Array Antenna Sidelobe Optimization Method and System Based on Improved Crossover Genetic Algorithm

Abstract

The present invention provides a method and system for optimizing the sidelobe of an array antenna based on an improved cross genetic algorithm, which relates to the field of computer science and technology and application technology. The present invention obtains the sidelobe design parameters of the array antenna, determines the sidelobe optimization parameters of the array antenna, and obtains the sidelobe pattern of the array antenna based on the above parameters; extracts the features of the antenna sidelobe pattern and analyzes them to obtain the anti-interference coefficient and the main-sidelobe difference coefficient of the pattern; constructs an optimization function for the sidelobe of the array antenna according to the anti-interference coefficient and the main-sidelobe difference coefficient; and solves the optimal sidelobe optimization parameters of the array antenna through an improved genetic algorithm. The present invention effectively balances multiple performance indicators of the array antenna, significantly improves the comprehensive performance of the antenna, not only ensures the signal transmission ability in the main lobe direction, but also enhances the interference suppression ability in the sidelobe direction, and has stronger adaptability.

Description

Technical Field

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

[0002] Array antennas are widely used in the fields of communication, radar, and electronic countermeasures. Their performance depends to a large extent on the pattern characteristics, especially the distribution of the main lobe and sidelobes. Traditional array antenna sidelobe optimization methods mainly rely on accurate mathematical models or analytical methods to optimize the pattern by adjusting the phase and weight of the array elements. However, in the case of a large array scale or complex optimization requirements, these methods often lead to high computational complexity and unsatisfactory optimization effects. In addition, traditional optimization algorithms are difficult to balance multiple pattern metrics (such as main lobe gain, sidelobe level, null depth, and half-power beamwidth), and cannot comprehensively improve the anti-interference performance and resolution ability of the antenna.

[0003] In the prior art, the publication number CN115510733A discloses an array antenna sidelobe optimization method based on an improved crossover genetic algorithm. In this prior art, the arrangement of the antenna array selects a circular array and uses an improved genetic algorithm for optimization. At the same time, constraints are added that the array radius size is fixed and the total number of placed array elements remains unchanged. When using the improved crossover genetic algorithm, a measurement process for the initial population and a method of selecting two different crossover methods according to a comparison set value are added to reduce the repetition rate. The optimal peak sidelobe level and the optimal radius of the sparse circular array are obtained through iteration, and the peak sidelobe level can be effectively reduced compared with the traditional method. However, this prior art still has defects. The main focus of the prior art is to reduce the peak sidelobe level, without comprehensively considering other key pattern metrics, such as main lobe gain, main-to-sidelobe ratio, null depth, and half-power beamwidth. This single optimization goal may not meet the higher requirements for the comprehensive performance of the array antenna, and it is difficult to ensure the signal transmission quality in the target direction of the antenna. 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 the background art section is only used to enhance the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose 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 art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An array antenna sidelobe optimization method based on an improved cross genetic algorithm, the specific steps include:

[0008] Step 1: Obtain the known array antenna sidelobe design parameters and array antenna sidelobe optimization parameters, and obtain the array antenna sidelobe pattern according to the array antenna sidelobe design parameters and array antenna sidelobe optimization parameters;

[0009] Step 2: Extract the features of the antenna sidelobe pattern to obtain the antenna sidelobe pattern feature data, analyze the antenna sidelobe pattern feature data to obtain the anti-interference coefficient and the main-lobe to sidelobe difference coefficient of the pattern; construct an array antenna sidelobe optimization function according to the anti-interference coefficient and the main-lobe to sidelobe difference coefficient;

[0010] Step 3: Combine the array antenna sidelobe optimization parameters into a chromosome vector, and randomly generate initial chromosome vectors as individuals in the initial population; use the array antenna sidelobe optimization function value as the fitness, and use the roulette wheel selection method to select individuals in the initial population for adaptive crossover and mutation operations;

[0011] Step 4: Perform selection, crossover, and mutation operations on the initial population in a loop, and place the chromosome of each of the two newly generated individuals obtained each time into the iterative population until the number of individual chromosomes in the iterative population reaches Arrange the initial population according to the fitness, select the first individuals to form the initial selection population, and merge the iterative population with the initial selection population to generate a new generation of initial population;

[0012] Step 5: Repeat Step 3 and Step 4 until the maximum number of iterations is reached; take the individual with the highest fitness in the last generation of the initial population generated after the last iteration operation as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

[0013] Furthermore, taking the center of the first element as the reference point and the array extension direction as the reference direction, establish a polar coordinate system; set the origin of the polar coordinates as the observation reference point, and the observation angle is the polar angle of the polar coordinates; the coordinate of the element center position is the coordinate of the center of each element in the polar coordinates.

[0014] Furthermore, the array antenna sidelobe design parameters include the number of elements, the operating frequency, and the element center position coordinates; the array antenna sidelobe optimization parameters include the element phase and the element weight;

[0015] The specific logic for obtaining the array antenna sidelobe pattern is as follows: Analyze the number of elements, the operating frequency, the element center position, the element phase, and the element weight to obtain the array antenna sidelobe pattern; the specific formula for obtaining the array antenna sidelobe pattern is:

[0016] ;

[0017] wherein, is the sidelobe pattern of the array antenna observed at the observation angle , is the observation angle value, is the element weight of the th element block, is the array wavenumber of the array, is the th polar radius of the element center position coordinate of the element block, is the th element phase of the element block, is the total number of elements, is the element block number index, .

[0018] Furthermore, the antenna sidelobe pattern characteristic data includes main lobe gain, maximum sidelobe level, main-to-sidelobe ratio, null depth, and half-power beam width;

[0019] The polar angles of the two element ends of the last section of elements are , respectively, and , then the main lobe region is , and the specific formula for calculating the main lobe gain is:

[0020] ;

[0021] wherein, is the main lobe gain;

[0022] The specific formula for calculating the maximum sidelobe level is:

[0023] ;

[0024] wherein, is the maximum sidelobe level;

[0025] The specific formula for calculating the main-to-sidelobe ratio is:

[0026] ;

[0027] wherein, is the main-to-sidelobe ratio;

[0028] The null depth was originally the minimum value within the null region range , and the null depth is calculated by directly finding the value within the entire range.

[0029] The specific formula for calculating the zero - point depth is as follows:

[0030] ;

[0031] Among them, is the zero - point depth;

[0032] The half - power beamwidth represents the angular range corresponding to both sides of the pattern when the signal intensity in the main lobe drops to half of the peak value. In the main lobe region, the signal intensity usually decreases from the center to both sides. Therefore, the specific formula for calculating the half - power beamwidth is:

[0033] ;

[0034] ;

[0035] Among them, is the half - power beamwidth, is the observation angle on the left side of the pattern when the signal intensity 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 intensity in the main lobe drops to half of the peak value;

[0036] The specific logic for generating the anti - interference coefficient is: analyze the maximum sidelobe level and the zero - point depth to generate the anti - interference coefficient; the specific formula for generating the anti - interference coefficient is:

[0037] ;

[0038] Among them, is the anti - interference coefficient, is the maximum sidelobe level, is the zero - point depth;

[0039] The specific logic for generating the main - sidelobe difference coefficient is: generate the main - sidelobe difference coefficient according to the main - lobe gain, the main - sidelobe ratio, and the half - power beamwidth; the specific formula for generating the main - sidelobe difference coefficient is:

[0040] ;

[0041] Among them, is the main - sidelobe difference coefficient, is the main - sidelobe ratio, is the main - lobe gain, is the half - power beamwidth.

[0042] Furthermore, the specific logic for constructing the sidelobe optimization function of the array antenna is: construct the sidelobe optimization function of the array antenna according to the anti - interference coefficient and the main - sidelobe difference coefficient; the sidelobe optimization function of the array antenna is expressed as:

[0043] ;

[0044] Among them, is the sidelobe optimization function of the array antenna, is the anti-interference coefficient, is the main-sidelobe difference coefficient.

[0045] Furthermore, the specific logic for selecting individuals is as follows: Normalize the fitness of each individual in the initial population to obtain the selection probability. The specific formula for calculating the selection probability is:

[0046] ;

[0047] Among them, is the selection probability of the th individual in the initial population, is the value of the sidelobe optimization function of the array antenna of the th individual in the initial population, is the population individual index, is the total number of individuals in the initial population;

[0048] Calculate the cumulative selection probability according to the selection probability. The specific formula for calculating the cumulative selection probability is:

[0049] ;

[0050] Among them, is the cumulative selection probability of the th individual in the initial population;

[0051] Generate a random number , , and find the individual that satisfies . If the number of individuals that satisfy is less than 2, reselect the random number. If the number of individuals that satisfy is greater than or equal to 2, randomly select two individuals for crossover operation.

[0052] Furthermore, the specific logic for performing adaptive crossover is as follows: Obtain the fitness rankings of the two individuals undergoing crossover. The fitness rankings are ranked from smallest to largest. Calculate the number of gene segments to be exchanged according to the fitness rankings of the two individuals undergoing crossover. The specific logic for calculating the number of gene segments to be exchanged is:

[0053] ;

[0054] Among them, is the number of gene segments to be exchanged, is the fitness ranking of one 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 an individual in the population, and ; is for to perform a ceiling operation;

[0055] Among the two individuals for crossover, randomly select gene segments for exchange operation.

[0056] The present invention further provides an array antenna sidelobe optimization system based on an improved crossover genetic algorithm, and the system is used to implement the array antenna sidelobe optimization method based on the improved crossover genetic algorithm, specifically including:

[0057] A data acquisition module, configured to acquire known array antenna sidelobe design parameters and array antenna sidelobe optimization parameters, and acquire the array antenna sidelobe pattern according to the array antenna sidelobe design parameters and the array antenna sidelobe optimization parameters;

[0058] A function construction module, configured to perform feature extraction on the antenna sidelobe pattern to obtain antenna sidelobe pattern feature data, analyze the antenna sidelobe pattern feature data to obtain the anti-interference coefficient and the main-lobe-to-sidelobe difference coefficient of the pattern; construct an array antenna sidelobe optimization function according to the anti-interference coefficient and the main-lobe-to-sidelobe difference coefficient;

[0059] An individual operation module, configured to combine the array antenna sidelobe optimization parameters into a chromosome vector, and randomly generate initial chromosome vectors as individuals in the initial population; use the array antenna sidelobe optimization function value as the fitness, and select individuals in the initial population for adaptive crossover and mutation operations by using the roulette wheel selection method;

[0060] A population selection module, configured to perform selection, crossover, and mutation operations on the initial population in a loop, and place the chromosome of each pair of newly generated individuals into the iterative population until the number of individual chromosomes in the iterative population reaches ; arrange the initial population according to the fitness, select the first individuals to form an initial selection population, and merge the iterative population with the initial selection population to generate a new generation of initial population;

[0061] A final solution module, configured to repeat the individual operation module and the population selection module until the maximum number of iterations is reached; use the individual with the highest fitness in the last generation of the initial population generated after the last iteration operation as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

[0062] Compared with the prior art, the beneficial effects of the present invention are:

[0063] The present invention and this technology comprehensively consider the pattern performance indicators such as main lobe gain, side lobe level, main-to-side lobe ratio, null depth, and half-power beam width, and use the anti-interference coefficient and the main-to-side difference coefficient as the optimization objective functions to effectively balance multiple performance requirements. It significantly improves the comprehensive performance of the antenna, ensuring both the signal transmission ability in the main lobe direction and enhancing the interference suppression ability in the side lobe direction, with stronger adaptability. Description of the Drawings

[0064] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0065] Figure 2 It is a schematic diagram of the overall system structure of the present invention. Detailed Embodiments

[0066] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific embodiments.

[0067] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not represent any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0068] Embodiment:

[0069] Please refer to Figure 1 , the present invention provides a technical solution:

[0070] An improved cross genetic algorithm-based side lobe optimization method for array antennas, the specific steps include:

[0071] Step 1: Obtain the known side lobe design parameters and side lobe optimization parameters of the array antenna, and obtain the side lobe pattern of the array antenna according to the side lobe design parameters and side lobe optimization parameters of the array antenna;

[0072] Among them, the arrangement of the array antenna adopts a uniform linear array.

[0073] Optimize the sidelobe optimization parameters of the known array antenna;

[0074] Taking the center of the first element as the reference point and the array extension direction as the reference direction, establish a polar coordinate system; set the origin of the polar coordinates as the observation reference point, and the observation angle is the polar angle of the polar coordinates; the coordinate of the element center position is the coordinate of the element center of each element in the polar coordinates.

[0075] The sidelobe design parameters of the array antenna include the number of elements, the operating frequency, and the coordinate of the element center position; the sidelobe optimization parameters of the array antenna include the element phase and the element weight;

[0076] Obtain the array wavenumber by analyzing the operating frequency; the specific formula is:

[0077] ;

[0078] where is the operating frequency, is the speed of light.

[0079] The specific logic for obtaining the sidelobe pattern of the array antenna is; analyze the number of elements, the operating frequency, the element center position, the element phase, and the element weight to obtain the sidelobe pattern of the array antenna; the specific formula for obtaining the sidelobe pattern of the array antenna is:

[0080] ;

[0081] where is the sidelobe pattern of the array antenna observed at the observation angle , is the observation angle value, is the element weight of the th block of elements, is the array wavenumber, is the th polar radius of the element center position coordinate of the th block of elements, is the element phase of the th block of elements, is the total number of elements, is the index of the block of elements,

[0082] Step 2: Extract the features of the antenna sidelobe pattern to obtain the sidelobe pattern feature data of the antenna, and analyze the sidelobe pattern feature data to obtain the anti-interference coefficient and the main-lobe-to-sidelobe difference coefficient of the pattern; construct an array antenna sidelobe optimization function according to the anti-interference coefficient and the main-lobe-to-sidelobe difference coefficient;

[0083] 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:

[0084] ;

[0085] in, is the main lobe gain;

[0086] The specific formula used to calculate the maximum sidelobe level is:

[0087] ;

[0088] in, is the maximum sidelobe level;

[0089] The specific formula for calculating the main-sidelobe ratio is:

[0090] ;

[0091] in, is the main lobe to side lobe ratio;

[0092] 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

[0093] The specific formula for calculating the zero depth is:

[0094] ;

[0095] in, is the zero depth;

[0096] 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:

[0097] ;

[0098] ;

[0099] in, is the half-power beamwidth, is the observation angle on the left side of the radiation pattern when the signal intensity in the main lobe drops to half of the peak value. is the observation angle on the right side of the radiation pattern when the signal intensity in the main lobe drops to half of the peak value.

[0100] The specific logic for generating the anti-interference coefficient is as follows: analyze the maximum sidelobe level and null depth to generate the anti-interference coefficient; the specific formula for generating the anti-interference coefficient is:

[0101] ;

[0102] where, 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 radiation power of the sidelobe in the radiation pattern. The higher the maximum sidelobe level, the easier it is for the antenna to receive interference signals, thus reducing the anti-interference ability. The null depth represents the minimum radiation power in the null region of the radiation pattern. The larger the value, the weaker the suppression ability of the antenna in the interference direction. Both the maximum sidelobe level and the null depth have a direct impact on interference. By simple linear addition, the impacts of the maximum sidelobe level and the null depth on the anti-interference performance are combined. The anti-interference coefficient provides a unified evaluation standard for the anti-interference performance. This formula provides a quantitative index for optimizing the antenna performance and can provide an important basis for optimizing the design parameters of the antenna.

[0103] The specific logic for generating the main-sidelobe difference coefficient is as follows: generate the main-sidelobe difference coefficient based on the main lobe gain, main-sidelobe ratio, and half-power beamwidth; the specific formula for generating the main-sidelobe difference coefficient is:

[0104] ;

[0105] where, is the main-sidelobe difference coefficient, is the main-sidelobe ratio, is the main lobe gain, is the half-power beam width. The main side lobe difference coefficient comprehensively measures the ability to distinguish between the main lobe and side lobe performance of the antenna pattern. The larger the value, the greater the performance difference between the main lobe and side lobe, and the higher the quality of the pattern. The main-to-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 the value, the stronger the concentration of the main lobe, the smaller the side lobe interference, the more accurate the signal direction distribution, and the higher the quality of the pattern. The main lobe gain represents the maximum radiation power in the main lobe direction, reflecting the signal radiation intensity of the antenna in the target direction. The larger the value, the stronger the signal of the antenna in the target direction, the better the coverage ability, and the higher the quality of the pattern. The half-power beam width represents the angular range where the main lobe power drops to half of the peak value, which is used to measure the antenna resolution ability; the smaller the 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 for the pattern performance when the main lobe concentration is insufficient. The main side lobe difference coefficient combines the main-to-side lobe ratio, main lobe gain, and half-power beam width, providing a unified index to measure the performance difference between the main lobe and side lobe, and further evaluating the pattern quality, which can provide an important basis for optimizing the design parameters of the antenna.

[0106] The specific logic for constructing the side lobe optimization function of the array antenna is as follows: construct the side lobe optimization function of the array antenna according to the anti-interference coefficient and the main side lobe difference coefficient; the side lobe optimization function of the array antenna is expressed as:

[0107] ;

[0108] where, is the side lobe optimization function of the array antenna, is the anti-interference coefficient, is the main side lobe difference coefficient. The side lobe 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 situation by measuring the performance difference between the main lobe and side lobe of the pattern. The larger the value, the more ideal the overall design. The anti-interference coefficient evaluates the overall design situation by analyzing the interference suppression ability of the side lobe. The smaller the value, the more ideal the overall design.

[0109] Step 3: Combine the side lobe optimization parameters of the array antenna into a chromosome vector, and randomly generate initial chromosome vectors as individuals in the initial population; use the side lobe optimization function value of the array antenna as the fitness, and adopt the roulette wheel selection method to select individuals in the initial population for adaptive crossover and mutation operations;

[0110] where, the chromosome vector is expressed as ;

[0111] The specific logic for selecting individuals is as follows: Normalize the fitness of each individual in the initial population to obtain the selection probability. The specific formula for calculating the selection probability is:

[0112] ;

[0113] where, is the selection probability of the th individual in the initial population, is the value of the array antenna sidelobe optimization function of the th individual in the initial population, is the population individual index, is the total number of individuals in the initial population;

[0114] Calculate the cumulative selection probability according to the selection probability. The specific formula for calculating the cumulative selection probability is:

[0115] ;

[0116] where, is the cumulative selection probability of the th individual in the initial population;

[0117] Generate a random number , , and find the individual that satisfies . If the number of individuals that satisfy is less than 2, reselect the random number. If the number of individuals that satisfy is greater than or equal to 2, randomly select two individuals for crossover operation.

[0118] The specific logic for performing adaptive crossover is as follows: Obtain the fitness rankings of the two individuals for crossover. The fitness rankings are ranked from smallest to largest. Calculate the number of gene segments to be exchanged based on the fitness rankings of the two individuals for crossover. The specific logic for calculating the number of gene segments to be exchanged is:

[0119] ;

[0120] where, is the number of gene segments to be exchanged, is the fitness ranking of one individual for crossover, is the fitness ranking of the other 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 ; is for Perform a ceiling operation; is the total number of chromosome gene segments;

[0121] Among the two individuals for crossover, randomly select gene segments for exchange operation.

[0122] Step 4: Perform selection, crossover, and mutation operations on the initial population in a loop, and place the chromosomes of the two newly generated individuals 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, select the first individuals to form the initial selection population, and merge the iterative population with the initial selection population to generate a new generation of initial population;

[0123] wherein, is the ceiling operation on and is the floor operation on ;

[0124] Step 5: Repeat Step 3 and Step 4 until the maximum number of iterations is reached; take the individual with the highest fitness in the last generation of the initial population generated after the last iteration operation as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

[0125] 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, and specifically includes:

[0126] A data acquisition module, configured to acquire known array antenna sidelobe design parameters and array antenna sidelobe optimization parameters, and obtain the array antenna sidelobe pattern according to the array antenna sidelobe design parameters and the array antenna sidelobe optimization parameters;

[0127] A function construction module, configured to extract features from the antenna sidelobe pattern to obtain antenna sidelobe pattern feature data, analyze the antenna sidelobe pattern feature data to obtain the anti-interference coefficient and the main-sidelobe difference coefficient of the pattern; construct an array antenna sidelobe optimization function according to the anti-interference coefficient and the main-sidelobe difference coefficient;

[0128] An individual operation module, configured to combine the array antenna sidelobe optimization parameters into a chromosome vector, and randomly generate initial chromosome vectors as individuals in the initial population; use the array antenna sidelobe optimization function value as the fitness, and adopt the roulette wheel selection method to select individuals in the initial population for adaptive crossover and mutation operations;

[0129] The population selection module is used to perform selection, crossover, and mutation operations on the initial population in a loop, and place the chromosomes of the two newly generated individuals obtained each time into the iterative population until the number of individual chromosomes in the iterative population reaches After arranging the initial population according to fitness, select the top individuals to form the initial selected population, and merge the iterative population with the initial selected population to generate a new generation of the initial population;

[0130] 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 the initial population generated after the last iteration operation is used as the optimal solution, and the optimal solution includes the array antenna sidelobe optimization parameters.

[0131] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to approximate the real situation as closely as possible. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0132] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0133] 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. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application should not easily think of changes or substitutions, and all should be covered within 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, randomly generate m initial chromosome vectors as individuals in the initial population; use the array antenna sidelobe optimization function value as the fitness, and use the roulette wheel selection method 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 the 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; The antenna sidelobe pattern characteristic data includes main lobe gain, maximum sidelobe level, main-sidelobe ratio, null depth and half-power beam width; 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: KG=SLL+ND Among them, KG is the anti-interference coefficient, SLL is the maximum sidelobe level, and ND is the null 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: Among them, KC is the main-side difference coefficient, MSLR is the main-side lobe ratio, G main is the main lobe gain and HPBW is the half-power beamwidth.

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: Where f is the operating frequency and c 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: Where F(θ) is the observed sidelobe pattern of the array antenna at the observation angle θ, θ is the observation angle value, and w i is the element weight of the ith array element, k is the array wave number; d i is the polar diameter of the coordinates of the center position of the i-th array element, is the array element phase of the i-th array element block, N is the total number of array elements, i is the array element block number index, i∈[1,N].

4. The method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm according to claim 3, characterized in that: The polar angles of the tail coordinates of the two elements in the last section are θ1 and θ2 respectively, and θ1<0<θ2, then the main lobe area is [θ1, θ2], and the specific formula for calculating the main lobe gain is: G main =max|F(θ)| θ∈[θ1,θ2] Among them, G main is the main lobe gain; The specific formula used to calculate the maximum sidelobe level is: SLL=max|F(θ)| θ∈(θ2,2π+θ1) Where SLL is the maximum sidelobe level; The specific formula for calculating the main-sidelobe ratio is: Among them, MSLR is the main lobe to side lobe ratio; The zero-point depth is originally the minimum value of |F(θ)| within the zero-point region. The zero-point depth is calculated by directly finding |F(θ)| in the entire range. The specific formula for calculating the zero depth is: ND=min|F(θ)| θ∈[0,2π] Among them, ND 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: HPBW=θ c -θ d Where PBW is the half-power beam width, θ c 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, θ d 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.

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: TX=KC-KG Among them, TX is the sidelobe optimization function of the array antenna, KG is the anti-interference coefficient, and KC is the main-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: Among them, p j is the selection probability of the jth individual in the initial population, TX j is the array antenna sidelobe optimization function value of the jth individual in the initial population, j is the individual index in the population, and m 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: Among them, P j Accumulate the selection probability for the jth individual in the initial population; Generate a random number r, r∈[0,1], and find j-1 <r<P j Individuals who satisfy P j-1 <r<P j If the number of individuals is less than 2, reselect the random number. If P is satisfied j-1 <r<P j 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: Among them, MN is the number of gene fragments to be exchanged, Rk a is the fitness ranking of the ath individual to be crossed, Rk b is the fitness ranking of the bth individual to be crossed, m is the total number of individuals in the initial population, N is the total number of array elements, a and b are the indices of individuals in the population, and a≠b; For Perform rounding operation upwards; In the two individuals of the crossover, MN gene fragments are randomly selected for exchange operation.

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; The individual operation module is used to combine the array antenna sidelobe optimization parameters into a chromosome vector, randomly generate m initial chromosome vectors as individuals in the initial population; use the array antenna sidelobe optimization function value as the fitness, and use the roulette wheel selection method 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; A 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; The antenna sidelobe pattern characteristic data includes main lobe gain, maximum sidelobe level, main-sidelobe ratio, null depth and half-power beam width; 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: KG=SLL+ND Among them, KG is the anti-interference coefficient, SLL is the maximum sidelobe level, and ND is the null 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: Among them, KC is the main-side difference coefficient, MSLR is the main-side lobe ratio, G main is the main lobe gain and HPBW is the half-power beamwidth.

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