An electromagnetic simulation method for unidirectional fine structure antenna

Abstract

The present invention discloses an electromagnetic simulation method for a unidirectional fine structure antenna. An antenna electromagnetic simulation object model is established to determine the calculation space range of the antenna size; a corresponding perfectly matched layer absorption term is added to a low-dispersion one-step frog leap hybrid implicit-explicit time-domain finite difference method to construct a convolutional perfectly matched layer suitable for the low-dispersion hybrid method; the convolutional perfectly matched layer suitable for the low-dispersion hybrid method is used to obtain the far-field data and artificial anisotropy parameters of the antenna, as well as the auxiliary field quantity, absorption term, magnetic field value, and electric field value at the next moment; the excitation source of the electric field is updated and the cyclic time step is updated. The present invention proposes an efficient combination method of a low-dispersion one-step frog leap hybrid implicit-explicit time-domain finite difference method and a convolutional perfectly matched layer, which not only effectively improves the dispersion characteristics and calculation accuracy for far-field simulation of unidirectional fine structure antennas, but also greatly simplifies the implementation process and improves the calculation efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of physics computing technology, and relates to an electromagnetic simulation method for a unidirectional fine structure antenna, specifically a method for realizing a convolutional perfectly matched layer by a low-dispersion hybrid implicit-explicit time-domain finite-difference method based on artificial anisotropic parameters and auxiliary variables in a Cartesian rectangular coordinate system. Background Art

[0002] The grid division method is crucial in simulation calculations, especially when solving open-domain space problems, the number of grid units required is quite large. Therefore, when using electronic computers for simulation calculations, sufficient memory is required to ensure the smooth completion of the entire simulation process. However, due to limited computer memory and high time cost, open-domain electromagnetic problems cannot be effectively handled. In order to solve the above problems, a special boundary must be set at an appropriate location, and the set truncation boundary must ensure that the external electromagnetic waves can be effectively eliminated, otherwise serious reflections will occur at the boundary, reducing the calculation accuracy, and even failing to complete the simulation calculation task. At present, the truncation boundary has developed from traditional methods including Mur absorption boundary to absorption boundary conditions with better absorbing performance. The most representative of them is the perfectly matched layer (PML), including Berenger perfectly matched layer (Berenger PML, BPML), uniaxial PML (UPML) and convolutional perfectly matched layer (CPML). CPML is the absorbing boundary condition with the best absorbing performance, and it still maintains very superior absorbing performance for evanescent waves and low-frequency waves.

[0003] With the rapid development of computer technology and the complexity of electromagnetic simulation problems, numerical calculation methods have replaced traditional analytical methods. Among them, the finite-difference time-domain method (FDTD), which ranks among the three mainstream numerical algorithms, is one of the most commonly used methods and has been widely used in many research fields. However, due to the strict time stability condition restrictions, the traditional FDTD method cannot efficiently and accurately simulate and analyze fine structures. In order to further weaken the above restrictions, scholars have proposed a series of improvement schemes. Among them, the hybrid implicit-explicit (HIE) FDTD has the advantage of weak conditional stability. The one-step frog leap HIE-FDTD (L-HIE-FDTD) method makes the time stability condition more relaxed. Its time step is only related to the spatial step in the two rough structure directions. However, with the improvement of computational efficiency, the one-step frog leap scheme will lead to serious numerical dispersion problems. In order to solve the problems of reduced computational accuracy caused by poor numerical dispersion characteristics, a low-dispersion one-step frog leap HIE-FDTD (A-HIE-FDTD) method based on artificial anisotropic parameters is proposed, which achieves improved computational accuracy while ensuring high computational efficiency. However, facing the dilemma that this method cannot be effectively applied to the far-field radiation problem of microwave devices with unidirectional fine structures, there is an urgent need for an efficient and stable absorption boundary suitable for the A-HIE-FDTD method. Summary of the invention

[0004] In order to overcome the defects of the prior art, the purpose of the present invention is to provide an electromagnetic simulation method for a unidirectional fine structure antenna, and propose a method for realizing a convolutional perfectly matched layer by a low-dispersion hybrid implicit-explicit time-domain finite-difference method based on artificial anisotropic parameters and auxiliary variables in a Cartesian rectangular coordinate system. The method effectively improves the numerical dispersion characteristics and calculation accuracy by introducing artificial anisotropic parameters, simplifies the implementation process through auxiliary variables, and improves the calculation efficiency.

[0005] The technical solution adopted by the present invention to solve the technical problem is:

[0006] In a first aspect, the present invention provides an electromagnetic simulation method for a unidirectional fine structure antenna, wherein the unidirectional fine structure refers to a small-sized structure in only one spatial direction; the method comprises the following steps:

[0007] Step S1, establishing an antenna electromagnetic simulation object model and determining the calculation space range of the antenna size;

[0008] Step S2, constructing a convolutional perfectly matched layer suitable for a low-dispersion mixing method;

[0009] The corresponding perfectly matched layer absorption term is added to the low-dispersion one-step leapfrog hybrid implicit-explicit finite-difference time-domain method to construct a convolutional perfectly matched layer suitable for the low-dispersion hybrid method; the far-field data of the antenna is obtained using the convolutional perfectly matched layer suitable for the low-dispersion hybrid method;

[0010] Step S3, based on the convolution perfectly matched layer suitable for the low dispersion hybrid method, obtaining artificial anisotropy parameters, as well as auxiliary field quantities, absorption terms, magnetic field values, and electric field values ​​at the next moment;

[0011] Step S4, updating the excitation source of the electric field and updating the cycle time step n;

[0012] The electric field excitation source J is added to the center position of the calculation area, and the updated excitation source value is assigned to the electric field component at the center position to complete the update of the electric field excitation source position and value; the loop time step n increases by 1 each time, and it is determined whether the simulation iteration step number N in step S1 is reached, where n ranges from 1 to N; if the simulation iteration step number N is not reached, the time step n increases by 1, and returns to step S3 to continue the loop; if the iteration step number is reached, that is, n=N, it ends and records the electromagnetic field components obtained in steps S3 to S4.

[0013] Preferably, the unidirectional fine structure antenna is a dual-band multiple-input multiple-output antenna with a unidirectional fine structure.

[0014] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method described.

[0015] In a third aspect, the present invention provides a computing device, comprising a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method described in the right is implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] The present invention proposes a method for efficiently combining a low-dispersion one-step frog leaping hybrid implicit-explicit time-domain finite-difference method with a convolutional perfectly matched layer, which realizes the fusion and expansion of artificial anisotropic parameters, auxiliary variables and perfectly matched layers. For the far-field simulation of unidirectional fine-structure antennas, it not only effectively improves the dispersion characteristics and calculation accuracy, but also greatly simplifies the implementation process and improves the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is an algorithm flow chart of the implementation method of the present invention;

[0019] Figure 2is a schematic diagram of a geometric model of an antenna structure of an embodiment of a method for implementing the present invention;

[0020] Figure 3 The antenna processing sample and the antenna testing environment in Embodiment 1 of the present invention;

[0021] Figure 4 is a comparison curve diagram of simulation results and measurement results of antenna S parameters under different CFLN conditions in Example 1 of the present invention;

[0022] Figure 5 is a comparison curve diagram of simulation results and measurement results of the antenna gain pattern under different CFLN conditions in Example 1 of the present invention;

[0023] Figure 6 is a relative error comparison curve diagram of the antenna S parameters under different CFLN conditions in Example 1 of the present invention;

[0024] Among them, all simulation results are derived from the implementation method of the present invention; Figure 2 P1 is the antenna port 1 in Embodiment 1 of the present invention, P2 is the antenna port 2, and other geometric dimension parameters are detailed in Table 1; Figure 3 (a) is the front and back photos of the antenna processing sample, and (b) is the antenna testing environment; Figure 4 (a) is a comparison curve of S11, (b) is a comparison curve of S21, and the stability constant (Courant-Friedrich-Levy number, CFLN) represents the ratio of the time step of the A-HIE-FDTD method involved in the method of the present invention to the traditional FDTD method, wherein the values ​​of CFLN are 1, 2, 4, and 8, respectively; Figure 5 (a) is the comparison curve at 2.45 GHz, and (b) is the comparison curve at 5 GHz, where the CFLN values ​​are 1, 2, 4, and 8 respectively; Figure 6 (a) is the relative error comparison curve of S11, and (b) is the relative error comparison curve of S21, where the CFLN values ​​are 1, 2, 4, and 8 respectively.

[0025] Table 1 Geometric dimensions of the antenna in Example 1 of the present invention

[0026] parameter L <![CDATA[l1]]> <![CDATA[l2]]> <![CDATA[l3]]> <![CDATA[l4]]> <![CDATA[l5]]> <![CDATA[l6]]> <![CDATA[l7]]> <![CDATA[l8]]> Dimensions (mm) 60 19.5 12 1.5 10 23.5 1.5 0.5 5 parameter W <![CDATA[w1]]> <![CDATA[w2]]> <![CDATA[w3]]> <![CDATA[w4]]> <![CDATA[w5]]> <![CDATA[w6]]> <![CDATA[w7]]> H Dimensions (mm) 24 17 7 1 1 11 3 12 0.8 DETAILED DESCRIPTION

[0027] The present invention is applied to a low-dispersion one-step frog leaping hybrid implicit-explicit time-domain finite-difference method in computational electromagnetics. The recursive formula involved can be obtained based on the low-dispersion one-step frog leaping hybrid implicit-explicit time-domain finite-difference method. The specific implementation method is specifically described using the electromagnetic simulation problem of an antenna as an example, and the actual application scope is not limited to this.

[0028] The present invention provides a method for realizing a low-dispersion hybrid convolutional perfect matching layer, specifically a method for realizing a convolutional perfect matching layer by a low-dispersion hybrid implicit-explicit finite-difference time-domain method based on artificial anisotropic parameters and auxiliary variables in a Cartesian rectangular coordinate system. The specific steps of the method are:

[0029] 1) Establish a simulation object model and determine the calculation space range

[0030] Establish an antenna electromagnetic simulation object model; according to the antenna electromagnetic simulation object model, obtain the calculation space range of the antenna size; the entire calculation space range is (X min ,Y min ,Z min )~(X max ,Y max ,Z max ), the spatial range size is (X max –X min )×(Y max –Y min )×(Z max –Z min ), the center position coordinates are ((X min +X max ) / 2,(Y min +Y max ) / 2,(Z min +Z max ) / 2). Among them, X min and X max Represents the minimum spatial position and maximum spatial position in the x direction, Y min and Y max Represents the minimum spatial position and maximum spatial position in the y direction, Z min and Z max They represent the minimum spatial position and the maximum spatial position in the z direction respectively;

[0031] Δx, Δy, Δz represent the spatial steps in the x, y, and z directions respectively, N represents the number of simulation iterations, n represents the time step (ranging from 1 to N); J represents the excitation source function.

[0032] 2) Constructing a convolutional perfectly matched layer suitable for low-dispersion hybrid methods

[0033] Considering linear, isotropic and non-dispersive media, the three-dimensional Maxwell equations inside the perfectly matched layer are expressed as

[0034] jωΓ=RΓ (1)

[0035] Γ=[E x ,E y ,Ez ,H x ,H y ,H z ] T (2)

[0036]

[0037] Where j represents an imaginary number, ω represents the angular velocity, Γ represents the electromagnetic field quantity, E x 、E y and E z represent the electric field in the x, y and z directions respectively, H x , H y and H z They represent the magnetic field in the x, y and z directions respectively. Formula (3) is the calculation formula for artificial anisotropy parameters. “0” represents the 0-element matrix (3×3), ε and μ represent the dielectric constant and magnetic permeability respectively, and α η and β η (η=x,y,z) represent the electric anisotropy parameter and the magnetic anisotropy parameter respectively and are considered to be completely equal. η Represents the stretching coordinate factor, see formula (4) for details:

[0038]

[0039]

[0040] Where ε0 represents the vacuum dielectric constant, σ η , κ η and α η are all non-negative real numbers, expressed as:

[0041]

[0042]

[0043] Where d represents the thickness of the absorption layer, ρ represents the distance from the electromagnetic field to the interface between the calculation area and the absorption layer, m and mα represent the order of the polynomial, and σ η represents the conductivity, Δη represents the spatial step in the η direction (η=x,y,z), σ ηmax , κ ηmax and α ηmax Respectively represent σ η , κ η and α η The maximum value of ηmax The value range of α is 5 to 11. ηmax The value range of σ is 0~0.05, ηmax See formula (6b).

[0044] In addition, according to the low-dispersion one-step leapfrog hybrid implicit-explicit finite-difference time-domain method, the entire electromagnetic iterative process needs to be split into two sub-processes, namely R = R1 + R2:

[0045]

[0046]

[0047] Where Δt represents the time step size, n, n+1 / 2 and n+1 represent the time index, I represents the identity matrix (6×6), ψ e and ψ h They represent the absorption terms of the electric field and magnetic field respectively, and the corresponding specific formulas will be listed in the subsequent steps. The convolutional fully matched layer is shown in formulas (3)-(7).

[0048] According to the weak conditional stability condition, the split R1 and R2 can be expressed as follows:

[0049]

[0050] Here

[0051]

[0052]

[0053]

[0054] where κ eη and κ hη denote the κ corresponding to the electric field and magnetic field respectively η (η=x,y,z).

[0055] 3) Based on the convolutional perfectly matched layer suitable for the low-dispersion hybrid method, the artificial anisotropy parameters of the antenna, as well as the auxiliary field quantity, absorption term, magnetic field value, and electric field value at the next moment are obtained;

[0056] Without loss of generality, it is assumed that the fine structure is constructed only along the y-axis. Substituting formula (8) into formula (7) yields

[0057]

[0058]

[0059]

[0060]

[0061] Where E and H represent the electric field [E x ,E y ,Ez ] and magnetic field quantity [H x ,H y ,H z ].

[0062] To implement the one-step leapfrog scheme, rewrite equations (12a)-(12b) to the previous time step:

[0063]

[0064]

[0065] Substituting equation (13b) into equation (12a) eliminates H n-1 / 2 Available

[0066]

[0067] Substituting formula (14) and (11b) into formula (11a), we can get E n+1 / 2 The time step formula is:

[0068]

[0069] Similarly, we can get H n+1 The time step formula is:

[0070]

[0071] Further introduction of auxiliary field quantity Ф η (η=x,y,z)

[0072]

[0073]

[0074] in, and Respectively represent the auxiliary field quantity Φ at time n+1 / 2 and n+1 η , and They represent the electric field E at time n+1 / 2 and time n-1 / 2 respectively. η , and Represents the magnetic field H at time n+1 and time n respectively η .

[0075] Using the auxiliary field quantity Φ in formulas (17a)-(17b) η , the final electromagnetic iterative formulas (15) and (16) can be expressed as follows:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] Here,

[0084]

[0085] b1=(κ ex Δx) -1 ,b2=(κ ey Δy) -1 ,b3=(κ ez Δz) -1 (20b)

[0086] c1=(κ hx Δx) -1 ,c2=(κ hy Δy) -1 ,c3=(κ hz Δz) -1 (20c)

[0087] d1=a1b1, d2=a1b2, d3=a1b3, d4=a2c1, d5=a2c2 (20d)

[0088] d6=a2c3,d7=d1d5,d8=d1d6,d9=d2d4,d 10 =d3d4 (20e)

[0089] The absorption term ψ can be expressed as

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] Among them, a η and b η It can be calculated by formula (5) (η=x,y,z), a eη and b eη They represent the electric field corresponding to a η and b η ; a hη and b hη They represent the magnetic field corresponding to a η and b η ; i, j, k represent the spatial coordinates in the x, y, and z directions respectively; i+, j+, k+ represent i+1 / 2, j+1 / 2, k+1 / 2 respectively; i-, j-, k- represent i-1 / 2, j-1 / 2, k-1 / 2 respectively; a1, a2, b1, b2, b3, c1, c2, c3, d1, d2, d3, d4, d5, d6, d7, d7, d9, d 10 、f a 、f b 、f c All represent the corresponding coefficients in formulas (18a)-(18c) and (19a)-(19c); the auxiliary field quantities and electromagnetic field quantities in formulas (18a)-(18c) and (19a)-(19c) can be expressed as The form represents Θ in the space coordinate of P1 at time T1 η; Among them, Θ represents the auxiliary field quantity and electromagnetic field quantity, including Φ, E, H; T1 represents the time index, including n-1, n-1 / 2, n, n+1 / 2, n+1; P1 represents the space coordinate, including (i-,j,k+), (i-,j+,k), (i-,j+,k+1), (i-,j+1,k+), (i,j-,k), (i,j-,k+), (i,j,k-), (i,j,k+), (i,j+,k-), (i,j+,k), (i,j+, k+),(i,j+,k+1),(i,j+1,k+),(i+,j-1,k),(i+,j-,k),(i+,j,k-),(i+,j,k),(i+,j,k+),(i+,j,k+1),( i+,j+,k),(i+,j+,k+1),(i+,j+1,k),(i+,j+1,k+),(i+1,j-,k),(i+1,j,k-),(i+1,j,k+),(i+1,j+,k); and They represent the electric field E in the η2 direction under the spatial coordinates of P2 at time T2. η1 and magnetic field H η1 Corresponding absorption terms; where T2 represents the time index, including n-1, n-1 / 2, n, n+1 / 2; P2 represents the spatial coordinate, including (i+,j,k), (i,j+,k), (i,j,k+), (i,j+,k+), (i+,j,k+), (i+,j+,k).

[0103] According to formulas (17a)-(17c) and (18a)-(18c), the numerical dispersion formula of the method of the present invention can be obtained:

[0104]

[0105] in,

[0106]

[0107]

[0108] For the calculation of artificial anisotropic parameters, the basic principle is to make the normalized numerical phase velocity A = v p / c is optimized to 1 in all propagation directions, which means that the numerical dispersion error |1-v p / c| is smaller, which is also the key to achieve low-dispersion one-step leapfrog mixing. Substituting into the numerical dispersion formula (27) we can obtain the numerical velocity v p =ω / k. Among them, c represents the speed of light in vacuum, ω represents the angular velocity, and k represents the numerical wave number.

[0109] According to the optimization principle, the solution process of artificial anisotropy parameters can be summarized into the following three steps:

[0110] (1) Set A to 1 and solve the three-variable linear equation in the numerical dispersion formula along three spatial directions to obtain the initial value of the artificial anisotropy parameter: and

[0111] (2) and Substitute the numerical dispersion formula to obtain The maximum value of A under max ), and set Q = |1-A max |, represents the maximum deviation of A from 1;

[0112] (3) Let A = (1-Q / 2) and substitute A into the numerical dispersion formula to obtain the final artificial anisotropy parameter α x ,α y and α z .

[0113] 4) Update the excitation source of the electric field

[0114] The electric field excitation source J is added to the center of the calculation area, the excitation source value is gradually updated, and the updated excitation source value is assigned to the electric field component at the center position to complete the update of the electric field excitation source position and value.

[0115] 5) Update the cycle time step n

[0116] The time step n increases by 1 each time the loop is repeated, and a determination is made as to whether the iteration number N in step 1) is reached; if the iteration number N is not reached, the time step n increases by 1, and the loop is continued by returning to step 3); if the iteration number is reached, that is, n=N, the loop is terminated, and the electromagnetic field components obtained in steps 3) to 5) are recorded.

[0117] Example 1

[0118] This embodiment is an electromagnetic simulation problem of an antenna. The geometric model diagram of the antenna structure is shown in Figure 2 As shown, the implementation process of the embodiment is the same as the specific implementation method part; the Gaussian excitation source J is set at the center of the simulation space, the space step is Δx=Δy=0.5mm and Δz=0.1mm, the grid resolution is 20, the CFLN is 1, 2, 4, and 8 respectively, and the thickness of the fully matched layer is 8 layers.

[0119] The antenna was experimentally measured in a microwave darkroom, and the processing samples and the test environment in the laboratory were as follows Figure 3As shown. It should be noted that all experimental results of the method of the present invention adopt the same experimental environment. The antenna experimental measurement adopts the antenna test system equipment (model SY-2-16) and vector network analyzer (model PNA-X5224A) of China Electronics Technology Group Corporation 41 Institute. The size of the microwave darkroom is 4m×3m×3m (length×width×height), the operating frequency is 1GHz~40GHz, the shielding performance is ≥80dB, and the quiet zone reflection level is ≤-30dB.

[0120] Figure 4 The S parameters obtained by simulation calculation and experimental results of the method of the present invention are shown in FIG. Figure 4 As shown in (a), the designed antenna has a frequency bandwidth of 0.1 GHz (2.4 GHz to 2.5 GHz) in the 2.45 GHz band and a frequency bandwidth of 2.8 GHz (4.5 GHz to 7.3 GHz) in the 5 GHz band, which fully complies with the IEEE802.11n standard in WLAN. Even if the CFLN is increased to 8, the simulation results of the method of the present invention are still very consistent with the experimental results, which effectively verifies its high calculation accuracy. In addition, by Figure 4 (b) It can be found that S 21 The simulation and experimental results are both lower than -15dB, achieving a good isolation level.

[0121] Figure 5 (a) and Figure 5 (b) shows the simulation results and measurement results of the gain pattern of the antenna at 2.45 GHz and 5 GHz, respectively, where the results shown are all gain pattern results on the xoy plane. It can be found that with the continuous increase of CFLN, although the simulation results of the A-HIE-FDTD method have a certain degree of deviation, it basically maintains a good degree of consistency and does not affect the overall simulation and evaluation of the antenna performance, which can effectively verify the high stability of the method of the present invention. Even if the CFLN rises to 8, the simulation results of the gain pattern calculated by the method of the present invention are still very close to the measurement results, which once again proves that the method of the present invention has a high calculation accuracy.

[0122] In order to further verify the calculation accuracy and stability of the method of the present invention, the relative error in formula (30) is introduced.

[0123]

[0124] Among them, Θ ori and θ exp They respectively represent the electromagnetic field values ​​calculated by the same simulation calculation method in the original simulation space and the extended simulation space.

[0125] It should be noted that the expanded simulation space adds 100 grid cells on the basis of the original simulation space, and the simulation results in the expanded simulation space are used as the reference standard for calculating the relative error. Figure 6 The relative error curve of the S parameters obtained by the simulation calculation of the method of the present invention under different CFLN conditions is shown in Figure 1. It can be found that even when the CFLN increases to 8, the relative error of the S parameters is lower than -50dB, which once again proves the high calculation accuracy of the method of the present invention.

[0126] The comparison of the calculation results of different algorithms is shown in Table 2. The optimization parameter α x , α y and α z The calculation of refers to step 3 in the specific implementation method). The relative error will continue to increase with the increase of CFLN. Even when CFLN rises to 8, the calculation time of the method of the present invention is only 26.64% of the traditional method, which proves its high calculation efficiency. Among them, the traditional method mentioned is the combination method of the traditional FDTD method and CPML (i.e. FDTD-CPML), the method of the present invention is the combination method of the A-HIE-FDTD method and CPML (i.e. A-HIE-CPML), and L-HIE-CPML represents the combination method of the L-HIE-FDTD method and CPML.

[0127] Table 2 Calculation results of different methods in Example 1

[0128]

[0129] In summary, the method of the present invention simulates and designs a dual-band multi-input multi-output antenna. By comparing the simulation and experimental results of scattering parameters, relative errors and radiation patterns, it verifies its effectiveness in dealing with the far-field simulation problem of microwave RF devices with unidirectional fine structures. In addition, compared with the combination of the traditional time-domain finite difference method and the convolutional perfectly matched layer, when the CFLN rises to 8, the time cost of the implementation method is reduced by 73.36%, and its superior absorption performance is verified by simulation and experimental analysis. As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention is described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that: it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein by equivalent; and 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. An electromagnetic simulation method for a unidirectional fine structure antenna, characterized in that: The method comprises the following steps: Step S1, establishing an antenna electromagnetic simulation object model and determining the calculation space range of the antenna size; Step S2, constructing a convolutional perfectly matched layer suitable for a low-dispersion mixing method; The corresponding perfectly matched layer absorption term is added to the low-dispersion one-step leapfrog hybrid implicit-explicit finite-difference time-domain method to construct a convolutional perfectly matched layer suitable for the low-dispersion hybrid method; the far-field data of the antenna is obtained using the convolutional perfectly matched layer suitable for the low-dispersion hybrid method; Step S3, based on the convolution perfectly matched layer suitable for the low dispersion hybrid method, obtaining artificial anisotropy parameters, as well as auxiliary field quantities, absorption terms, magnetic field values, and electric field values ​​at the next moment; Step S4, updating the excitation source of the electric field and updating the cycle time step n; Add the electric field excitation source J to the center of the calculation area, and assign the updated excitation source value to the electric field component at the center to complete the update of the position and value of the electric field excitation source; the loop time step n increases by 1 each time, and it is determined whether the simulation iteration step number N in step S1 is reached, where n ranges from 1 to N; if the simulation iteration step number N is not reached, the time step n increases by 1, and returns to step S3 to continue the loop; if the iteration step number is reached, that is, n=N, it ends, and the electromagnetic field components obtained in steps S3 to S4 are recorded; The specific process of step S2 is: Considering a linear, isotropic and non-dispersive medium, the three-dimensional Maxwell equations inside the perfectly matched layer are expressed as jωΓ=RΓ (1) Γ=[E x ,E y ,E z ,H x ,H y ,H z ] T (2) Where j represents an imaginary number, ω represents the angular velocity, Γ represents the electromagnetic field quantity, E x 、E y and E z represent the electric field in the x, y and z directions respectively, H x , H y and H z Represent the magnetic field in the x, y and z directions respectively, "0" represents a 0-element matrix, ε and μ represent the dielectric constant and magnetic permeability respectively, α η and β η (η=x, y, z) represent the electric anisotropy parameter and the magnetic anisotropy parameter respectively and are considered to be completely equal; S η Represents the stretching coordinate factor, see formula (4) for details: Where ε0 represents the vacuum dielectric constant, σ η , κ η and α η are all non-negative real numbers, expressed as: Where d represents the thickness of the absorption layer, ρ represents the distance from the electromagnetic field to the interface between the calculation area and the absorption layer, m and mα represent the order of the polynomial, and σ η represents the conductivity, Δη represents the spatial step in the η direction, σ ηmax , κ ηmax and α ηmax Respectively represent σ η , κ η and α η The maximum value of In addition, according to the low-dispersion one-step leapfrog hybrid implicit-explicit finite-difference time-domain method, the entire electromagnetic iterative process needs to be split into two sub-processes, namely R = R1 + R2: Where Δt represents the time step size, n, n+1 / 2 and n+1 represent the time index, I represents the identity matrix (6×6), ψ e and ψ h Represent the absorption terms of electric field and magnetic field respectively; according to the weak condition stability condition, R1 and R2 are expressed as follows: where κ eη and κ hη denote the κ corresponding to the electric field and magnetic field respectively η .

2. The method according to claim 1, characterized in that: The specific process of step S1 is: establish an antenna electromagnetic simulation object model; obtain the calculation space range of the antenna size according to the antenna electromagnetic simulation object model; the entire calculation space range is (X min ,Y min ,Z min )~(X max ,Y max ,Z max ), the spatial range size is (X max –X min )×(Y max –Y min )×(Z max –Z min ), the center position coordinates are ((X min +X max ) / 2,(Y min +Y max ) / 2,(Z min +Z max ) / 2); where X min and X max Represents the minimum spatial position and maximum spatial position in the x direction, Y min and Y max Represents the minimum spatial position and maximum spatial position in the y direction, Z min and Z max They represent the minimum and maximum spatial positions in the z direction respectively.

3. The method according to claim 1, characterized in that: The calculation of the artificial anisotropy parameters in step S3 is as follows: The numerical wave vectors in the three spatial directions of x, y, and z Substituting into the numerical dispersion formula, we can obtain the numerical velocity v p =ω / k, and further obtain the normalized numerical phase velocity AA = v p / c and numerical dispersion error |1-AA|; where c is the speed of light in vacuum, ω is the angular velocity, k is the numerical wave number, θ and denote the polarization angle and azimuth angle in the spherical coordinate system respectively.

4. The method according to claim 1, characterized in that: The specific process of step S3 is: Without loss of generality, assume that the unidirectional fine structure is constructed along the y-axis direction; Substituting formula (8) into formulas (7a)-(7b) yields Where E and H represent the electric field [E x ,E y ,E z ] and magnetic field quantity [H x ,H y ,H z ]; To implement the low-dispersion one-step leapfrog hybrid implicit-explicit FDTD method, equations (12a)-(12b) are rewritten as the previous time step: Substituting equation (13b) into equation (12a) eliminates H n-1 / 2 Available Substituting formula (14) and (11b) into formula (11a), we can get E n+1 / 2 The time step formula is: Similarly, we can get H n+1 The time step formula is: Further introduce auxiliary field quantity Ф η (η=x,y,z) in, and Respectively represent the auxiliary field quantity Φ at time n+1 / 2 and n+1 η , and They represent the electric field E at time n+1 / 2 and time n-1 / 2 respectively. η , and Represents the magnetic field H at time n+1 and time n respectively η ; Using the auxiliary field quantity Φ in formulas (17a)-(17b) η , the final electromagnetic iterative formulas (15) and (16) can be expressed as follows: Here, b1=(k ex Δx) -1 ,b2=(k ey Δy) -1 ,b3=(k ez Δz) -1 (20b) c1=(k hx Δx) -1 ,c2=(κ hy Δy) -1 ,c3=(κ hz Δz) -1 (20c) d1=a1b1, d2=a1b2, d3=a1b3, d4=a2c1, d5=a2c2(20d) d6=a2c3,d7=d1d5,d8=d1d6,d9=d2d4,d 10 =d3d4(20e) The absorption term ψ can be expressed as Among them, a eη and b eη They represent the electric field corresponding to a η and b η ; a hη and b hη They represent the magnetic field corresponding to a η and b η ; i, j, k represent the spatial coordinates in the x, y, and z directions respectively, i+, j+, k+ represent i+1 / 2, j+1 / 2, k+1 / 2 respectively, i-, j-, k- represent i-1 / 2, j-1 / 2, k-1 / 2 respectively; a1, a2, b1, b2, b3, c1, c2, c3, d1, d2, d3, d4, d5, d6, d7, d7, d9, d 10 、f a 、f b 、f c represents the coefficient of the common correspondence; represents Θ in the space coordinate of P1 at time T1 η , Θ=Φ,E,H, T1=n-1,n-1 / 2,n,n+1 / 2,n+1; P1 represents the spatial coordinate combination in the x, y, and z directions; and They represent the electric field E in the η2 direction under the spatial coordinates of P2 at time T2. η1 and magnetic field H η1 The corresponding absorption terms, T2 = n-1, n-1 / 2, n, n+1 / 2, P2 represents the spatial coordinate combination in the x, y, and z directions, η1≠η2; η1 = x, y, z; η2 = x, y, z; According to formulas (17a)-(17c) and (18a)-(18c), the numerical dispersion formula can be obtained: in, 5. The method according to claim 1, characterized in that: The unidirectional fine structure antenna is a dual-band multiple-input multiple-output antenna with a unidirectional fine structure.

6. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 5.

7. A computing device, comprising a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method according to any one of claims 1 to 5 is implemented.

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