Array antenna sparse design method, computer equipment and readable storage medium

Abstract

The invention discloses an array antenna sparse design method, computer equipment and a readable storage medium. The method comprises the steps of presetting an initial uniform array of an array antenna based on an effective undirected radiation energy demand for the array antenna; sparse processing is carried out on the initial uniform array, and a middle sparse array in which a plurality of antenna units are distributed differently is obtained; respectively calculating a directivity coefficient and a sidelobe weighted value of the middle sparse array; and based on the directivity coefficient and the sidelobe weighted value of the middle sparse array, selecting the optimal middle sparse array in the directivity coefficient and the sidelobe weighted value of the middle sparse array, and if the average sidelobe of the middle sparse array is within a preset threshold value, taking the middle sparse array as the optimal distribution of the antenna units. The sparse array distribution can improve the normal main lobe gain of the antenna, reduce the output power of the radio frequency module of the array antenna and improve the stability of the array antenna under the same number of units.

Description

Technical Field

[0001] The present application relates to the technical field of array antennas, and in particular to a sparse design method for array antennas, a computer device, and a readable storage medium. Background Art

[0002] An array antenna is an antenna system that arranges multiple unit antennas in a specific geometric shape and works together. Through reasonable design and control, the radiation characteristics of these antenna units can be superimposed on each other to produce the desired beam directivity and gain characteristics. Array antennas are widely used in communications, radars, satellites, navigation, electronic countermeasures and other fields. At present, most long-range phased array radars and three-coordinate phased array radars use planar array antennas. A planar array antenna can be decomposed into multiple sub-array antennas or decomposed into multiple linear arrays. There are two main ways to arrange the elements of a planar array antenna: rectangular grid array arrangement and triangular grid array arrangement. The latter can be regarded as a planar array antenna arranged in a rectangular grid with a large spacing between two elements. Large planar array antennas with uniform spacing have the characteristics of high gain, narrow beam, easy azimuth, and two-dimensional electronic scanning in elevation. However, it consumes more elements, making the array antenna expensive. Summary of the invention

[0003] The main technical problem solved by the present application is to provide a sparse design method for array antennas to solve the problem of consuming a large number of array elements and making the array antennas expensive.

[0004] In order to solve the above technical problems, a technical solution adopted in this application is to provide a sparse design method for an array antenna, comprising:

[0005] Based on the requirement for effective isotropic radiation energy of the array antenna, presetting an initial uniform array of antenna units in the array antenna;

[0006] Setting a sparse rate, sparsely processing the initial uniform array, and obtaining a plurality of intermediate sparse arrays with different antenna unit distributions; and respectively calculating directivity coefficients and sidelobe weighted values ​​of the intermediate sparse arrays;

[0007] Based on the directivity coefficient and the sidelobe weighted value of the intermediate sparse array, the intermediate sparse array with the best directivity coefficient and the best sidelobe weighted value in the intermediate sparse array is selected, and if the average sidelobe of the intermediate sparse array is within a preset threshold, the intermediate sparse array is used as the optimal distribution of the antenna unit. The present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method when executing the computer program.

[0008] The present application also provides a computer-readable storage medium having a computer program stored thereon, characterized in that the steps of the method are implemented when the computer program is executed by a processor.

[0009] The beneficial effects of the present application are: sparse array distribution can improve the antenna normal main lobe gain, reduce the output power of the array antenna RF module, and improve the stability of the array antenna under the same number of units; the comparison between sparse array distribution and uniform arrays of the same size and the same number of units; the sparse array distribution gain is about 3dB higher than the uniform array of the same number, and saves 75% of the number of channels compared to the uniform array of the same size. Sparse array distribution can effectively reduce the sidelobe level of the specified airspace, and can well control the sidelobe level of the specified airspace by adjusting the feeding amplitude of each antenna unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a structural schematic diagram according to an embodiment of the present application;

[0011] Figure 2 is a structural schematic diagram of another side according to an embodiment of the present application;

[0012] Figure 3 is a front view structural schematic diagram of another side according to an embodiment of the present application;

[0013] Figure 4 is a schematic diagram of the structure inside the outer cover according to an embodiment of the present application;

[0014] Figure 5 is a schematic structural diagram of the back side of the intermediate plate according to an embodiment of the present application;

[0015] Figure 6 is a front view structural schematic diagram of a middle plate according to an embodiment of the present application;

[0016] Figure 7 is a schematic diagram of the structure of a hub according to an embodiment of the present application;

[0017] Figure 8 is a structural schematic diagram of an antenna unit according to an embodiment of the present application;

[0018] Fig. 9 is a schematic diagram of the structure of the other side of the antenna unit according to an embodiment of the present application;

[0019] Fig.10 It is a schematic diagram of the composition structure of the outer cover according to an embodiment of the present application;

[0020] Fig.11 is a schematic diagram of a sparse method flow according to an embodiment of the present application;

[0021] Fig.12It is a specific flow chart of the sparse method according to an embodiment of the present application;

[0022] Fig.13 is a flowchart of a sparse algorithm according to an embodiment of the present application;

[0023] Fig.14 is a schematic diagram of sparse array distribution according to an embodiment of the present application;

[0024] Fig.15 is a schematic diagram of a 10×10 sparse array according to an embodiment of the present application;

[0025] Fig.16 is a schematic diagram of a 10×10 common square array layout according to an embodiment of the present application;

[0026] Fig.17 This is a schematic diagram of a 20×20 common square array layout according to an embodiment of the present application;

[0027] Fig.18 This is a comparison diagram of the E-plane directional pattern simulation results of three array arrangements according to an embodiment of the present application;

[0028] Fig.19 It is a comparison chart of the simulation results of the H-plane directional patterns of three array arrangements according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions 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 part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of this application.

[0030] It should be noted that when an element is referred to as being "fixed on" or "set on" another component, it can be directly on the other component or indirectly set on the other component; when a component is referred to as being "connected to" another component, it can be directly connected to the other component or indirectly connected to the other component.

[0031] It should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0032] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "multiple" and "several" mean two or more, unless otherwise clearly and specifically defined.

[0033] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so that people familiar with this technology can understand and read them. They are not used to limit the conditions under which this application can be implemented, so they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in this application without affecting the effects and purposes that can be achieved by this application.

[0034] For the description of this application, the non-limiting Figure 4 The marks "front", "rear", "up", "down", "left" and "right" shown in the figure are used to facilitate understanding of the embodiment and are not intended to limit the present application. Among them, the front-to-back direction represents the longitudinal direction, the left-to-right direction represents the transverse direction, and the up-down direction represents the vertical direction.

[0035] Figure 1-Figure 10 An embodiment of the sparse design method of array antenna of the present application is shown, including an outer cover 1, an intermediate plate 2, antenna units 3, a bottom shell 4 and a wiring hub 5. The outer cover 1 and the bottom shell 4 cover both sides of the intermediate plate 2. The antenna units 3 are arranged on the intermediate plate 2 and extend toward the outer cover 1. The antenna units 3 are unevenly sparsely arrayed on the intermediate plate 2. The wiring hub 5 is arranged on the bottom shell 4. The connecting wires of the antenna units 3 converge on the wiring hub 5. The wiring hub 5 is used to connect the power amplifier unit through a bundled cable.

[0036] In this application, the array antenna is a passive array and is placed separately. The active devices including phase shift and amplitude control network are integrated in the power amplifier unit and placed away from the array antenna. They are connected in the middle by a bundled cable. One power amplifier unit is connected to a bundled cable, thereby connecting all antenna units 3 on an intermediate board 2. The array antenna frame is simple and the array antenna has high reliability. The antenna units 3 are unevenly sparsely arrayed on the intermediate board 2, thereby reducing antenna elements and reducing costs. At the same time, it prevents the occurrence of grating lobes and can well control the side lobe level in the specified airspace.

[0037] The outer cover 1 is frame-shaped and has a three-layer structure. Fig.10As shown, there are an outer layer 11, a middle layer 12 and an inner layer 13 in sequence, and the inner layer 13 is adjacent to the middle plate 2. The inner layer 13 and the outer layer 11 are made of epoxy resin quartz cloth, and the middle layer 12 is made of hard foam.

[0038] The inner layer 13 and the outer layer 11 include multiple layers of epoxy resin quartz cloth, which are formed by high-temperature pressing of multiple layers of quartz cloth and hydroamine. While having high strength, the epoxy resin quartz cloth has a smaller loss angle, which can reduce the loss of antenna signals. The thickness of each layer of quartz cloth in the epoxy resin quartz cloth is 0.3mm-0.8mm, and the thickness of the inner layer 13 and the outer layer 11 can be 0.6mm-3mm, such as 0.6mm, 0.8mm, 1mm, 1.5mm, 2mm and 3mm. The relative dielectric constant of quartz cloth is about 3.8, and the loss tangent value is low. The relative dielectric constant of hydroamine is about 3.3, and the loss tangent value is about 4 / 10,000.

[0039] In some embodiments, the thickness of the inner layer 13 is less than the thickness of the outer layer 11. The thicker outer layer 11 can ensure that the outer layer 11 has a better protective effect, and the thinner inner layer 13 can reduce the loss of the antenna signal.

[0040] The middle layer 12 is a hard foam, and the thickness of the middle layer 12 is 1 mm-5 mm, such as 1 mm, 2 mm, 3 mm, 4 mm and 5 mm, etc. The relative dielectric constant of the middle layer 12 is 1.02-1.06, and the loss tangent value is about 1 / 10,000.

[0041] The normal wave transmittance of the outer cover 1 at 5.5GHz is 93.2%, and the comprehensive wave transmittance is 90.4%. The insertion loss caused by its wave transmittance is 0.43dB, and a 0.5dB insertion loss margin is left for the outer cover 1 in engineering.

[0042] The middle plate 2 can be an integral structure or formed by splicing a plurality of sub-plates 21. When it is an integral structure, the antenna units 3 are unevenly sparsely arrayed on the middle plate 2. When it is formed by splicing a plurality of sub-plates 21, the antenna units 3 are unevenly sparsely arrayed on the sub-plates 21, and the sub-plates 21 are spliced ​​to form the middle plate 2. The middle plate 2 is formed by splicing a plurality of sub-plates 21, and the number of sub-plates 21 is 4, 6, or 8, etc. When the number of sub-plates 21 is set to 4, the 4 sub-plates 21 are arranged vertically and horizontally, and the sub-plates 21 are symmetrically arranged with the longitudinal center line and the transverse center line, and the 4 sub-plates 21 are connected together by a cross 22.

[0043] The bottom shell 4 is provided corresponding to the middle board 2 or the sub-board 21. When the middle board 2 is an integrated structure, one bottom shell 4 is provided. When the middle board 2 is formed by splicing a plurality of sub-boards 21, the number of bottom shells 4 corresponds to the number of sub-boards 21, and each sub-board 21 corresponds to one bottom shell 4. The connection lines of the antenna unit 3 on the sub-board 21 converge on the line collection board 5 corresponding to the bottom shell 4.

[0044] The bottom shell 4 is trumpet-shaped, with the trumpet mouth adjacent to the middle plate 2 having a larger diameter, and the trumpet mouth away from the middle plate 2 having a smaller diameter. A plurality of hubs 5 are provided, arranged at the trumpet mouth away from the middle plate 2. The trumpet-shaped shape of the bottom shell 4 can facilitate the integration of the connecting wires of the antenna unit 3 into the inner wall of the bottom shell 4, and reduce the space occupied by the connecting wires enclosed by the outer cover 1 and the bottom shell 4. This can also prevent the connecting wires from interfering with each other when they are laid. The connecting wires are guided and converged by the bottom shell 4, and the hub 5 seals the feed connector on the inner side of the bottom shell 4, thereby improving the feeding reliability of the antenna unit 3 and facilitating the plug-in connection with the bundled cable.

[0045] A plurality of connection terminals 51 are arranged on both side surfaces of the wiring hub 5 . The connection terminals 51 adjacent to the middle board 2 are connected to the connection wires of the antenna unit 3 , and the connection terminals 51 away from the middle board 2 are connected to the bundled cables.

[0046] The antenna unit 3 includes a fixing seat 31 and a radiating element 32. The radiating element 32 is fixed on the fixing seat 31. A bayonet adapted to the fixing seat 31 is provided on the middle plate 2, and the fixing seat 31 is fixed at the bayonet. The fixing seat 31 is in a stepped shape. The radiating element 32 includes a fixing portion 321, a main body 322 and a branch portion 323. The fixing portion 321 is in a square structure and is fixed on the fixing seat 31. The main body 322 is vertically arranged on the fixing portion 321. A plurality of branch portions 323 are provided, extending outward from the side of the main body 322, and the branch portions 323 are evenly distributed on both sides of the main body 322. There are 10 branch portions 323, and the 10 branch portions 323 are located on both sides of the main body 322, and are symmetrically arranged with respect to the vertical center line of the main body 322. The branch portions 323 are, from top to bottom, a first branch portion 323 , a second branch portion 323 , a third branch portion 323 , a fourth branch portion 323 and a fifth branch portion 323 .

[0047] A dielectric plate 324 is provided on the lower side of the main body 322 on the side opposite to the fixing portion 321. The upper end of the dielectric plate 324 is located between the first branch portion 323 and the second branch portion 323. A microstrip line is provided on the dielectric plate 324 for feeding the antenna unit 3. A grounding hole 325 is provided on the dielectric plate 324 for grounding.

[0048] The main body 322 on the upper side of the fixing part 321 has a protruding part 327, the upper end of which is located between the first branch part 323 and the second branch part 323. The protruding part 327 is used to enhance the structural strength of the lower side of the fixing part 321 and adjust the performance of the antenna unit 3.

[0049] A choke slot 326 is provided at the main body 322 on the upper side of the fixing portion 321, and the upper end of the choke slot 326 extends to the upper side of the second branch portion 323. The lower end of the choke slot 326 penetrates the fixing portion 321 and extends in a direction away from the dielectric plate 324. The choke slot 326 is used to ensure that only the first branch portion 323 is fed with power.

[0050] like Fig.11 As shown, the present application provides a sparse design method for an array antenna, comprising the following steps:

[0051] Step S1), based on the effective isotropic radiation energy requirement of the array antenna, presetting the initial uniform array of the array antenna;

[0052] Step S2), setting a sparse rate, sparse the initial uniform array, and obtaining a plurality of intermediate sparse arrays with different antenna unit distributions; respectively calculating the directivity coefficients and sidelobe weighted values ​​of the intermediate sparse arrays;

[0053] Step S3), based on the directivity coefficient and sidelobe weighted value of the intermediate sparse array, select the intermediate sparse array with the best directivity coefficient and sidelobe weighted value in the intermediate sparse array; if the average sidelobe of the intermediate sparse array is within a preset threshold, the intermediate sparse array is used as the optimal distribution of antenna units.

[0054] The main effects of using a sparse array distribution method for the antenna units in this application are as follows:

[0055] 1) Sparse array distribution can improve the antenna normal main lobe gain, reduce the output power of the array antenna RF module, and improve the stability of the array antenna with the same number of units. The simulation experiment below discusses the comparison between the sparse array and the uniform array of the same size and the uniform array of the same number of units. The results show that the sparse array gain is about 3dB higher than the uniform array of the same number, and saves 75% of the number of channels compared with the uniform array of the same size.

[0056] 2) The C-band array is not large. Even if a sparse array of 100 units is arranged within a 10-wavelength square, the array size does not exceed 0.82m×0.82m, making it easy to set up and use.

[0057] 3) Sparse array distribution can effectively reduce the sidelobe level in a specified airspace. When the phased array scanning angle is not too large, the sidelobe level does not increase significantly. At this time, the sidelobe can be controlled by appropriately adjusting the feeding amplitude of each unit.

[0058] Sparse arrays select feed antenna units from grid points in a uniform array according to a certain sparsity rate. For example, 100 antenna units can be arranged on a grid point with a spacing of half a wavelength at 20*20. Sparse arrays can increase the spacing between array elements and reduce mutual coupling between array elements through sparseness, so that the normal gain of the array approaches the theoretical design value. The normal gain of the first array is expressed as:

[0059] G array =10·log 10 (N)+G cell ,

[0060] Among them G array is the array normal gain, N is the number of antenna elements, G cell is the unit gain.

[0061] The gain of the antenna unit is closely related to the total normal gain of the array. When the feed loss is small, the maximum gain of the antenna unit is determined by its beam width, and the phased array scanning beam width determines the antenna unit beam width.

[0062] The phased array scanning beam space is pitch 0-50 degrees and horizontal ±35 degrees. The unit gain derived from it is expressed as:

[0063]

[0064] The antenna aperture area corresponding to this gain value is 0.75 wavelengths × 0.75 wavelengths × 0.25 wavelengths. Considering that the average distance between antenna units will increase after the sparse array distribution, the inductive resonance area occupied by the unit can be appropriately increased, so that the unit gain can be appropriately increased. Therefore, the maximum unit design gain is set to 12.5dBi. According to the antenna unit gain requirements, lobe width requirements and antenna polarization requirements. The antenna radiator is made of pure metal to reduce insertion loss; the feeding structure adopts microstrip coupling feeding, and the radiation efficiency within the bandwidth should reach more than 85%.

[0065] The array gain can be increased by reasonably increasing the antenna unit gain. When increasing the antenna unit gain, the gain value in the scanning state needs to be taken into account. Therefore, the lobe width of the antenna unit should be equivalent to the scanning space, thereby limiting the maximum gain of the unit.

[0066] The limitation of antenna array beam width also determines the G array The maximum value, G array It can also be expressed according to the second array normal gain as:

[0067]

[0068] HP 3dB is the H-plane lobe width of the array normal beam, EP3dB is the E-plane lobe width of the array normal beam. According to the first array normal gain formula, the second array normal gain formula, and the selection of unit antennas, the number of array antenna units N can be obtained. Of course, when designing array antennas, it is also necessary to comprehensively consider the engineering complexity of array antennas, the reduction in gain after sparse array distribution, the change in lobe width, etc., and try to reduce the number of units (channels) as much as possible.

[0069] To maximize the antenna normal EIRP (effective isotropic radiated power) and minimize the total power consumption and heat dissipation of the antenna, the output power of each T component should be consistent, that is, the signal power reaching the antenna unit is equal and the phase is the same. At this time, the antenna EIRP can be expressed as.

[0070] EIRP = 20·log 10 (N)+P in +G cell ,

[0071] Where P in is the signal power reaching the antenna unit.

[0072] Periodic array thinning will lead to an increase in the sidelobe level of the pattern, and a reasonable sparse method can effectively control the sidelobe level, especially the sidelobe level in a specified airspace. The sparse algorithm is used to optimize the sidelobe level value in the specified airspace, thereby performing array sparseness. The overall design process is as follows: Fig.12 shown.

[0073] Here are the steps:

[0074] 1) According to the EIRP requirements of the array antenna, the number of sparse arrays N is obtained through the first array normal gain formula and the second array normal gain formula;

[0075] 2) Considering the sidelobe level value to be controlled, the initial uniform array number M is obtained;

[0076] 3) Optimizing sparse array layout through sparse algorithms;

[0077] 4) Calculate the average sidelobe level value in the specified airspace at each scanning angle. If the value exceeds the standard, the output level of the T component needs to be controlled;

[0078] 5) Calculate the maximum output level of each T component;

[0079] 6) Record the power consumption of the array antenna;

[0080] 7) Increase or decrease the uniform array size M, repeat steps 2) to 6), and compare the power consumption of the array antenna until the best M value is found. The antenna array element layout at this time is the best layout.

[0081] The sparse algorithm is suitable for this application mainly because it has the following characteristics:

[0082] 1) Sparse algorithms use the coding of variables to be optimized as the operation object, especially for some discrete optimization problems that only have code concepts but cannot be accurately formulated, or optimization problems that are difficult to handle using existing mathematical analysis tools. In this application, the layout of unit antennas is such an inconvenient discrete optimization problem. It is difficult to find an optimization path using mathematical analysis, but it is easy to parameterize using binary coding and use sparse algorithms to solve the problem. In the optimization calculation process, we can draw on concepts such as chromosomes and genes in biology, imitate the mechanisms of inheritance and evolution of organisms in nature, and conveniently apply genetic operation operators.

[0083] 2) The objective function of the sparse algorithm may not be concisely expressed by mathematical formulas, especially certain statistical values, average values, etc. The objective function needs to rely on numerical calculations. In this application, the objective function is the average sidelobe value and array directivity coefficient of a certain spatial domain, and the average sidelobe itself is a statistical value. This kind of objective function cannot or is difficult to be differentiated, or even does not have a derivative at all. For the optimization and combinatorial optimization problems of this type of objective function, sparse algorithms are very practical.

[0084] 3) Sparse algorithms are convenient for handling multi-extreme value optimization problems. There may be many optimal distributions of the sparse array distribution of this application, and the use of other algorithms will often fall into local optimality. In addition, the optimal solution formed by the sparse array distribution often only needs to meet the engineering requirements, so sparse algorithms are suitable for handling such problems.

[0085] The sparse algorithm uses population search technology to represent the population as a set of problem solutions. It generates a new generation of population by applying a series of genetic operations such as selection, crossover and mutation to the current population, and gradually evolves the population to a state that contains an approximate optimal solution.

[0086] The sparse algorithm is used to perform sparse optimization of planar arrays. The algorithm flow is: first, generate a binary initial population that meets a certain sparse rate, then calculate the fitness of each individual in this population, and perform genetic operations such as selection, crossover and mutation on the individuals in this population according to the fitness of the individuals, select and retain the best individuals, and finally ensure that the sparse rate of individuals in the newly generated population remains unchanged. For the evolved offspring population, the fitness is calculated to determine whether the maximum generation has been reached (no exit condition is set here). If the maximum generation has been reached, the spatial average sidelobe of the optimal solution is determined. If it is better than -20dBc, the uniform grid size (array size) is increased; if it is worse than -19dBc, the uniform grid size (array size) is reduced until the termination condition is reached. The operation flow chart is as follows. Fig.13As shown. The design target of the normal beam average sidelobe is between -20dBc and -22dBc, which does not meet the index requirements. The reason for this setting is: the larger the phased array scanning angle, the higher the sidelobe level. Therefore, when the array is optimized, the normal direction sidelobe should be slightly lower than the index requirement, so that it will be close to the index requirement in the scanning state. Overall, the average index is the best. Scanning angles that do not meet the index requirements can be compensated by amplitude weighting. The specific process is described as follows:

[0087] 1) Initial condition setting: Determine the initial uniform array spacing to be 0.7 center frequency (5.5GHz) free space wavelength, i.e. 38.2mm. This distance is the minimum distance between units, which can meet the antenna unit array spacing requirement of 10.5-12dBi gain;

[0088] The initial uniform array size is set to 20×20, and the plane size of the array is 0.82m×0.82m. The total number of units is determined to be 100. This number takes into account the complexity of array antenna implementation and the feasibility of indicators. If the number of units is too large, the number of transmitting components and cable components will be too large, the array antenna will be complex and the cost will increase; if the number of units is too small, the EIRP indicator of the array antenna will be difficult to meet, and higher requirements will be placed on the output power of a single T component, which is not conducive to the implementation of a single T component.

[0089] 100 units are sparsely designed on a 20×20 grid, with an initial sparse rate of 25%; units may or may not appear at the intersection of the grid, and grids cannot appear at other locations. 1 indicates that there is a unit at the grid point, and 0 indicates that there is no unit. A distribution is a string of 400 binary numbers, which represents a distribution form, that is, individuals in the sparse algorithm. The number of individuals in the first generation population is set to 100.

[0090] The number of genetic generations is set to 600 (NP). When the number of generations is not completed, the algorithm will not exit without judgment.

[0091] The crossover probability was set to 80% and the mutation probability was set to 5%.

[0092] 2) Generation of constrained individuals:

[0093] Each individual is a 20*20 binary matrix, with g(i,j)=0 or 1, i and j are integers between 1 and 10. The matrix meets the following conditions: 1. There must be units on the grids at the four corners; 2. The array meets left-right symmetry and top-bottom symmetry. If the first restriction is not made, the overall array size cannot be limited; if the second restriction is not made, the symmetry of the directional pattern cannot be met, and it will bring a burden to the subsequent RF interface docking design. As shown in the following formula. The final sparse array distribution can be seen Fig.14, where there are circles, there are cells. Taking the center of the array as the origin, the cells in the four quadrants are all inverted and symmetrical. It can be expressed as:

[0094]

[0095] 3) Fitness calculation:

[0096] The directional pattern after considering the sparse array distribution is expressed as:

[0097]

[0098] Among them, f0(φ,θ) is the unit direction diagram, φ is the corresponding direction of the direction diagram, is the projection vector of the target direction vector on the XOY plane and the angle between the X axis, θ is the angle between the target direction vector and the Z axis, N, M are the number of antenna units in the horizontal and elevation dimensions of the antenna array, n∈N, m∈M, λ is the free space wavelength corresponding to the electromagnetic wave of the working frequency, θ0 and is the spatial angle that the array antenna pattern needs to cover, dx is the spacing between the antenna units in the horizontal dimension, and dy is the spacing between the antenna units in the elevation dimension.

[0099] First, we need to calculate the When calculating, it is necessary to import the unit pattern design result f0(φ,θ), which is actually a matrix. According to the pattern multiplication principle, it is necessary to perform a dot multiplication operation on the unit pattern and the matrix factor.

[0100] The normalized directional pattern is expressed as:

[0101]

[0102] The first objective function is the directivity coefficient gain value, expressed as:

[0103]

[0104] The second objective function is the average sidelobe value in the airspace Ω1 (α1∈(0°,30°), β1∈(-25°,25°)) of the geodetic coordinate system OX1Y1Z1. Transform Ω1 to the airspace Ω in the array coordinate system, and then use numerical methods to count all the sidelobes in the airspace (sidelobe: the directivity coefficient of any point in the airspace Ω is higher than the directivity coefficient of the 8 adjacent airspace points around it. In addition, the level at this point should be higher than -30dBc, that is, it is considered to be a sidelobe), and then calculate its average value, which is the following formula. K is the number of all sidelobes in the airspace Ω, which needs to be counted by numerical methods, At the side lobe (the side lobe is located at The second objective function is expressed as:

[0105]

[0106] The overall objective function should be a comprehensive function of MSLL1 and MSLL2, which requires MSLL1 to be as large as possible and MSLL2 to be as small as possible (ultimately, by adjusting the array size, the minimum MSLL2 is controlled between -19dBC and -20dBC). After the sparse array is distributed, the antenna array directivity coefficient can approach the theoretical value described by the first array normal gain formula, but it cannot be higher than this value. Therefore, in the sparse state, it is mainly determined by the number of units, so the change is small, and the size of the side lobe is closely related to the array form, so the weight should be larger. Therefore, the objective function value is expressed as:

[0107] MSLL=0.2·MSLL1-0.8·MSLL2,

[0108] According to the objective function value, calculate the fitness function of each antenna unit:

[0109]

[0110] Among them, MSLL max is the maximum value of the objective function in a population, MSLL min is the minimum value of the objective function in the population, MSLL i is the objective function value of the i-th individual in the population, and the fitness function fit i The maximum value of is 1 and the minimum value is 0. The fitness of all individuals in a population of one generation is calculated in turn.

[0111] 4) Select and retain the best individual:

[0112] Save the value of the individual with the highest fitness and its objective function value.

[0113] 5) Selection and crossover operations:

[0114] The individuals with the highest fitness are directly retained in the next generation group.

[0115] In addition, all individuals in the group are selected and crossed in turn. The selection operation is to make mating choices for individuals in the group. The mating individuals are selected according to the probability determined by the following formula, and the probability of being selected is expressed as:

[0116]

[0117] In the order of selection, individuals mate with each other, and the mating position is randomly selected. After mating, there are still two individuals. In addition, if the entire population NP has not been traversed during the selection process, and 80% of the individuals have been selected, the subsequent individuals will not be selected; if the entire population NP has been traversed during the selection process, and the individuals selected are less than 80%, continue to randomly select until the selection rate of 80% is met. When there are not enough individuals in the group, new individuals are randomly generated to keep the number of individuals in each generation unchanged.

[0118] 6) Mutation operation:

[0119] The binary codes in all individuals are traversed and inverted with a probability of 5%. After the traversal of the codes in the individuals is completed. If the sum of all codes is greater than 100, all 1 codes are inverted with a probability of 5% until the sum of the codes is 100; if the sum of all codes is less than 100, all 0 codes are inverted with a probability of 5% until the sum of the codes is 100. The above operation is performed for all individuals in the group.

[0120] 7) Iterative calculation:

[0121] Generate a new generation of population according to operations E and F. Repeat steps 3) to 6) until the set number of generations is completed. Calculate the best individuals in all generations, save the distribution, and save the two objective functions.

[0122] 8) Average sidelobe level determination:

[0123] Determine whether its average sidelobe is between the preset thresholds -20dBc and -22dBc. If so, the calculation is terminated. The optimal individual obtained at this time is the final design result of the sparse array distribution. If it is too large, it means that the current array sparsity rate is too small, which can be improved by reducing the uniform array scale or reducing the array element spacing (for the convenience of design, the array element spacing has been fixed, so the algorithm can only reduce the scale); if it is too small, it means that the current array sparsity rate is too high, which can be improved by increasing the uniform array scale or increasing the array element spacing (for the convenience of design, the array element spacing has been fixed, so the algorithm can only reduce the scale).

[0124] 9) Repeated iteration:

[0125] When the sidelobe level does not meet the requirements, the sparse algorithm calculation needs to be repeated until the average sidelobe requirements are met.

[0126] Considering that there are certain probabilistic problems in the optimization process of the sparse algorithm, the population size setting and genetic algebra setting of the algorithm itself can be as large as possible. In addition, the design should be repeated several times to ensure the optimality of the array antenna solution. The sparse algorithm is used to perform sparse optimization arrangement of rectangular planar array antennas to reduce antenna elements and reduce costs, while preventing the occurrence of grating lobes and being able to well control the sidelobe level in the specified airspace.

[0127] In the following, two layout modes, directional array layout and sparse array distribution, are compared to illustrate the effect of sparse array distribution.

[0128] The layout of 10×10 sparse array and 10×10, 20×20 normal square array is as follows Figure 15 to Figure 17 As shown in the figure, the number of array elements required for the three methods are 100, 100, and 400 respectively, and the array dimensions are 1344mm×1344mm, 704mm×704mm, and 1344mm×1344mm respectively. That is, the number of unit channels of the 10×10 ordinary square array and the sparse array is equal; the array dimensions of the 20×20 ordinary square array and the sparse array are the same;

[0129] The simulation comparison of the normal radiation patterns of the E-plane and H-plane at the center frequency of 3.3GHz of the three array modes is shown below: Fig.18 The following is a comparison of the simulation results of the E-plane radiation patterns of the three array arrangements. The normal gain of the sparse array is 30.8dBi, and the 3dB beamwidth is 4.1°; the normal gain of the 10×10 square array is 27.9dBi, and the 3dB beamwidth is 7°; the normal gain of the 20×20 square array is 33.9dBi, and the 3dB beamwidth is 3.5°.

[0130] Fig.19 The following is a comparison of the simulation results of the H-plane radiation patterns of the three array arrangements. The normal gain of the sparse array is 30.8dBi, and the 3dB beamwidth is 3.6°; the normal gain of the 10×10 square array is 27.9dBi, and the 3dB beamwidth is 7.1°; the normal gain of the 20×20 square array is 33.9dBi, and the 3dB beamwidth is 3.5°.

[0131] The data comparison of the gain, 3dB beamwidth and array size of the E-plane and H-plane of the three array arrangements is shown in the table.

[0132] Table 1 Comparison of the results of three array layouts

[0133] Array form Sparse arrays 10×10 square array 20×20 square array E-plane gain (dBi) 30.8 27.9 33.9 H plane gain (dBi) 30.8 27.9 33.9 E-plane 3dB beamwidth (°) 4.1 7 3.5 H plane 3dB beamwidth (°) 3.6 7.1 3.5 Array size (mm×mm) 1344×1344 704×704 1344×1344 Number of array elements 100 100 400

[0134] From the above, we can see that: 1. The gain of the sparse array is about 3dB higher than that of the uniform array with the same number of channels, and about 3dB lower than that of the uniform array with the same size. The sidelobe level is relatively high, but the average sidelobe level in the specified airspace can be reduced or increased through the algorithm. 2. The lobe width of the sparse array is between the two uniform arrays, but closer to the uniform array of the same size; the sparse array saves about 75% of the number of channels compared to the uniform array of the same size.

[0135] It can be seen that the present application discloses a C-band array antenna. In the present application, the array antenna is a passive array and is placed separately. The active devices including the phase shift and amplitude control network are integrated in the power amplifier unit and placed away from the array antenna. The middle is connected by a cluster cable. One power amplifier unit is connected to a cluster cable, thereby connecting all the antenna units on an intermediate board. The array antenna frame is simple, the array antenna has high reliability, and the antenna units are unevenly sparsely arrayed on the intermediate board, so as to reduce the number of antenna elements and reduce costs. At the same time, it prevents the occurrence of grating lobes and can well control the side lobe level in the specified airspace.

[0136] The above are merely embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structural transformations made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A sparse design method for an array antenna, characterized in that: Includes steps: Based on the requirement for effective isotropic radiation energy of the array antenna, presetting an initial uniform array of antenna units in the array antenna; Setting a sparse rate, sparsely processing the initial uniform array, and obtaining a plurality of intermediate sparse arrays with different antenna unit distributions; respectively calculating the directivity coefficient and the sidelobe weighted value of the intermediate sparse array; Based on the directivity coefficients and sidelobe weighted values ​​of the intermediate sparse array, the intermediate sparse array with the best directivity coefficients and sidelobe weighted values ​​in the intermediate sparse array is selected; if the average sidelobe of the intermediate sparse array is within a preset threshold, the intermediate sparse array is used as the optimal distribution of the antenna units.

2. The sparse design method for array antenna according to claim 1, characterized in that: The requirement of the effective isotropic radiation energy of the antenna, the effective isotropic radiation energy is expressed as: EIRP=20·log 10 (N)+P in +G cell , Among them, P in To achieve the signal power at the antenna unit, G cell is the unit gain, and N is the number of antenna elements.

3. The sparse design method for array antenna according to claim 2, characterized in that: The intermediate sparse array is obtained according to the array normal gain, and the array normal gain is expressed as: G array =10·log 10 (N)+G cell , Among them, G array is the array normal gain, HP 3dB is the H-plane lobe width of the array normal beam, EP 3dB is the E-plane lobe width of the array normal beam.

4. The array antenna sparse design method according to claim 3, characterized in that: The sparse algorithm is used to optimize the sidelobe level value in the specified spatial domain, and the array antenna is sparsely processed. The steps are as follows: 1) according to the effective isotropic radiation energy of the array antenna, obtaining the number of the antenna units involved in the initial uniform array; 2) according to the sidelobe level value to be controlled, the distribution of the initial uniform array is obtained; 3) optimizing the distribution of the initial uniform array by the sparse algorithm to form the intermediate sparse array; 4) calculating the average sidelobe level value in the specified spatial domain under each scanning angle of the intermediate sparse array, and if the average sidelobe level value exceeds the standard, controlling the output level of the T component; 5) Calculating the maximum output level of each T component; 6) recording the power consumption of the intermediate sparse array; 7) increasing or decreasing the number of antenna units in the initial uniform array, repeating steps 2) to 6), comparing the power consumption of the intermediate sparse array, and selecting the distribution of the antenna units when the power consumption of the intermediate sparse array is the smallest as the optimal distribution of the antenna units.

5. The array antenna sparse design method according to claim 4, characterized in that: The constraints of the sparse algorithm are set, and the four corners of the array antenna are all set to have the antenna units.

6. The array antenna sparse design method according to claim 4, characterized in that: The constraints of the sparse algorithm are set to set the antenna units on the array antenna to be symmetrical along the longitudinal direction.

7. The array antenna sparse design method according to claim 4, characterized in that: The constraints of the sparse algorithm are set to set the antenna units on the array antenna to be symmetrical in the horizontal direction.

8. The array antenna sparse design method according to any one of claims 4 to 7, characterized in that: The distribution process of optimizing the initial uniform array by the sparse algorithm is as follows: 1) Initial condition setting: determining the spacing between the antenna units in the initial uniform array; setting the scale of the initial uniform array, determining the total number of antenna units, and sparsely arranging the antenna units; 2) Generation of constrained individuals: Each of the antenna elements is set as a binary matrix, with g(i,j)=0 or 1, i and j are integers between 1 and 10, including: 3) Fitness calculation: The directional pattern after sparse array distribution is expressed as: Among them, f0(φ,θ) is the unit direction diagram, φ is the corresponding direction of the direction diagram, is the projection vector of the target direction vector on the XOY plane and the angle between the X axis, θ is the angle between the target direction vector and the Z axis, N, M are the number of antenna units in the horizontal and elevation dimensions of the antenna array, n∈N, m∈M, λ is the free space wavelength corresponding to the electromagnetic wave of the working frequency, θ0 and is the spatial angle that the array antenna pattern needs to cover, dx is the spacing of the antenna units in the horizontal dimension, and dy is the spacing of the antenna units in the elevation dimension; First, calculate the space Import unit pattern results during calculation Perform dot multiplication operation on the unit direction diagram and the array factor; The first objective function is the directivity coefficient gain value, and the normalized directivity pattern is expressed as: The first objective function is expressed as: The second objective function is the average sidelobe value in the spatial domain Ω1 of the geodetic coordinate system. The spatial domain Ω1 of the geodetic coordinate system is transformed into the spatial domain Ω in the array coordinate system. All sidelobes in the spatial domain Ω are counted by numerical method, and then the average value is calculated. The second objective function is expressed as: Where K is the number of all side lobes in the spatial domain Ω, is the directivity coefficient at the side lobe; The overall objective function is a comprehensive function of MSLL1 and MSLL2, and the overall objective function value is expressed as: MSLL=0.2·MSLL1-0.8·MSLL2, According to the objective function value, the fitness function of each antenna unit is calculated: Among them, MSLL max is the maximum value of the objective function in a population, MSLL min is the minimum value of the objective function in the population, MSLL i is the objective function value of the i-th individual in the population, and the fitness function fit i The maximum value of is 1 and the minimum value is 0. The fitness of all individuals in a generation population is calculated in sequence. 4) Select and retain the best individual: Save the value of the individual with the highest fitness and its objective function value; 5) Selection and crossover operations: The individuals with the highest fitness are directly retained in the next generation group; The individuals in all groups are selected and crossed one by one. The selection operation is to make mating choices for the individuals in the group. The mating individuals are selected according to the probability determined by the following formula. The probability of being selected is expressed as: In the order of selection, individuals are mated with each other, and the mating position is randomly selected. After mating, there are still two individuals. If the entire population NP has not been traversed during the selection process, and 80% of the individuals have been selected, the subsequent individuals will not be selected. If the entire population NP has been traversed during the selection process, and the individuals selected are less than 80%, continue to randomly select until the selection rate of 80% is met. When there are not enough individuals in the group, new individuals are randomly generated to keep the number of individuals in each generation unchanged. 6) Mutation operation: The binary codes in all individuals are traversed and inverted with a probability of 5%. After the traversal of the codes in the individuals is completed; if the sum of all codes is greater than 100, all 1 codes are inverted with a probability of 5% until the sum of the codes is 100; if the sum of all codes is less than 100, all 0 codes are inverted with a probability of 5% until the sum of the codes is 100; the above operation is performed for all individuals in the group; 7) Iterative calculation: Generate a new generation of population according to step 5) and step 6); re-traverse the process of step 3) to step 6); until the set number of generations is completed by iteration; calculate the optimal individuals in all generations, save the distribution, and save the two objective functions; 8) Average sidelobe level determination: It is determined whether the average side lobe is within a preset threshold value of -20 dBc to -22 dBc. If so, the calculation is terminated. The optimal individual obtained at this time is the optimal distribution of the antenna unit.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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