zero depth is: ; in, is the zero depth; The half-power beamwidth indicates the angle range on both sides of the pattern when the signal strength in the main lobe drops to half of the peak value. In the main lobe area, the signal strength usually decreases from the center to both sides, so the specific formula for calculating the half-power beamwidth is: ; ; in, is the half-power beamwidth, The observation angle on the left side of the pattern when the signal strength in the main lobe drops to half of the peak value is The observation angle on the right side of the pattern when the signal strength in the main lobe drops to half of the peak value; The specific logic for generating the anti-interference coefficient is: the maximum sidelobe level and the zero point depth are analyzed to generate the anti-interference coefficient; the specific formula for generating the anti-interference coefficient is: ; in, is the anti-interference coefficient, is the maximum sidelobe level, is the zero depth; The specific logic for generating the main-side difference coefficient is: generating the main-side difference coefficient according to the main lobe gain, the main-side lobe ratio and the half-power beam width; the specific formula for generating the main-side difference coefficient is: ; in, is the principal-side difference coefficient, is the main lobe to side lobe ratio, is the main lobe gain, is the half-power beamwidth.

[0011] Furthermore, the specific logic for constructing the array antenna sidelobe optimization function is as follows: constructing the array antenna sidelobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; the array antenna sidelobe optimization function is expressed as: ; in, is the array antenna sidelobe optimization function, is the anti-interference coefficient, The principal-side difference coefficient.

[0012] Furthermore, the specific logic for selecting individuals is: the fitness of each individual in the initial population is normalized to obtain the selection probability, and the specific formula for calculating the selection probability is: ; in, is the first The probability of selection of an individual, is the first The sidelobe optimization function value of each individual array antenna, is the population individual index, is the total number of individuals in the initial population; The cumulative selection probability is calculated based on the selection probability. The specific formula for calculating the cumulative selection probability is: ; in, is the first Each individual accumulates selection probability; Generate a random number , , find satisfaction If an individual satisfies The number of individuals is less than 2, then reselect the random number. If If the number of individuals is greater than or equal to 2, two individuals are randomly selected for crossover operation.

[0013] Furthermore, the specific logic for performing adaptive crossover is: obtaining the fitness rankings of the two individuals to be crossed, the fitness rankings are ranked from small to large, and the number of gene fragments to be exchanged is calculated according to the fitness rankings of the two individuals to be crossed. The specific logic for calculating the number of gene fragments to be exchanged is: ; in, is the number of gene fragments to be exchanged, is the fitness ranking of an individual undergoing crossover, The fitness ranking of another individual for crossover, is the total number of individuals in the initial population, is the total number of array elements, is the index of the individual in the population, and ; For Perform rounding operation upwards; In the two individuals of the crossover, randomly select The gene fragments were exchanged.

[0014] The present invention further provides an array antenna sidelobe optimization system based on an improved crossover genetic algorithm, the system is used to implement the array antenna sidelobe optimization method based on the improved crossover genetic algorithm, specifically comprising: A data acquisition module is used to acquire known array antenna side lobe design parameters and array antenna side lobe optimization parameters, and acquire the array antenna side lobe pattern according to the array antenna side lobe design parameters and the array antenna side lobe optimization parameters; A function construction module is used to extract features of the antenna side lobe pattern, obtain characteristic data of the antenna side lobe pattern, analyze the characteristic data of the antenna side lobe pattern to obtain the anti-interference coefficient and the main-side difference coefficient of the pattern; and construct an array antenna side lobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; Individual operation module, used to combine the array antenna sidelobe optimization parameters into a chromosome vector, randomly generated The initial chromosome vectors are used as individuals in the initial population; the value of the array antenna sidelobe optimization function is used as the fitness, and the roulette wheel selection method is used to select individuals in the initial population for adaptive crossover and mutation operations; The population selection module is used to perform selection, crossover and mutation operations on the initial population cycle, and place the two newly obtained individual chromosomes each time into the iterative population until the number of individual chromosomes in the iterative population reaches Arrange the initial population according to fitness and select the first Individuals constitute the initial selection population, and the iterative population is merged with the initial selection population to generate a new generation of initial population; The final solution module is used to repeat the individual operation module and the population selection module until the maximum number of iterations is reached; the individual with the highest fitness in the last generation of initial population generated after the last iteration operation is completed is taken as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention and technology comprehensively consider the main lobe gain, side lobe level, main-side lobe ratio, null depth and half-power beam width and other directional performance indicators, and use the anti-interference coefficient and main-side difference coefficient as the optimization objective function, effectively balancing multiple performance requirements. The comprehensive performance of the antenna is significantly improved, which not only ensures the signal transmission capability in the main lobe direction, but also enhances the interference suppression capability in the side lobe direction, and has stronger adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0017] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0019] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] Example: See also Figure 1 , the present invention provides a technical solution: An array antenna sidelobe optimization method based on an improved crossover genetic algorithm, the specific steps comprising: Step 1: Obtain known array antenna side lobe design parameters and array antenna side lobe optimization parameters, and obtain the array antenna side lobe pattern according to the array antenna side lobe design parameters and the array antenna side lobe optimization parameters; The array antenna is arranged in a uniform linear array.

[0021] Optimize the known array antenna sidelobe optimization parameters; A polar coordinate system is established with the center of the first array element as the reference point and the array extension direction as the reference direction; the origin of the polar coordinates is set as the observation reference point, and the observation angle is the polar angle of the polar coordinates; the coordinates of the array element center position are the coordinates of the array element center of each array element in the polar coordinates.

[0022] The array antenna sidelobe design parameters include the number of array elements, the operating frequency and the coordinates of the center position of the array element; the array antenna sidelobe optimization parameters include the array element phase and the array element weight; The array wave number is obtained by analyzing the operating frequency; the specific formula is: ; in, is the operating frequency, The speed of light.

[0023] The specific logic for obtaining the side lobe pattern of the array antenna is: the number of array elements, the operating frequency, the center position of the array element, the array element phase, and the array element weight are analyzed to obtain the side lobe pattern of the array antenna; the specific formula for obtaining the side lobe pattern of the array antenna is: ; in, For the observation angle The observed sidelobe pattern of the array antenna is is the observation angle value, For the The element weight of the block element, is the array wave number, For the The polar radius of the coordinates of the center position of the block array element, For the The element phase of the block element, is the total number of array elements, is the array element block number index, .

[0024] Step 2: Extract the characteristics of the antenna side lobe pattern to obtain the characteristic data of the antenna side lobe pattern, analyze the characteristic data of the antenna side lobe pattern to obtain the anti-interference coefficient and the main-side difference coefficient of the pattern; construct the array antenna side lobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; The antenna sidelobe pattern characteristic data include main lobe gain, maximum sidelobe level, main-sidelobe ratio, null depth and half-power beam width. The polar angles of the tail coordinates of the two elements in the last section are , ,and , then the main lobe area is , the specific formula for calculating the main lobe gain is: ; in, is the main lobe gain; The specific formula based on which the maximum sidelobe level is calculated is: ; in, is the maximum sidelobe level; The specific formula for calculating the main-sidelobe ratio is: ; in, is the main lobe to side lobe ratio; The zero point depth is originally within the zero point area The minimum value of The value is small or even close to 0, so the present invention directly calculates The zero depth is calculated by

[0025] The specific formula for calculating the zero depth is: ; in, is the zero depth; The half-power beamwidth indicates the angle range on both sides of the pattern when the signal strength in the main lobe drops to half of the peak value. In the main lobe area, the signal strength usually decreases from the center to both sides, so the specific formula for calculating the half-power beamwidth is: ; ; in, is the half-power beamwidth, The observation angle on the left side of the pattern when the signal strength in the main lobe drops to half of the peak value is The observation angle on the right side of the pattern when the signal strength in the main lobe drops to half of the peak value.

[0026] The specific logic for generating the anti-interference coefficient is: the maximum sidelobe level and the zero point depth are analyzed to generate the anti-interference coefficient; the specific formula for generating the anti-interference coefficient is: ; in, is the anti-interference coefficient, is the maximum sidelobe level, is the null depth; the anti-interference coefficient comprehensively measures the performance of the antenna in suppressing interference. The larger the value, the worse the anti-interference ability; the maximum sidelobe level represents the maximum radiated power of the sidelobe in the pattern. The higher the maximum sidelobe level means that the antenna is more likely to receive interference signals, thereby reducing the anti-interference ability. The null depth represents the minimum radiated power in the null area of ​​the pattern. The larger the value, the weaker the antenna's ability to suppress the interference direction. Both the maximum sidelobe level and the null depth have a direct impact on interference. Through simple linear addition, the effects of the maximum sidelobe level and the null depth on anti-interference performance are combined. The anti-interference coefficient provides a unified evaluation standard for anti-interference performance. This formula provides a quantitative indicator for antenna performance optimization and can provide an important basis for optimizing antenna design parameters.

[0027] The specific logic for generating the main-side difference coefficient is: generating the main-side difference coefficient according to the main lobe gain, the main-side lobe ratio and the half-power beam width; the specific formula for generating the main-side difference coefficient is: ; in, is the principal-side difference coefficient, is the main lobe to side lobe ratio, is the main lobe gain, is the half-power beamwidth. The main-side difference coefficient comprehensively measures the ability of the main lobe and side lobe performance difference of the antenna pattern. The larger the value, the greater the performance difference between the main lobe and the side lobe, and the higher the pattern quality. The main-side lobe ratio represents the ratio of the main lobe gain to the maximum side lobe level, which is used to measure the concentration of the main lobe and the interference ability of the side lobe. The larger its value, the stronger the main lobe concentration, the smaller the side lobe interference, the more accurate the signal direction distribution, and the higher the pattern quality; the main lobe gain represents the maximum radiated power in the main lobe direction, reflecting the signal radiation intensity of the antenna in the target direction. The larger its value, the stronger the signal in the target direction, the better the coverage capability, and the higher the pattern quality. The half-power beamwidth represents the angle range where the main lobe power drops to half of the peak value, which is used to measure the antenna resolution capability; the smaller its value, the narrower the main lobe direction distribution, the higher the antenna resolution, and the signal is concentrated in the target direction. It is used to penalize the situation where the main lobe gain is low and the beam width is large, indicating the penalty on the pattern performance when the main lobe concentration is insufficient. The main-side lobe difference coefficient combines the main-side lobe ratio, main lobe gain and half-power beam width to provide a unified indicator to measure the performance difference between the main lobe and the side lobe, and then evaluate the pattern quality, which can provide an important basis for optimizing the design parameters of the antenna.

[0028] The specific logic for constructing the array antenna sidelobe optimization function is as follows: construct the array antenna sidelobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; the array antenna sidelobe optimization function is expressed as: ; in, is the array antenna sidelobe optimization function, is the anti-interference coefficient, Main-side lobe difference coefficient. The sidelobe optimization function of the array antenna is used to comprehensively measure the performance of the antenna pattern. The larger the value, the better the main lobe performance of the pattern, the smaller the side lobe interference, and the more ideal the overall design. The main-side lobe difference coefficient evaluates the overall design by measuring the performance difference between the main lobe and the side lobe of the pattern. The larger the value, the more ideal the overall design. The anti-interference coefficient evaluates the overall design by analyzing the ability of the side lobe to suppress interference. The smaller the value, the more ideal the overall design. Step 3: Combine the array antenna sidelobe optimization parameters into a chromosome vector and randomly generate The initial chromosome vectors are used as individuals in the initial population; the value of the array antenna sidelobe optimization function is used as the fitness, and the roulette wheel selection method is used to select individuals in the initial population for adaptive crossover and mutation operations; Among them, the chromosome vector is expressed as ; The specific logic for selecting individuals is: the fitness of each individual in the initial population is normalized to obtain the selection probability. The specific formula for calculating the selection probability is: ; in, is the first The probability of selection of an individual, is the first The sidelobe optimization function value of each individual array antenna, is the population individual index, is the total number of individuals in the initial population; The cumulative selection probability is calculated based on the selection probability. The specific formula for calculating the cumulative selection probability is: ; in, is the first Each individual accumulates selection probability; Generate a random number , , find satisfaction If an individual satisfies The number of individuals is less than 2, then reselect the random number. If If the number of individuals is greater than or equal to 2, two individuals are randomly selected for crossover operation.

[0029] The specific logic for performing adaptive crossover is: obtaining the fitness rankings of the two individuals to be crossed, the fitness rankings are ranked from small to large, and the number of gene fragments to be exchanged is calculated according to the fitness rankings of the two individuals to be crossed. The specific logic for calculating the number of gene fragments to be exchanged is: ; in, is the number of gene fragments to be exchanged, is the fitness ranking of an individual undergoing crossover, The fitness ranking of another individual for crossover, is the total number of individuals in the initial population, is the total number of array elements, is the index of the individual in the population, and ; For Perform rounding operation upwards; is the total number of chromosome gene segments; In the two individuals of the crossover, randomly select The gene fragments were exchanged.

[0030] Step 4: Perform selection, crossover and mutation operations on the initial population, and place the two newly generated individual chromosomes obtained each time into the iterative population until the number of individual chromosomes in the iterative population reaches Arrange the initial population according to fitness and select the first Individuals constitute the initial selection population, and the iterative population is merged with the initial selection population to generate a new generation of initial population; in, For Perform rounding operation upwards. For Perform a rounding down operation; Step 5: Repeat steps 3 and 4 until the maximum number of iterations is reached; the individual with the highest fitness in the last generation of initial population generated after the last iteration operation is completed is taken as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

[0031] The present invention further provides an array antenna sidelobe optimization system based on an improved crossover genetic algorithm, the system is used to implement the array antenna sidelobe optimization method based on the improved crossover genetic algorithm, specifically comprising: A data acquisition module is used to acquire known array antenna side lobe design parameters and array antenna side lobe optimization parameters, and acquire the array antenna side lobe pattern according to the array antenna side lobe design parameters and the array antenna side lobe optimization parameters; A function construction module is used to extract features of the antenna side lobe pattern, obtain characteristic data of the antenna side lobe pattern, analyze the characteristic data of the antenna side lobe pattern to obtain the anti-interference coefficient and the main-side difference coefficient of the pattern; and construct an array antenna side lobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; Individual operation module, used to combine the array antenna sidelobe optimization parameters into a chromosome vector, randomly generated The initial chromosome vectors are used as individuals in the initial population; the value of the array antenna sidelobe optimization function is used as the fitness, and the roulette wheel selection method is used to select individuals in the initial population for adaptive crossover and mutation operations; The population selection module is used to perform selection, crossover and mutation operations on the initial population cycle, and place the two newly obtained individual chromosomes each time into the iterative population until the number of individual chromosomes in the iterative population reaches Arrange the initial population according to fitness and select the first Individuals constitute the initial selection population, and the iterative population is merged with the initial selection population to generate a new generation of initial population; The final solution module is used to repeat the individual operation module and the population selection module until the maximum number of iterations is reached; the individual with the highest fitness in the last generation of initial population generated after the last iteration operation is completed is taken as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

[0032] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0033] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0034] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0035] The above is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any technical personnel familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm, characterized in that: The specific steps include: Step 1: Obtain known array antenna side lobe design parameters and array antenna side lobe optimization parameters, and obtain the array antenna side lobe pattern according to the array antenna side lobe design parameters and the array antenna side lobe optimization parameters; Step 2: Extract the characteristics of the antenna side lobe pattern to obtain the characteristic data of the antenna side lobe pattern, analyze the characteristic data of the antenna side lobe pattern to obtain the anti-interference coefficient and the main-side difference coefficient of the pattern; construct the array antenna side lobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; Step 3: Combine the array antenna sidelobe optimization parameters into a chromosome vector and randomly generate The initial chromosome vectors are used as individuals in the initial population; the value of the array antenna sidelobe optimization function is used as the fitness, and the roulette wheel selection method is used to select individuals in the initial population for adaptive crossover and mutation operations; Step 4: Perform selection, crossover and mutation operations on the initial population, and place the two newly generated individual chromosomes obtained each time into the iterative population until the number of individual chromosomes in the iterative population reaches Arrange the initial population according to fitness and select the first Individuals constitute the initial selection population, and the iterative population is merged with the initial selection population to generate a new generation of initial population; Step 5: Repeat steps 3 and 4 until the maximum number of iterations is reached; the individual with the highest fitness in the last generation of initial population generated after the last iteration operation is completed is taken as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

2. The method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm according to claim 1, characterized in that: A polar coordinate system is established with the center of the first array element as the reference point and the array extension direction as the reference direction; the origin of the polar coordinates is set as the observation reference point, and the observation angle is the polar angle of the polar coordinates; the coordinates of the array element center position are the coordinates of the array element center of each array element in the polar coordinates.

3. The method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm according to claim 1, characterized in that: The array antenna sidelobe design parameters include the number of array elements, the operating frequency and the coordinates of the center position of the array element; the array antenna sidelobe optimization parameters include the array element phase and the array element weight; The array wave number is obtained by analyzing the operating frequency; the specific formula is: ； in, is the operating frequency, is the speed of light; The specific logic for obtaining the side lobe pattern of the array antenna is: the number of array elements, the operating frequency, the coordinates of the center position of the array element, the array element phase, and the array element weight are analyzed to obtain the side lobe pattern of the array antenna; the specific formula for obtaining the side lobe pattern of the array antenna is: ； in, For the observation angle The observed sidelobe pattern of the array antenna is is the observation angle value, For the The element weight of the block element, is the array wave number; For the The polar radius of the coordinates of the center position of the block array element, For the The element phase of the block element, is the total number of array elements, is the array element block number index, .

4. The method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm according to claim 1, characterized in that: The antenna sidelobe pattern characteristic data includes main lobe gain, maximum sidelobe level, main-sidelobe ratio, null depth and half-power beam width; The polar angles of the tail coordinates of the two array elements in the last section are , ,and , then the main lobe area is , the specific formula for calculating the main lobe gain is: ； in, is the main lobe gain; The specific formula used to calculate the maximum sidelobe level is: ； in, is the maximum sidelobe level; The specific formula for calculating the main-sidelobe ratio is: ； in, is the main lobe to side lobe ratio; The zero point depth is originally within the zero point area The minimum value of Calculate the zero depth in this way; The specific formula for calculating the zero depth is: ； in, is the zero depth; The half-power beamwidth represents the angle range on both sides of the pattern when the signal strength in the main lobe drops to half of the peak value. In the main lobe area, the signal strength decreases from the center to both sides, so the specific formula for calculating the half-power beamwidth is: ； ； in, is the half-power beamwidth, The observation angle on the left side of the pattern when the signal strength in the main lobe drops to half of the peak value is The observation angle on the right side of the pattern when the signal strength in the main lobe drops to half of the peak value; The specific logic for generating the anti-interference coefficient is: the maximum sidelobe level and the zero point depth are analyzed to generate the anti-interference coefficient; the specific formula for generating the anti-interference coefficient is: ； in, is the anti-interference coefficient, is the maximum sidelobe level, is the zero depth; The specific logic for generating the main-to-side difference coefficient is: generating the main-to-side difference coefficient according to the main lobe gain, the main-to-side lobe ratio and the half-power beam width; the specific formula for generating the main-to-side difference coefficient is: ； in, is the principal-side difference coefficient, is the main lobe to side lobe ratio, is the main lobe gain, is the half-power beamwidth.

5. The method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm according to claim 1, characterized in that: The specific logic for constructing the array antenna sidelobe optimization function is as follows: construct the array antenna sidelobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; the array antenna sidelobe optimization function is expressed as: ； in, is the array antenna sidelobe optimization function, is the anti-interference coefficient, The principal-side difference coefficient.

6. The method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm according to claim 1, characterized in that: The specific logic for selecting individuals is: the fitness of each individual in the initial population is normalized to obtain the selection probability. The specific formula for calculating the selection probability is: ； in, is the first The probability of selection of an individual, is the first The sidelobe optimization function value of each individual array antenna, is the index of individuals in the population, is the total number of individuals in the initial population; The cumulative selection probability is calculated based on the selection probability. The specific formula for calculating the cumulative selection probability is: ； in, is the first Each individual accumulates selection probability; Generate a random number , , find satisfaction If an individual satisfies The number of individuals is less than 2, then reselect the random number. If If the number of individuals is greater than or equal to 2, two individuals are randomly selected for crossover operation.

7. The method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm according to claim 1, characterized in that: The specific logic for performing adaptive crossover is: obtaining the fitness rankings of the two individuals to be crossed, the fitness rankings are ranked from small to large, and the number of gene fragments to be exchanged is calculated according to the fitness rankings of the two individuals to be crossed. The specific logic for calculating the number of gene fragments to be exchanged is: ； in, is the number of gene fragments to be exchanged, For the cross The fitness ranking of each individual, For the cross The fitness ranking of each individual, is the total number of individuals in the initial population, is the total number of array elements, is the index of the individual in the population, and ; For Perform rounding operation upwards; In the two individuals of the crossover, randomly select The gene fragments were exchanged.

8. An array antenna sidelobe optimization system based on an improved crossover genetic algorithm, characterized in that: The system is used to implement the array antenna sidelobe optimization method based on the improved crossover genetic algorithm according to any one of claims 1 to 7, and specifically includes: A data acquisition module is used to acquire known array antenna side lobe design parameters and array antenna side lobe optimization parameters, and acquire the array antenna side lobe pattern according to the array antenna side lobe design parameters and the array antenna side lobe optimization parameters; A function construction module is used to extract features of the antenna side lobe pattern, obtain characteristic data of the antenna side lobe pattern, analyze the characteristic data of the antenna side lobe pattern to obtain the anti-interference coefficient and the main-side difference coefficient of the pattern; and construct an array antenna side lobe optimization function according to the anti-interference coefficient and the main-side difference coefficient; Individual operation module, used to combine the array antenna sidelobe optimization parameters into a chromosome vector, randomly generated The initial chromosome vectors are used as individuals in the initial population; the value of the array antenna sidelobe optimization function is used as the fitness, and the roulette wheel selection method is used to select individuals in the initial population for adaptive crossover and mutation operations; The population selection module is used to perform selection, crossover and mutation operations on the initial population cycle, and place the two newly obtained individual chromosomes each time into the iterative population until the number of individual chromosomes in the iterative population reaches Arrange the initial population according to fitness and select the first Individuals constitute the initial selection population, and the iterative population is merged with the initial selection population to generate a new generation of initial population; The final solution module is used to repeat the individual operation module and the population selection module until the maximum number of iterations is reached; the individual with the highest fitness in the last generation of initial population generated after the last iteration operation is completed is taken as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

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