A method for optimizing array antenna sidelobe based on improved crossover genetic algorithm

Abstract

The present invention discloses a sidelobe optimization method for an array antenna based on an improved crossover genetic algorithm, which belongs to the field of computer technology and applications. In order to achieve the optimization goal of low sidelobe, a circular array is selected for the arrangement of the antenna array and optimized using an improved genetic algorithm. At the same time, the constraints of a fixed array radius and a constant total number of array elements are added. When the improved crossover genetic algorithm is adopted, the measurement process of the initial population and the method of selecting two different crossover methods according to the comparison set value to reduce the repetition rate are added. The optimal peak sidelobe level and the optimal radius of the sparse circular array are obtained through iteration, which can effectively reduce the peak sidelobe level compared with the traditional method. The present invention has the characteristics of not being easily trapped in the local optimum, high search accuracy, fast convergence speed, etc., and has broad application prospects in 5G communication technology and radar anti-interference systems.

Description

Technical Field

[0001] The invention relates to an array antenna sidelobe optimization method based on an improved crossover genetic algorithm, and belongs to the field of computer science, technology and applications. Background Art

[0002] Modern antenna design often requires good directivity, low sidelobe levels, and high antenna gain. However, a single antenna often fails to meet these requirements. In the 5G era, sparse array antennas are created by removing elements from a uniform array and randomly distributing them within the array. This requires reducing the number of elements and lowering costs to achieve the same performance as a full array while ensuring efficient resource utilization.

[0003] Array antenna optimization is a research hotspot applied in modern antenna design, but some traditional optimization methods can only achieve the effect of reducing sidelobes, but do not have the characteristics of fast convergence speed, high search accuracy and low computational complexity. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a method for optimizing the side lobes of array antennas based on an improved crossover genetic algorithm. A circular array is selected for the arrangement of the antenna array and optimized using an improved crossover genetic algorithm. At the same time, the constraints of a fixed array radius and a constant total number of array elements are added. When the improved crossover genetic algorithm is used, the measurement process of the initial population and the method of selecting two different crossover methods based on the comparison set value to reduce the repetition rate are added. The optimal radius of the sparsely distributed circular array is iteratively optimized, and the peak sidelobe level can be effectively reduced after embedding the function model. The present invention has the characteristics of not being easily trapped in the local optimum, high search accuracy, and fast convergence speed. It has broad application prospects in 5G communication technology and radar anti-interference systems.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] A method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm is provided. The array antenna is a circular array antenna. The improved crossover genetic algorithm is used to optimize the peak sidelobe level of the circular array antenna. The specific steps are as follows:

[0007] Step 1, construct an initial population describing the circular array antenna;

[0008] Step 2: Perform the first iteration based on the initial population and measure the result of the first iteration;

[0009] Step 3: replace the individuals in the measurement results obtained in step 2 that do not meet the set convergence requirements with empirical values that meet the set convergence requirements;

[0010] Step 4: Perform selection, crossover, and mutation operations on the population obtained in step 3;

[0011] Step 5: Repeat steps 2-4 to optimize the number of ring layers and obtain the optimal number of ring layers;

[0012] Step 6: Keep the optimal number of circular ring layers obtained in step 5, repeat steps 2-4, and output the optimal peak sidelobe level and the optimal radius of the circular array.

[0013] Furthermore, before optimizing the number of inner ring layers, the size of the outermost ring is kept unchanged.

[0014] Furthermore, an antenna unit is fixedly placed at the center of the circular array antenna.

[0015] Furthermore, the total number of antenna units of the circular array antenna is fixed.

[0016] Furthermore, in step 4, before performing the crossover operation, the individual correlation between the current population and the previous iteration result is calculated first. If the individual correlations are all greater than the set threshold, a crossover operation is performed with a half probability. Otherwise, a crossover is performed among individuals whose gene segment differences exceed the set difference value.

[0017] Furthermore, the fitness function of the improved crossover genetic algorithm is:

[0018]

[0019]

[0020] Among them, MSLL is the fitness function, F(φ) is the direction function, F dB (φ) represents the function of the normalized pattern, φ is the azimuth angle with the positive direction of the x-axis as the reference direction, S represents the sidelobe interval of the pattern, λ is the wavelength, R is the radius of the circular array, d i represents the azimuth angle of the i-th antenna unit, and (φ0,θ0) represents the pointing direction of the main beam of the circular array.

[0021] Furthermore, the crossover probability is 0.8 and the mutation probability is 0.05.

[0022] Furthermore, the radius of each level of the circular array is greater than 0 and less than or equal to 4.7.

[0023] Furthermore, the objective function selected by the optimization method is the same as the fitness function of the improved crossover genetic algorithm.

[0024] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects: the present invention can further improve on the traditional genetic algorithm, measure the first-generation initialized population and select the better solution to replace it, and at the same time, add preprocessing to the crossover process of the genetic algorithm. When using the genetic algorithm with improved crossover, the optimal peak sidelobe level and the optimal radius of the sparse circular array are obtained through iteration, which can effectively reduce the peak sidelobe level compared with the traditional method. The algorithm is not easily trapped in the local optimum, has high search accuracy, and fast convergence speed. It has broad application prospects in 5G communication technology and radar anti-interference systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is the arrangement of the elements of the circular array when the number of layers is not optimized in the present invention;

[0026] Figure 2 It is the iterative speed curve calculated by MATLAB software for the circular array when the number of layers is not optimized in the present invention;

[0027] Figure 3 The antenna radiation pattern of the circular array is calculated using MATLAB software when the number of layers is not optimized in the present invention;

[0028] Figure 4 It is the three-dimensional directional pattern of the circular array when the number of layers is not optimized in the present invention;

[0029] Figure 5 This is an arrangement of elements of a circular array in one embodiment of the present invention;

[0030] Figure 6 is an iterative velocity curve calculated by MATLAB software for a circular array in one embodiment of the present invention;

[0031] Figure 7 The antenna radiation pattern of the circular array is calculated using MATLAB software in one embodiment of the present invention;

[0032] Figure 8 In one embodiment of the present invention, a circular array is optimized using a genetic algorithm for a three-dimensional directional pattern;

[0033] Figure 9 is another embodiment of the present invention, an arrangement of elements of a circular array;

[0034] Figure 10 In another embodiment of the present invention, the iterative velocity curve of the circular array is calculated using MATLAB software;

[0035] Figure 11 In another embodiment of the present invention, the antenna radiation pattern of the circular array is calculated using MATLAB software;

[0036] Figure 12 In another embodiment of the present invention, the circular array adopts a three-dimensional directional pattern optimized by an improved crossover genetic algorithm;

[0037] Figure 13 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0038] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be interpreted as limiting the present invention.

[0039] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as such, will not be interpreted in an idealized or overly formal sense.

[0040] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings:

[0041] This paper proposes a method for optimizing antenna array sidelobe based on an improved crossover genetic algorithm. To achieve the goal of low sidelobe optimization, a circular array is selected for the antenna array layout and optimized using an improved genetic algorithm. Simultaneously, constraints are added: the array radius is fixed and the total number of array elements remains constant. Using the improved crossover genetic algorithm, the optimal radius of the sparsely distributed circular array is iteratively found. Embedding a function model effectively reduces the peak sidelobe level. This method is characterized by its resistance to local optima, high search accuracy, and rapid convergence, making it beneficial for planar array antenna design and 5G mobile communication applications.

[0042] The present invention provides an array antenna sidelobe optimization method based on an improved crossover genetic algorithm, wherein the array antenna includes a circular array structure composed of antenna units; the antenna units in the circular array are placed layer by layer. This optimization method first optimizes the number of layers within the circular surface, and then uses an improved crossover genetic algorithm to optimize the number of array elements in each layer within the circular surface and the spacing between adjacent layers. It is a circular array structure composed of omnidirectional antenna units; the antenna array elements are placed layer by layer within the circular surface, the center array element is fixedly placed, the spacing between adjacent array elements on the same layer is the same, the spacing between different layers is different, and the radius of the outermost array is fixed. Figure 13 As shown in the figure, the improved crossover genetic algorithm is used to optimize the peak sidelobe level. The specific steps are as follows:

[0043] Step 1, construct an initial population describing the circular array antenna;

[0044] Step 2: Perform the first iteration based on the initial population and measure the result of the first iteration;

[0045] Step 3: replace the individuals in the measurement results obtained in step 2 that do not meet the set convergence requirements with empirical values that meet the set convergence requirements;

[0046] Step 4: Perform selection, crossover, and mutation operations on the population obtained in step 3;

[0047] Step 5: Optimize the number of inner ring layers of the population, and repeat steps 2-4 to obtain the optimal number of rings;

[0048] Step 6: Keep the optimal number of circular rings obtained in step 5, repeat steps 2-4 to optimize the population, and iteratively output the optimal value of the peak sidelobe level and the optimal radius of the circular array.

[0049] Before optimizing the number of ring layers, the size of the outermost ring remains unchanged. The specific function to call is Population_Init.

[0050] Among them, the optimization method improves the crossover process of the genetic algorithm. Before crossover of the population after the second generation, in order to reduce the probability of repetition, a set value is first given and compared with the results of the individuals of the previous generation. If it is greater than this value, a crossover operation is performed on half the probability. If it is less than or equal to the set value, screening crossover is performed among individuals with large differences in gene fragments. This method accelerates the convergence speed of the algorithm and improves stability.

[0051] The optimization method selects the peak sidelobe level as the fitness function, and the specific function is as follows:

[0052]

[0053]

[0054] Where F(φ) is the pattern function, λ is the wavelength, R is the radius of the circular array, φ is the azimuth angle with the positive direction of the x-axis as the reference direction, and d i represents the azimuth angle of the i-th array element, the main beam pointing direction is (φ0,θ0), MSLL is the fitness function, F dB (φ) represents the function of the normalized pattern, and S represents the sidelobe interval of the pattern.

[0055] The objective function selected by the optimization method is the same as the fitness function.

[0056] In one embodiment, Figure 1As shown in the figure, without optimizing the number of layers, the array antenna consists of 120 elements arranged in a circular array in a plane. The entire circular array consists of four layers of elements. A single element is fixed at the center of the first layer, and the remaining three layers are distributed in a stepwise manner, with the distance between adjacent circles being sparsely spaced. Each element is an ideal omnidirectional antenna unit. The peak sidelobe level of the unoptimized circular array is calculated, but the simulation is complex and slow, and is prone to getting stuck in a local solution.

[0057] In this embodiment, the circular array was not optimized. The maximum radius of the circular array was 4.7. Taking the center of the circle as the reference, the distances from the center of the circle to the four layers of circular rings on the circular surface were 0, 1.9896, 2.9487, and 4.7000, respectively. The number of array elements in each layer was 1, 25, 31, and 63, respectively. The various characteristics of the array antenna were obtained by simulation calculation using MATLAB software.

[0058] Figure 2 This is the iterative speed curve of the array antenna sidelobe level fitness function calculated using MATLAB software. The maximum number of iterations is set to 200 and the population size is 50. It can be seen that the peak sidelobe level obtained after iteration is -16.3092dB.

[0059] Figure 3 This is the radiation pattern of a circular array antenna calculated using MATLAB software. The horizontal axis represents the normalized antenna element phase, and the vertical axis represents the sidelobe level. The objective function is the same as the fitness function, with the phase range between -180° and 180° and the sidelobe level range between 0 and -40 dB. It can be seen that the peak sidelobe level is the same as the value obtained after iteration: -16.3092 dB.

[0060] Figure 4 is the three-dimensional radiation pattern of the circular array antenna.

[0061] In one embodiment, Figure 5 As shown, the optimized array antenna consists of 120 elements arranged in a circular array in a plane. The entire circular array consists of seven layers of elements. A fixed element is placed at the center of the first layer, and the remaining six layers are distributed in a progressively smaller pattern, with the distance between adjacent rings being thinned out. Each element is an ideal omnidirectional antenna unit. Using a general genetic algorithm to optimize this circular array can reduce the peak sidelobe level, but the simulation computational complexity is high and the speed is slow, requiring further targeted improvements.

[0062] In this embodiment, a genetic algorithm was used to optimize the number of elements in each ring of the entire circular array and the distances between different layers. The maximum radius of the circular array was 4.7. With the center as the reference, the distances from the center of the seven ring layers on the circular surface were 0, 0.5803, 1.1377, 2.0203, 2.9215, 3.7532, and 4.7000, respectively. The number of elements in each layer was 1, 7, 14, 25, 31, 31, and 11, respectively. The various characteristics of the array antenna were simulated and calculated using MATLAB software.

[0063] Figure 6 This is the iterative speed curve of the fitness function of the array antenna sidelobe level optimization method calculated using MATLAB software. The optimization algorithm sets the maximum number of iterations to 200 and the population size to 50. It can be seen that the optimization method tends to be stable around 135 generations, and the optimal peak sidelobe level obtained after iteration is -23.6739dB.

[0064] Figure 7 This figure shows the radiation pattern of the optimized circular array antenna calculated using MATLAB software. The solid line represents the azimuth pattern at an azimuth angle of φ = 0°; the dashed line represents the elevation pattern at an azimuth angle of φ = 90°. The horizontal axis represents the normalized antenna element phase, and the vertical axis represents the sidelobe level. The objective function is the same as the fitness function, with the phase range between -180° and 180° and the sidelobe level range between 0 and -40 dB. It can be seen that the peak sidelobe level after optimization is the same as the iteratively obtained optimal value, which is -23.6739 dB.

[0065] Figure 8 is the three-dimensional radiation pattern of the optimized circular array antenna.

[0066] In one embodiment, Figure 9 As shown, the optimized array antenna consists of 120 elements arranged in a circular array in a plane. The entire circular array consists of seven layers of elements. A fixed element is placed at the center of the first layer, and the remaining six layers are distributed in a progressively smaller pattern, with the distance between adjacent rings being sparsely spaced. Each element is an ideal omnidirectional antenna unit. Using a genetic algorithm with improved population initialization and improved crossover to optimize this circular array not only further reduces the peak sidelobe level but also accelerates convergence, reduces simulation computational complexity, and reduces the likelihood of the simulation process falling into local optimal solutions.

[0067] In this embodiment, a genetic algorithm with an improved population initialization process and improved crossover is used to optimize the number of elements in each ring of the entire circular array and the distances between different layers. The maximum radius of the circular array is 4.7. With the center of the circle as the reference, the distances from the center of the seven ring layers on the circular surface are 0, 0.6398, 1.3636, 2.1027, 2.9634, 3.7954, and 4.7000, respectively. The number of elements in each layer is 1, 8, 17, 25, 29, 29, and 11, respectively. The various characteristics of the array antenna are simulated and calculated using MATLAB software.

[0068] Figure 10 This is the iterative speed curve of the fitness function of the array antenna sidelobe level optimization method calculated by MATLAB software. The optimization algorithm sets the maximum number of iterations to 200 and the population size to 50. It can be seen that the optimal peak sidelobe level obtained by the optimization method after iteration is -25.1338dB. Compared with the traditional method, it tends to be stable around 100 generations and has a faster convergence speed.

[0069] Figure 11 This figure shows the radiation pattern of the optimized circular array antenna calculated using MATLAB software. The solid line represents the azimuth pattern at an azimuth angle of φ = 0°; the dashed line represents the elevation pattern at an azimuth angle of φ = 90°. The horizontal axis represents the normalized antenna element phase, and the vertical axis represents the sidelobe level. The objective function is the same as the fitness function, with the phase range between -180° and 180° and the sidelobe level range between 0 and -40 dB. The peak sidelobe level after optimization is the same as the optimal value obtained by iteration, at -25.1338 dB, representing a 1.4599 dB improvement compared to the traditional method.

[0070] Figure 12 It is the three-dimensional radiation pattern of the circular array antenna after optimization by improved crossover genetic algorithm.

[0071] In summary, the present invention discloses a sidelobe optimization method for an array antenna based on an improved crossover genetic algorithm, which belongs to the field of computer technology and applications. In order to achieve the optimization goal of low sidelobe, a circular array is selected for the arrangement of the antenna array and optimized using an improved genetic algorithm. At the same time, constraints on the array aperture, the number of array elements, and the array element aperture are added. When the improved crossover genetic algorithm is adopted, the optimal radius of the sparsely distributed circular array is iteratively optimized, and the peak sidelobe level can be effectively reduced after embedding the function model. The present invention has the characteristics of not being easily trapped in local optimality, high search accuracy, and fast convergence speed, and has broad application prospects in 5G communication technology and radar anti-interference systems.

[0072] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as such, will not be interpreted in an idealized or overly formal sense.

[0073] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person familiar with the technology can understand and think of any changes or replacements within the technical scope disclosed by the present invention, which should be included in the scope of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for optimizing the sidelobe of an array antenna based on an improved crossover genetic algorithm, wherein the array antenna is a circular array antenna, characterized in that: The circular array antenna uses an improved crossover genetic algorithm to optimize its peak sidelobe level. The specific steps are as follows: Step 1, construct an initial population describing the circular array antenna; Step 2: Perform the first iteration based on the initial population and measure the result of the first iteration; Step 3: replace the individuals in the measurement results obtained in step 2 that do not meet the set convergence requirements with empirical values that meet the set convergence requirements; Step 4: Perform selection, crossover, and mutation operations on the population obtained in step 3; Step 5: Repeat steps 2-4 to optimize the number of ring layers and obtain the optimal number of ring layers; Step 6: Keep the optimal number of circular ring layers obtained in step 5, repeat steps 2-4, and output the optimal peak sidelobe level and the optimal radius of the circular array; In step 4, before performing the crossover operation, the individual correlation between the current population and the previous iteration result is calculated. If the individual correlations are all greater than the set threshold, a crossover operation is performed with a split-half probability. Otherwise, a crossover is performed among individuals whose gene segment differences exceed the set difference value. The fitness function of the improved crossover genetic algorithm is: Among them, MSLL is the fitness function, F(φ) is the direction pattern function, and F dB (φ) represents the function of the normalized pattern, φ is the azimuth angle with the positive direction of the x-axis as the reference direction, S represents the sidelobe interval of the pattern, λ is the wavelength, R is the radius of the circular array, d i represents the azimuth angle of the i-th antenna unit, and (φ0,θ0) represents the pointing direction of the main beam of the circular array.

2. The improved cross array antenna sidelobe optimization method according to claim 1, characterized in that: Before optimizing the number of inner ring layers, keep the size of the outermost ring unchanged.

3. The improved cross array antenna sidelobe optimization method according to claim 1, characterized in that: An antenna unit is fixedly placed at the center of the circular array antenna.

4. The improved cross array antenna sidelobe optimization method according to claim 1, characterized in that: The total number of antenna units of the circular array antenna is fixed.

5. The method for optimizing the sidelobe of an improved cross array antenna according to claim 1, wherein: The crossover probability is 0.8 and the mutation probability is 0.

05.

6. The improved cross array antenna sidelobe optimization method according to claim 1, characterized in that: The radius of each level of the circular array is greater than 0 and less than or equal to 4.

7.

7. The improved cross array antenna sidelobe optimization method according to claim 1, characterized in that: The objective function selected by the optimization method is the same as the fitness function of the improved crossover genetic algorithm.

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