Planar array beam forming design method based on genetic algorithm

Abstract

The invention relates to a planar array beamforming design method based on a genetic algorithm, and the method comprises the steps: simulating a planar phased-array antenna through employing an HFSS after a beamforming design target of the phased-array antenna is determined, and exporting an initial normalized directional diagram as an optimization object of an optimization algorithm; constructing constraint conditions, sketching an expected antenna directional diagram according to the constraint conditions, establishing an optimization problem, and judging whether the antenna directional diagram reaches a shaping design target or not in an iteration process by utilizing a fitness function; determining the mutation probability and the number of iterations, determining the number of individuals in an initial population, randomly initializing the gene length of the individuals in the population according to the number of the individuals, and carrying out iterative optimization on the antenna initial normalization directional diagram; and inputting an optimized result into an HFSS simulation model, and finally meeting a beamforming design target through judgment and comparison. The method has the advantages that the beamforming design is carried out through the weighting of the phase weighting bit, the sidelobe level of the directional diagram can be reduced, and the design of the flat-topped beam of the directional diagram is realized.

Description

Technical Field

[0001] The invention relates to the field of antennas, and in particular to a planar array beamforming design method based on genetic algorithms. Background Art

[0002] Array antennas are limited by the antenna equivalent aperture and the number of array elements. The antenna pattern beam width, sidelobe size, and zero point position are basically fixed. In special use environments, it is usually necessary to change the beam shape of the array antenna pattern according to actual use requirements, that is, beamforming. Existing array antenna beamforming design algorithms often use genetic algorithms, which usually achieve beamforming by changing the antenna amplitude and phase distribution. For phased array beamforming, using both at the same time will reduce the antenna EIRP value. At the same time, amplitude control will introduce new devices, which increases the production cost. In addition, traditional genetic algorithms usually need to construct an objective function, set different weight values ​​for different elevation angles, and use the difference between the pattern function and the objective function to achieve beamforming. Each time a beam of a different shape is achieved, the objective function needs to be modified again. At the same time, during the algorithm iteration process, the weights of different elevation angles need to be continuously adjusted to achieve the required beamforming results, which increases the workload of algorithm debugging. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a planar array beamforming design method based on genetic algorithm to solve the shortcomings of the prior art.

[0004] The object of the present invention is achieved by the following technical solution: a planar array beamforming design method based on genetic algorithm, the method comprising: S1. After determining the design goal of the phased array antenna beamforming, use HFSS to simulate the planar phased array antenna and derive the initial normalized radiation pattern containing the antenna sidelobe level and 3dB beam width information as the optimization object of the optimization algorithm; S2. Construct constraints, use the constraints to outline the desired antenna pattern, establish an optimization problem, and use the fitness function to determine whether the antenna pattern reaches the shaping design goal during the iteration process; S3, determine the mutation probability and the number of iterations, determine the number of individuals in the initial population, and use this to randomly initialize the gene lengths of the individuals in the population, and iteratively optimize the initial normalized antenna pattern; S4. Input the optimized results into the established HFSS simulation model of the planar phased array antenna, and finally meet the beamforming design goals through judgment and comparison.

[0005] The steps of S3 include: Determine the number of individuals in the initial population, and use this to randomly initialize the gene length of the individuals in the population to be an m×n binary code sequence, where m is the number of antenna units and n is the number of quantized bits of the optimization variable; The individuals in the initial population are the phase excitation matrix of the planar phased array antenna, and the phase excitation matrix is ​​converted into a one-dimensional column vector; The phase matrix excitation matrix is ​​used as an iterative optimization variable, and the initial normalized radiation pattern of the antenna is iteratively optimized using an optimization algorithm.

[0006] The individuals in the initial population are the planar phased array antenna phase excitation matrix, and converting the phase excitation matrix into a one-dimensional column vector includes: The planar phased array antenna phase excitation matrix phase that generates the binary coding sequence is ,in, For the i Line j Column phase value, N x Represents the number of individuals, N y Represents the number of array antenna elements; The planar phased array antenna phase excitation matrix is ​​converted into a one-dimensional column vector to generate the initial population: .

[0007] The iterative optimization process of the optimization algorithm includes: Calculate the fitness function value of each individual in the population according to the directional graph, perform crossover and mutation operations on the individuals, generate a new population, and complete an iteration; The individuals with different fitness function values ​​are sorted, the fitness function value of the new population is repeatedly calculated and iterated until the preset number of iterations is reached or the fitness function value is 0, then the iteration is stopped and the current optimal individual, i.e., the phase excitation matrix, is output.

[0008] The steps of S4 include: The optimized phase excitation matrix value is input into the established HFSS simulation model of the planar phased array antenna to obtain the shaped antenna pattern; Compare with the initial beamforming design target to determine whether the beamforming design target set before the optimization algorithm iteration starts is met; If it is satisfied, the optimization is completed. If it is not satisfied, the mutation probability, the initial population size and the number of iterations are adjusted, and the phase excitation matrix is ​​iteratively optimized again through the optimization algorithm until the beamforming design goal is met.

[0009] The constraints include: SLL1=pattern(theta<-FT_BW|theta>FT_BW); FT_phi=pattern(theta>=-FT_BW&theta<=FT_BW); fit1=SLL1-SLL; FIT1=-K-FT_phi; Among them, FT_phi and SLL1 are the inner and outer level values ​​of the flat-top beam width respectively, theta is the antenna elevation angle, pattern is the antenna pattern level value, FT_BW is the shaping target, that is, the flat-top beam width, SLL is the sidelobe level value, K is the flat-top beam widening factor, and fit1 and FIT1 are used to establish the fitness function.

[0010] The fitness function is established according to the constraint conditions, and the fitness function value is calculated by the formula FIT=sum(fit1(fit1>0))+sum(FIT1(FIT1>0)). The fitness function value FIT is used to determine whether the antenna pattern meets the requirements during the shaping iteration process. The smaller the FIT value, the closer the shaped pattern is to the beamforming design target.

[0011] The invention has the following advantages: a planar array beamforming design method based on genetic algorithm, which designs beamforming only by weighting the phase weighted bit, can reduce the sidelobe level of the pattern, realize the flat-top beam design of the pattern, avoids the influence of the EIRP value of the phased array antenna when the phase amplitude is weighted at the same time, does not need a specific target pattern function, and only needs to input the target beam characteristics in actual use, reduces the calculation amount to a certain extent, reduces the calculation time, and can flexibly control the beam width and sidelobe level. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided below in conjunction with the drawings is not intended to limit the scope of protection of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application. The present invention is further described below in conjunction with the drawings.

[0014] like Figure 1 As shown, the present invention specifically relates to a planar array beamforming design method based on genetic algorithm, which specifically includes the following contents: Step 1: Determine the phased array antenna beamforming design objectives based on actual usage, such as flat-top beam width and sidelobe height.

[0015] Step 2: Use HFSS to simulate the planar phased array antenna and derive the initial normalized radiation pattern containing the antenna sidelobe level and 3dB beam width information as the optimization object of the algorithm.

[0016] Step 3: Construct constraints to replace the objective function in the traditional solution, use the constraints to outline the desired antenna pattern, and establish an optimization problem. Use the fitness function to determine whether the antenna pattern meets the shaping design goal during the iteration process.

[0017] Furthermore, the optimization problem constraints are as follows: SLL1=pattern(theta<-FT_BW|theta>FT_BW); FT_phi=pattern(theta>=-FT_BW&theta<=FT_BW); fit1=SLL1-SLL; FIT1=-K-FT_phi; Among them, FT_phi and SLL1 are the inner and outer level values ​​of the flat-top beam width respectively; theta is the antenna elevation angle; pattern is the antenna pattern level value; FT_BW is the shaping target, that is, the flat-top beam width; SLL is the sidelobe level value; K is the flat-top beam widening factor, which is a constant of 1; fit1 and FIT1 are used to establish the fitness function.

[0018] The fitness function is established according to the constraints and is used to calculate the fitness function value. The calculation expression is as follows: FIT=sum(fit1(fit1>0))+sum(FIT1(FIT1>0)); The fitness function value FIT is used to determine whether the antenna pattern meets the requirements during the shaping iteration process. The smaller the FIT value, the closer the beamforming pattern is to the beamforming design target.

[0019] Step 4: Determine the appropriate mutation probability, for example: 0~1. The larger the mutation probability, the lower the probability of falling into the local optimal solution and the slower the convergence speed.

[0020] Step 5: Determine the number of iterations. An appropriate number of iterations can help reduce the calculation time while achieving the desired effect, for example: 200~1000.

[0021] Step 6: Determine the number of individuals in the initial population, and use this to randomly initialize the gene length of the individuals in the population to be an m×n binary code sequence, where m is the number of antenna units and n is the number of bits of the optimization variable quantization. Specifically, according to the number of phased array antenna units, set a reasonable number of individuals in the population, for example: 10~100; The individuals in the initial population are the phase excitation matrix of the planar phased array antenna. The phase excitation matrix is ​​converted into a one-dimensional column vector. The conversion steps are as follows: First, generate the planar phased array antenna phase excitation matrix phase of the binary coding sequence, which is: , in For the i Line j Column phase value, N x Represents the number of individuals, N y Represents the number of array antenna elements.

[0022] The planar phased array antenna phase excitation matrix is ​​converted into a one-dimensional column vector to generate the initial population: .

[0023] Step 7: Use the phase excitation matrix phase as an iterative optimization variable and use the optimization algorithm to iteratively optimize the initial normalized pattern of the antenna. The iterative optimization process of the algorithm is as follows: First, the fitness function value of each individual in the population is calculated according to the directional graph, and the individuals are selected for crossover and mutation operations to generate a new population and complete an iteration; The individuals with different fitness function values ​​are sorted, the fitness function value of the new population is repeatedly calculated and iterated until the preset number of iterations is reached or the fitness function value is 0, then the iteration is stopped and the current optimal individual, i.e., the phase excitation matrix, is output.

[0024] Step 8. Input the optimized phase excitation matrix value into the established planar phased array antenna HFSS simulation model to obtain the antenna radiation pattern after shaping, and compare it with the initial beamforming design target to determine whether it meets the beamforming design target set before the algorithm iteration starts. If it does, the optimization is completed. If it does not, the mutation probability, initial population number and number of iterations can be appropriately adjusted, and the phase excitation matrix can be re-iteratively optimized using the optimization algorithm until the beamforming design target is met.

[0025] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used for various other combinations, modifications and improvements, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art do not deviate from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A planar array beamforming design method based on genetic algorithm, characterized by: The method comprises: S1. After determining the design goal of the phased array antenna beamforming, use HFSS to simulate the planar phased array antenna and derive the initial normalized radiation pattern containing the antenna sidelobe level and 3dB beam width information as the optimization object of the optimization algorithm; S2. Construct constraints, use the constraints to outline the desired antenna pattern, establish an optimization problem, and use the fitness function to determine whether the antenna pattern reaches the shaping design goal during the iteration process; S3, determine the mutation probability and the number of iterations, determine the number of individuals in the initial population, and use this to randomly initialize the gene lengths of the individuals in the population, and iteratively optimize the initial normalized antenna pattern; S4. Input the optimized results into the established HFSS simulation model of the planar phased array antenna, and finally meet the beamforming design goals through judgment and comparison.

2. The planar array beamforming design method based on genetic algorithm according to claim 1, characterized in that: The steps of S3 include: Determine the number of individuals in the initial population, and use this to randomly initialize the gene length of the individuals in the population to be an m×n binary code sequence, where m is the number of antenna units and n is the number of quantized bits of the optimization variable; The individuals in the initial population are the phase excitation matrix of the planar phased array antenna, and the phase excitation matrix is ​​converted into a one-dimensional column vector; The phase matrix excitation matrix is ​​used as an iterative optimization variable, and the initial normalized radiation pattern of the antenna is iteratively optimized using an optimization algorithm.

3. The planar array beamforming design method based on genetic algorithm according to claim 2, characterized in that: The individuals in the initial population are the planar phased array antenna phase excitation matrix, and converting the phase excitation matrix into a one-dimensional column vector includes: The planar phased array antenna phase excitation matrix phase that generates the binary coding sequence is ,in, For the i Line j Column phase value, N x Represents the number of individuals, N y Represents the number of array antenna elements; The planar phased array antenna phase excitation matrix is ​​converted into a one-dimensional column vector to generate the initial population: .

4. The planar array beamforming design method based on genetic algorithm according to claim 2, characterized in that: The iterative optimization process of the optimization algorithm includes: Calculate the fitness function value of each individual in the population according to the directional graph, perform crossover and mutation operations on the individuals, generate a new population, and complete an iteration; The individuals with different fitness function values ​​are sorted, the fitness function value of the new population is repeatedly calculated and iterated until the preset number of iterations is reached or the fitness function value is 0, then the iteration is stopped and the current optimal individual, i.e., the phase excitation matrix, is output.

5. The planar array beamforming design method based on genetic algorithm according to claim 1, characterized in that: The steps of S4 include: The optimized phase excitation matrix value is input into the established HFSS simulation model of the planar phased array antenna to obtain the shaped antenna pattern; Compare with the initial beamforming design target to determine whether the beamforming design target set before the optimization algorithm iteration starts is met; If it is satisfied, the optimization is completed. If it is not satisfied, the mutation probability, the initial population size and the number of iterations are adjusted, and the phase excitation matrix is ​​iteratively optimized again through the optimization algorithm until the beamforming design goal is met.

6. The planar array beamforming design method based on genetic algorithm according to claim 1, characterized in that: The constraints include: SLL1=pattern(theta<-FT_BW|theta>FT_BW); FT_phi=pattern(theta>=-FT_BW&theta<=FT_BW); fit1=SLL1-SLL; FIT1=-K-FT_phi; Among them, FT_phi and SLL1 are the inner and outer level values ​​of the flat-top beam width respectively, theta is the antenna elevation angle, pattern is the antenna pattern level value, FT_BW is the shaping target, that is, the flat-top beam width, SLL is the sidelobe level value, K is the flat-top beam widening factor, and fit1 and FIT1 are used to establish the fitness function.

7. The planar array beamforming design method based on genetic algorithm according to claim 6, characterized in that: The fitness function is established according to the constraint conditions, and the fitness function value is calculated by the formula FIT=sum(fit1(fit1>0))+sum(FIT1(FIT1>0)). The fitness function value FIT is used to determine whether the antenna pattern meets the requirements during the shaping iteration process. The smaller the FIT value, the closer the shaped pattern is to the beamforming design target.

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