WO2024078157A1 - Vivaldi天线中透镜的优化方法、系统及相关设备 - Google Patents

Vivaldi天线中透镜的优化方法、系统及相关设备 Download PDF

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WO2024078157A1
WO2024078157A1 PCT/CN2023/115063 CN2023115063W WO2024078157A1 WO 2024078157 A1 WO2024078157 A1 WO 2024078157A1 CN 2023115063 W CN2023115063 W CN 2023115063W WO 2024078157 A1 WO2024078157 A1 WO 2024078157A1
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lens
individual
genetic
initial population
preset
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French (fr)
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喻伟晟
郭嘉帅
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深圳飞骧科技股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Definitions

  • the present invention belongs to the field of antennas, and in particular relates to a method and system for optimizing a lens in a Vivaldi antenna and related equipment.
  • Vivaldi antenna also known as tapered slot antenna (TSA) is an ideal antenna for broadband applications.
  • TSA tapered slot antenna
  • Vivaldi antenna has the advantages of wide bandwidth, moderate gain, slow change with operating frequency, and stable phase center.
  • Vivaldi antenna is a directional antenna, its gain is still too low for many applications. Therefore, it is of great significance to study the high gain technology of Vivaldi antenna. However, it is difficult to achieve increased gain in a wide bandwidth.
  • the main method to improve the gain of Vivaldi antennas is to load metamaterials at the front end of the antenna.
  • the antenna gain can usually only be improved within a narrow band, so it cannot solve the problem of increasing the gain within a wide band.
  • Dielectric lenses usually have a wider operating frequency band, so dielectric-loaded Vivaldi antennas have become a research hotspot.
  • Existing research shows that lenses can improve the gain of Vivaldi antennas, but the increase in gain is not ideal, and the gain can only be increased within certain frequency bands.
  • the use of traditional scanning parameter optimization to design lenses in research still has problems such as time-consuming and labor-intensive, and single lens function.
  • the embodiments of the present invention provide a method, system and related equipment for optimizing a lens in a Vivaldi antenna, aiming to solve the problem of unsatisfactory gain effect of a dielectric lens in a traditional Vivaldi antenna gain design.
  • an embodiment of the present invention provides a method for optimizing a lens in a Vivaldi antenna, the method comprising the following steps:
  • genetic calculation is performed on the individuals in the initial population according to the individual fitness to obtain genetic individuals;
  • the initial population is updated and iterated with the genetic individuals, and then optimized using the genetic algorithm;
  • the optimal individual is output according to the genetic individual, and the structural parameters corresponding to the optimal individual are output as the final parameters of the lens.
  • the lens includes a conical lens, a truncated cone lens and a hemispherical lens
  • the structural parameters include: the bottom diameter of the conical lens, the top diameter of the truncated cone lens, the bottom diameter of the hemispherical lens, the height of the conical lens, the height of the truncated cone lens, the slit width of the lens, and the slit spacing of the lens.
  • the number of the structural parameters is defined as n
  • the size of the initial population is defined as N
  • the individual fitness is F(x)
  • min freq and max freq represent the preset optimized minimum frequency and the preset optimized maximum frequency, respectively
  • Gi(x) and Ri(x) represent the target gain and the actual gain, respectively
  • HPBWE i (x) and HPBWHi(x) represent the 3dB beamwidth of the E plane and the H plane of the individual at the frequency point i, respectively
  • K is a real number greater than 1
  • ⁇ 1 and ⁇ 2 are preset weights.
  • the preset individual selection rule is specifically:
  • All the individuals in the initial population are sorted according to the size of the individual fitness, and the individuals that meet the preset ratio are selected for crossover mutation.
  • the preset termination condition is specifically:
  • the individual with the highest individual fitness is maintained in the initial population for at least 15 update iterations.
  • the size N of the initial population is 10.
  • the preset optimized minimum frequency min freq is 2 GHz
  • the preset optimized maximum frequency max freq is 24 GHz.
  • an embodiment of the present invention further provides a system for optimizing a lens in a Vivaldi antenna, comprising:
  • An initialization module is used to construct an initial population for a genetic algorithm using the structural parameters of the lens as optimization parameters;
  • a simulation module used for performing lens modeling in a simulation environment according to individuals in the initial population to obtain simulation modeling data
  • a fitness calculation module used for calculating the individual fitness of the individual according to the simulation modeling data
  • a genetic module used for performing genetic calculation on the individuals in the initial population according to the individual fitness according to a preset individual selection rule to obtain genetic individuals;
  • Iterative output module used to determine whether the genetic algorithm reaches the preset termination condition:
  • the initial population is updated and iterated with the genetic individuals, and then optimized using the genetic algorithm;
  • the optimal individual is output according to the genetic individual, and the structural parameters corresponding to the optimal individual are output as the final parameters of the lens.
  • an embodiment of the present invention further provides a computer 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 computer program, the steps in the method for optimizing the lens in the Vivaldi antenna as described in any one of the above embodiments are implemented.
  • an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the method for optimizing the lens in the Vivaldi antenna as described in any one of the above embodiments are implemented.
  • the beneficial effects achieved by the present invention are that a genetic algorithm is used to optimize the parameters of a dielectric lens and pattern optimization is added to an objective function. While achieving high gain and ultra-wideband through parameters, the E-plane and H-plane beam widths of the antenna are optimized to be approximately equal, and ultimately the gain of the antenna after the lens is loaded is significantly improved in the entire operating frequency band, and equal beam widths of the E-plane and H-plane are achieved at multiple frequency points.
  • FIG1 is a flowchart of the steps of a method for optimizing a lens in a Vivaldi antenna provided by an embodiment of the present invention
  • FIG2 is a schematic diagram of the structure of a lens in a Vivaldi antenna provided by an embodiment of the present invention
  • FIG3 is a schematic diagram of a Vivaldi antenna S11 curve provided by an embodiment of the present invention.
  • FIG4 is a schematic diagram of a Vivaldi antenna gain curve provided in an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of the structure of a lens optimization system in a Vivaldi antenna provided by an embodiment of the present invention.
  • FIG. 6 is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention.
  • FIG. 1 is a flowchart of the steps of a method for optimizing a lens in a Vivaldi antenna provided by an embodiment of the present invention.
  • the optimization method includes the following steps:
  • Figure 2 is a schematic diagram of the structure of the lens in the Vivaldi antenna provided in an embodiment of the present invention.
  • the lens in the Vivaldi antenna is mainly composed of three parts, among which the conical lens plays the role of a fixed structure in the Vivaldi antenna, the truncated cone lens is responsible for improving the gain of the low-frequency part, and the hemispherical lens is responsible for improving the gain of the high-frequency part.
  • the structural parameters of the above three lenses are used as optimization parameters of the genetic algorithm in an embodiment of the present invention to construct an initial population.
  • the lens includes a conical lens, a truncated cone lens and a hemispherical lens
  • the structural parameters include: the bottom diameter of the conical lens, the top diameter of the truncated cone lens, the bottom diameter of the hemispherical lens, the height of the conical lens, the height of the truncated cone lens, the slit width of the lens, and the slit spacing of the lens.
  • dD1 represents the bottom diameter of the conical lens
  • dD2 represents the top diameter of the truncated cone lens
  • dD3 represents the bottom diameter of the hemispherical lens
  • dH1 represents the height of the conical lens
  • dD2 represents the height of the truncated cone lens
  • dw represents the slit width of the lens
  • ds represents the slit spacing of the lens.
  • the embodiment of the present invention uses CST as the simulation environment.
  • the number of the structural parameters is defined as n
  • the size of the initial population is defined as N
  • the individual fitness is F(x)
  • min freq and max freq represent the preset optimized minimum frequency and the preset optimized maximum frequency, respectively
  • Gi(x) and Ri(x) represent the target gain and the actual gain, respectively
  • HPBWE i (x) and HPBWHi(x) represent the 3dB beamwidth of the E plane and the H plane of the individual at the frequency point i, respectively
  • K is a real number greater than 1
  • ⁇ 1 and ⁇ 2 are preset weights.
  • the section passing through the maximum radiation direction and parallel to the electric field vector is called the E plane
  • the section passing through the maximum radiation direction and parallel to the magnetic field vector is called the H plane.
  • the lens needs to be optimized within the broadband.
  • the gain constraints of equations (2) and (3) make the gain of the optimal individual close to the target gain, thereby achieving the high gain characteristics of the antenna.
  • equation (4) represents the constraints on the 3dB beam width of the E plane and H plane of the individual radiation pattern. After optimization, the 3dB beam width of the E plane and H plane of the individual can be approximately equal;
  • the output of f 2 (x) is the absolute value of the difference between HPBWE i (x) and HPBWH i (x) divided by K.
  • K is a real number greater than 1. In the embodiment of the present invention, it is used to adjust the size of f 2 (x) so that f 1 (x) and f 2 (x) are at the same order of magnitude. Therefore, it can be set as needed.
  • K is 3 in the embodiment of the present invention.
  • N of the initial population is 10. N also represents the initial The number of individuals in the population. In genetic algorithms, the more individuals there are, the more accurate the results will be, but at the same time, the more time it will take, so the value needs to match the size of the optimization parameter.
  • the preset optimized minimum frequency min freq is 2 GHz
  • the preset optimized maximum frequency max freq is 24 GHz.
  • the preset individual selection rule is specifically:
  • All the individuals in the initial population are sorted according to the size of the individual fitness, and the individuals that meet the preset ratio are selected for crossover mutation.
  • scanning parameter optimization is usually used as a method for selecting the optimal result
  • the embodiment of the present invention controls the optimization direction of the lens through an objective function, so that it evolves in a favorable direction and finally selects the best result, thus saving time and effort.
  • the initial population is updated and iterated with the genetic individuals, and then optimized using the genetic algorithm;
  • the optimal individual is output according to the genetic individual, and the structural parameters corresponding to the optimal individual are output as the final parameters of the lens.
  • the genetic algorithm is an iterative cycle algorithm, which continuously updates the population so that the individuals after cross-inheritance are closer and closer to the expected results.
  • the preset termination condition is specifically:
  • the individual with the highest individual fitness is maintained in the initial population for at least 15 update iterations.
  • the termination condition of the genetic algorithm set in the embodiment of the present invention is related to the individual itself. When the individual undergoes enough cycles, it is considered that the structural parameter corresponding to the individual can best reflect a higher gain effect.
  • the optimization method of the lens in the Vivaldi antenna provided by the embodiment of the present invention is based on the The S11 curve of the Vivaldi antenna with the dielectric lens as the component obtained by optimizing the mirror structure is shown in Figure 3, the gain curve is shown in Figure 4, and the 3dB beam width at different frequencies of the E plane and the H plane is shown in Table 1 below.
  • the S11 of the Vivaldi antenna is greater than -10dB near 3GHz, and the S11 in the other frequency bands is less than -10dB.
  • the antenna meets the requirements of wideband operation.
  • the feed antenna gain is 5.3-10dBi
  • the lens antenna gain is 8-19.1dBi.
  • the antenna gain is increased by 2.7dB (2GHz) at the lowest and 9.3dB (16GHz) at the highest.
  • the difference between the E-plane and H-plane beam widths of the Vivaldi antenna becomes smaller after the lens is loaded.
  • the difference between the E-plane and H-plane beam widths at each frequency point in the table is less than 10°, which proves the practicality of the genetic algorithm used in the embodiment of the present invention to optimize the lens parameters.
  • the beneficial effects achieved by the present invention are that a genetic algorithm is used to optimize the parameters of the dielectric lens and pattern optimization is added to the objective function. While achieving high gain and ultra-wideband through parameters, the E-plane and H-plane beam widths of the antenna are optimized to be approximately equal, so that the gain of the antenna after the lens is loaded is significantly improved in the entire working frequency band, and equal beam widths of the E-plane and H-plane are achieved at multiple frequency points.
  • the embodiment of the present invention further provides a system 200 for optimizing a lens in a Vivaldi antenna.
  • FIG. 5 is a schematic diagram of the structure of the system for optimizing a lens in a Vivaldi antenna provided by the embodiment of the present invention, including:
  • Initialization module 201 used to construct an initial population for genetic algorithm using the structural parameters of the lens as optimization parameters;
  • a simulation module 202 is used to perform lens modeling in a simulation environment according to individuals in the initial population to obtain simulation modeling data;
  • a fitness calculation module 203 used to calculate the individual fitness of the individual according to the simulation modeling data
  • Genetic module 204 used for performing genetic calculation on the individuals in the initial population according to the individual fitness according to a preset individual selection rule to obtain genetic individuals;
  • Iteration output module 205 is used to determine whether the genetic algorithm reaches a preset termination condition:
  • the initial population is updated and iterated with the genetic individuals, and then optimized using the genetic algorithm;
  • the optimal individual is output according to the genetic individual, and the structural parameters corresponding to the optimal individual are output as the final parameters of the lens.
  • the optimization system 200 for the lens in the Vivaldi antenna can implement the steps in the optimization method for the lens in the Vivaldi antenna in the above embodiment, and can achieve the same technical effects. Please refer to the description in the above embodiment, which will not be repeated here.
  • An embodiment of the present invention further provides a computer device.
  • the computer device 300 includes: a memory 302, a processor 301, and a computer program stored in the memory 302 and executable on the processor 301.
  • the processor 301 calls the computer program stored in the memory 302 to execute the steps of the method for optimizing the lens in the Vivaldi antenna provided in the embodiment of the present invention, which specifically includes the following steps in conjunction with FIG. 1 :
  • genetic calculation is performed on the individuals in the initial population according to the individual fitness to obtain genetic individuals;
  • the initial population is updated and iterated with the genetic individuals, and then optimized using the genetic algorithm;
  • the optimal individual is output according to the genetic individual, and the structural parameters corresponding to the optimal individual are output as the final parameters of the lens.
  • the lens includes a conical lens, a truncated cone lens and a hemispherical lens
  • the structural parameters include: the bottom diameter of the conical lens, the top diameter of the truncated cone lens, the bottom diameter of the hemispherical lens, the height of the conical lens, the height of the truncated cone lens, the slit width of the lens, and the slit spacing of the lens.
  • the number of the structural parameters is defined as n
  • the size of the initial population is defined as N
  • the individual fitness is F(x)
  • min freq and max freq represent the preset optimized minimum frequency and the preset optimized maximum frequency, respectively
  • Gi(x) and Ri(x) represent the target gain and the actual gain, respectively
  • HPBWE i (x) and HPBWHi(x) represent the 3dB beamwidth of the E plane and the H plane of the individual at the frequency point i, respectively
  • K is a real number greater than 1
  • ⁇ 1 and ⁇ 2 are preset weights.
  • the preset individual selection rule is specifically:
  • All the individuals in the initial population are sorted according to the size of the individual fitness, and the individuals that meet the preset ratio are selected for crossover mutation.
  • the preset termination condition is specifically:
  • the individual with the highest individual fitness is maintained in the initial population for at least 15 update iterations.
  • the size N of the initial population is 10.
  • the preset optimized minimum frequency min freq is 2 GHz
  • the preset optimized maximum frequency max freq is 24 GHz.
  • the computer device 300 provided in the embodiment of the present invention can implement the steps in the method for optimizing the lens in the Vivaldi antenna in the above embodiment, and can achieve the same technical effect. Please refer to the description in the above embodiment, which will not be repeated here.
  • An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored.
  • a computer program is stored.
  • the various processes and steps in the method for optimizing the lens in the Vivaldi antenna provided in the embodiment of the present invention are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
  • the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).
  • the technical solution of the present invention can be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.
  • a storage medium such as ROM/RAM, a magnetic disk, or an optical disk
  • a terminal which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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Abstract

本发明属于天线领域，尤其涉及一种Vivaldi天线中透镜的优化方法、系统及相关设备，所述方法包括以透镜的结构参数为优化参数，构建用于遗传算法的初始种群；根据初始种群中的个体在仿真环境中进行透镜建模，得到仿真建模数据；根据仿真建模数据计算个体的个体适应度；根据预设个体选择规则对初始种群中的个体进行遗传计算，得到遗传个体；判断遗传算法是否达到预设终止条件：若否，则以遗传个体更新迭代初始种群，再利用遗传算法进行优化；若是，则根据遗传个体输出最优个体，并将最优个体对应的结构参数输出为透镜的最终参数。本发明使用了遗传算法来优化介质透镜的参数，使得加载透镜后天线的增益在整个工作频段都得到了明显提升。

Description

Vivaldi天线中透镜的优化方法、系统及相关设备 技术领域
本发明属于天线领域，尤其涉及一种Vivaldi天线中透镜的优化方法、系统及相关设备。
背景技术
Vivaldi天线，也称锥形槽天线(Tapered Slot Antenna，TSA)，是一种针对宽带应用的理想型天线，Vivaldi天线作为行波天线之一，具有带宽较宽、增益适中且随工作频率变化缓慢、相位中心稳定等优点。Vivaldi天线虽然属于定向天线，但其增益对于很多应用仍然太低，因此研究Vivaldi天线的高增益技术具有重要意义，但是，要实现在宽频带内提高增益是比较困难的。
目前，提高Vivaldi天线增益的方法主要是在天线前端加载超材料，但由于超材料固有的窄带特性，通常只能在窄带内提高天线增益，所以并不能解决宽频带内提高增益的问题。介质透镜通常具有较宽的工作频带，因此介质加载的Vivaldi天线成为了研究热点。现有的研究中体现出透镜能够提高Vivaldi天线的增益，但其增益提高的幅度并不理想，且只能在部分频段内提高增益。另外，研究中使用传统扫参优化设计透镜，依然存在耗时费力、透镜功能单一等问题。
发明内容
本发明实施例提供一种Vivaldi天线中透镜的优化方法、系统及相关设备，旨在解决传统的Vivaldi天线增益设计中对于介质透镜的增益效果不理想的问题。
第一方面，本发明实施例提供一种Vivaldi天线中透镜的优化方法，所述优化方法包括以下步骤：
以透镜的结构参数为优化参数，构建用于遗传算法的初始种群；
根据所述初始种群中的个体在仿真环境中进行透镜建模，得到仿真建模数据；
根据所述仿真建模数据计算所述个体的个体适应度；
根据预设个体选择规则对所述初始种群中的所述个体按照所述个体适应度进行遗传计算，得到遗传个体；
判断所述遗传算法是否达到预设终止条件：
若否，则以所述遗传个体更新迭代所述初始种群，再利用所述遗传算法进行优化；
若是，则根据所述遗传个体输出最优个体，并将所述最优个体对应的所述结构参数输出为所述透镜的最终参数。
更进一步地，所述透镜包括圆锥形透镜、圆台形透镜和半球形透镜，所述结构参数包括：所述圆锥形透镜的底面直径、所述圆台形透镜的顶面直径、所述半球形透镜的底面直径、所述圆锥形透镜的高度、所述圆台形透镜的高度、所述透镜的开缝宽度、所述透镜的开缝间距。
更进一步地，根据所述仿真建模数据计算所述个体的个体适应度的步骤中，定义所述结构参数的数量为n，所述初始种群的大小为N，其中的所述个体适应度为F(x)，所述个体适应度F(x)满足以下关系式(1)：
F(x)＝ω1f1(x)+ω2f2(x),x＝[x1,x2,x3......,xN]    (1)；
关系式(1)中的约束满足以下关系式(2)至(4)：


以上关系式(2)至(4)中，min freq和max freq分别表示预设优化最小频率和预设优化最大频率，Gi(x)和Ri(x)分别表示目标增益和实际增益，HPBWEi(x)和HPBWHi(x)分别代表所述个体在频点i时的E面和H面的3dB波束宽度，K为大于1的实数，ω1和ω2为预设权重。
更进一步地，所述预设个体选择规则具体为：
将所述初始种群中的所有所述个体按照所述个体适应度的大小进行排序，并从中筛选出满足预设比例的所述个体进行交叉变异。
更进一步地，所述预设终止条件具体为：
拥有最高的所述个体适应度的所述个体在所述初始种群中保持了至少15次所述更新迭代。
更进一步地，所述初始种群的大小N取值为10。
更进一步地，所述预设优化最小频率min freq为2GHz，所述预设优化最大频率max freq为24GHz。
第二方面，本发明实施例还提供一种Vivaldi天线中透镜的优化系统，包括：
初始化模块，用于以透镜的结构参数为优化参数，构建用于遗传算法的初始种群；
仿真模块，用于根据所述初始种群中的个体在仿真环境中进行透镜建模，得到仿真建模数据；
适应度计算模块，用于根据所述仿真建模数据计算所述个体的个体适应度；
遗传模块，用于根据预设个体选择规则对所述初始种群中的所述个体按照所述个体适应度进行遗传计算，得到遗传个体；
迭代输出模块，用于判断所述遗传算法是否达到预设终止条件：
若否，则以所述遗传个体更新迭代所述初始种群，再利用所述遗传算法进行优化；
若是，则根据所述遗传个体输出最优个体，并将所述最优个体对应的所述结构参数输出为所述透镜的最终参数。
第三方面，本发明实施例还提供一种计算机设备，包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如上述实施例中任意一项所述的Vivaldi天线中透镜的优化方法中的步骤。
第四方面，本发明实施例还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如上述实施例中任意一项所述的Vivaldi天线中透镜的优化方法中的步骤。
本发明所达到的有益效果，由于使用了遗传算法来优化介质透镜的参数，并在目标函数中加入了方向图优化，在通过参数实现高增益、超宽带的同时，将天线E面和H面波束宽度优化为近似相等，最终使得加载透镜后天线的增益在整个工作频段都得到了明显提升，并且在多个频点实现了E面和H面等波束宽度。
附图说明
图1是本发明实施例提供的Vivaldi天线中透镜的优化方法的步骤流程框图；
图2是本发明实施例提供的Vivaldi天线中透镜的结构示意图；
图3是本发明实施例提供的Vivaldi天线S11曲线示意图；
图4是本发明实施例提供的Vivaldi天线增益曲线示意图；
图5是本发明实施例提供的Vivaldi天线中透镜的优化系统的结构示意图
图6是本发明实施例提供的计算机设备的结构示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实 施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
请参照图1，图1是本发明实施例提供的Vivaldi天线中透镜的优化方法的步骤流程框图，所述优化方法包括以下步骤：
S1、以透镜的结构参数为优化参数，构建用于遗传算法的初始种群。
具体的，请参照图2，图2是本发明实施例提供的Vivaldi天线中透镜的结构示意图，一般的，Vivaldi天线中的透镜主要由三部分组成，其中，圆锥形透镜在Vivaldi天线中起到固定结构的作用，圆台形透镜负责提高低频部分的增益，半球形透镜负责提高高频部分的增益，在本发明实施例中，通过对于上述三种透镜的结构参数，本发明实施例中使用其作为遗传算法的优化参数来构建初始种群。
更进一步地，所述透镜包括圆锥形透镜、圆台形透镜和半球形透镜，所述结构参数包括：所述圆锥形透镜的底面直径、所述圆台形透镜的顶面直径、所述半球形透镜的底面直径、所述圆锥形透镜的高度、所述圆台形透镜的高度、所述透镜的开缝宽度、所述透镜的开缝间距。具体的，对应图2中的标记，dD1表示所述圆锥形透镜的底面直径，dD2表示所述圆台形透镜的顶面直径，dD3表示所述半球形透镜的底面直径，dH1表示所述圆锥形透镜的高度，dD2表示所述圆台形透镜的高度，dw表示所述透镜的开缝宽度，ds表示所述透镜的开缝间距。
S2、根据所述初始种群中的个体在仿真环境中进行透镜建模，得到仿真建模数据。
示例性的，本发明实施例使用CST作为仿真环境。
S3、根据所述仿真建模数据计算所述个体的个体适应度。
更进一步地，根据所述仿真建模数据计算所述个体的个体适应度的步骤中，定义所述结构参数的数量为n，所述初始种群的大小为N，其中的所述个体适应度为F(x)，所述个体适应度F(x)满足以下关系式(1)：
F(x)＝ω1f1(x)+ω2f2(x),x＝[x1,x2,x3......,xN]     (1)；
关系式(1)中的约束满足以下关系式(2)至(4)：


以上关系式(2)至(4)中，min freq和max freq分别表示预设优化最小频率和预设优化最大频率，Gi(x)和Ri(x)分别表示目标增益和实际增益，HPBWEi(x)和HPBWHi(x)分别代表所述个体在频点i时的E面和H面的3dB波束宽度，K为大于1的实数，ω1和ω2为预设权重。
对于线极化天线，通过最大辐射方向并平行于电场矢量的剖面称为E面，通过最大辐射方向并平行于磁场矢量的剖面称为H面，在本发明实施例中，考虑到宽频带特性，需要对透镜在宽频带内进行优化。通过关系式(2)、(3)的增益约束使得最优个体的增益能接近目标增益，从而实现天线高增益的特性，最后由关系式(4)表示对个体方向图E面和H面3dB波束宽度的约束，经过优化可使得个体E面和H面3dB波束宽度近似相等；
对于所述目标增益和所述实际增益，如果实际增益大于期望值则Qi(x)＝0，否则Qi(x)等于目标增益与实际增益的差值；
f2(x)输出的是HPBWEi(x)和HPBWHi(x)差值的绝对值除以K，HPBWEi(x)和HPBWHi(x)的差值越小f2(x)越接近于0，则个体的适应度越高，其中，K为大于1的实数，在本发明实施例中用于调节f2(x)的大小，使f1(x)和f2(x)处于同一个量级，因此可以根据需要来设置，示例性的，本发明实施例中K取值3。
更进一步地，所述初始种群的大小N取值为10。N同时也代表了所述初始 种群中个体的数目，在遗传算法中，个体数目越多则结果的准确性越高，但同时所花费的时间也越多，因此取值需要与优化参数的大小相互匹配。
更进一步地，所述预设优化最小频率min freq为2GHz，所述预设优化最大频率max freq为24GHz。
S4、根据预设个体选择规则对所述初始种群中的所述个体按照所述个体适应度进行遗传计算，得到遗传个体。
更进一步地，所述预设个体选择规则具体为：
将所述初始种群中的所有所述个体按照所述个体适应度的大小进行排序，并从中筛选出满足预设比例的所述个体进行交叉变异。
作为对比，现有技术中通常使用扫参优化作为选择最优结果的方法，而本发明实施例则是通过目标函数控制透镜的优化方向，使其朝着有利方向进化，最终选出最好结果，因此更加省时省力。
S5、判断所述遗传算法是否达到预设终止条件：
若否，则以所述遗传个体更新迭代所述初始种群，再利用所述遗传算法进行优化；
若是，则根据所述遗传个体输出最优个体，并将所述最优个体对应的所述结构参数输出为所述透镜的最终参数。
具体的，遗传算法属于迭代循环的算法，通过不断的更新种群，使得经过交叉遗传后的个体越来越接近与预期的结果。
更进一步地，所述预设终止条件具体为：
拥有最高的所述个体适应度的所述个体在所述初始种群中保持了至少15次所述更新迭代。通过确定在多次迭代中的所述个体拥有最接近于预期值的表现，本发明实施例设定的遗传算法终止条件与所述个体本身相关，当所述个体经历足够多的循环时，则认为该个体对应的结构参数最能体现出更高的增益效果。
示例性的，通过本发明实施例提供的Vivaldi天线中透镜的优化方法在以透 镜结构为优化参数得到的介质透镜为组件的Vivaldi天线S11曲线如图3所示，增益曲线如图4所示，E面和H面不同频点处3dB波束宽度示意如下表1所示。
表1 E面和H面不同频点处3dB波束宽度示意表
可以看出，Vivaldi天线在加载本发明实施例得到的结构参数的透镜后在3GHz附近S11大于-10dB，其余频段S11均小于-10dB，整体来说天线符合宽频带工作的要求；在工作频段内，馈源天线增益为5.3-10dBi，透镜天线增益为8-19.1dBi，加载透镜后天线增益最低提高了2.7dB(2GHz)，最高提高了9.3dB(16GHz)；而对于馈源和透镜天线不同频率E面和H面3dB波束宽度比较，加载透镜后Vivaldi天线的E面和H面波束宽度差异变小，表中各频点E面和H面波束宽度差值均小于10°，证明了本发明实施例使用的遗传算法对透镜参数进行优化的实用性。
本发明所达到的有益效果，由于使用了遗传算法来优化介质透镜的参数，并在目标函数中加入了方向图优化，在通过参数实现高增益、超宽带的同时，将天线E面和H面波束宽度优化为近似相等，最终使得加载透镜后天线的增益在整个工作频段都得到了明显提升，并且在多个频点实现了E面和H面等波束宽度。
本发明实施例还提供一种Vivaldi天线中透镜的优化系统200，请参照图5，图5是本发明实施例提供的Vivaldi天线中透镜的优化系统的结构示意图，包括：
初始化模块201，用于以透镜的结构参数为优化参数，构建用于遗传算法的初始种群；
仿真模块202，用于根据所述初始种群中的个体在仿真环境中进行透镜建模，得到仿真建模数据；
适应度计算模块203，用于根据所述仿真建模数据计算所述个体的个体适应度；
遗传模块204，用于根据预设个体选择规则对所述初始种群中的所述个体按照所述个体适应度进行遗传计算，得到遗传个体；
迭代输出模块205，用于判断所述遗传算法是否达到预设终止条件：
若否，则以所述遗传个体更新迭代所述初始种群，再利用所述遗传算法进行优化；
若是，则根据所述遗传个体输出最优个体，并将所述最优个体对应的所述结构参数输出为所述透镜的最终参数。
所述Vivaldi天线中透镜的优化系统200能够实现如上述实施例中的Vivaldi天线中透镜的优化方法中的步骤，且能实现同样的技术效果，参上述实施例中的描述，此处不再赘述。
本发明实施例还提供一种计算机设备，请参照图6，图6是本发明实施例提供的计算机设备的结构示意图，所述计算机设备300包括：存储器302、处理器301及存储在所述存储器302上并可在所述处理器301上运行的计算机程序。
所述处理器301调用所述存储器302存储的计算机程序，执行本发明实施例提供的Vivaldi天线中透镜的优化方法中的步骤，请结合图1，具体包括：
以透镜的结构参数为优化参数，构建用于遗传算法的初始种群；
根据所述初始种群中的个体在仿真环境中进行透镜建模，得到仿真建模数据；
根据所述仿真建模数据计算所述个体的个体适应度；
根据预设个体选择规则对所述初始种群中的所述个体按照所述个体适应度进行遗传计算，得到遗传个体；
判断所述遗传算法是否达到预设终止条件：
若否，则以所述遗传个体更新迭代所述初始种群，再利用所述遗传算法进行优化；
若是，则根据所述遗传个体输出最优个体，并将所述最优个体对应的所述结构参数输出为所述透镜的最终参数。
更进一步地，所述透镜包括圆锥形透镜、圆台形透镜和半球形透镜，所述结构参数包括：所述圆锥形透镜的底面直径、所述圆台形透镜的顶面直径、所述半球形透镜的底面直径、所述圆锥形透镜的高度、所述圆台形透镜的高度、所述透镜的开缝宽度、所述透镜的开缝间距。
更进一步地，根据所述仿真建模数据计算所述个体的个体适应度的步骤中，定义所述结构参数的数量为n，所述初始种群的大小为N，其中的所述个体适应度为F(x)，所述个体适应度F(x)满足以下关系式(1)：
F(x)＝ω1f1(x)+ω2f2(x),x＝[x1,x2,x3......,xN]    (1)；
关系式(1)中的约束满足以下关系式(2)至(4)：


以上关系式(2)至(4)中，min freq和max freq分别表示预设优化最小频率和预设优化最大频率，Gi(x)和Ri(x)分别表示目标增益和实际增益，HPBWEi(x)和HPBWHi(x)分别代表所述个体在频点i时的E面和H面的3dB波束宽度，K为大于1的实数，ω1和ω2为预设权重。
更进一步地，所述预设个体选择规则具体为：
将所述初始种群中的所有所述个体按照所述个体适应度的大小进行排序，并从中筛选出满足预设比例的所述个体进行交叉变异。
更进一步地，所述预设终止条件具体为：
拥有最高的所述个体适应度的所述个体在所述初始种群中保持了至少15次所述更新迭代。
更进一步地，所述初始种群的大小N取值为10。
更进一步地，所述预设优化最小频率min freq为2GHz，所述预设优化最大频率max freq为24GHz。
本发明实施例提供的计算机设备300能够实现如上述实施例中的Vivaldi天线中透镜的优化方法中的步骤，且能实现同样的技术效果，参上述实施例中的描述，此处不再赘述。
本发明实施例还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，该计算机程序被处理器执行时实现本发明实施例提供的Vivaldi天线中透镜的优化方法中的各个过程及步骤，且能实现相同的技术效果，为避免重复，这里不再赘述。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)或随机存取存储器(Random Access Memory，简称RAM)等。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本发明各个实施例所述的方法。
上面结合附图对本发明的实施例进行了描述，所揭露的仅为本发明较佳实施例而已，但是本发明并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本发明的启示下，在不脱离本发明宗旨和权利要求所保护的范围情况下，还可做出很多形式用等同变化，均属于本发明的保护之内。

Claims (10)

  1. 一种Vivaldi天线中透镜的优化方法，其特征在于，所述优化方法包括以下步骤：
    以透镜的结构参数为优化参数，构建用于遗传算法的初始种群；
    根据所述初始种群中的个体在仿真环境中进行透镜建模，得到仿真建模数据；
    根据所述仿真建模数据计算所述个体的个体适应度；
    根据预设个体选择规则对所述初始种群中的所述个体按照所述个体适应度进行遗传计算，得到遗传个体；
    判断所述遗传算法是否达到预设终止条件：
    若否，则以所述遗传个体更新迭代所述初始种群，再利用所述遗传算法进行优化；
    若是，则根据所述遗传个体输出最优个体，并将所述最优个体对应的所述结构参数输出为所述透镜的最终参数。
  2. 如权利要求1所述的Vivaldi天线中透镜的优化方法，其特征在于，所述透镜包括圆锥形透镜、圆台形透镜和半球形透镜，所述结构参数包括：所述圆锥形透镜的底面直径、所述圆台形透镜的顶面直径、所述半球形透镜的底面直径、所述圆锥形透镜的高度、所述圆台形透镜的高度、所述透镜的开缝宽度、所述透镜的开缝间距。
  3. 如权利要求1所述的Vivaldi天线中透镜的优化方法，其特征在于，根据所述仿真建模数据计算所述个体的个体适应度的步骤中，定义所述结构参数的数量为n，所述初始种群的大小为N，其中的所述个体适应度为F(x)，所述个体适应度F(x)满足以下关系式(1)：
    F(x)＝ω1f1(x)+ω2f2(x),x＝[x1,x2,x3......,xN]    (1)；
    关系式(1)中的约束满足以下关系式(2)至(4)：


    以上关系式(2)至(4)中，min freq和max freq分别表示预设优化最小频率和预设优化最大频率，Gi(x)和Ri(x)分别表示目标增益和实际增益，HPBWEi(x)和HPBWHi(x)分别代表所述个体在频点i时的E面和H面的3dB波束宽度，K为大于1的实数，ω1和ω2为预设权重。
  4. 如权利要求1所述的Vivaldi天线中透镜的优化方法，其特征在于，所述预设个体选择规则具体为：
    将所述初始种群中的所有所述个体按照所述个体适应度的大小进行排序，并从中筛选出满足预设比例的所述个体进行交叉变异。
  5. 如权利要求1所述的Vivaldi天线中透镜的优化方法，其特征在于，所述预设终止条件具体为：
    拥有最高的所述个体适应度的所述个体在所述初始种群中保持了至少15次所述更新迭代。
  6. 如权利要求3所述的Vivaldi天线中透镜的优化方法，其特征在于，所述初始种群的大小N取值为10。
  7. 如权利要求3所述的Vivaldi天线中透镜的优化方法，其特征在于，所述预设优化最小频率min freq为2GHz，所述预设优化最大频率max freq为24GHz。
  8. 一种Vivaldi天线中透镜的优化系统，其特征在于，包括：
    初始化模块，用于以透镜的结构参数为优化参数，构建用于遗传算法的初 始种群；
    仿真模块，用于根据所述初始种群中的个体在仿真环境中进行透镜建模，得到仿真建模数据；
    适应度计算模块，用于根据所述仿真建模数据计算所述个体的个体适应度；
    遗传模块，用于根据预设个体选择规则对所述初始种群中的所述个体按照所述个体适应度进行遗传计算，得到遗传个体；
    迭代输出模块，用于判断所述遗传算法是否达到预设终止条件：
    若否，则以所述遗传个体更新迭代所述初始种群，再利用所述遗传算法进行优化；
    若是，则根据所述遗传个体输出最优个体，并将所述最优个体对应的所述结构参数输出为所述透镜的最终参数。
  9. 一种计算机设备，其特征在于，包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如权利要求1至7中任意一项所述的Vivaldi天线中透镜的优化方法中的步骤。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1至7中任意一项所述的Vivaldi天线中透镜的优化方法中的步骤。
PCT/CN2023/115063 2022-10-13 2023-08-25 Vivaldi天线中透镜的优化方法、系统及相关设备 WO2024078157A1 (zh)

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