Sparse antenna array synthesis method based on neural network intelligent solution

Abstract

The invention relates to a sparse antenna array synthesis method based on neural network intelligent solution, and the method comprises the steps: carrying out the full-wave simulation of an antenna unit according to an array synthesis requirement, obtaining a unit active directional diagram, and constructing a unit active directional diagram database; constructing an initial sample set according to the unit active directional diagram and the array numerical simulation model; randomly dividing the initial sample set into a training set and a verification set, carrying out offline training by utilizing the training set, and carrying out array comprehensive solution on verification set data; judging whether the resolving result meets the arraying requirement or not, if not, adopting a convex optimization algorithm to assist calculation, updating the data set, and retraining; otherwise, outputting the neural network model; and through the model, according to the unit spacing constraint and the active directional diagram, sparse antenna array synthesis is carried out, and array topology is output. The method can achieve the comprehensive settlement of the antenna array, is high in speed, is high in efficiency, and better accords with a real array working environment.

Description

Technical Field

[0001] The present invention relates to the technical field of phased array antennas, and in particular to a sparse antenna array synthesis method based on neural network intelligent solution. Background Art

[0002] Phased array antennas are widely used in various radar communications, remote sensing detection, and astronomical systems. As a key component for the conversion of space electromagnetic waves and guided waves in electronic systems, their performance indicators directly affect the system performance. Array antenna synthesis technology can reversely synthesize the number of elements, arrangement, element spacing, excitation amplitude and phase of the array antenna according to the expected radiation pattern performance, which is the starting point and key to antenna design.

[0003] The cost and power consumption of active phased arrays mainly come from the transceiver components. Especially for high-gain antennas, reducing the number of active channels will greatly reduce the complexity and cost of the system, and sparse array technology is applied. Compared with the traditional uniform array method, the distribution of antenna units is not limited to rectangular or active triangular grids, and the unit spacing is no longer limited to half a wavelength. High-gain, high-resolution narrow beam radiation can be achieved with less hardware equipment, which brings greater flexibility to system design and reduces the hardware cost of the system.

[0004] However, sparse or sparse arrays will lead to the generation of scanning grating lobes, which need to be solved by unit position arrangement and excitation amplitude and phase optimization. The key to the design lies in an effective array synthesis algorithm. Array synthesis is often a multi-objective, multi-parameter nonlinear optimization problem. Traditional exhaustive methods, analytical algorithms, gradient iterative optimization algorithms, random optimization algorithms, convex optimization algorithms and their hybrid solutions show their respective advantages for different synthesis problems. However, when considering the optimization of sparse arrays, the convergence and computational efficiency of the algorithm will be difficult to guarantee when considering the synthesis of multi-dimensional variables such as unit mutual coupling and array position. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a sparse antenna array synthesis method based on neural network intelligent solution, so as to solve the problem that the traditional array synthesis method converges slowly and is easy to fall into the local optimal solution.

[0006] To achieve the above-mentioned object of the invention, the present invention provides a sparse antenna array synthesis method based on neural network intelligent solution, comprising the following steps:

[0007] Step S1, according to the comprehensive requirements of the array, perform full-wave simulation of the antenna unit, obtain the unit active pattern, and construct a unit active pattern database; the comprehensive requirements of the array include array requirements and expected radiation performance, the array requirements include sparsity rate, array dimension, unit spacing constraints, and the expected radiation performance includes gain, scanning angle, beam width, peak sidelobe, and axial ratio;

[0008] Step S2, obtaining the whole array pattern according to the unit active pattern and the array numerical simulation model, and constructing an initial sample set according to the whole array pattern and array parameters;

[0009] Step S3, randomly dividing the initial sample set into a training set and a validation set, and using the training set to perform offline neural network model training;

[0010] Step S4: Based on the trained neural network model, perform array comprehensive solution on the verification set data;

[0011] Step S5, judging whether the solution result of the validation set data meets the array requirements; if not, using a convex optimization algorithm to assist in the calculation, and using the data obtained by the auxiliary calculation to update the data set and retrain the neural network model; otherwise, outputting the neural network model;

[0012] Step S6: Perform sparse antenna array synthesis through the output neural network model according to the input array synthesis requirements and the unit active radiation pattern of the antenna array, and output the array topology.

[0013] According to a technical solution of the present invention, in step S1, it specifically includes:

[0014] Step S11: According to the unit spacing constraint and the antenna unit form, the array element spacing is changed, and a full-wave simulation is performed to obtain the unit active radiation pattern. where θ and are the elevation angle and azimuth angle of the antenna coordinate system;

[0015] Step S12: the active directional pattern of the unit is adjusted according to the operating frequency f. Sampling is performed to obtain the unit active directional diagram corresponding to different operating frequencies Step S13: construct the unit active pattern data set according to the corresponding relationship between the array topology and the unit active pattern, wherein the samples in the unit active pattern data set are represented as

[0016] According to a technical solution of the present invention, the antenna unit form is determined according to the comprehensive requirements of the array:

[0017] The antenna array aperture is determined according to the gain, the antenna element scale is obtained according to the sparsity rate, and the antenna unit type is determined according to the scanning angle and gain. When the antenna scanning gain loss is small, a wide-beam antenna unit is used for full-wave simulation. When the antenna gain requirement is high, a high-gain antenna unit is used for full-wave simulation.

[0018] According to a technical solution of the present invention, in step S11, the full-wave simulation includes: according to the unit spacing constraint Γ=[d xmin ,d xmax ,d ymin ,d ymax ], within the cell spacing range of the cell spacing constraint, set the array topology set L = {[x p ,y q ]|p=1,2,…,N x ,q=1,2,…N y}, select the unit and a group of adjacent units for full-wave simulation, change the unit spacing, and obtain the unit active pattern The unit spacing range is expressed as: d x =|x p -x p-1 |∈[d xmin ,d xmax ],d y =|y q -y q-1 |∈[d ymin ,d ymax ], where p and q represent the unit numbers along the x-axis and y-axis respectively, and d x represents the unit spacing along the x-axis, d y represents the unit spacing along the y-axis, d xmin represents the minimum cell spacing along the x-axis, d ymin Indicates the minimum cell spacing along the y-axis.

[0019] According to a technical solution of the present invention, in the step S2, it specifically includes:

[0020] Step S21, according to the array comprehensive requirements and the unit active directional pattern, the directional pattern is calculated through the array numerical simulation model to obtain the directional pattern of the entire array, which is expressed as:

[0021]

[0022] in, represents the whole array pattern of the antenna array with array topology l and operating frequency f; I p,qrepresents the excitation amplitude of the antenna unit; k represents the wave number corresponding to the operating frequency f, k = 2πf / c, c is the speed of light; w = {w p,q |p=1,2,…,N x ,q=1,2,…N y} represents the phase of the antenna array. The phase w of the antenna array is generated according to the array topology and can be expressed as Where k0 represents the wave number k0 = 2πf0 / c when working at frequency f = f0, c is the speed of light, and the scanning is hour, N x 、N y Indicates the number of array elements distributed along the x-axis and y-axis;

[0023] Step S22: construct an initial sample set according to the unit active directional pattern and the whole array directional pattern, wherein each sample in the initial sample set includes an input vector and a sample label, and the initial sample set is represented as S={(X m ,Y m )|m=1,2,…M};

[0024] Among them, X m is the input vector, including the unit active pattern, the whole array pattern, the beam width, and the sidelobe level, expressed as in is the unit active pattern of the antenna array, and its operating frequency is f, represents the entire array pattern of the antenna array, Indicates scanning to The beam width at Indicates scanning to The sidelobe level at

[0025] Y m is the sample label, including the array topology of the antenna array represented by Y m =L.

[0026] According to a technical solution of the present invention, when the desired radiation performance also includes cross-polarization, in full-wave simulation, the cross-polarized unit active pattern component is extracted, and the input vector also includes the whole array cross-polarization level, and is expressed as

[0027]

[0028] Among them, E f,co Indicates the cross-polarization level of the entire array.

[0029] According to a technical solution of the present invention, in step S3, it specifically includes:

[0030] Step S31, dividing the initial sample set into a training set and a validation set in a ratio of 8:2 by a random division method, and normalizing them;

[0031] Step S32, constructing a neural network model, and determining the number of network layers and the number of neurons in each layer according to the input vector dimension and the output vector dimension; wherein the array element scale is independent of the input vector dimension and is related to the output vector dimension, and the neural network model adopts a double hidden layer structure feedforward network model, and the number of neurons in the two hidden layers is the same, and the number of neurons is determined according to the array dimension;

[0032] Step S33, setting the number of training times, training target, learning rate, transfer function, training function and training error;

[0033] Step S34, substituting the verification set into the neural network model for verification to determine whether the training error meets the requirements;

[0034] Step S35: When the training error does not meet the requirement, return to step S32 to adjust the network parameters and retrain;

[0035] Step S36: When the output meets the training error requirement, the network model and weight coefficients are saved to obtain the offline trained neural network model.

[0036] According to a technical solution of the present invention, in step S5, it specifically includes:

[0037] Convex optimization model is established, expressed as

[0038]

[0039] Among them, E ref represents the expected pattern, and ε represents the fitting error;

[0040] Through the convex optimization model, the set of optimized array topology L'={[x p ',y q ']|p=1,2,…,N x ,q=1,2,…N y};

[0041] The optimized data is added to the training set to form a new training set S'={(X' m ,Y' m )|m=1,2,…M+M a}, retrain the network using the new training set, and at the same time, the set of optimization results can be directly output as comprehensive results.

[0042] According to one aspect of the present invention, an electronic device is provided, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes the above-mentioned sparse antenna array synthesis method based on neural network intelligent solution.

[0043] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, and when the computer instructions are executed by a processor, the above-mentioned sparse antenna array synthesis method based on neural network intelligent solution is implemented.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The sparse antenna array synthesis method based on neural network intelligent solution provided by the present invention establishes an artificial neural network with array pattern and pattern performance index as input and unit pattern and array topology as output, avoiding cumbersome manual synthesis and feedback process, improving multi-objective optimization efficiency, and having more general practicality; traditional array synthesis algorithms are often based on the element optimal idea, respectively optimizing the unit pattern and array topology to the optimal solution, and then directly combining them, and the combined result may not be the optimal solution. The present invention realizes the intelligent solution of multi-objective optimization by introducing artificial neural network, and introduces the complementary design of unit pattern and array topology based on the idea of ​​system optimality to achieve the optimization of system effect.

[0046] The present invention adopts a multi-layer neural network training method for array synthesis, which effectively avoids the problems of slow convergence and easy falling into local optimal solutions of traditional array synthesis methods. Through array forward analysis combined with network training, the multi-dimensional parameter constraint relationship of array synthesis can be intelligently obtained to obtain a sparse topology that meets the design constraints.

[0047] The present invention introduces the relationship between array topology change and array element mutual coupling effect into array synthesis, and obtains the unit active directional pattern under different topology rules as the element factor of array synthesis through neural network training prediction, which is more in line with the actual array working environment; and the network training based on full-wave simulation data can intelligently fit the nonlinear change relationship, which has advantages over conventional algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flowchart schematically shows a sparse antenna array synthesis method based on neural network intelligent solution provided in accordance with one embodiment of the present invention;

[0050] Figure 2 A schematic diagram showing a principle diagram of a sparse antenna array synthesis method based on neural network intelligent solution provided in accordance with an embodiment of the present invention;

[0051] Figure 3 Schematically showing a flow chart of offline neural network training according to one embodiment of the present invention;

[0052] Figure 4 Schematically showing a schematic diagram of coupling area of ​​unit modeling in a planar array according to one embodiment of the present invention;

[0053] Figure 5 Schematically showing a low sidelobe array pattern obtained by using a sparse array according to one embodiment of the present invention;

[0054] Figure 6 A schematic diagram schematically shows the topological positions of array elements corresponding to a sparse array low sidelobe pattern according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The description of the embodiments of this specification should be combined with the corresponding drawings, which should be considered as part of the complete specification. In the drawings, the shape or thickness of the embodiments may be enlarged and indicated for simplification or convenience. Furthermore, the parts of each structure in the drawings will be described separately. It is worth noting that the elements not shown in the drawings or not described in words are in a form known to ordinary technicians in the relevant technical field.

[0056] The description of the embodiments herein and any reference to directions and orientations are only for the convenience of description and are not to be construed as any limitation on the scope of protection of the present invention. The following description of the preferred embodiments may involve combinations of features, which may exist independently or in combination, and the present invention is not particularly limited to the preferred embodiments. The scope of the present invention is defined by the claims.

[0057] like Figures 1 to 6As shown, a sparse antenna array synthesis method based on neural network intelligent solution of the present invention includes the following steps:

[0058] Step S1, according to the comprehensive requirements of the array, perform full-wave simulation of the antenna unit, obtain the unit active pattern, and construct a unit active pattern database; the comprehensive requirements of the array include array requirements and expected radiation performance, the array requirements include sparsity rate, array dimension, unit spacing constraints, and the expected radiation performance includes gain, scanning angle, beam width, peak sidelobe, axial ratio, and further, cross-polarization level;

[0059] In step S1, it specifically includes:

[0060] Step S11: According to the unit spacing constraint and the antenna unit form, the array element spacing is changed, and a full-wave simulation is performed to obtain the unit active radiation pattern. where θ and are the elevation angle and azimuth angle of the antenna coordinate system;

[0061] The antenna unit form is determined according to the comprehensive array requirements: the array aperture of the antenna is determined according to the gain, the array element scale of the antenna is obtained according to the sparsity rate, and the unit type of the antenna is determined according to the scanning angle and gain; when the antenna scanning gain loss is small, a wide-beam antenna unit is used for full-wave simulation; when the antenna gain requirement is high, a high-gain antenna unit is used for full-wave simulation.

[0062] Model the wide-angle scanning unit and high-gain antenna unit separately. A specific radiation unit model can be selected as a representative to optimize the unit model under the radiation boundary condition. Array modeling is performed based on the unit model. Since antenna mutual coupling is related to the array element spacing and configuration, the adjacent unit in the array contributes the most to unit coupling. Therefore, after selecting the unit form, full-wave simulation is performed in combination with the minimum array sparsity requirement. The steps of full-wave simulation include:

[0063] According to the cell spacing constraint Γ = [d xmin ,d xmax ,d ymin ,d ymax ], within the cell spacing range of the cell spacing constraint, set the array topology set L = {[x p ,y q ]|p=1,2,…,N x ,q=1,2,…N y}, select the unit and a group of adjacent units for full-wave simulation, change the unit spacing, and obtain the unit active pattern The cell spacing range is expressed as:

[0064] d x =|x p -x p-1|∈[d xmin ,d xmax ],d y =|y q -y q-1 |∈[d ymin ,d ymax ],

[0065] Where p and q represent the numbers of the antenna units along the x-axis and y-axis respectively, and d x represents the unit spacing along the x-axis, d y represents the unit spacing along the y-axis, d xmin represents the minimum cell spacing along the x-axis, d ymin Indicates the minimum cell spacing along the y-axis.

[0066] When changing the cell spacing, use step x =0.01λ, step y =0.01λ changes the unit spacing, expressed as changing the unit spacing d x =[0.45λ:0.01λ:λ], d y =[0.45λ:0.01λ:λ].

[0067] In order to meet the needs of planar array synthesis, different azimuth planes are The active pattern is sampled In order to reduce the amount of data and speed up training, the azimuth sampling data set can be selected as

[0068] Step S12: the unit active pattern is adjusted according to the operating frequency f. Sampling is performed to obtain the unit active directional diagram corresponding to different operating frequencies

[0069] Step S13: construct a unit active pattern data set according to the corresponding relationship between the array topology and the unit active pattern. The samples in the unit active pattern data set are represented as

[0070] l represents the unit active pattern The corresponding array topology is the geometric arrangement of antenna units in the antenna array. The set L of array topologies includes several array topologies l in different forms, and the unit spacing of each array topology l is different.

[0071] Step S2, obtaining the whole array pattern according to the unit active pattern and the array numerical simulation model, and constructing an initial sample set according to the whole array pattern and array parameters;

[0072] In step S2, it specifically includes:

[0073] Step S21: According to the array comprehensive requirements and the unit active directional pattern, the directional pattern is calculated through the array numerical simulation model to obtain the directional pattern of the entire array, which is expressed as:

[0074]

[0075] in, represents the whole array pattern of the antenna array with array topology l and operating frequency f; I p,q represents the excitation amplitude of the antenna unit. The excitation amplitude of the antenna unit can be uniformly weighted to generate the initial distribution; k represents the wave number corresponding to the operating frequency f, k = 2πf / c, c is the speed of light; w represents the phase of the antenna array. The phase w is generated according to the array topology and can be expressed as w = {w p,q |p=1,2,…,N x ,q=1,2,…N y} represents the phase of the antenna array. The phase w is generated according to the array topology and can be expressed as

[0076]

[0077] Where k0 represents the wave number k0 = 2πf0 / c when working at frequency f = f0, c is the speed of light, and the scanning is hour, N x 、N y Indicates the number of array elements distributed along the x-axis and y-axis.

[0078] By setting the excitation amplitude I and phase w of the antenna unit, the whole array radiation pattern with different amplitude weighting and different scanning directions is generated to obtain the corresponding beam width Sidelobe level As the data of the training dataset.

[0079] Considering the amplitude and phase quantization effects of the analog array, the weights are set according to the number of quantization bits and accuracy of the chip components. Generally speaking, for a 6-bit phase shifter, the excitation phase w = [0:5.325°:360°] can be set.

[0080] Step S22: construct an initial sample set according to the unit active pattern and the expected radiation performance. Each sample in the initial sample set includes an input vector and a sample label. The initial sample set is represented by S={(X m ,Y m )||m=1,2,…M};

[0081] Among them, X m is the input vector, including the unit active pattern, the whole array pattern, the beam width, and the sidelobe level, expressed as in is the unit active pattern of the antenna array, and its operating frequency is f, represents the entire array pattern of the antenna array, Indicates scanning to The beam width at Indicates scanning to The sidelobe level at

[0082] Y m is a vector of sample labels, including a collection of array topologies, denoted as Y m =L.

[0083] When the expected radiation performance also includes cross-polarization, in full-wave simulation, the cross-polarization unit active pattern component is extracted, and the input vector also includes the cross-polarization level of the entire array, which is expressed as

[0084]

[0085] Among them, E f,co Indicates the cross-polarization level of the entire array.

[0086] Step S3, randomly dividing the initial sample set into a training set and a validation set, and using the training set to perform offline neural network model training;

[0087] In step S3, it specifically includes:

[0088] Step S31, dividing the initial sample set into a training set and a validation set in a ratio of 8:2 by a random division method, and normalizing them;

[0089] Step S32, constructing a neural network model, and determining the number of network layers and the number of neurons in each layer according to the input vector dimension and the output vector dimension; wherein the array element scale is independent of the input vector dimension and is related to the output vector dimension, and the neural network model adopts a double hidden layer structure feedforward network model, and the number of neurons in the two hidden layers is the same, and the number of neurons is determined according to the array dimension;

[0090] Step S33, setting the number of training times, training target, learning rate, transfer function, training function and training error;

[0091] Optionally, you can set the transfer function to the transfer and purelin functions, and the training function to the trainlm function.

[0092] Step S34, substituting the verification set into the neural network model for verification to determine whether the training error meets the requirements;

[0093] Step S35: When the training error does not meet the requirement, return to step S32 to adjust the network parameters and retrain;

[0094] Step S36: When the output meets the training error requirement, the network model and weight coefficients are saved to obtain the offline trained neural network model.

[0095] When training a feedforward neural network model, the model convergence is accelerated by reasonably setting the network parameters. At the same time, the corresponding output vector dimension is modified according to the array scale, and the number of hidden layer neurons is reasonably set to improve the convergence speed and prediction accuracy.

[0096] Step S4: Based on the trained neural network model, perform array comprehensive solution on the verification set data;

[0097] The model verification is carried out based on the trained neural network model. The verification set data is substituted into the array comprehensive solution to obtain the normalized array topological position and amplitude and phase weights.

[0098] Step S5, judging whether the solution result of the validation set data meets its array requirements, if not, using a convex optimization algorithm to assist in the calculation, and using the data obtained by the auxiliary calculation to update the data set and retrain the neural network model; otherwise, outputting the neural network model;

[0099] When verifying the solution results of the validation set data, verify whether the solution results meet the sparse rate requirements, array dimension requirements, and unit spacing constraint requirements. If they meet, output the neural network model; if not, use convex optimization to assist in the calculation of the solution results.

[0100] In step S5, the convex optimization algorithm is used to assist the calculation, specifically including:

[0101] Convex optimization model is established, expressed as

[0102]

[0103] Among them, min means taking the minimum, st means the constraint condition, E ref represents the expected pattern, and ε represents the fitting error.

[0104] Using the convex optimization algorithm, the array topology set L'={[x p ',y q ']|p=1,2,…,N x ,q=1,2,…N y};

[0105] The optimized data is added to the training set to form a new training set S'={(X' m ,Y' m)|m=1,2,…M+M a}, retrain the network using the new training set, and at the same time, the set of optimization results can be directly output as comprehensive results.

[0106] Determine whether the array requirements are met. If the constraints are not met, use a convex optimization algorithm to assist in the calculation, and update the calculated data to the data set to retrain the network model. Furthermore, other types of optimization algorithms can also be used to generate new training data sets and update the network model online. Map the normalized output results to variables and output the optimized array topology position and channel amplitude and phase results.

[0107] Step S6: Perform sparse antenna array synthesis through the output neural network model according to the input unit spacing constraint and active radiation pattern, and output array topology, excitation amplitude and excitation phase.

[0108] Compared with traditional synthesis algorithms, the present invention can intelligently obtain the nonlinear relationship between the array's multi-dimensional parameters and the radiation pattern through forward analysis simulation of the array and training of the neural network model, and intelligently solve multiple radiation expectation values, which is conducive to reverse synthesis design under multi-variable coupling and multi-objective constraints. By adopting the method of offline training and online updating, the array topology excitation that meets the index requirements can be quickly obtained.

[0109] According to one aspect of the present invention, an electronic device includes: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes the above-mentioned sparse antenna array synthesis method based on neural network intelligent solution.

[0110] According to one aspect of the present invention, a computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the above-mentioned sparse antenna array synthesis method based on neural network intelligent solution is implemented.

[0111] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0112] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for those skilled in the art, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A sparse antenna array synthesis method based on neural network intelligent solution, characterized in that: The following steps are involved: Step S1, according to the comprehensive requirements of the array, perform full-wave simulation of the antenna unit, obtain the unit active pattern, and construct a unit active pattern database; the comprehensive requirements of the array include array requirements and expected radiation performance, the array requirements include sparsity rate, array dimension, unit spacing constraints, and the expected radiation performance includes gain, scanning angle, beam width, peak sidelobe, and axial ratio; Step S2, obtaining the whole array pattern according to the unit active pattern and the array numerical simulation model, and constructing an initial sample set according to the whole array pattern and array parameters; Step S3, randomly dividing the initial sample set into a training set and a validation set, and using the training set to perform offline neural network model training; Step S4: Based on the trained neural network model, perform array comprehensive solution on the verification set data; Step S5, judging whether the solution result of the verification set data meets its array requirements, if not, using a convex optimization algorithm to assist in the calculation, and using the data obtained by the auxiliary calculation to update the data set and retrain the neural network model; otherwise, outputting the neural network model; Step S6: Perform sparse antenna array synthesis through the output neural network model according to the input array synthesis requirements and the unit active radiation pattern of the antenna array, and output the array topology.

2. The sparse antenna array synthesis method based on neural network intelligent solution according to claim 1 is characterized in that: In the step S1, it specifically includes: Step S11: According to the unit spacing constraint and the antenna unit form, the array element spacing is changed, and a full-wave simulation is performed to obtain the unit active radiation pattern. where θ and are the elevation angle and azimuth angle of the antenna coordinate system; Step S12: the active directional pattern of the unit is adjusted according to the operating frequency f. Sampling is performed to obtain the unit active directional diagram corresponding to different operating frequencies Step S13: construct the unit active pattern data set according to the corresponding relationship between the array topology and the unit active pattern, wherein the samples in the unit active pattern data set are represented as 3. The sparse antenna array synthesis method based on neural network intelligent solution according to claim 2 is characterized in that: The antenna unit form is determined according to the comprehensive requirements of the array: The antenna array aperture is determined according to the gain, the antenna element scale is obtained according to the sparsity rate, and the antenna unit type is determined according to the scanning angle and gain. When the antenna scanning gain loss is small, a wide-beam antenna unit is used for full-wave simulation. When the antenna gain requirement is high, a high-gain antenna unit is used for full-wave simulation.

4. The sparse antenna array synthesis method based on neural network intelligent solution according to claim 3 is characterized in that: In the step S11, the full-wave simulation includes: According to the cell spacing constraint Γ = [d xmin ,d xmax ,d ymin ,d ymax ], within the cell spacing range of the cell spacing constraint, set the array topology set L = {[x p ,y q ]|p=1,2,…,N x ,q=1,2,…N y }, select the antenna unit and a group of adjacent antenna units by partitioning to perform full-wave simulation, change the unit spacing, and obtain the unit active radiation pattern The unit spacing range is expressed as: d x =|x p -x p-1 |∈[d xmin ,d xmax ],d y =|y q -y q-1 |∈[d ymin ,d ymax ], where p and q represent the numbers of the antenna units along the x-axis and y-axis respectively, and d x represents the unit spacing along the x-axis, d y represents the unit spacing along the y-axis, d xmin represents the minimum cell spacing along the x-axis, d ymin Indicates the minimum cell spacing along the y-axis.

5. The sparse antenna array synthesis method based on neural network intelligent solution according to claim 3 is characterized in that: In the step S2, it specifically includes: Step S21, according to the array comprehensive requirements and the unit active directional pattern, the directional pattern is calculated through the array numerical simulation model to obtain the directional pattern of the entire array, which is expressed as: in, represents the whole array pattern of the antenna array with array topology l and operating frequency f; I p,q represents the excitation amplitude of the antenna unit; k represents the wave number corresponding to the operating frequency f, k = 2πf / c, c is the speed of light; w = {w p,q |p=1,2,…,N x ,q=1,2,…N y } represents the phase of the antenna array. The phase w of the antenna array is generated according to the array topology and can be expressed as p=1,2,…,N x ,q=1,2,…N y , where k0 represents the wave number k0 = 2πf0 / c when working at frequency f = f0, c is the speed of light, and the scan is hour, N x 、N y Indicates the number of array elements distributed along the x-axis and y-axis; Step S22: construct an initial sample set according to the unit active directional pattern and the whole array directional pattern, wherein each sample in the initial sample set includes an input vector and a sample label, and the initial sample set is represented as S={(X m ,Y m )|m=1,2,…M}; Among them, X m is the input vector, including the unit active pattern, the whole array pattern, the beam width, and the sidelobe level, expressed as in is the unit active pattern of the antenna array, and its operating frequency is f, represents the entire array pattern of the antenna array, Indicates scanning to The beam width at Indicates scanning to The sidelobe level at Y m is the sample label, including the set of array topologies of the antenna array, denoted as Y m =L.

6. The sparse antenna array synthesis method based on neural network intelligent solution according to claim 5 is characterized in that: When the desired radiation performance also includes cross-polarization, in full-wave simulation, the cross-polarization unit active pattern component is extracted, and the input vector also includes the whole array cross-polarization level, which is expressed as Among them, E f,co Indicates the cross-polarization level of the entire array.

7. The sparse antenna array synthesis method based on neural network intelligent solution according to claim 1 is characterized in that: In the step S3, it specifically includes: Step S31, dividing the initial sample set into a training set and a validation set in a ratio of 8:2 by a random division method, and normalizing them; Step S32, constructing a neural network model, and determining the number of network layers and the number of neurons in each layer according to the input vector dimension and the output vector dimension; wherein the array element scale is independent of the input vector dimension and is related to the output vector dimension, and the neural network model adopts a double hidden layer structure feedforward network model, and the number of neurons in the two hidden layers is the same, and the number of neurons is determined according to the array dimension; Step S33, setting the number of training times, training target, learning rate, transfer function, training function and training error; Step S34, substituting the verification set into the neural network model for verification to determine whether the training error meets the requirements; Step S35: When the training error does not meet the requirement, return to step S32 to adjust the network parameters and retrain; Step S36: When the output meets the training error requirement, the network model and weight coefficients are saved to obtain the offline trained neural network model.

8. The sparse antenna array synthesis method based on neural network intelligent solution according to claim 1 is characterized in that: In step S5, the convex optimization algorithm is used to assist the calculation, which specifically includes: Convex optimization model is established, expressed as min||w||0 Among them, E ref represents the expected pattern, and ε represents the fitting error; Through the convex optimization model, the set of optimized array topology L'={[x p ',y q ']|p=1,2,…,N x ,q=1,2,…N y }; The optimized data is added to the training set to form a new training set S'={(X' m ,Y' m )|m=1,2,…M+M a }, retrain the network using the new training set, and at the same time, the set of optimization results can be directly output as the comprehensive results.

9. An electronic device, characterized in that: include: One or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes the sparse antenna array synthesis method based on neural network intelligent solution as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, implement the sparse antenna array synthesis method based on neural network intelligent solution as described in any one of claims 1 to 8.

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