Sparse arrangement method and device, electronic equipment and readable storage medium

Abstract

The application provides a sparse array method and device, electronic equipment and a readable storage medium. An optimal array is obtained according to a required antenna aperture and a sparse rate and based on a genetic algorithm, and the optimal array is composed of multiple effective elements and ineffective elements. Unit antennas are constructed based on phase centers of the effective elements in the optimal array, and each unit antenna is composed of a single effective element or an effective element and an ineffective element adjacent to the effective element. Finally, a high-aperture-efficiency sparse array is obtained by splicing the constructed multiple unit antennas. The present application can improve the unit antenna gain, narrow the unit antenna beam width, and reduce the mutual coupling between the elements by constructing unit antennas of different shapes to fill the entire array, thereby improving the aperture efficiency and sidelobe level of the sparse array antenna, and greatly reducing the power consumption and cost of the system.

Description

Technical Field

[0001] This invention relates to the field of antenna technology, and more specifically, to a sparse array method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] The contradiction between countermeasures and countermeasures in modern radar is becoming increasingly intense. Survivability in harsh environments, especially those with strong electromagnetic interference, has become a crucial indicator of modern radar combat capability. Phased array radar is a radar system with strong survivability and has become a major development direction for modern radar. Compared with other antennas, phased array radar antennas have advantages such as extremely high beam controllability and extremely fast system response speed. Large-aperture phased array radar antennas have high gain, narrow beam, and low sidelobe level, but they have a large number of channels, high power consumption, and high cost. Sparse arraying, through optimization algorithms to optimize the position of array elements, can significantly reduce the number of channels of the array antenna while maintaining high gain and narrow beam for the same aperture, effectively reducing the cost of phased array antennas.

[0003] Optimization algorithms such as genetic algorithms, immune algorithms, and particle swarm optimization can be used to optimize the position of array elements, thereby obtaining sparse antenna arrays with lower sidelobes. Many optimization schemes exist in the existing technology to achieve the design of sparse arrays; however, most of these schemes only optimize the arrangement of array elements or subarrays, without considering the utilization rate of the element antenna aperture. Summary of the Invention

[0004] The objectives of this invention include, for example, providing a sparse array method, apparatus, electronic device, and readable storage medium that can improve the aperture efficiency and sidelobe level of a sparse array antenna.

[0005] The embodiments of the present invention can be implemented as follows:

[0006] In a first aspect, the present invention provides a sparse array method, the method comprising:

[0007] Based on the required antenna aperture and sparsity, and using a genetic algorithm, the optimal array is obtained, which consists of multiple effective array elements and ineffective array elements;

[0008] Based on the phase center of each effective element in the optimal array, a unit antenna is constructed. Each unit antenna is composed of a single effective element or an effective element and an adjacent ineffective element.

[0009] A high-aperture-efficiency sparse array is obtained by splicing together multiple constructed element antennas.

[0010] In an optional implementation, the step of obtaining the optimal array based on the required antenna aperture and sparsity using a genetic algorithm includes:

[0011] Obtain the required antenna aperture, and determine the array size based on the required antenna aperture and the set spacing information. The array has multiple array elements.

[0012] Based on the set sparsity rate and the size of the array, an original population is constructed. The original population includes multiple individuals, and each individual contains multiple elements corresponding to the array. Each element represents whether the corresponding array element is valid.

[0013] An objective function is constructed based on the normal sidelobe level of the array to optimize the original population and obtain the optimal individual.

[0014] The optimal array is constructed based on the optimal individual.

[0015] In an optional implementation, the step of constructing an objective function based on the normal sidelobe level of the array to optimize the original population includes:

[0016] The corresponding objective function is constructed based on the normal sidelobe level of the array corresponding to each individual in the original population.

[0017] The individuals in the original population are subjected to multiple rounds of iterative optimization until the iteration termination condition is met, at which point the optimal individual corresponding to the maximum objective function is obtained.

[0018] In an optional implementation, the step of performing multi-round iterative optimization on individuals in the original population includes:

[0019] In each iteration, the function value of the objective function corresponding to each individual in the current iteration is calculated;

[0020] Individuals whose objective function value is less than a preset threshold are filtered out, and individuals whose objective function value is greater than or equal to the preset threshold are selected to enter the next iteration of optimization.

[0021] In an optional implementation, the step of performing multi-round iterative optimization on individuals in the original population includes:

[0022] In each iteration, multiple individuals are grouped into groups of individuals;

[0023] When the generated random number is less than or equal to the set crossover rate, some elements are exchanged among the individuals in the group to generate new individuals;

[0024] The newly generated individuals will be included in the next iteration of optimization.

[0025] In an optional implementation, the step of performing multi-round iterative optimization on individuals in the original population includes:

[0026] In each iteration, for each individual, when the generated random number is less than or equal to the set mutation rate, some elements in the individual are transformed to generate a mutated individual;

[0027] The resulting mutated individuals will be included in the next iteration of optimization.

[0028] In an optional implementation, the method further includes:

[0029] Simulation tests were conducted based on the high-aperture efficiency sparse array to obtain the antenna pattern of the high-aperture efficiency sparse array.

[0030] Secondly, the present invention provides a sparse array device, the device comprising:

[0031] The acquisition module is used to obtain the optimal array based on the required antenna aperture and sparsity, and on a genetic algorithm. The optimal array consists of multiple effective array elements and ineffective array elements.

[0032] A construction module is used to construct unit antennas based on the phase centers of each effective array element in the optimal array, wherein each unit antenna is composed of a single effective array element or a single effective array element and an adjacent ineffective array element.

[0033] The splicing module is used to splice multiple constructed element antennas to obtain a high-aperture efficiency sparse array.

[0034] Thirdly, the present invention provides an electronic device including one or more storage media and one or more processors communicating with the storage media. The one or more storage media store machine-executable instructions that can be executed by the processor. When the electronic device is running, the processor executes the machine-executable instructions to perform the steps of the method described in any of the foregoing embodiments.

[0035] Fourthly, the present invention provides a computer-readable storage medium storing machine-executable instructions, which, when executed, implement the method steps described in any of the foregoing embodiments.

[0036] The beneficial effects of the embodiments of the present invention include, for example:

[0037] This application provides a sparse array method, apparatus, electronic device, and readable storage medium. Based on the required antenna aperture and sparsity, an optimal array is obtained using a genetic algorithm. This optimal array consists of multiple effective and ineffective elements. Unit antennas are constructed based on the phase centers of each effective element in the optimal array. Each unit antenna consists of a single effective element or an effective element and its adjacent ineffective element. Finally, the constructed unit antennas are spliced ​​together to obtain a high-aperture-efficiency sparse array. This scheme improves the gain and narrows the beamwidth of unit antennas by constructing unit antennas of different shapes to fill the entire array surface, thereby reducing the mutual coupling between elements and improving the aperture efficiency and sidelobe level of the sparse array antenna, significantly reducing system power consumption and cost. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart of the sparse array method provided in the embodiments of this application;

[0040] Figure 2 for Figure 1 A flowchart of the sub-steps included in step S101;

[0041] Figure 3 for Figure 2 A flowchart of the sub-steps included in step S1031;

[0042] Figure 4 for Figure 3 One of the flowcharts for the sub-steps included in step S1032;

[0043] Figure 5 for Figure 3 The second flowchart of the sub-steps included in step S1032;

[0044] Figure 6 for Figure 3 The third flowchart of the sub-steps included in step S1032;

[0045] Figure 7 A schematic diagram of a uniformly arranged array constructed for an embodiment of this application;

[0046] Figure 8 A schematic diagram of the optimal array obtained by optimizing an embodiment of this application;

[0047] Figure 9 This is a schematic diagram of the fitness value curve during the genetic algorithm optimization process in the embodiments of this application;

[0048] Figure 10 A schematic diagram of a high-aperture efficiency sparse array provided in an embodiment of this application;

[0049] Figure 11 Antenna pattern of a high-aperture efficiency sparse array provided in the embodiments of this application;

[0050] Figure 12 The antenna pattern of a uniformly arranged array;

[0051] Figure 13 The antenna pattern is for a typical sparse array.

[0052] Figure 14 A structural block diagram of the electronic device provided in the embodiments of this application;

[0053] Figure 15 This is a functional block diagram of the sparse array device provided in the embodiments of this application.

[0054] Icons: 110 - Storage medium; 120 - Processor; 130 - Sparse array device; 131 - Acquisition module; 132 - Construction module; 133 - Assembly module; 140 - Communication interface. Detailed Implementation

[0055] To optimize the element positions and achieve sparsely distributed antenna arrays with lower sidelobes, existing technologies have proposed numerous improvement schemes. For example, an optimization method for low-sidelobe linear sparsely distributed array antennas based on sequential quadratic programming has been proposed. This method offers fast iteration speed and good sidelobe characteristics, making it suitable for the optimized design of linear sparsely distributed array antennas. Furthermore, a sparse array method based on element subarrays is provided. This method constructs regular element subarrays from antenna elements, and then uses density-weighted sparse antenna arrays on the antenna array surface with random distribution. This array method can maximally suppress grating lobes, and the regularly distributed small antenna subarrays facilitate mass production of T / R components. Additionally, a sparse array method satisfying multiple constraints is provided. This method, when the fitness function is a multi-directional peak sidelobe level value, can effectively reduce the maximum sidelobe level value at each angle within the radar scanning range by approximately 3 dB.

[0056] However, existing design methods only optimize sparse arrays by considering the arrangement of array elements or subarrays, without taking into account the aperture utilization of individual antenna elements. This results in problems such as low aperture utilization, low gain, and wide beamwidth, leading to low aperture efficiency of sparse array antennas and limited sidelobe suppression.

[0057] To address the aforementioned issues, this application provides a sparse array scheme. An optimal array is determined using a genetic algorithm, and unit antennas are constructed based on the phase centers of each effective element in the optimal array. Finally, a high-aperture-efficiency sparse array is obtained by splicing multiple unit antennas. This scheme, by constructing unit antennas of different shapes to cover the entire array surface, can improve the gain of the unit antennas, narrow their beamwidth, and thus reduce the mutual coupling between elements. This improves the aperture efficiency and sidelobe level of the sparse array antenna, significantly reducing system power consumption and cost.

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0059] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0060] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0061] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0062] Please see Figure 1 This is a flowchart of a sparse array method provided in an embodiment of this application. The method steps defined in the flowchart of this sparse array method can be implemented by an electronic device, such as a personal computer, laptop, or server. The following will describe... Figure 1 The specific process shown will be explained in detail.

[0063] S101, based on the required antenna aperture and sparsity, and using a genetic algorithm, an optimal array is obtained, which consists of multiple effective array elements and ineffective array elements.

[0064] S102, construct a unit antenna based on the phase center of each effective array element in the optimal array, wherein each unit antenna is composed of a single effective array element or a valid array element and an adjacent ineffective array element.

[0065] S103 is a high-aperture-efficiency sparse array obtained by splicing together multiple unit antennas.

[0066] In this embodiment, the antenna aperture can be set according to actual needs, and the antenna aperture is determined by the overall size of the formed antenna array. For example, when the formed antenna array is square, the antenna aperture can be the size of the diagonal of the square.

[0067] Generally, an antenna array should have multiple array elements arranged in multiple rows and columns, which can be activated simultaneously during antenna array operation. However, in practical applications, the cost of chips is relatively high, and the more chips used, the higher the cost. Furthermore, the high power consumption of chips puts pressure on antenna heat dissipation. Therefore, a sparsity ratio is usually set so that the actual operating array elements do not cover the entire array surface; only elements in certain locations may be operational.

[0068] The percentage of operable array elements in the entire array can be determined by the set sparsity rate, which ranges from 0 to 1. When the sparsity rate is 0, it means that no array elements are operable in the entire array. When the sparsity rate is 1, it means that all array elements in the entire array are operable. When the sparsity rate is 0.5, it means that array elements at 50% of the positions in the entire array are operable.

[0069] Given a fixed antenna aperture and sparsity, the performance of the antenna array varies depending on the arrangement of the operational array elements. Therefore, in order to find the optimal array, this embodiment employs a genetic algorithm for iterative optimization to find the optimal array that achieves the best possible performance for the antenna array.

[0070] In this embodiment, the optimal array consists of multiple effective array elements and invalid array elements. A effective array element is an array element that actually exists at the corresponding position and is set to operate. An invalid array element may be an array element that is not set at the corresponding position, i.e., the position is blank, or it may be an array element that is set at the corresponding position but is not fed, i.e., it is not set to operate.

[0071] After determining the optimal array, it can be determined whether each position in the optimal array corresponds to an array element or a powered and operational array element. In this embodiment, after determining the optimal array and its effective array elements, the phase center of each effective array element can be determined. Based on this, a unit antenna can be constructed. The construction of the unit antenna can be based on the arrangement of effective and ineffective array elements in the optimal array, and the shape of the unit antenna can also be different. For example, some unit antennas can be composed of a single effective array element, while some unit antennas can be composed of an effective array element and its adjacent ineffective array element.

[0072] The constructed multiple element antennas can be spliced ​​together to obtain the required high-aperture efficiency sparse array. The splicing of multiple elements is similar to the splicing of "Tetris". After splicing, all element antennas can cover the entire array surface.

[0073] In this embodiment, given the required antenna aperture and sparsity, a genetic algorithm is first used to find the optimal array. Then, based on the effective elements in the optimal array, unit antennas are constructed, and a high-aperture-efficiency sparse array is obtained by splicing these unit antennas. By designing antenna elements of different shapes, similar to Tetris blocks, to cover the entire array surface, the gain of the unit antennas can be improved and the beamwidth of the unit antennas can be narrowed. Furthermore, splicing unit antennas of different shapes to cover the entire array surface reduces the mutual coupling between array elements, thereby improving the aperture efficiency and sidelobe level of the sparse array, and significantly reducing the power consumption and cost of the system.

[0074] Genetic algorithms, a part of evolutionary computation, are computational models that simulate Darwin's genetic selection and natural selection processes in biological evolution. They are methods for searching for optimal solutions by simulating natural evolution. Please refer to [link to relevant documentation]. Figure 2 In this embodiment, when using a genetic algorithm to obtain the optimal array in step S101 above, it can be achieved in the following way:

[0075] S1011, Obtain the required antenna aperture, and determine the array size based on the required antenna aperture and the set spacing information. The array has multiple array elements.

[0076] S1012, Based on the set sparsity rate and the size of the array, an original population is constructed. The original population includes multiple individuals, and each individual contains multiple elements corresponding to the array. Each element represents whether the corresponding array element is valid.

[0077] S1013, construct an objective function based on the normal sidelobe level of the array to optimize the original population and obtain the optimal individual.

[0078] S1014, construct the optimal array based on the optimal individual.

[0079] In this embodiment, the overall size of the array can be determined based on the required antenna aperture. Combined with the spacing information, including the spacing dx and dy between array elements in the x and y directions, the scale of the array can be determined. Specifically, the scale of the array refers to the number of array elements, for example, Nx × Ny.

[0080] With a fixed array size, the set sparsity ratio determines the percentage of array elements that can be designated as effective array elements. For example, if the sparsity ratio is 0.5, then elements representing 50% of the array's positions can be designated as effective array elements.

[0081] Thus, based on the population size set in the genetic algorithm, a corresponding number of individuals can be constructed to form the original population. In this embodiment, each individual contains multiple elements, and each individual can be the amplitude matrix of the antenna array, arranged in the same way as the antenna array, for example, it can be Nx×Ny as mentioned above. Each element can be 0 or 1. 0 indicates that the corresponding array element is an invalid element, that is, the array element corresponding to this element is an unfed element or there is no actual array element set at the corresponding position in the antenna array. 1 indicates that the corresponding array element is a valid element, that is, there is an array element actually set at the corresponding position in the antenna array and this element is a fed element.

[0082] In addition, other parameters of the genetic algorithm can be set, such as the maximum number of iterations, the maximum iteration duration, the crossover rate Pc, and the mutation rate Pm. Based on the constructed original population, an objective function is constructed using the normal sidelobe level of the array corresponding to each individual. This objective function is used to guide the optimization of the original population. The optimal individual is determined when the iteration stops. The optimal array can then be determined based on this optimal individual.

[0083] Please see Figure 3 In this embodiment, the step of optimizing the original population using the objective function as a guide can be achieved in the following way:

[0084] S10131, the corresponding objective function is constructed based on the normal sidelobe level of the array corresponding to each individual in the original population.

[0085] S10132, Perform multiple rounds of iterative optimization on the individuals in the original population until the iteration termination condition is met, and obtain the optimal individual corresponding to the maximum objective function.

[0086] In this embodiment, the absolute value of the normal sidelobe level of the array corresponding to an individual can be used as the objective function, and the function value of the objective function is the fitness value. A higher fitness value indicates better correlation performance of the corresponding array. Therefore, multiple rounds of iterative optimization can be performed on individuals in the original population based on the objective function.

[0087] The iteration can be stopped when the termination conditions are met, such as when the number of iterations reaches the maximum number of iterations, the iteration duration reaches the maximum iteration duration, or the objective function converges and no longer changes.

[0088] When iteration stops, the absolute value of the normal sidelobe level of the array corresponding to each individual in the population at this time is calculated, which is also the fitness value. The individual with the highest fitness value is the optimal individual.

[0089] In this embodiment, when iteratively optimizing the population, any one or more combinations of selection, crossover, and mutation operations can be used for optimization.

[0090] Please see Figure 4 In one possible implementation, iterative optimization of the population can be achieved in the following way:

[0091] S101321A, in each iteration, calculate the function value of the objective function corresponding to each individual in the current iteration.

[0092] S101322A, filter out individuals whose objective function value is less than a preset threshold, and select individuals whose objective function value is greater than or equal to the preset threshold to enter the next iteration of optimization.

[0093] The selection operation for the population involves choosing individuals with high fitness to proceed to subsequent iterations and eliminating individuals with low fitness, thereby continuously optimizing the population. Optionally, in each iteration round, the objective function value of the array corresponding to each individual, i.e., the fitness value, can be calculated. Individuals with fitness values ​​greater than or equal to a preset threshold are allowed to proceed to subsequent iterations, while other individuals are eliminated.

[0094] In addition, please see Figure 5 In another implementation, the iterative optimization of the population can also be achieved in the following way:

[0095] S101321B, in each iteration, groups multiple individuals together.

[0096] S101322B, when the generated random number is less than or equal to the set crossover rate, some elements are exchanged among the individuals in the group to generate a new individual.

[0097] S101323B will generate new individuals that will be entered into the next iteration of optimization.

[0098] Crossover in a population involves gene exchange between multiple individuals. Multiple individuals can be grouped into groups of two, three, or any other number of individuals. In each iteration, the system generates a random number between 0 and 1. The crossover rate is also set to a number between 0 and 1. Crossover occurs when the generated random number is less than the set crossover rate. Therefore, the crossover rate can be set according to the desired crossover intensity; a higher crossover frequency requires a higher rate, and vice versa.

[0099] In addition, the crossover rate can also be set to a variable number. For example, in the early iterations, the crossover rate can be set to be larger to perform a stronger crossover operation in the early stages of the iteration, and in the later iterations, the crossover rate can be set to be smaller to perform a weaker crossover operation in the later stages of the iteration.

[0100] When performing a crossover operation on a group of individuals, partial elements can be exchanged between the individuals in the group to create a new individual. For example, if the group contains two individuals, partial elements can be exchanged between those two individuals; or if the group contains three individuals, partial elements can be exchanged sequentially among those three individuals.

[0101] By generating new individuals to continue the subsequent iterative optimization, we can avoid getting stuck in local optima in the later stages of optimization, thus making it impossible to find the optimal individual.

[0102] In addition, please see Figure 6 In another possible implementation, the iterative optimization of the population can also be achieved in the following way:

[0103] S101321C: In each iteration, for each individual, when the generated random number is less than or equal to the set mutation rate, some elements in the individual are transformed to generate a mutated individual.

[0104] S101322C will generate mutated individuals that will be optimized in the next iteration round.

[0105] In this embodiment, the mutation operation on the population is equivalent to mutating a single individual. Similarly, in each iteration, the system generates a random number and compares it with the set mutation rate. If the random number is less than or equal to the mutation rate, some elements in the individual can be transformed. For example, the original 1 can be transformed into 0, and the original 0 can be transformed into 1, thereby generating a mutated individual.

[0106] By generating variant individuals based on existing individuals, the goal of diversifying the population can also be achieved, thus avoiding the population iteration from getting stuck in local optima.

[0107] It should be noted that in this embodiment, when optimizing the population, one of the selection operation, crossover operation, and mutation operation can be used alone, or multiple operations can be combined for optimization. This embodiment does not limit this.

[0108] After optimization based on the above methods and determining the final optimal individual element, the optimal array corresponding to the optimal individual element is determined. Based on the arrangement of effective and ineffective array elements in the optimal array, element antennas of different shapes can be constructed, and all element antennas can be spliced ​​together to fill the entire array surface to obtain a high-aperture-efficiency sparse array. The phase center of each element antenna is the phase center of the effective element in the element antenna. This design improves the aperture utilization of the element antennas, increases gain, narrows the beam velocity, and does not affect the synthesis of the array antenna pattern.

[0109] Based on this, simulation tests can be conducted using high-aperture efficiency sparse arrays to obtain the antenna pattern of the high-aperture efficiency sparse array.

[0110] In this embodiment, it is assumed that the uniformly arranged array size is 16×16, with a total of 256 array elements, and the element spacing d x =d y =0.5λ, array arrangement as follows Figure 7 As shown in the figure. Using a genetic algorithm, the population is iteratively optimized with a sparsity ratio of 0.5. The final optimal array arrangement can be obtained as follows. Figure 8 As shown in the diagram. The black boxes indicate valid array elements, while the white boxes indicate invalid array elements. Figure 9 The figure shows the change in fitness value during the iterative process in the genetic algorithm.

[0111] exist Figure 8 Based on the aforementioned optimal array, unit antennas are constructed, and a high-aperture efficiency sparse array is obtained by splicing these unit antennas, such as... Figure 10 As shown. Figure 10 The various shapes of the Tetris-like squares in the image represent the unit antennas. The dots indicate the phase center of the unit antenna, which is also the location of the phase center of the effective array element in the unit antenna.

[0112] Figure 11 The image shows the antenna pattern of the resulting high-aperture-efficiency sparse array, while... Figure 12 The image shows the antenna pattern of a uniform 16×16 array. Figure 13The image shows the antenna pattern of a typical sparse array (i.e., only optimization was performed and no element antennas were constructed).

[0113] As shown in the figure, for a uniformly arranged 16×16 array, the gain is 30.3 dBi, and the sidelobe level is 13.3 dB with uniform feeding. A common sparse array, optimized using a genetic algorithm, has a gain of 27.2 dBi and a sidelobe level of 17.33 dB with uniform feeding. A high-aperture-efficiency sparse array formed by high-aperture-utilization element antenna arrays has a gain of 28.9 dBi and a sidelobe level of 18.4 dB with uniform feeding. Therefore, in this embodiment, using high-aperture-utilization element antenna arrays to form a high-aperture-efficiency sparse array can effectively improve the aperture efficiency and sidelobe level of the sparse antenna array, with a gain increase of 1.7 dB compared to a common sparse array antenna. For a monopulse radar, this means a 3.4 dB increase in the overall link gain, significantly enhancing the radar system's detection power. If the same detection power were achieved solely by increasing the system's transmit power, the power consumption and cost would be several times higher than the high-aperture-efficiency sparse antenna array described in this application.

[0114] Please see Figure 14 This is a schematic diagram of exemplary components of an electronic device provided in an embodiment of this application. The electronic device can be, for example, a personal computer, a laptop computer, or a server. The electronic device may include a storage medium 110, a processor 120, a sparse array device 130, and a communication interface 140. In this embodiment, the storage medium 110 and the processor 120 are both located within the electronic device and are separately configured. However, it should be understood that the storage medium 110 may also be independent of the electronic device and can be accessed by the processor 120 through a bus interface. Alternatively, the storage medium 110 may also be integrated into the processor 120, for example, as a cache and / or general-purpose registers.

[0115] The sparse array device 130 can be understood as the aforementioned electronic device, or the processor 120 of the electronic device, or as a software functional module that implements the aforementioned sparse array method under the control of the electronic device, independent of the aforementioned electronic device or processor 120.

[0116] like Figure 15 As shown, the sparse array device 130 may include an acquisition module 131, a construction module 132, and a splicing module 133. The functions of each functional module of the sparse array device 130 will be described in detail below.

[0117] The module 131 is used to obtain the optimal array based on the required antenna aperture and sparsity, and on a genetic algorithm. The optimal array consists of multiple effective array elements and ineffective array elements.

[0118] It is understood that the obtaining module 131 can be used to perform the above step S101. For details on the implementation of the obtaining module 131, please refer to the above content related to step S101.

[0119] The construction module 132 is used to construct a unit antenna based on the phase center of each effective array element in the optimal array, wherein each unit antenna is composed of a single effective array element or a valid array element and an adjacent invalid array element.

[0120] It is understood that the building module 132 can be used to perform the above step S102. For details on the implementation of the building module 132, please refer to the above content related to step S102.

[0121] The splicing module 133 is used to splice multiple unit antennas to obtain a high-aperture-efficiency sparse array.

[0122] It is understood that the splicing module 133 can be used to perform the above step S103. For details on the implementation of the splicing module 133, please refer to the above content related to step S103.

[0123] In one possible implementation, the obtaining module 131 described above can be used to:

[0124] Obtain the required antenna aperture, and determine the array size based on the required antenna aperture and the set spacing information. The array has multiple array elements.

[0125] Based on the set sparsity rate and the size of the array, an original population is constructed. The original population includes multiple individuals, and each individual contains multiple elements corresponding to the array. Each element represents whether the corresponding array element is valid.

[0126] An objective function is constructed based on the normal sidelobe level of the array to optimize the original population and obtain the optimal individual.

[0127] The optimal array is constructed based on the optimal individual.

[0128] In one possible implementation, the obtaining module 131 described above can be used to:

[0129] The corresponding objective function is constructed based on the normal sidelobe level of the array corresponding to each individual in the original population.

[0130] The individuals in the original population are subjected to multiple rounds of iterative optimization until the iteration termination condition is met, at which point the optimal individual corresponding to the maximum objective function is obtained.

[0131] In one possible implementation, the obtaining module 131 described above can be used to:

[0132] In each iteration, the function value of the objective function corresponding to each individual in the current iteration is calculated;

[0133] Individuals whose objective function value is less than a preset threshold are filtered out, and individuals whose objective function value is greater than or equal to the preset threshold are selected to enter the next iteration of optimization.

[0134] In one possible implementation, the obtaining module 131 described above can be used to:

[0135] In each iteration, multiple individuals are grouped into groups of individuals;

[0136] When the generated random number is less than or equal to the set crossover rate, some elements are exchanged among the individuals in the group to generate new individuals;

[0137] The newly generated individuals will be included in the next iteration of optimization.

[0138] In one possible implementation, the above-described obtaining module can be used to:

[0139] In each iteration, for each individual, when the generated random number is less than or equal to the set mutation rate, some elements in the individual are transformed to generate a mutated individual;

[0140] The resulting mutated individuals will be included in the next iteration of optimization.

[0141] In one possible implementation, the sparse array device 130 further includes a simulation module, which can be used for:

[0142] Simulation tests were conducted based on the high-aperture efficiency sparse array to obtain the antenna pattern of the high-aperture efficiency sparse array.

[0143] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0144] Furthermore, embodiments of this application also provide a computer-readable storage medium storing machine-executable instructions, which, when executed, implement the sparse array method provided in the above embodiments.

[0145] Specifically, the computer-readable storage medium can be a general-purpose storage medium, such as a removable disk or hard disk. When the computer program on the computer-readable storage medium is run, it can execute the aforementioned sparse array. The processes involved in the execution of the executable instructions on the computer-readable storage medium can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0146] In summary, the sparse array method, apparatus, electronic device, and readable storage medium provided in this application obtain an optimal array based on a genetic algorithm, according to the required antenna aperture and sparsity. This optimal array consists of multiple effective and ineffective array elements. Unit antennas are constructed based on the phase centers of each effective element in the optimal array. Each unit antenna consists of a single effective element or an effective element and its adjacent ineffective element. Finally, a high-aperture-efficiency sparse array is obtained by splicing together the constructed unit antennas. This scheme, by constructing unit antennas of different shapes to splice and cover the entire array surface, can improve the gain of unit antennas, narrow the beamwidth of unit antennas, and thus reduce the mutual coupling between array elements, thereby improving the aperture efficiency and sidelobe level of the sparse array antenna and significantly reducing the power consumption and cost of the system.

[0147] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A sparse array arrangement method, characterized in that, The method includes: Based on the required antenna aperture and sparsity, and using a genetic algorithm, the optimal array is obtained, which consists of multiple effective array elements and ineffective array elements; Based on the phase center of each effective element in the optimal array, a unit antenna is constructed. Each unit antenna is composed of a single effective element or an effective element and an adjacent ineffective element. The shapes of the unit antennas are different, and the phase center of the unit antenna is the phase center of the effective element in the unit antenna. A high-aperture-efficiency sparse array is obtained by splicing together multiple unit antennas. The multiple unit antennas include unit antennas composed of individual effective array elements, and unit antennas composed of effective array elements and adjacent ineffective array elements. After splicing, all unit antennas cover the entire array surface to improve the unit antenna gain, narrow the unit antenna beamwidth, and improve the aperture efficiency and sidelobe level of the sparse array.

2. The sparse array method according to claim 1, characterized in that, The step of obtaining the optimal array based on the required antenna aperture and sparsity using a genetic algorithm includes: Obtain the required antenna aperture, and determine the array size based on the required antenna aperture and the set spacing information. The array has multiple array elements. Based on the set sparsity rate and the size of the array, an original population is constructed. The original population includes multiple individuals, and each individual contains multiple elements corresponding to the array. Each element represents whether the corresponding array element is valid. An objective function is constructed based on the normal sidelobe level of the array to optimize the original population and obtain the optimal individual. The optimal array is constructed based on the optimal individual.

3. The sparse array method according to claim 2, characterized in that, The step of constructing an objective function based on the normal sidelobe level of the array to optimize the original population includes: The corresponding objective function is constructed based on the normal sidelobe level of the array corresponding to each individual in the original population. The individuals in the original population are subjected to multiple rounds of iterative optimization until the iteration termination condition is met, at which point the optimal individual corresponding to the maximum objective function is obtained.

4. The sparse array method according to claim 3, characterized in that, The step of performing multiple rounds of iterative optimization on individuals in the original population includes: In each iteration, the function value of the objective function corresponding to each individual in the current iteration is calculated; Individuals whose objective function value is less than a preset threshold are filtered out, and individuals whose objective function value is greater than or equal to the preset threshold are selected to enter the next iteration of optimization.

5. The sparse array method according to claim 3, characterized in that, The step of performing multiple rounds of iterative optimization on individuals in the original population includes: In each iteration, multiple individuals are grouped into groups of individuals; When the generated random number is less than or equal to the set crossover rate, some elements are exchanged among the individuals in the group to generate a new individual; The newly generated individuals will be included in the next iteration of optimization.

6. The sparse array method according to claim 3, characterized in that, The step of performing multiple rounds of iterative optimization on individuals in the original population includes: In each iteration, for each individual, when the generated random number is less than or equal to the set mutation rate, some elements in the individual are transformed to generate a mutated individual; The resulting mutated individuals will be included in the next iteration of optimization.

7. The sparse array method according to any one of claims 1-6, characterized in that, The method further includes: Simulation tests were conducted based on the high-aperture efficiency sparse array to obtain the antenna pattern of the high-aperture efficiency sparse array.

8. A sparse array device, characterized in that, The apparatus for implementing the sparse array method according to any one of claims 1-7 comprises: The acquisition module is used to obtain the optimal array based on the required antenna aperture and sparsity, and on a genetic algorithm. The optimal array consists of multiple effective array elements and ineffective array elements. A construction module is used to construct unit antennas based on the phase centers of each effective array element in the optimal array, wherein each unit antenna is composed of a single effective array element or a single effective array element and an adjacent ineffective array element. The splicing module is used to splice multiple constructed element antennas to obtain a high-aperture efficiency sparse array.

9. An electronic device, characterized in that, The device includes one or more storage media and one or more processors communicating with the storage media. The one or more storage media store machine-executable instructions that can be executed by the processor. When the electronic device is running, the processor executes the machine-executable instructions to perform the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores machine-executable instructions that, when executed, implement the steps of the method described in any one of claims 1-7.

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