Terahertz phased array sidelobe suppression method and device based on improved genetic algorithm

Abstract

The present invention provides a method and device for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm. The method includes: initializing a first population; repeatedly performing the following steps: dividing the first population into multiple first sub-populations, and respectively constructing fitness functions of the multiple first sub-populations based on multiple optimization objectives for suppressing sidelobes of the terahertz phased array; performing selection, fusion, crossover, and mutation on the individuals of the multiple sub-populations to obtain a third population; dividing the third population into multiple second sub-populations, and constructing fitness functions of the multiple second sub-populations; respectively performing selection operations on the individuals of the multiple second sub-populations; inserting the individuals in the multiple second sub-populations after the selection operations into the first population; when the number of iterations reaches the maximum number of iterations, outputting the sub-array spacing combination corresponding to the target individual from the multiple individuals that simultaneously satisfy the multiple optimization objectives for suppressing sidelobes of the terahertz phased array. The present invention can obtain a global optimal solution and improve the optimization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of terahertz wireless communication, and in particular, to a method and device for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm. Background Art

[0002] At present, with the increase in wireless devices in the microwave and millimeter-wave frequency bands, the limited spectrum resources have become increasingly crowded, and the bandwidth allocated to each wireless device is relatively narrow, thus restricting the transmission rate of wireless communication. However, the terahertz frequency band has rich spectrum resources to be developed and utilized, providing support for high-speed wireless communication.

[0003] In a terahertz communication scenario, dynamically scanning a terahertz beam can enable multiple high-transmission-rate wireless communication devices to be connected to a communication network simultaneously. A feasible technical solution is that a terahertz phased array antenna unit is connected to a chip, and by adjusting the feeding phase provided by the chip to each antenna unit in real time, the direction of the terahertz beam can be controlled in real time. However, affected by the processing technology, the edge spacing of the terahertz phased array antenna subarray connected to the chip is usually greater than half a wavelength, resulting in a high sidelobe level of the terahertz beam, and the main lobe energy is not concentrated, reducing the gain and efficiency of the phased array antenna. In addition, due to the low power of the terahertz signal source and the large transmission loss and path loss experienced by terahertz waves when propagating in the air, suppressing the sidelobes of the terahertz beam in the terahertz communication field and making the energy concentrated on the main lobe become particularly important for improving the transmission range, anti-interference ability, and transmission rate of wireless communication.

[0004] Suppressing the sidelobe level of a large-scale terahertz phased array distributed subarray is complex and belongs to a non-linear optimization problem, which has the characteristics of a huge amount of calculation and complex constraint conditions, making it difficult for conventional analytical methods to obtain the global optimal solution for such problems. The genetic algorithm is applicable to solving such non-linear problems and has been widely used in the design of array antennas. However, the standard genetic algorithm requires artificial and repeated adjustment of the weight coefficients of each optimization objective when solving multi-objective optimization, resulting in low optimization efficiency, and if the weight coefficients are not properly allocated, it is easy to fall into a local optimal solution. Summary of the Invention

[0005] The present invention provides a method and device for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm, aiming to solve the defect that the standard genetic algorithm in the prior art requires artificial and repeated adjustment of the weight coefficients of each optimization objective when solving multi-objective optimization, resulting in low optimization efficiency, and if the weight coefficients are not properly allocated, it is easy to fall into a local optimal solution. The present invention can obtain the global optimal solution and improve the optimization efficiency after determining the optimized spacing arrangement of the subarray and without involving the optimization of the array unit structure, so as to mainly concentrate the radiation energy on the main lobe and improve the transmission distance, anti-interference ability, and transmission rate of the terahertz wireless communication system.

[0006] The present invention provides a method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm, including:

[0007] Initializing a first population;

[0008] Repeatedly execute the following steps until the number of iterations reaches the maximum number of iterations:

[0009] Dividing the first population into multiple first sub-populations, and respectively constructing fitness functions for the multiple first sub-populations based on multiple terahertz phased array sidelobe suppression optimization objectives;

[0010] Based on the fitness functions of the multiple first sub-populations, respectively perform selection operations on the individuals of the multiple first sub-populations;

[0011] Fusing the multiple first sub-populations after the selection operation, and randomly arranging the individuals to obtain a second population;

[0012] Performing crossover operation and mutation operation on the second population to obtain a third population;

[0013] Dividing the third population into multiple second sub-populations, and respectively constructing fitness functions for the multiple second sub-populations based on the multiple terahertz phased array sidelobe suppression optimization objectives;

[0014] Based on the fitness functions of the multiple second sub-populations, respectively perform selection operations on the individuals of the multiple second sub-populations;

[0015] Inserting the individuals in the multiple second sub-populations after the selection operation into the first population;

[0016] When the number of iterations reaches the maximum number of iterations, determine target individuals whose fitness meets preset conditions from multiple individuals that simultaneously meet the multiple terahertz phased array sidelobe suppression optimization objectives, and output the sub-array spacing combinations corresponding to the target individuals.

[0017] According to the method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm provided by the present invention, the multiple terahertz phased array sidelobe suppression optimization objectives include: synchronously suppressing the sidelobe levels of the radiation patterns in the E-plane and H-plane at different scanning angles during two-dimensional dynamic scanning of the phased array beam.

[0018] According to the method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm provided by the present invention, the initialization of the first population includes:

[0019] Set initialization parameters, where the initialization parameters include: the sub-array spacing set of the phased array, the center operating frequency of the phased array antenna, the maximum scanning angle of the main lobe of the terahertz beam, the number of individuals, the number of bits for binary encoding of a single variable, the maximum number of iterations, the crossover probability, the mutation probability, and the generation gap parameter between the parent and offspring generations; where the sub-array spacing in the sub-array spacing set is the distance between the edges of two adjacent sub-arrays.

[0020] According to a terahertz phased array sidelobe suppression method based on an improved genetic algorithm provided by the present invention, the step of dividing the first population into multiple first sub-populations and respectively constructing the fitness functions of the multiple first sub-populations based on multiple terahertz phased array sidelobe suppression optimization objectives includes:

[0021] Evenly divide the first population into four first sub-populations;

[0022] Determine the difference between the peak main lobe level and the peak sidelobe level of the E-plane radiation pattern when the main beam of the phased array is perpendicular to the array plane as the fitness function of the first of the multiple first sub-populations;

[0023] Determine the difference between the peak main lobe level and the peak sidelobe level of the H-plane radiation pattern when the main beam of the phased array is perpendicular to the array plane as the fitness function of the second of the multiple first sub-populations;

[0024] Determine the difference between the peak main lobe level and the peak sidelobe level of the E-plane radiation pattern when the main beam of the phased array points to the maximum scanning angle of the main lobe of the terahertz beam as the fitness function of the third of the multiple first sub-populations;

[0025] Determine the difference between the peak main lobe level and the peak sidelobe level of the H-plane radiation pattern when the main beam of the phased array points to the maximum scanning angle of the main lobe of the terahertz beam as the fitness function of the fourth of the multiple first sub-populations.

[0026] According to a terahertz phased array sidelobe suppression method based on an improved genetic algorithm provided by the present invention, the step of respectively performing selection operations on the individuals of the multiple first sub-populations based on the fitness functions of the multiple first sub-populations includes:

[0027] According to the generation gap parameter between the parent and offspring generations, respectively perform selection operations on the individuals in the multiple first sub-populations based on the fitness functions of the multiple first sub-populations; where the fitness of the individuals in the multiple first sub-populations is proportional to the probability of being selected.

[0028] According to a terahertz phased array sidelobe suppression method based on an improved genetic algorithm provided by the present invention, the step of performing crossover operations and mutation operations on the second population to obtain a third population includes:

[0029] Based on the crossover probability, different individuals in the second population are randomly paired, and partial fragments of the gene sequences are swapped by single-point crossover;

[0030] Based on the mutation probability, partial genes of the individuals in the second population after the crossover operation are mutated into allelic genes to obtain a third population.

[0031] According to a method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm provided by the present invention, the inserting the individuals in the multiple second sub-populations after the selection operation into the first population includes:

[0032] Randomly arranging the individuals in the multiple second sub-populations after the selection operation and inserting them into the first population;

[0033] Eliminating the individuals in the first population with fitness lower than a first preset value.

[0034] According to a method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm provided by the present invention, when the number of iterations reaches the maximum number of iterations, determining a target individual with fitness meeting preset conditions from multiple individuals that simultaneously satisfy multiple sidelobe suppression optimization objectives of the terahertz phased array includes:

[0035] When the number of iterations reaches the maximum number of iterations, determining a target individual with each fitness greater than a second preset value and the largest total fitness from multiple individuals that simultaneously satisfy multiple sidelobe suppression optimization objectives of the terahertz phased array.

[0036] The present invention also provides a device for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm, including:

[0037] An initialization module for initializing a first population;

[0038] An iteration module for repeatedly executing the following until the number of iterations reaches the maximum number of iterations:

[0039] Dividing the first population into multiple first sub-populations, and respectively constructing fitness functions of the multiple first sub-populations based on multiple sidelobe suppression optimization objectives of the terahertz phased array;

[0040] Based on the fitness functions of the multiple first sub-populations, respectively performing a selection operation on the individuals in the multiple first sub-populations;

[0041] Fusing the multiple first sub-populations after the selection operation, and randomly arranging the individuals to obtain a second population;

[0042] Performing a crossover operation and a mutation operation on the second population to obtain a third population;

[0043] Divide the third population into multiple second sub - populations, and construct the fitness functions of the multiple second sub - populations respectively based on the multiple terahertz phased array sidelobe suppression optimization objectives;

[0044] Based on the fitness functions of the multiple second sub - populations, perform selection operations on the individuals of the multiple second sub - populations respectively;

[0045] Insert the individuals in the multiple second sub - populations after the selection operation into the first population;

[0046] An output module, configured to, when the number of iterations reaches the maximum number of iterations, determine target individuals whose fitness meets a preset condition from multiple individuals that simultaneously meet the multiple terahertz phased array sidelobe suppression optimization objectives, and output the sub - array spacing combinations corresponding to the target individuals.

[0047] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the terahertz phased array sidelobe suppression method based on an improved genetic algorithm as described in any one of the above are implemented.

[0048] The present invention also provides a non - transitory computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the terahertz phased array sidelobe suppression method based on an improved genetic algorithm as described in any one of the above are implemented.

[0049] The method and device for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm provided by the present invention improve the multi-objective optimization strategy of the genetic algorithm. First, the first population is divided into multiple first sub-populations, and fitness functions of multiple first sub-populations are respectively constructed based on multiple terahertz phased array sidelobe suppression optimization objectives; based on the fitness functions of multiple first sub-populations, selection operations are respectively performed on the individuals of multiple first sub-populations; subsequently, the multiple first sub-populations after the selection operation are fused, and the individuals are randomly arranged to obtain a second population; the second population is subjected to crossover operation and mutation operation to obtain a third population; the third population is divided into multiple second sub-populations, and fitness functions of multiple second sub-populations are respectively constructed based on multiple terahertz phased array sidelobe suppression optimization objectives; based on the fitness functions of multiple second sub-populations, selection operations are respectively performed on the individuals of multiple second sub-populations; the individuals in the multiple second sub-populations after the selection operation are inserted into the first population; since there is no need to repeatedly adjust the weight coefficients of each optimization objective, the optimization efficiency can be improved, and there is no problem of improper configuration of weight coefficients, and the problem of falling into local optimal solutions can be avoided. After multiple iterations, the global optimal solution that meets multiple terahertz phased array sidelobe suppression optimization objectives can be obtained in the feasible solution space. Therefore, after determining the optimized spacing arrangement of the sub-arrays and without involving the optimization of the array unit structure, the present invention can obtain the global optimal solution and improve the optimization efficiency, so as to mainly concentrate the radiation energy on the main lobe, and improve the transmission distance, anti-interference ability and transmission rate of the terahertz wireless communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 is a schematic flowchart of the method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm provided by an embodiment of the present invention;

[0052] Figure 2 is a schematic diagram of a terahertz phased array antenna model provided by an embodiment of the present invention;

[0053] Figure 3a is the E-plane radiation pattern provided by an embodiment of the present invention when the beam pointing is ;

[0054] Figure 3b is the H-plane radiation pattern provided by an embodiment of the present invention when the beam pointing is ;

[0055] Figure 3c is the E-plane radiation pattern when the beam pointing is ;

[0056] Figure 3d is the H-plane radiation pattern when the beam pointing is ;

[0057] Figure 3e is the E-plane radiation pattern when the beam pointing is ;

[0058] Figure 3f is the H-plane radiation pattern when the beam pointing is ;

[0059] Figure 4a is the E-plane radiation pattern when the beam pointing is ;

[0060] Figure 4b is the H-plane radiation pattern when the beam pointing is ;

[0061] Figure 4c is the E-plane radiation pattern when the beam pointing is ;

[0062] Figure 4d is the H-plane radiation pattern when the beam pointing is ( θ = 30°);

[0063] Figure 4e is the E-plane radiation pattern when the beam pointing is ( θ = 60°);

[0064] Figure 4f is the H-plane radiation pattern when the beam pointing is ( θ = 60°);

[0065] Figure 5 is the structural schematic diagram of the terahertz phased array sidelobe suppression device based on the improved genetic algorithm provided by the embodiments of the present invention;

[0066] Figure 6 is the structural schematic diagram of the electronic device provided by the embodiments of the present invention; Detailed implementation manners

[0067] To make the objectives, technical solutions and advantages of the present invention more clear, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts belong to the scope of protection of the present invention.

[0068] The following will describe Figure 1 the method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm of the present invention with reference to FIGS.

[0069] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm provided by an embodiment of the present invention. As Figure 1 shown, the method may include the following steps:

[0070] Step 101, initialize the first population;

[0071] Step 102, determine whether the number of iterations has reached the maximum number of iterations. If so, go to step 110. If not, go to step 103;

[0072] Step 103, divide the first population into multiple first sub-populations, and take the synchronous suppression of the sidelobe levels of the E-plane and H-plane radiation patterns at different scanning angles during the two-dimensional dynamic scanning of the phased array beam as multiple optimization objectives for suppressing sidelobes of the terahertz phased array, and construct fitness functions for the multiple first sub-populations respectively based on the multiple optimization objectives for suppressing sidelobes of the terahertz phased array;

[0073] Step 104, perform a selection operation on the individuals of the multiple first sub-populations respectively based on the fitness functions of the multiple first sub-populations;

[0074] Step 105, fuse the multiple first sub-populations after the selection operation, and randomly arrange the individuals to obtain the second population;

[0075] Step 106, perform a crossover operation and a mutation operation on the second population to obtain the third population;

[0076] Step 107, divide the third population into multiple second sub-populations, and construct fitness functions for the multiple second sub-populations respectively based on the multiple optimization objectives for suppressing sidelobes of the terahertz phased array;

[0077] Step 108, perform a selection operation on the individuals of the multiple second sub-populations respectively based on the fitness functions of the multiple second sub-populations;

[0078] Step 109: Insert the individuals in multiple second subpopulations after the selection operation into the first population, and then go to Step 102;

[0079] Step 110: When the number of iterations reaches the maximum number of iterations, determine the target individuals whose fitness meets the preset conditions from multiple individuals that simultaneously satisfy multiple optimization objectives for sidelobe suppression of the terahertz phased array, and output the subarray spacing combinations corresponding to the target individuals.

[0080] In Step 101, the initialization process of the first population may include: setting initialization parameters, and the initialization parameters may include the following parameters:

[0081] 1) The set U of subarray spacings of the phased array, where the subarray spacing is the distance between the edges of two adjacent subarrays. For example: λ0 = 1 mm, U = {0.5λ0, 0.5625λ0, 0.625λ0, 0.6875λ0,..., 3λ0} = {0.5 mm, 0.5625 mm, 0.625 mm, 0.6875 mm,..., 3 mm}, this embodiment is not limited to this.

[0082] 2) The center operating frequency of the phased array antenna. For example: the center operating frequency f of the phased array antenna is 300 GHz, this embodiment is not limited to this.

[0083] 3) The maximum scanning angle of the main lobe of the terahertz beam. For example This embodiment is not limited to this.

[0084] 4) The number of individuals. For example: the number of individuals NIND in the first population is 1000, this embodiment is not limited to this.

[0085] 5) The number of bits of binary encoding for a single variable PRECI = 20, this embodiment is not limited to this.

[0086] 6) The maximum number of iterations MAXGEN = 200, this embodiment is not limited to this.

[0087] 7) The crossover probability P c = 0.8, this embodiment is not limited to this, and only needs to satisfy 0 < P c < 1.

[0088] 8) The mutation probability P m = 0.01, this embodiment is not limited to this, and only needs to satisfy 0 < P m < 1.

[0089] 9) The generation gap parameter GGAP between the parent and offspring generations = 0.9, this embodiment is not limited to this, and only needs to satisfy 0 < GGAP < 1.

[0090] Regarding the terahertz phased array antenna, the overall terahertz phased array antenna includes M×M sub-arrays (M is a natural number greater than or equal to 2, for example, M = 25). Each sub-array includes 4×4 antenna elements. Each antenna element is square with a side length of half a wavelength λ0 / 2 (for example, λ0 = 1 mm, and half a wavelength λ0 / 2 = 0.5 mm). The antenna elements that make up the sub-array are closely adjacent without gaps. Each sub-array is fed by a single chip for each antenna element, and each pin of the chip is connected to an antenna element. The M×M sub-arrays form (M–1)×(M–1) intervals, and the size of these sub-array intervals (i.e., the sub-array spacing) is used as an optimization variable to achieve the suppression of the sidelobe level of the terahertz phased array antenna.

[0091] To reduce the computational complexity, as Figure 2 shown, the overall layout of the terahertz phased antenna array satisfies the four-fold rotational symmetry property, so the number of optimization variables can be reduced to (M–1) / 2. The optimization variables are G1, G2, G3, …, G k (k = (M–1) / 2), and considering the processing accuracy and feasibility, the specific values of the optimization variables are selected from the set U of sub-array intervals rather than arbitrary values.

[0092] Each individual chromosome in the first population consists of (M–1) / 2 optimization variables, and each gene of each chromosome is composed of a binary code with a length of PRECI×(M–1) / 2 bits.

[0093] Regarding terahertz waves, terahertz waves generally refer to electromagnetic waves in the frequency range of 0.1–10 THz, which are located between the microwave and infrared bands in the electromagnetic spectrum. It has been experimentally verified that the transmission rate of terahertz wireless communication with a carrier frequency of 300 GHz can reach 102.4 Gbps. In contrast, the transmission rate of millimeter-wave wireless communication with an experimentally verified carrier frequency of 60 GHz is only 3.5 Gbps.

[0094] In step 103, based on each terahertz phased array sidelobe suppression optimization objective, the fitness function of each first sub-population is constructed.

[0095] Optionally, multiple terahertz phased array sidelobe suppression optimization objectives include: dividing the first population into multiple first sub-populations, and synchronously suppressing the sidelobe levels of the radiation patterns in the E-plane and H-plane at different scanning angles during the two-dimensional dynamic scanning of the phased array beam.

[0096] Taking the synchronous suppression of the sidelobe levels of the radiation patterns in the E-plane and H-plane at different scanning angles during the two-dimensional dynamic scanning of the phased array beam as multiple terahertz phased array sidelobe suppression optimization objectives, and using an improved genetic algorithm for multiple iterations, the synchronous suppression of the sidelobe levels of the radiation patterns in the E-plane and H-plane at different scanning angles during the two-dimensional dynamic scanning of the phased array beam can be achieved.

[0097] Specifically, the radiation pattern E of the phased array corresponding to each individual in the first sub-population can be calculated through Expression (1). total :

[0098] E total = E single × AF(1)

[0099] where E single represents the E-plane or H-plane radiation pattern of a single antenna element, and AF represents the array factor. In this embodiment, the antenna element can be a microstrip antenna with a center operating frequency of 300 GHz, but this embodiment is not limited thereto.

[0100] The array factor can be calculated through Expression (2):

[0101]

[0102] where A mn represents the feeding amplitude a mn and feeding phase provided by the chip to the mn-th antenna element; k0 represents the free-space wavenumber; (x mn , y mn ) represents the coordinates of the mn-th antenna element, θ represents the elevation angle in the spherical coordinate system, represents the azimuth angle in the spherical coordinate system.

[0103] It should be noted that each sub-array is fed by a single chip for the antenna elements therein. To improve the gain of the phased array, the feeding amplitude provided by the chip for each antenna element is a mn = 1.

[0104] The feeding phase required for the mn-th antenna element can be calculated through Expression (3):

[0105]

[0106] where represents the maximum scanning angle of the main lobe of the terahertz beam.

[0107] Exemplarily, the chip provides 6-bit feeding phase regulation, that is, the phase space of 0° - 360° is evenly divided into 2 6 equal parts, and the phases that can be provided are n × 5.625° (n = 0, 1, 2,..., 63). Each antenna element is set to select the value closest to its required feeding phase as its actual received feeding phase.

[0108] Preferably, step 103 may include the following steps:

[0109] Step 1031: Divide the first population evenly into four first sub-populations, namely sub-population 11, sub-population 12, sub-population 13, and sub-population 14;

[0110] Step 1032: Determine the difference between the main lobe level peak and the side lobe level peak of the E-plane radiation pattern when the phased array main beam is perpendicular to the array surface as the fitness function of the first first sub-population (i.e., sub-population 11);

[0111] Step 1033: Determine the difference between the main lobe level peak and the side lobe level peak of the H-plane radiation pattern when the phased array main beam is perpendicular to the array surface as the fitness function of the second first sub-population (i.e., sub-population 12);

[0112] Step 1034: Determine the difference between the main lobe level peak and the side lobe level peak of the E-plane radiation pattern when the phased array main beam points to the maximum scanning angle of the main lobe of the terahertz beam (e.g., θ d = 60°) as the fitness function of the third first sub-population (i.e., sub-population 13);

[0113] Step 1035: Determine the difference between the main lobe level peak and the side lobe level peak of the H-plane radiation pattern when the phased array main beam points to the maximum scanning angle of the main lobe of the terahertz beam (e.g., θ d = 60°) as the fitness function of the fourth first sub-population (i.e., sub-population 14).

[0114] In this embodiment, the first population (i.e., the original population) is evenly split into four sub-populations, and fitness functions are constructed for the four sub-populations respectively based on four optimization objectives.

[0115] In step 104, selection operations are performed on the individuals of the multiple first sub-populations respectively based on the fitness functions of the multiple first sub-populations.

[0116] In one embodiment, according to the generation gap parameter between the parent generation and the offspring generation, selection operations are performed on the individuals in the multiple first sub-populations respectively based on the fitness functions of the multiple first sub-populations; wherein, the fitness of the individuals in the multiple first sub-populations is proportional to the probability of being selected. Optionally, the roulette wheel method is used to perform selection operations on the individuals in the multiple first sub-populations respectively. This embodiment is not limited thereto, and other methods can also be used for individual selection operations.

[0117] Exemplarily, according to the generation gap parameter GGAP (e.g., GGAP = 0.9), selection operations are respectively performed on the individuals in sub-populations 11, 12, 13, and 14 using the roulette wheel method based on different fitness functions. The larger the fitness value of an individual, the greater the probability of being selected and retained. After the selection operation, the number of individuals in each sub-population is reduced from NIND / 4 to NIND×GGAP / 4 (e.g., NIND = 1000, GGAP = 0.9, NIND×GGAP / 4 = 225).

[0118] In this embodiment, since each sub-population performs the selection operation for its respective single optimization objective, it avoids the problem of repeatedly adjusting the weight coefficients of each optimization objective or falling into a local optimal solution due to improper configuration of the weight coefficients, thereby improving the optimization efficiency and searching for the global optimal solution in the feasible solution space.

[0119] In step 105, for example, the sub-populations 11, 12, 13, and 14 after the selection operation are merged into a new population, that is, the second population, with an individual size of 225×4 = 900.

[0120] The individuals within the second population are randomly arranged, and the individual size of the second population is smaller than that of the first population. The random arrangement of the individuals within the second population ensures that when the population is evenly divided again, some individuals in each sub-population are from the original sub-populations 11, 12, 13, and 14, so that excellent individuals can be evolved after multiple iterations to simultaneously meet all optimization objectives.

[0121] In step 106, the second population is subjected to crossover operation and mutation operation to obtain a new population, that is, the third population.

[0122] In one embodiment, step 106 may include the following sub-steps:

[0123] Step 1061: Based on the crossover probability, different individuals in the second population are randomly paired, and partial fragments of the gene sequences are swapped by single-point crossover;

[0124] Step 1062: Based on the mutation probability, some genes of the individuals in the second population after the crossover operation are mutated into allelic genes to obtain the third population.

[0125] In step 1061, based on the crossover probability P c (e.g., P c = 0.8), different individuals in the newly merged second population are randomly paired, and partial fragments of the gene sequences are swapped by single-point crossover to form new individuals, that is, the recombination of (M–1) / 2 sub-array spacing optimization variables that make up the individuals is realized, and M can be 25.

[0126] In step 1062, based on the mutation probability P m (e.g., P m = 0.01), individuals in the newly fused second population perform mutation operations, and some genes in the individual genes mutate into their alleles, thereby forming new individuals, that is, changing the values of the variables.

[0127] In this embodiment, crossover operations and mutation operations on the second population can be implemented.

[0128] In step 107, the third population is divided into multiple second subpopulations (e.g., subpopulation 21, subpopulation 22, subpopulation 23, and subpopulation 24), and fitness functions for multiple second subpopulations are respectively constructed based on multiple terahertz phased array sidelobe suppression optimization objectives.

[0129] Preferably, step 107 may include the following steps:

[0130] Step 1071: The third population is evenly divided into four second subpopulations, namely subpopulation 21, subpopulation 22, subpopulation 23, and subpopulation 24; the individual scale of each subpopulation can be 225, and the radiation pattern of the phased array corresponding to each individual is calculated;

[0131] Step 1072: The difference between the peak value of the main lobe level and the peak value of the sidelobe level of the E-plane radiation pattern when the main beam of the phased array is perpendicular to the array plane is determined as the fitness function of the first second subpopulation (i.e., subpopulation 21);

[0132] Step 1073: The difference between the peak value of the main lobe level and the peak value of the sidelobe level of the H-plane radiation pattern when the main beam of the phased array is perpendicular to the array plane is determined as the fitness function of the second second subpopulation (i.e., subpopulation 22);

[0133] Step 1074: The difference between the peak value of the main lobe level and the peak value of the sidelobe level of the E-plane radiation pattern when the main beam of the phased array points to the maximum scan angle of the main lobe of the terahertz beam (e.g., θ d = 60°) is determined as the fitness function of the third second subpopulation (i.e., subpopulation 23);

[0134] Step 1035: The difference between the peak value of the main lobe level and the peak value of the sidelobe level of the H-plane radiation pattern when the main beam of the phased array points to the maximum scan angle of the main lobe of the terahertz beam (e.g., θ d = 60°) is determined as the fitness function of the fourth second subpopulation (i.e., subpopulation 24).

[0135] In this embodiment, the third population (i.e., the new population after crossover and mutation operations) is evenly split into four subpopulations, and fitness functions are constructed for the four subpopulations respectively based on four optimization objectives.

[0136] In step 108, according to the generation gap parameter GGAP (for example, GGAP = 0.9), a roulette wheel method is used to perform a selection operation on the individuals in sub-populations 21, 22, 23, and 24 based on their respective different fitness functions. The greater the fitness of an individual, the greater the probability of being selected and retained. After the selection operation, the individual scale in each sub-population can be reduced to 202.

[0137] In step 109, the individuals in the multiple second sub-populations after the selection operation are inserted into the first population, thereby updating the first population.

[0138] In one embodiment, step 109 may include: randomly arranging the individuals in the multiple second sub-populations after the selection operation and inserting them into the first population; eliminating the individuals in the first population with fitness lower than the first preset value.

[0139] It should be noted that a fitness lower than the first preset value means a low fitness value, and the first preset value can be specifically set according to actual applications.

[0140] Specifically, sub-populations 21, 22, 23, and 24 after the selection operation are randomly arranged and inserted into the first population, and in order to keep the scale of the first population unchanged, the individuals with low internal fitness values are eliminated.

[0141] After step 109 is executed, it jumps to step 102, and each time it iterates, the iteration count is incremented by 1. If the current iteration count is less than the maximum iteration count (for example, MAXGEN = 200), then steps 103 - 109 are continued.

[0142] In step 110, when the iteration count reaches the maximum iteration count, first, multiple individuals that simultaneously meet multiple terahertz phased array sidelobe suppression optimization goals are screened out, then the target individuals whose fitness meets the preset conditions are screened out from them, and finally, the sub-array spacing combination corresponding to the target individuals is output.

[0143] In one embodiment, step 110 may include: when the iteration count reaches the maximum iteration count, determining the target individuals whose fitness for each item is greater than the second preset value and whose total fitness is the largest from the multiple individuals that simultaneously meet multiple terahertz phased array sidelobe suppression optimization goals.

[0144] Specifically, the second preset value can be 10 dB. When the number of iterations reaches the maximum number of iterations (for example, MAXGEN = 200), from multiple individuals that simultaneously meet multiple terahertz phased array sidelobe suppression optimization objectives, determine the target individual whose fitness values are all greater than 10 dB and whose total fitness value is the largest. Finally, output the subarray spacing combination corresponding to the target individual. For example: {G1 = 0.5λ0, G2 = 0.5625λ0, G3 = 1.0625λ0, G4 = 2.6875λ0, G5 = 0.5λ0, G6 = 0.75λ0, G7 = 2.125λ0, G8 = 0.5625λ0, G9 = 1.125λ0, G 10 = 2.75λ0, G 11 = 1.5λ0, G 12 = 1.5625λ0}, this embodiment is not limited thereto.

[0145] As Figures 3a - 3f shown, after applying the terahertz phased array sidelobe suppression method based on the improved genetic algorithm of this embodiment, when the phased array realizes two-dimensional beam scanning the difference between the peak main lobe level and the peak sidelobe level is always 11 dB within the range of elevation angle θ = 0°–60°.

[0146] As Figures 4a - 4f shown, after applying the terahertz phased array sidelobe suppression method based on the improved genetic algorithm of this embodiment, when the phased array realizes two-dimensional beam scanning the difference between the peak main lobe level and the peak sidelobe level is always 22.5 dB within the range of elevation angle θ = 0°–60°.

[0147] Obviously, after applying the terahertz phased array sidelobe suppression method based on the improved genetic algorithm of this embodiment, the sidelobe levels of the E-plane and H-plane radiation patterns at different scanning angles during two-dimensional dynamic scanning of the phased array beam are effectively suppressed.

[0148] The method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm provided in this embodiment improves the multi-objective optimization strategy of the genetic algorithm. First, the first population is divided into multiple first sub-populations, and fitness functions of the multiple first sub-populations are respectively constructed based on multiple optimization objectives for suppressing sidelobes of the terahertz phased array; based on the fitness functions of the multiple first sub-populations, selection operations are respectively performed on the individuals of the multiple first sub-populations; subsequently, the multiple first sub-populations after the selection operations are fused, and the individuals are randomly arranged to obtain a second population; crossover operations and mutation operations are performed on the second population to obtain a third population; the third population is divided into multiple second sub-populations, and fitness functions of the multiple second sub-populations are respectively constructed based on multiple optimization objectives for suppressing sidelobes of the terahertz phased array; based on the fitness functions of the multiple second sub-populations, selection operations are respectively performed on the individuals of the multiple second sub-populations; the individuals in the multiple second sub-populations after the selection operations are inserted into the first population; since it is not necessary to repeatedly adjust the weight coefficients of each optimization objective, the optimization efficiency can be improved, and there is no problem of improper configuration of the weight coefficients, and the problem of falling into a local optimal solution can be avoided. After multiple iterations, a global optimal solution that satisfies multiple optimization objectives for suppressing sidelobes of the terahertz phased array can be obtained in the feasible solution space. Therefore, after determining the optimized spacing arrangement of the sub-arrays and without involving the optimization of the array unit structure, the present invention can obtain a global optimal solution and improve the optimization efficiency, so as to mainly concentrate the radiation energy on the main lobe, and improve the transmission distance, anti-interference ability and transmission rate of the terahertz wireless communication system.

[0149] The terahertz phased array sidelobe suppression device based on the improved genetic algorithm provided by the present invention will be described below. The terahertz phased array sidelobe suppression device described below can be correspondingly referred to the terahertz phased array sidelobe suppression method described above.

[0150] Please refer to Figure 5 , Figure 5 FIG. is a schematic structural diagram of a terahertz phased array sidelobe suppression device based on an improved genetic algorithm provided by an embodiment of the present invention. As Figure 5 shown, the device may include:

[0151] An initialization module 10, configured to initialize the first population;

[0152] An iteration module 20, configured to loop and execute the following until the number of iterations reaches the maximum number of iterations:

[0153] Divide the first population into multiple first sub-populations, and respectively construct fitness functions of the multiple first sub-populations based on multiple optimization objectives for suppressing sidelobes of the terahertz phased array;

[0154] Perform a selection operation on the individuals of the multiple first subpopulations respectively based on the fitness functions of the multiple first subpopulations;

[0155] Fuse the multiple first subpopulations after the selection operation, and randomly arrange the individuals to obtain a second population;

[0156] Perform a crossover operation and a mutation operation on the second population to obtain a third population;

[0157] Divide the third population into multiple second subpopulations, and respectively construct the fitness functions of the multiple second subpopulations based on the multiple terahertz phased array sidelobe suppression optimization objectives;

[0158] Perform a selection operation on the individuals of the multiple second subpopulations respectively based on the fitness functions of the multiple second subpopulations;

[0159] Insert the individuals in the multiple second subpopulations after the selection operation into the first population;

[0160] An output module 30, configured to, when the number of iterations reaches the maximum number of iterations, determine a target individual whose fitness meets a preset condition from multiple individuals that simultaneously meet the multiple terahertz phased array sidelobe suppression optimization objectives, and output the subarray spacing combination corresponding to the target individual.

[0161] Optionally, the multiple terahertz phased array sidelobe suppression optimization objectives include: synchronous suppression of the sidelobe levels of the E-plane and H-plane radiation patterns at different scanning angles during two-dimensional dynamic scanning of the phased array beam.

[0162] Optionally, the initialization module 10 is specifically configured to: set initialization parameters, where the initialization parameters include: a set of subarray spacings of the phased array, the center operating frequency of the phased array antenna, the maximum scanning angle of the main lobe of the terahertz beam, the number of individuals, the number of bits of binary coding for a single variable, the maximum number of iterations, the crossover probability, the mutation probability, and the generation gap parameter between the parent and offspring; where the subarray spacing in the set of subarray spacings is the distance between the edges of two adjacent subarrays.

[0163] Optionally, the iteration module 20 is specifically configured to:

[0164] Evenly divide the first population into four first subpopulations;

[0165] Determine the difference between the peak value of the main lobe level and the peak value of the sidelobe level of the E-plane radiation pattern when the main beam of the phased array is perpendicular to the array plane as the fitness function of the first of the first subpopulations;

[0166] The difference between the peak value of the main lobe level and the peak value of the side lobe level of the H-plane radiation pattern when the main beam of the phased array is perpendicular to the array plane is determined as the fitness function of the second-mentioned first sub-population;

[0167] The difference between the peak value of the main lobe level and the peak value of the side lobe level of the E-plane radiation pattern when the main beam of the phased array points to the maximum scanning angle of the main lobe of the terahertz beam is determined as the fitness function of the third-mentioned first sub-population;

[0168] The difference between the peak value of the main lobe level and the peak value of the side lobe level of the H-plane radiation pattern when the main beam of the phased array points to the maximum scanning angle of the main lobe of the terahertz beam is determined as the fitness function of the fourth-mentioned first sub-population.

[0169] Optionally, the iteration module 20 is specifically configured to:

[0170] According to the generation gap parameter between the parent generation and the offspring generation, based on the fitness functions of the multiple first sub-populations, selection operations are respectively performed on the individuals in the multiple first sub-populations; wherein, the fitness of the individuals in the multiple first sub-populations is proportional to the probability of being selected.

[0171] Optionally, the iteration module 20 is specifically configured to:

[0172] Based on the crossover probability, different individuals in the second population are randomly paired, and partial fragments of the gene sequences are cross-exchanged at a single point;

[0173] Based on the mutation probability, some genes of the individuals in the second population after the crossover operation are mutated into alleles to obtain a third population.

[0174] Optionally, the iteration module 20 is specifically configured to:

[0175] Randomly arrange the individuals in the multiple second sub-populations after the selection operation and insert them into the first population;

[0176] Eliminate the individuals in the first population whose fitness is lower than the first preset value.

[0177] Optionally, the output module 30 is specifically configured to:

[0178] When the number of iterations reaches the maximum number of iterations, from the multiple individuals that simultaneously meet the multiple terahertz phased array side lobe suppression optimization objectives, determine the target individual whose fitness values are all greater than the second preset value and whose total fitness is the largest.

[0179] Figure 6 Illustrates a schematic diagram of the physical structure of an electronic device, such as Figure 6As shown in the figure, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communication interface 820, and the memory 830 complete mutual communication through the communication bus 840. The processor 810 may call the logical instructions in the memory 830 to execute the terahertz phased array sidelobe suppression method based on the improved genetic algorithm. The method includes:

[0180] Initialize the first population;

[0181] Loop through the following steps until the number of iterations reaches the maximum number of iterations:

[0182] Divide the first population into multiple first sub-populations, and construct the fitness functions of the multiple first sub-populations respectively based on multiple terahertz phased array sidelobe suppression optimization objectives;

[0183] Based on the fitness functions of the multiple first sub-populations, perform selection operations on the individuals of the multiple first sub-populations respectively;

[0184] Fuse the multiple first sub-populations after the selection operation, and arrange the individuals randomly to obtain the second population;

[0185] Perform crossover operation and mutation operation on the second population to obtain the third population;

[0186] Divide the third population into multiple second sub-populations, and construct the fitness functions of the multiple second sub-populations respectively based on the multiple terahertz phased array sidelobe suppression optimization objectives;

[0187] Based on the fitness functions of the multiple second sub-populations, perform selection operations on the individuals of the multiple second sub-populations respectively;

[0188] Insert the individuals in the multiple second sub-populations after the selection operation into the first population;

[0189] When the number of iterations reaches the maximum number of iterations, determine the target individuals whose fitness meets the preset conditions from the multiple individuals that simultaneously meet the multiple terahertz phased array sidelobe suppression optimization objectives, and output the sub-array spacing combination corresponding to the target individuals.

[0190] In addition, when the logical instructions in the above-mentioned memory 830 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0191] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the terahertz phased array sidelobe suppression method based on the improved genetic algorithm provided by the above-mentioned various methods. This method includes:

[0192] Initialize the first population;

[0193] Loop and execute the following steps until the number of iterations reaches the maximum number of iterations:

[0194] Divide the first population into multiple first sub-populations, and respectively construct the fitness functions of the multiple first sub-populations based on multiple terahertz phased array sidelobe suppression optimization objectives;

[0195] Based on the fitness functions of the multiple first sub-populations, respectively perform selection operations on the individuals of the multiple first sub-populations;

[0196] Fuse the multiple first sub-populations after the selection operation, and randomly arrange the individuals to obtain a second population;

[0197] Perform crossover operations and mutation operations on the second population to obtain a third population;

[0198] Divide the third population into multiple second sub-populations, and respectively construct the fitness functions of the multiple second sub-populations based on the multiple terahertz phased array sidelobe suppression optimization objectives;

[0199] Based on the fitness functions of the multiple second sub-populations, respectively perform selection operations on the individuals of the multiple second sub-populations;

[0200] Insert the individuals in the selected and operated multiple second sub-populations into the first population;

[0201] When the number of iterations reaches the maximum number of iterations, determine a target individual whose fitness meets the preset conditions from multiple individuals that simultaneously meet the multiple terahertz phased array sidelobe suppression optimization objectives, and output the sub-array spacing combination corresponding to the target individual.

[0202] On the other hand, the present invention also provides a non-transitory computer-readable storage medium, on which

[0203] is stored a computer program, which when executed by a processor is configured to execute the terahertz phased array sidelobe suppression method based on the improved genetic algorithm provided above, and the method includes:

[0204] Initialize the first population;

[0205] Loop and execute the following steps until the number of iterations reaches the maximum number of iterations:

[0206] Divide the first population into multiple first sub-populations, and respectively construct the fitness functions of the multiple first sub-populations based on multiple terahertz phased array sidelobe suppression optimization objectives; Based on the fitness functions of the multiple first sub-populations, respectively perform selection operations on the individuals in the multiple first sub-populations;

[0207] Fuse the multiple first sub-populations after the selection operation, and randomly arrange the individuals to obtain a second population;

[0208] Perform crossover operation and mutation operation on the second population to obtain a third population; Divide the third population into multiple second sub-populations, and respectively construct the fitness functions of the multiple second sub-populations based on the multiple terahertz

[0209] phased array sidelobe suppression optimization objectives;

[0210] Based on the fitness functions of the multiple second sub-populations, for the multiple second sub-populations

[0211] respectively perform selection operations on the individuals;

[0212] Insert the individuals in the selected and operated multiple second sub-populations into the first

[0213] population;

[0214] When the number of iterations reaches the maximum number of iterations, determine a target individual whose fitness meets the preset conditions from multiple individuals that simultaneously meet the multiple terahertz phased array sidelobe suppression optimization objectives, and output the sub-array spacing combination corresponding to the target individual.

[0215] The device embodiments described above are merely illustrative, where the units described as separation parts

[0216] may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0217] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course also by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0218] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm, characterized in that Including: Initializing the first population; Repeatedly execute the following steps until the number of iterations reaches the maximum number of iterations: Evenly divide the first population into four first sub-populations; When the main beam of the phased array is perpendicular to the array surface E The difference between the peak value of the main lobe level and the peak value of the side lobe level of the surface radiation pattern is determined as the fitness function of the first-mentioned first sub-population; When the main beam of the phased array is perpendicular to the array surface H The difference between the peak value of the main lobe level and the peak value of the side lobe level of the surface radiation pattern is determined as the fitness function of the second said first sub-population; When the main beam of the phased array points to the maximum scanning angle of the main lobe of the terahertz beam, E the difference between the peak value of the main lobe level and the peak value of the side lobe level of the surface radiation pattern is determined as the fitness function of the third said first sub-population; When the main beam of the phased array is directed to the maximum scanning angle of the main lobe of the terahertz beam, H the difference between the peak value of the main lobe level and the peak value of the side lobe level of the surface radiation pattern is determined as the fitness function of the fourth said first sub-population; Based on the fitness functions of the four first sub-populations, perform selection operations on the individuals of the four first sub-populations respectively; Fuse the four first sub-populations after the selection operation, and randomly arrange the individuals to obtain the second population; Perform crossover operation and mutation operation on the second population to obtain the third population; Divide the third population into multiple second sub-populations, and construct the fitness functions of the multiple second sub-populations respectively based on the multiple terahertz phased array sidelobe suppression optimization objectives; Based on the fitness functions of the multiple second sub-populations, perform selection operations on the individuals of the multiple second sub-populations respectively; Insert the individuals in the multiple second sub-populations after the selection operation into the first population; When the number of iterations reaches the maximum number of iterations, determine the target individuals whose fitness meets the preset conditions from the multiple individuals that simultaneously meet the multiple terahertz phased array sidelobe suppression optimization objectives, and output the sub-array spacing combination corresponding to the target individuals.

2. The method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm according to claim 1, wherein The multiple optimization objectives for terahertz phased array sidelobe suppression include: synchronous suppression of the sidelobe levels of the radiation patterns in different scanning angles during two-dimensional dynamic scanning of the phased array beam E plane and H plane 3. The method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm according to claim 2, wherein The initialization of the first population includes: Setting initialization parameters, where the initialization parameters include: the set of sub-array spacings of the phased array, the center operating frequency of the phased array antenna, the maximum scanning angle of the main lobe of the terahertz beam, the number of individuals, the number of binary encoding bits for a single variable, the maximum number of iterations, the crossover probability, the mutation probability, and the generation gap parameter between the parent and offspring; where the sub-array spacing in the set of sub-array spacings is the distance between the edges of two adjacent sub-arrays.

4. The method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm according to claim 3, wherein The performing selection operations on the individuals of the multiple first sub-populations respectively based on the fitness functions of the multiple first sub-populations includes: According to the generation gap parameter between the parent and offspring, perform selection operations on the individuals in the multiple first sub-populations based on the fitness functions of the multiple first sub-populations; where the fitness of the individuals in the multiple first sub-populations is proportional to the probability of being selected.

5. The method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm according to any one of claims 1 to 3, characterized in that, The performing crossover operation and mutation operation on the second population to obtain the third population includes: Based on the crossover probability, randomly pair different individuals in the second population, and perform single-point crossover to exchange partial fragments of the gene sequences; Based on the mutation probability, mutate partial genes of the individuals in the second population after the crossover operation into alleles to obtain the third population.

6. The method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm according to any one of claims 1 to 3, characterized in that, The inserting the individuals in the multiple second sub-populations after the selection operation into the first population includes: Randomly arrange the individuals in the multiple second sub-populations after the selection operation, and insert them into the first population; Eliminate the individuals in the first population whose fitness is lower than the first preset value.

7. The method for suppressing sidelobes of a terahertz phased array based on an improved genetic algorithm according to any one of claims 1 to 3, characterized in that, The determining the target individuals whose fitness meets the preset conditions from the multiple individuals that simultaneously meet the multiple terahertz phased array sidelobe suppression optimization objectives when the number of iterations reaches the maximum number of iterations includes: When the number of iterations reaches the maximum number of iterations, a target individual with fitness values all greater than a second preset value and the largest total fitness is determined from multiple individuals that simultaneously satisfy the multiple terahertz phased array sidelobe suppression optimization objectives.

8. A terahertz phased array sidelobe suppression device based on an improved genetic algorithm, characterized in that, Including: An initialization module for initializing a first population; An iteration module for repeatedly performing the following until the number of iterations reaches the maximum number of iterations: Dividing the first population evenly into four first sub-populations; When the main beam of the phased array is perpendicular to the array plane E The difference between the peak value of the main lobe level and the peak value of the side lobe level of the surface radiation pattern is determined as the fitness function of the first sub-population When the main beam of the phased array is perpendicular to the array surface, H the difference between the peak value of the main lobe level and the peak value of the side lobe level of the surface radiation pattern is determined as the fitness function of the second said first sub-population; When the main beam of the phased array points to the maximum scanning angle of the main lobe of the terahertz beam, E the difference between the peak value of the main lobe level and the peak value of the side lobe level of the surface radiation pattern is determined as the fitness function of the third said first sub-population; When the main beam of the phased array points to the maximum scanning angle of the main lobe of the terahertz beam, H the difference between the peak value of the main lobe level and the peak value of the side lobe level of the surface radiation pattern is determined as the fitness function of the fourth said first sub-population; Performing selection operations on the individuals of the four first sub-populations respectively based on the fitness functions of the four first sub-populations; Fusing the four first sub-populations after the selection operations, and randomly arranging the individuals to obtain a second population; Performing crossover operations and mutation operations on the second population to obtain a third population; Dividing the third population into multiple second sub-populations, and respectively constructing fitness functions for the multiple second sub-populations based on the multiple terahertz phased array sidelobe suppression optimization objectives; Performing selection operations on the individuals of the multiple second sub-populations respectively based on the fitness functions of the multiple second sub-populations; Inserting the individuals in the multiple second sub-populations after the selection operations into the first population; An output module for, when the number of iterations reaches the maximum number of iterations, determining a target individual whose fitness meets the preset conditions from multiple individuals that simultaneously satisfy the multiple terahertz phased array sidelobe suppression optimization objectives, and outputting the sub-array spacing combination corresponding to the target individual.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the program, it implements the steps of the terahertz phased array sidelobe suppression method based on an improved genetic algorithm according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the terahertz phased array sidelobe suppression method based on an improved genetic algorithm according to any one of claims 1 to 7.

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