Irregular subarray partitioning design method for low sidelobe wide-angle scanning array antennas

Abstract

This invention discloses an irregular subarray partitioning design method for a low-sidelobe wide-angle scanning array antenna, solving the problem of slow sidelobe level calculation and long optimization time in existing technologies. The method includes: discretizing the array surface into a set of array elements; arranging and combining the array elements in the set to obtain a set of subarray position combinations, and constructing a dictionary matrix; transforming the subarray partitioning problem into a set-covering problem, solving for a binary selection vector to make the dictionary matrix and the binary selection vector form a single row vector, thus obtaining a set of candidate subarray combinations covering the array surface; constructing an irregular subarray partitioning design model; filtering the candidate subarray combinations to obtain the optimal subarray partitioning result; and using the optimal subarray partitioning result, employing the Grey Wolf optimization algorithm to optimize the excitation amplitude of the optimal subarray partitioning result to reduce the radiation pattern sidelobe level. This method replaces the direct calculation of sidelobe levels, thereby improving computational efficiency.

Description

Technical Field

[0001] This invention relates to the field of antenna design technology, and in particular to an irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna. Background Technology

[0002] Phased array antenna systems can achieve real-time control of the excitation amplitude and phase of each element in the array. Therefore, they have been widely used in many fields such as communications and radar. However, the high cost of transceiver components limits the widespread application of phased array antennas. To reduce the cost of phased array antennas, researchers have proposed subarray partitioning methods, which reduce the number of components to lower costs.

[0003] However, if a regular subarray partitioning method is used, the phase centers of the array antenna subarrays will be periodically arranged with large spacing (exceeding the operating wavelength), resulting in grating lobes during scanning. To suppress grating lobes, the subarray spacing can be reduced or the periodicity of the subarray phase center arrangement can be broken. Reducing the subarray spacing will produce severe mutual coupling effects. Therefore, breaking the periodicity of the subarray phase center arrangement is a feasible method to suppress grating lobes. Thus, an irregular subarray partitioning method was proposed. Meanwhile, considering the design difficulty of the subarray feed network in practical engineering, a multi-unit domino subarray, similar to a Tetris puzzle, is typically used to design the transmitting antenna.

[0004] Based on the scanning performance requirements of the array beam, typical subarray structures can currently be divided into finite field-of-view scanning structures and wide bandwidth scanning structures. Wide bandwidth scanning structures employ phase delay at the element level and time delay at the subarray level. This architecture not only reduces the number of required time delay modules but also reduces pattern loss caused by beam squint, achieving wide bandwidth scanning performance.

[0005] Sidelobe level is one of the important parameters in the comprehensive evaluation of antenna array radiation patterns. However, wide-bandwidth angle scanning structures typically have a slow sidelobe level calculation speed, resulting in a long optimization time. Summary of the Invention

[0006] This invention provides an irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna, which solves the problem of slow sidelobe level calculation and long optimization time in the prior art, and realizes a replacement for direct sidelobe level calculation, thereby improving the calculation efficiency.

[0007] This invention provides a method for irregular subarray partitioning design of a low sidelobe wide-angle scanning array antenna, the method comprising:

[0008] The array surface is discretized into a set of array elements. The array elements in the set of array elements are arranged and combined to obtain a set of subarray position combinations. A dictionary matrix is ​​constructed based on the set of array elements and the set of subarray position combinations.

[0009] The subarray partitioning problem is transformed into a set covering problem. By solving the binary selection vector, the dictionary matrix and the binary selection vector are made into a row vector, thereby obtaining the candidate subarray combination set covering the array.

[0010] An irregular subarray partitioning design model is constructed, and the candidate subarray combination set is screened to obtain the optimal subarray partitioning result;

[0011] Using the optimal subarray partitioning result, the excitation amplitude of the optimal subarray partitioning result is optimized by the Grey Wolf optimization algorithm to reduce the sidelobe level of the radiation pattern.

[0012] In one possible implementation, the array elements corresponding to each subarray position combination in the subarray position combination set are physically adjacent in the array surface.

[0013] In one possible implementation, the dictionary matrix is ​​a binary matrix; wherein, when an element L(m,n) = 1 in the dictionary matrix, it indicates that the nth element belongs to the position combination of the mth subarray; when an element L(m,n) = 0 in the dictionary matrix, it indicates that the nth element does not belong to the position combination of the mth subarray.

[0014] In one possible implementation, the dictionary matrix ensures that each element in the set of elements is uniquely covered.

[0015] In one possible implementation, the step of filtering the candidate subarray combination set to obtain the optimal subarray partitioning result includes:

[0016] Calculate the phase center coordinates corresponding to each subarray position combination in the subarray position combination set, and obtain the corresponding first horizontal axis value set and first vertical axis value set based on the phase center coordinates;

[0017] Calculate the phase center coordinates of each candidate subarray combination in the candidate subarray combination set, and perform statistics based on the phase center coordinates to obtain the corresponding second horizontal axis value set and second vertical axis value set;

[0018] Traverse the elements in the first horizontal axis value set and determine the number of times the j-th element appears in the second horizontal axis value set. Use this number as the number of phase centers of the j-th subarray.

[0019] Iterate through the elements in the first set of values ​​for the vertical axis and determine the number of times the i-th element appears in the second set of values ​​for the vertical axis. Use this number as the number of phase centers of the i-th subarray.

[0020] The variance of each candidate subarray combination is calculated based on the number of phase centers of the j-th column subarray and the number of phase centers of the i-th row subarray, and the candidate subarray combination with the minimum variance is determined as the optimal subarray partitioning result.

[0021] In one possible implementation, the irregular subarray partitioning design model is represented as:

[0022] Find X = [x1, x2, ..., x R ] T

[0023]

[0024] stXL = 1;

[0025]

[0026] Where X represents the binary selection vector; N represents the sum of the first horizontal coordinate X data value and the first vertical coordinate Y data value; v n represents the concatenated vector of row and column statistical features; F(v) represents the variance of the candidate subarray combination; L represents the dictionary matrix; p represents the maximum number of rows of all possible positions of the phase center corresponding to each subarray position combination in the subarray position combination set; q represents the maximum number of columns of all possible positions of the phase center corresponding to each subarray position combination in the subarray position combination set. x represents the number of phase centers of the i-th row subarray; r Indicates whether to select the r-th candidate subarray; R represents the number of phase centers of the j-th subarray; R represents the number of candidate subarray combinations; XL represents a single row vector.

[0027] In one possible implementation, the step of using the optimal subarray partitioning result and employing the Grey Wolf optimization algorithm to optimize the excitation amplitude of the optimal subarray partitioning result to reduce the sidelobe level of the radiation pattern includes:

[0028] An optimization model is constructed; the optimization variable of the optimization model is the excitation amplitude of the optimal subarray partitioning result, and the optimization objective of the optimization model is to minimize the maximum sidelobe level PSLL of the radiation pattern corresponding to the optimal subarray partitioning result.

[0029] The optimization model is solved using the Grey Wolf Optimization Algorithm to obtain the excitation amplitude of the optimized subarray partitioning result; wherein, the Grey Wolf Optimization Algorithm uses a nonlinear convergence factor to adjust the search step size when solving the optimization model.

[0030] In one possible implementation, the radiation pattern is represented as:

[0031]

[0032] Where I is the number of array elements in the array element set; K represents the set of row numbers for all possible positions of the phase center corresponding to the subarray combination in the candidate subarray combination set; x k The x-axis coordinate represents the position of the phase center of the k-th candidate subarray combination; y k The y-coordinate represents the position of the phase center of the k-th candidate subarray combination; u represents the projection of the ray extending arbitrarily into space from the antenna location onto the x-axis; v represents the projection of the ray extending arbitrarily into space from the antenna location onto the y-axis; u0 is the first parameter; v0 is the second parameter; α k The excitation amplitude of the k-th candidate subarray combination is represented by λ; the wavelength is represented by δ. ip λ is the attribution factor; λ0 represents the resonant wavelength; xi The x-axis coordinate represents the position of the phase center of the i-th candidate subarray combination; yi The y-axis coordinate represents the position of the phase center of the i-th candidate subarray combination; F(u,v) represents the radiation pattern when the parameters are u and v.

[0033] In one possible implementation, the optimization model is expressed as:

[0034]

[0035] Where Q represents the excitation amplitude vector; α P α represents the excitation amplitude of the p-th candidate subarray combination; i denoted by , where represents the excitation magnitude of the i-th candidate subarray combination; P represents the number of candidate subarray combinations.

[0036] In one possible implementation, the shape of the subarray position combination includes: a 2-unit rectangle, an L-shape, or a T-shape, and the subarray size can be expanded.

[0037] The present invention provides one or more technical solutions, which have at least the following technical effects or advantages: The present invention employs an irregular subarray partitioning design method for low sidelobe wide-angle scanning array antennas. The physical array surface is abstracted as a discrete set, facilitating subsequent combination optimization and the application of mathematical tools. It supports arbitrary array element arrangement, does not rely on regular grids, and adapts to the needs of complex antenna designs. An irregular subarray partitioning design model is constructed on the candidate subarray combination set to select the optimal partitioning. This optimizes sidelobes and array complexity, avoiding engineering infeasibility caused by a single objective. Screening is only performed on the candidate set, significantly reducing the search space, improving efficiency, and compensating for potential sidelobe problems left over from the subarray partitioning stage. The excitation amplitude of the optimal subarray partitioning result is optimized through the Grey Wolf optimization algorithm. This method combines mathematical modeling and intelligent optimization, significantly improving radiation pattern performance while ensuring coverage completeness, and is suitable for the design of high-performance phased array systems. Attached Figure Description

[0038] Figure 1 A flowchart illustrating the steps of an irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna provided in an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of the subarray position combination set provided in an embodiment of the present invention;

[0040] Figure 3a This is a graph showing the globally optimal result output by the X-axis algorithm provided in this embodiment of the invention.

[0041] Figure 3b The output array structure diagram is provided by the method in the embodiments of the present invention;

[0042] Figure 4a This is a schematic diagram of the optimal irregular subarray provided in an embodiment of the present invention;

[0043] Figure 4b This is a schematic diagram of a suboptimal irregular subarray provided in an embodiment of the present invention;

[0044] Figure 4c This is a schematic diagram of a regular subarray provided in an embodiment of the present invention;

[0045] Figure 5a The optimal irregular matrix scan to θ = 30° is provided in the embodiments of the present invention. Normalized power pattern;

[0046] Figure 5b The suboptimal irregular array provided in this embodiment of the invention is scanned to θ = 30°. Normalized power pattern;

[0047] Figure 5c The regular array provided in this embodiment of the invention is scanned to θ = 30°. The normalized power pattern. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0049] This invention provides a method for irregular subarray partitioning design of a low sidelobe wide-angle scanning array antenna, see [link to relevant documentation]. Figure 1 The method includes the following steps S101 to S104.

[0050] S101, discretize the array surface into a set of array elements, arrange and combine the array elements in the set of array elements to obtain a set of subarray position combinations, and construct a dictionary matrix based on the set of array elements and the set of subarray position combinations.

[0051] Specifically, the array elements corresponding to each subarray position combination in the subarray position combination set are physically adjacent in the array surface.

[0052] A dictionary matrix uniquely covers each element in the set of array elements. The dictionary matrix is ​​a binary matrix; where an element L(m,n) = 1 indicates that the nth element belongs to the mth subarray position combination; and an element L(m,n) = 0 indicates that the nth element does not belong to the mth subarray position combination.

[0053] Here, the shape of the subarray position combination includes, but is not limited to, a 2-unit rectangle, an L-shape, or a T-shape, and the subarray size can be expanded.

[0054] For example, participate Figure 2 ,exist Figure 2 In the structure shown, the array face set is represented as: A = {1, 2, 3, 4, 5, 6}. Taking dominoes (two-unit subarrays) as an example, seven different subarray position combinations can be obtained:

[0055]

[0056] Secondly, construct a dictionary matrix L, which represents the relationship between the set of all distinct subarray positions and the set of array faces.

[0057] Wherein, the dictionary matrix L is represented as:

[0058]

[0059] Each row of the dictionary matrix L represents a submatrix, and each column represents a cell position in the matrix's aperture. Its internal elements are:

[0060]

[0061] S102 transforms the subarray partitioning problem into a set covering problem. By solving the binary selection vector, the dictionary matrix and the binary selection vector are made into a row vector, thus obtaining the candidate subarray combination set of the covering surface.

[0062] For example, the problem of partitioning the matrix into subarrays can be transformed into a set covering problem, which can be described mathematically as follows: the linear superposition of several rows of the dictionary matrix L can yield an exact row vector, i.e., x T L = 1.

[0063] by Figure 2 For example, there are three possible outcomes for the selected vector x that meet the conditions: {1,0,1,0,0,0,1}, {0,1,0,1,1,0,0}, and {0,0,0,0,1,1,1}.

[0064] S103, Construct an irregular subarray partitioning design model, filter the candidate subarray combination set, and obtain the optimal subarray partitioning result;

[0065] Specifically, in step S103, the candidate subarray combination set is screened to obtain the optimal subarray partitioning result, including the following steps S1031 to S1035.

[0066] S1031, calculate the phase center coordinates corresponding to each subarray position combination in the subarray position combination set, and obtain the corresponding first horizontal axis value set and first vertical axis value set based on the phase center coordinates;

[0067] S1032, calculate the phase center coordinates of each candidate subarray combination in the candidate subarray combination set, and perform statistics based on the phase center coordinates to obtain the corresponding second horizontal axis value set and second vertical axis value set.

[0068] S1033, traverse the elements in the first horizontal axis value set, and determine the number of times the j-th element appears in the second horizontal axis value set, and take this number as the number of phase centers of the j-th subarray;

[0069] S1034, traverse the elements in the first vertical axis value set, and determine the number of times the i-th element appears in the second vertical axis value set, and take the number as the number of phase centers of the i-th subarray;

[0070] S1035, calculate the variance of each candidate subarray combination based on the number of phase centers of the j-th column subarray and the number of phase centers of the i-th row subarray, and determine the candidate subarray combination with the minimum variance as the optimal subarray partitioning result.

[0071] Here, the irregular subarray partitioning design model is represented as:

[0072] Find X = [x1, x2, ..., x R ] T

[0073]

[0074] stXL = 1 (1.4);

[0075]

[0076] Where X represents the binary selection vector; N represents the sum of the first horizontal coordinate X data value and the first vertical coordinate Y data value; v n represents the concatenated vector of row and column statistical features; F(v) represents the variance of the candidate subarray combination; L represents the dictionary matrix; p represents the maximum number of rows of all possible positions of the phase center corresponding to each subarray position combination in the subarray position combination set; q represents the maximum number of columns of all possible positions of the phase center corresponding to each subarray position combination in the subarray position combination set. x represents the number of phase centers of the i-th row subarray; r Indicates whether to select the r-th candidate subarray; R represents the number of phase centers of the j-th subarray; R represents the number of candidate subarray combinations; XL represents a single row vector.

[0077] For example, taking S1 as an example, the position coordinates of array elements 1 and 2 are (1, 1) and (1, 2), so the phase center of this subarray is (1, 1.5). Similarly, the phase centers of the remaining six subarrays with different positions are (1, 2.5), (2, 1.5), (2, 2.5), (1.5, 1), (1.5, 2), and (1.5, 3).

[0078] Therefore, the first x-coordinate data value of all possible positions of the phase center of the subarray position combination set is 3 (x-coordinate X = 1, 1.5, 2), and the first y-coordinate data value is 5 (y-coordinate Y = 1, 1.5, 2, 2.5, 3). For the first partition result, i.e., the first value of the x-coordinate X of the binary selection vector, S1, S3, and S7 were selected. At this time, the total number of phase centers in each row and each column of the candidate subarray combination set is (1, 1, 1) and (0, 2, 0, 0, 1), respectively.

[0079] Calculate the total number of subarray phase centers in each row and column for each array partitioning scheme, and solve for the variance of this data. For example, see... Figure 4c , Figure 4c The regular subarray shown uses a 1×2 subarray, so the subarray phase centers are only distributed in columns 1, 3, 5, 7, 9, and 11, while columns 2, 4, 6, 8, and 10 have no subarray phase centers. That is, the distribution of the number of subarray phase centers in each column is [12 0 12 0 12 0 12 0 12 0 12 0 12 0], and its column variance is 39.2727.

[0080] This invention uses the total variance as the optimization objective. An optimization model is established, as shown in formula (1.4). Specifically, in formula (1.4), the variance corresponding to the number of phase centers of the row-arranged subarrays is expressed as:

[0081]

[0082] The variance corresponding to the number of phase centers of the subarray arranged in columns is expressed as follows:

[0083]

[0084] This optimization problem is a nonlinear pure integer programming problem, which can be solved using the Gurobi solver.

[0085] S104. Using the optimal subarray partitioning result, the gray wolf optimization algorithm is used to optimize the excitation amplitude of the optimal subarray partitioning result in order to reduce the sidelobe level of the radiation pattern.

[0086] Specifically, in step S104, the excitation amplitude of the optimal subarray partitioning result is optimized using the Grey Wolf optimization algorithm to reduce the sidelobe level of the radiation pattern, including the following steps S1041 to S1042.

[0087] S1041, Construct an optimization model; The optimization variable of the optimization model is the excitation amplitude of the optimal subarray partitioning result, and the optimization objective of the optimization model is to minimize the maximum sidelobe level PSLL of the radiation pattern corresponding to the optimal subarray partitioning result;

[0088] Here, the optimization model is represented as:

[0089] Find Q = [α1, α2, ..., α P ] T

[0090] min PSLL(1.7);

[0091] stα i ≥0.2i=1,2,…,P

[0092] α i≤1i=1,2,…,P

[0093] Where Q represents the excitation amplitude vector; α P α represents the excitation amplitude of the p-th candidate subarray combination; i represents the excitation amplitude of the i-th candidate subarray combination; P represents the number of candidate subarray combinations; PSLL represents the maximum sidelobe level.

[0094] S1042, the Grey Wolf Optimization Algorithm is used to solve the optimization model to obtain the excitation amplitude of the optimal subarray partitioning result after optimization; wherein, the Grey Wolf Optimization Algorithm uses a nonlinear convergence factor to adjust the search step size when solving the optimization model.

[0095] Here, the optimization model is represented as:

[0096]

[0097] Where I is the number of array elements in the array element set; K represents the set of row numbers for all possible positions of the phase center corresponding to the subarray combination in the candidate subarray combination set; x k The x-axis coordinate represents the position of the phase center of the k-th candidate subarray combination; y k The y-coordinate represents the position of the phase center of the k-th candidate subarray combination; u represents the projection of the ray extending arbitrarily into space from the antenna location onto the x-axis; v represents the projection of the ray extending arbitrarily into space from the antenna location onto the y-axis; u0 is the first parameter; v0 is the second parameter; α k The excitation amplitude of the k-th candidate subarray combination is represented by λ; the wavelength is represented by δ. ip λ is the attribution factor; λ0 represents the resonant wavelength; xi The x-axis coordinate represents the position of the phase center of the i-th candidate subarray combination; yi The y-axis coordinate represents the position of the phase center of the i-th candidate subarray combination; F(u,v) represents the radiation pattern when the parameters are u and v.

[0098] For example, for a planar phased array precisely covered by P subarrays, its radiation pattern can be expressed as formula (1.8), based on the optimal subarray partitioning result obtained in step S103 and the nonlinear convergence factor. The gray wolf optimization algorithm is used to optimize the excitation amplitude of the optimal subarray partitioning result to reduce the sidelobe level, as shown in formula (1.7). The value of Y ranges from [0.2, 1], and PSLL is the maximum sidelobe level.

[0099] Understandably, the nonlinear convergence factor This was obtained using existing papers. Among them, t max t represents the maximum number of iterations; t represents the current number of iterations.

[0100] This invention proposes a method to replace direct calculation of sidelobe levels, thereby improving computational efficiency. Research has shown a correlation between sidelobe levels and the periodicity of the subarray phase centers. For periodically arranged subarrays, their phase centers tend to cluster in specific rows (Y-axis direction) or columns (X-axis direction), causing the number of phase centers in each row or column to deviate from a uniform distribution. Therefore, the greater the variance of the subarray partitioning structure, the more periodic it is, and vice versa. Thus, the irregularity of the subarray arrangement can be described by the variance of the total number of subarray phase centers in each row and column of the array distribution. The smaller the variance, the more irregular the subarray arrangement, and the lower the corresponding sidelobe level.

[0101] In a simulation experiment provided by this invention, a rectangular array with a half-wavelength spacing of 432 (M=12, N=36) units is used, and an L-shaped eight-unit subarray is adopted.

[0102] In existing technologies, using the X algorithm to find all 12,845,409 partitioning schemes that meet the exact coverage requirement takes approximately 1.296 × 10⁻⁶ time. 6 The time required to select the optimal partitioning scheme is approximately 8.896 × 10 seconds. 6 Seconds. The globally optimal result found is as follows: Figure 3a As shown. Under the same conditions, this invention took 489.4 seconds to output the result. Figure 3b This is the array structure output by the minimum variance method.

[0103] SLL = min{SLL broadsie +SLL scan}(1.9);

[0104] Where SLL represents the maximum sidelobe level of the radiation pattern, SLL broadsie SLL scan These represent the sidelobe levels at static and scanned to [20°, 0°], respectively.

[0105] Table 1 presents a comparison of the electrical sweep performance of the exact coverage solutions obtained by the X algorithm and the minimum variance method. As can be seen from Table 1, the electrical sweep performance of the output structure of the method of this invention is basically consistent with that of the optimal solution obtained by global search. Under constant amplitude conditions, the difference in the highest sidelobe level between the two is approximately 0.5 dB; after optimizing the excitation amplitude, the difference in the highest sidelobe level is only approximately 0.1 dB. Intuitively, the method of this invention can effectively and quickly obtain the target solution.

[0106] Table 1 Comparison of scanning performance between the two methods

[0107]

[0108] Simulation Experiment 2:

[0109] (1) Simulation parameters

[0110] A rectangular array with 144 (M=N=12) elements and a half-wavelength spacing is used, employing a two-element subarray. A broadband condition f / f0=1.2 is set under a wide bandwidth and wide-angle scanning structure.

[0111] (2) Simulation content and results

[0112] First, the optimal and second-best irregular subarray partitioning schemes are obtained using the minimum variance criterion, and their variances are compared with those of the regular subarray (the worst irregular subarray partitioning scheme) (see Table 2.1). For details, see... Figure 4a For the optimal irregular subarray, see [link / reference]. Figure 4b It is a suboptimal irregular subarray. Figure 4c The first subarray is a regular subarray. The optimal and second-best irregular subarrays have the same total variance. The optimal irregular subarray has consistent row and column variances, while the second-best irregular subarray has a smaller column variance, sometimes even better than the optimal one. The regular subarray has a zero row variance but a large column variance, resulting in the largest total variance.

[0113] Next, the excitation amplitude of the optimal subarray partitioning result is optimized using the Grey Wolf optimization algorithm to obtain three arrays, which are then scanned:

[0114]

[0115] The corresponding sidelobe levels (see Table 2.2). The optimal irregular subarray is in and The scanning directions have similar sidelobe levels, while the suboptimal irregular subarrays have... The directional scanning sidelobe level is the lowest, superior to the sidelobe level in that direction of the optimal irregular subarray. The regular array in... The scanning sidelobe level in the direction is poor, but... The scanning sidelobe level is optimal in this direction. Scan to... At that time, the sidelobe level of the regular array was about 8dB higher than that of the irregular array, when scanning to... At that time, it was more than 10dB higher.

[0116] See Figure 5a The optimal irregular matrix is ​​scanned to θ = 30°. Normalized power direction; see Figure 5b The suboptimal irregular matrix is ​​scanned to θ = 30°. Normalized power direction; see Figure 5c The regular matrix is ​​scanned to θ = 30°. The normalized power pattern.

[0117] According to Tables 2.1 and 2.2, the smaller the variance of the subarray rows (columns), the better. The better the scanning sidelobe level in the direction, the more effectively the irregular subarray widens the scanning angle. Therefore, judging the scanning capability of the irregular array only requires calculating its variance, reducing complexity. The irregular subarray partitioning design method of this invention can achieve better electrical performance under non-single scanning angles, and achieves high efficiency with an output structure that meets the requirements of low sidelobe level and wide-angle scanning.

[0118] Table 2.1 Variance calculation results for the three arrays

[0119] Optimal irregular matrix Suboptimal irregular array Rule Array Total variance var(v) 0.6493 0.6493 18.7826 <![CDATA[Row variance var(v row )]]> 0.6640 0.7549 0 <![CDATA[Column variance var(v col )]]> 0.6640 0.5731 39.2727

[0120] Table 2.2 Scanning performance information of the three arrays

[0121]

[0122]

[0123] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this invention can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0124] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present invention.

Claims

1. A method for irregular subarray partitioning design of a low sidelobe wide-angle scanning array antenna, characterized in that, include: The array surface is discretized into a set of array elements. The array elements in the set of array elements are arranged and combined to obtain a set of subarray position combinations. A dictionary matrix is ​​constructed based on the set of array elements and the set of subarray position combinations. The subarray partitioning problem is transformed into a set covering problem. By solving the binary selection vector, the dictionary matrix and the binary selection vector are made into a row vector, thereby obtaining the candidate subarray combination set covering the array. An irregular subarray partitioning design model is constructed, and the candidate subarray combination set is screened to obtain the optimal subarray partitioning result; The process of filtering the candidate subarray combination set to obtain the optimal subarray partitioning result includes: Calculate the phase center coordinates corresponding to each subarray position combination in the subarray position combination set, and obtain the corresponding first horizontal axis value set and first vertical axis value set based on the phase center coordinates; Calculate the phase center coordinates of each candidate subarray combination in the candidate subarray combination set, and perform statistics based on the phase center coordinates to obtain the corresponding second horizontal axis value set and second vertical axis value set; Iterate through the elements in the first set of horizontal axis values ​​and determine the first... The number of times each element appears in the second horizontal axis value set is used as the number of times the element appears. The number of phase centers of the subarray; Iterate through the elements in the set of values ​​for the first vertical axis and determine the first... The number of times each element appears in the set of values ​​on the second vertical axis is taken as the number of times the element appears. The number of phase centers of the subarray; According to the The number of phase centers of the subarray and the first The variance of each candidate subarray combination is obtained by calculating the number of phase centers of the row subarray, and the candidate subarray combination with the minimum variance is determined as the optimal subarray partitioning result. Using the optimal subarray partitioning result, the excitation amplitude of the optimal subarray partitioning result is optimized using the Grey Wolf optimization algorithm to reduce the sidelobe level of the radiation pattern; the process of using the optimal subarray partitioning result and optimizing the excitation amplitude of the optimal subarray partitioning result using the Grey Wolf optimization algorithm to reduce the sidelobe level of the radiation pattern includes: An optimization model is constructed; the optimization variable of the optimization model is the excitation amplitude of the optimal subarray partitioning result, and the optimization objective of the optimization model is to minimize the maximum sidelobe level PSLL of the radiation pattern corresponding to the optimal subarray partitioning result. The optimization model is solved using the Grey Wolf Optimization Algorithm to obtain the excitation amplitude of the optimized subarray partitioning result; wherein, the Grey Wolf Optimization Algorithm uses a nonlinear convergence factor to adjust the search step size when solving the optimization model.

2. The irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna according to claim 1, characterized in that, The array elements corresponding to each subarray position combination in the subarray position combination set are physically adjacent in the array surface.

3. The irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna according to claim 1, characterized in that, The dictionary matrix is ​​a binary matrix; wherein, the elements in the dictionary matrix... When, it indicates the first Each array element belongs to the first The positional combination of subarrays; the elements in the dictionary matrix When, it indicates the first The individual element does not belong to the first Individual formation positional combinations.

4. The irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna according to claim 1, characterized in that, The dictionary matrix ensures that each element in the set of array elements is uniquely covered.

5. The irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna according to claim 1, characterized in that, The irregular subarray partitioning design model is represented as follows: ； in, Represents a binary selection vector; This represents the sum of the first horizontal coordinate (X) data value and the first vertical coordinate (Y) data value; This represents a vector concatenated from row and column statistical features; This represents the variance of the candidate subarray combination; Represents a dictionary matrix; This represents the maximum number of rows for all possible positions of the phase center corresponding to each subarray position combination in the subarray position combination set; This represents the maximum number of columns corresponding to all possible positions of the phase center in the set of subarray position combinations; Indicates the first The number of phase centers of the row subarray; Indicate whether to select the first option A candidate subarray; Indicates the first The number of phase centers of the subarray; Indicates the number of candidate subarray combinations; This represents a single-row vector.

6. The irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna according to claim 1, characterized in that, The radiation pattern is represented as follows: ； in, It is the number of array elements in the array element set; This represents the set of row numbers for all possible positions of the phase center corresponding to a subarray combination in the candidate subarray combination set; Indicates the first The x-axis coordinate of the phase center position of each candidate subarray combination; Indicates the first The y-axis coordinate of the phase center position of each candidate subarray combination; This represents the projection of the ray extending arbitrarily into space from the location of the antenna onto the horizontal X-axis. This represents the projection of the ray extending arbitrarily into space from the location of the antenna onto the vertical Y-axis. The first parameter; This is the second parameter; Indicates the first The incentive magnitude of each candidate subarray combination; Indicates wavelength; As aggregator factor; Indicates the resonant wavelength; Indicates the first The x-axis coordinate of the phase center position of each candidate subarray combination; Indicates the first The y-axis coordinate of the phase center position of each candidate subarray combination; The parameter is , Direction diagram at time.

7. The irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna according to claim 1, characterized in that, The optimization model is expressed as follows: ； in, Represents the excitation amplitude vector; Indicates the first The incentive magnitude of each candidate subarray combination; Indicates the first The incentive magnitude of each candidate subarray combination; Indicates the number of candidate subarray combinations; This indicates the maximum sidelobe level.

8. The irregular subarray partitioning design method for a low sidelobe wide-angle scanning array antenna according to claim 1, characterized in that, The shape of the subarray position combination includes: 2-unit rectangle, L-shape or T-shape, and the subarray size can be expanded.

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