Antenna array optimization method, terminal device, and storage medium

Abstract

The application discloses an antenna array optimization method, a terminal device and a storage medium, and relates to the technical field of antennas, and comprises the following steps: acquiring antenna data of each antenna included in a to-be-optimized antenna array, encoding each individual in an initial population reflected by the antenna data to obtain an intermediate population, decoding and calculating the intermediate population to obtain a virtual array element of each individual, calculating individual fitness of the individual according to the virtual array element, performing a preset crossover operation and a preset mutation operation on the individual according to the individual fitness, obtaining an optimized array element position, and forming an L-shaped antenna array by using straight-line arrays corresponding to the optimized array element position. The individual fitness is calculated to avoid overfitting of the optimization process to low sidelobes, and the straight-line arrays corresponding to the optimized array element position are used to form an L-shaped antenna array, so that the length and width of the limited space are fully utilized, the horizontal and elevation apertures are maximized, and better angle resolution is realized.

Description

Technical Field

[0001] This application relates to the field of antenna technology, and in particular to antenna array optimization methods, terminal devices, and storage media. Background Technology

[0002] Antenna technology has been widely applied in many high-tech fields such as radar, communications, wireless positioning, astronomical observation, and electronic warfare. By adjusting the arrangement of multiple antenna array elements and the signal phase relationship, this technology enables directional transmission and reception of signals, significantly improving the angular resolution, signal gain, and anti-interference capability of equipment.

[0003] Traditional sparse array design reduces physical size by decreasing the number of array elements, thereby reducing hardware costs and simplifying system architecture, which is advantageous when resources or space are limited. However, this design method has a significant drawback: increasing the element spacing often leads to higher sidelobe levels, generating strong sidelobe effects that interfere with the main beam signal and reduce signal detection accuracy. Especially in high-precision scenarios such as radar and communication, the unbalanced sidelobe performance severely impacts the overall performance of the equipment.

[0004] Although genetic algorithms, as a global optimization algorithm, have demonstrated their advantages in antenna array optimization design, capable of finding near-optimal solutions in a large search space, traditional genetic algorithms have shortcomings when applied to sparse array optimization. Their fitness function is prone to overfitting during the optimization process, causing the algorithm to achieve extremely low sidelobe levels at a certain angle while neglecting performance at other angles, resulting in unbalanced sidelobe levels across multiple angles. This lack of systematic verification across multiple angles prevents the final designed array from maintaining balanced sidelobe performance across various angles.

[0005] Therefore, for antenna array design under limited space conditions, how to make full use of space while improving angular resolution and effectively suppressing sidelobes has become an urgent problem to be solved in the field of antenna array optimization design.

[0006] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0007] The main objective of this application is to provide an antenna array optimization method, terminal device, and storage medium, which aims to address the technical problem of how to improve angular resolution and effectively suppress sidelobes while making full use of space in antenna array design under limited space conditions.

[0008] To achieve the above objectives, this application proposes an antenna array optimization method, which includes:

[0009] Obtain antenna data for each antenna included in the antenna array to be optimized;

[0010] Encode each individual in the initial population reflected by the antenna data to obtain an intermediate population, and decode the intermediate population to obtain the virtual array element for each individual;

[0011] The individual fitness of each individual is calculated based on the virtual array elements. Based on the individual fitness, the individuals are subjected to preset crossover and preset mutation operations to obtain the optimized array element positions.

[0012] The optimized array elements are arranged into an L-shaped antenna array by forming linear arrays.

[0013] In one embodiment, the antenna includes a transmitting antenna and a receiving antenna, and the step of acquiring antenna data of each antenna included in the antenna array to be optimized includes:

[0014] The number of transmit and receive antennas on the antenna array to be optimized is determined based on the number of channels. Based on the element size reflected by the number of channels, the horizontal spacing between the target transmit antennas and the target receive antennas in the horizontal direction, and the elevation spacing between the target transmit antennas and the target receive antennas in the elevation direction are determined. The physical apertures of the target transmit horizontal antennas and the target receive horizontal antennas in the horizontal direction are determined using a preset space size, as well as the physical apertures of the target receive transmit antennas and the target receive elevation antennas in the elevation direction. The angular resolution of the target horizontal antennas in the horizontal direction is determined using preset application requirements, and the angular resolution of the target elevation antennas in the elevation direction is determined to be less than a certain value.

[0015] In one embodiment, the step of encoding each volume in the array element positions reflected by the antenna data to obtain the intermediate population further includes:

[0016] The genetic parameters in the preset genetic algorithm are initialized to obtain the working frequency, initial number of genetic iterations, initial crossover probability, initial mutation probability, initial population size, and position step size.

[0017] In one embodiment, the step of encoding each individual in the initial population reflected by the antenna data to obtain an intermediate population includes:

[0018] An initial population is constructed based on antenna data. The initial population includes multiple individuals, each of which consists of two genes. Each gene represents the position information of the transmitting element of the transmitting antenna and the position information of the receiving element of the receiving antenna, respectively.

[0019] Based on the position information of the transmitting antenna array elements and the receiving antenna array elements corresponding to each individual, the corresponding individuals are encoded to generate an intermediate population. Each individual in the intermediate population carries a target gene, which is the encoded position information of the transmitting antenna array elements and the encoded position information of the receiving antenna array elements.

[0020] In one embodiment, the step of decoding the intermediate population to obtain the virtual array element for each individual includes:

[0021] Decode the target gene of each individual in the intermediate population to obtain the actual physical location of the array element corresponding to each individual;

[0022] Substituting the actual physical positions of the array elements into the preset virtual array element calculation formula yields the virtual array elements corresponding to each individual element.

[0023] In one embodiment, the step of calculating the individual fitness of an individual based on virtual array elements includes:

[0024] Substitute the wavelength and aperture of the virtual array element into the preset angle resolution calculation formula to obtain the virtual angle resolution. Determine whether the virtual angle resolution is greater than the target antenna angle resolution reflected by the antenna data corresponding to the virtual array element. The target antenna angle resolution is the target horizontal antenna angle resolution or the target elevation antenna angle resolution.

[0025] If the virtual angular resolution is greater than the target antenna angular resolution, then the individual fitness of the individual corresponding to the virtual angular resolution is set to 0.

[0026] If the virtual angular resolution is less than or equal to the target antenna angular resolution, the virtual element positions and wavelengths in the virtual array elements are substituted into the preset radiation pattern calculation formula to obtain a multi-angle radiation pattern. The target sidelobe value in the multi-angle radiation pattern is then obtained, and the individual fitness of the individual corresponding to the virtual angular resolution is determined based on the target sidelobe value.

[0027] In one embodiment, the step of performing preset crossover and preset mutation operations on individuals based on their fitness to obtain optimized array element positions includes:

[0028] Using a preset selection strategy, the fitness of a target individual is obtained when the individual fitness is greater than the preset individual fitness, and the individual corresponding to the fitness of the target individual is taken as the parent.

[0029] Randomly pair individuals from the parent generation and cross them to generate offspring individuals;

[0030] Randomly mutate the offspring individuals to obtain the target parameters. Replace the real values ​​on the genes of the corresponding offspring individuals with the target parameters to obtain the optimized array element positions.

[0031] In one embodiment, after obtaining the optimized element positions, the method further includes:

[0032] Determine the current number of genetic iterations and check if the current number of genetic iterations is greater than the initial number of genetic iterations.

[0033] If the current number of genetic iterations is greater than the initial number of genetic iterations, then the step of forming an L-shaped antenna array from the linear arrays corresponding to the optimized array element positions is executed.

[0034] If the current genetic iteration is less than or equal to the initial genetic iteration number, then the step of calculating the individual fitness of the individual based on the virtual array elements is executed.

[0035] In addition, to achieve the above objectives, this application also proposes a terminal device, the terminal device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the antenna array optimization method described above.

[0036] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the antenna array optimization method described above.

[0037] One or more technical solutions proposed in this application have at least the following technical effects:

[0038] The proposed method involves acquiring antenna data from each antenna in the antenna array to be optimized, encoding each individual in the initial population reflected by the antenna data to obtain an intermediate population, decoding the intermediate population to obtain virtual array elements for each individual, calculating the individual fitness based on the virtual array elements, performing preset crossover and mutation operations on the individuals based on the individual fitness, obtaining the optimized array element positions, and assembling the linear arrays corresponding to the optimized array element positions into an L-shaped antenna array.

[0039] This application avoids overfitting to low sidelobes during the optimization process by calculating individual fitness, thus enabling the optimized array to have superior overall performance. At the same time, the linear arrays corresponding to the optimized array element positions are combined into an L-shaped antenna array to make full use of the limited length and width of the space, maximizing the horizontal and elevation apertures and achieving better angular resolution. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating an embodiment of the antenna array optimization method of this application.

[0043] Figure 2 This is a schematic diagram showing the physical location of the antenna;

[0044] Figure 3 This is a schematic diagram showing the location of the virtual antenna;

[0045] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the antenna array optimization method in this application embodiment.

[0046] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0047] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0048] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The main solution of this application embodiment is as follows: obtain the antenna data of each antenna included in the antenna array to be optimized, encode each individual in the initial population reflected by the antenna data to obtain an intermediate population, decode and calculate the intermediate population to obtain the virtual array element of each individual, calculate the individual fitness of the individual based on the virtual array element, perform preset crossover operation and preset mutation operation on the individual based on the individual fitness to obtain the optimized array element position, and form an L-shaped antenna array by arranging the linear arrays corresponding to the optimized array element positions.

[0050] Traditional sparse array design reduces physical size by decreasing the number of array elements, thereby reducing hardware costs and simplifying system architecture, which is advantageous in resource-constrained or space-limited situations. However, this design method has a significant drawback: increased element spacing often leads to higher sidelobe levels, generating strong sidelobe effects that interfere with the main beam signal and reduce signal detection accuracy. Especially in high-precision scenarios such as radar and communications, the unbalanced sidelobe performance severely impacts the overall performance of the equipment.

[0051] Although genetic algorithms, as a global optimization algorithm, have demonstrated their advantages in antenna array optimization design, capable of finding near-optimal solutions in a large search space, traditional genetic algorithms have shortcomings when applied to sparse array optimization. Their fitness function is prone to overfitting during the optimization process, causing the algorithm to achieve extremely low sidelobe levels at a certain angle while neglecting performance at other angles, resulting in unbalanced sidelobe levels across multiple angles. This lack of systematic verification across multiple angles prevents the final designed array from maintaining balanced sidelobe performance across various angles.

[0052] Therefore, for antenna array design under limited space conditions, how to make full use of space while improving angular resolution and effectively suppressing sidelobes has become an urgent problem to be solved in the field of antenna array optimization design.

[0053] This application provides a solution that avoids overfitting to low sidelobes during the optimization process by calculating individual fitness, thereby enabling the optimized array to have superior overall performance. At the same time, the linear arrays corresponding to the optimized array element positions are combined into an L-shaped antenna array to make full use of the limited space length and width, maximizing the horizontal and elevation apertures and achieving better angular resolution.

[0054] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or terminal device capable of performing the above functions. The following description uses a terminal device as an example to illustrate this embodiment and the subsequent embodiments.

[0055] Based on this, embodiments of this application provide an antenna array optimization method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the antenna array optimization method of this application.

[0056] In this embodiment, the antenna array optimization method includes steps S10 to S40:

[0057] Step S10: Obtain antenna data for each antenna included in the antenna array to be optimized.

[0058] In one feasible implementation, step S10 includes step S11:

[0059] Step S11: Determine the number of transmitting antennas and receiving antennas on the antenna array to be optimized based on the number of channels; determine the horizontal spacing between the target transmitting antennas and the target receiving antennas in the horizontal direction, and the elevation spacing between the target transmitting antennas and the target receiving antennas in the elevation direction, based on the element size reflected by the number of channels; determine the physical aperture of the target transmitting horizontal antenna and the target receiving horizontal antenna in the horizontal direction, and the physical aperture of the target receiving transmitting antenna and the target receiving elevation antenna in the elevation direction, using a preset space size; and determine the angular resolution of the target horizontal antenna in the horizontal direction and the angular resolution of the target elevation antenna in the elevation direction, using preset application requirements.

[0060] In this embodiment, the number N of transmitting antennas to be configured on the antenna array to be optimized is determined based on the number of channels on the device where the antenna array to be optimized is located. t The number of receiving antennas N r .

[0061] Since one channel corresponds to one antenna element, the number of channels equals the number of antenna elements. Therefore, the number of channels on the device reflects the element size. Based on the element size, the minimum horizontal spacing d between the transmitting antennas of the antenna array to be optimized in the horizontal direction can be determined. tx (i.e., the horizontal spacing between the target transmitting antennas) and the minimum horizontal spacing d of the receiving antennas rx (i.e., the horizontal spacing between the target receiving antennas) and the minimum elevation spacing d of the transmitting antennas in the elevation direction. ty (i.e., the target transmitting antenna elevation distance) and the minimum receiving antenna elevation distance d ry (i.e., the elevation distance of the target receiving antenna).

[0062] Simultaneously, based on the available space size of the antenna array that can actually be installed on the equipment (i.e., the preset space size), the maximum horizontal transmit antenna physical aperture L of the antenna matrix to be optimized is determined. ty (i.e., the physical aperture of the target transmitting horizontal antenna) and the maximum receiving horizontal antenna physical aperture L rx (i.e., the physical aperture of the target receiving horizontal antenna), and the maximum transmitting elevation antenna physical aperture L in the elevation direction. ty (i.e., the physical aperture of the target transmitting elevation antenna) and the maximum receiving elevation antenna physical aperture L ry (i.e., the physical aperture of the target receiving elevation antenna).

[0063] Simultaneously, based on the application requirements (i.e., preset application requirements) of the antenna array installed on the equipment, the minimum horizontal antenna angle resolution res of the image array to be optimized in the horizontal direction is determined. x(i.e., the target horizontal antenna angular resolution), and the minimum elevation antenna angular resolution res in the elevation direction. y (i.e., the target elevation antenna angle resolution).

[0064] Step S20: Encode each individual in the initial population reflected by the antenna data to obtain an intermediate population, and decode the intermediate population to obtain the virtual array element for each individual.

[0065] In one possible implementation, step S21 is included before step S20:

[0066] Step S21: Initialize the genetic parameters in the preset genetic algorithm to obtain the working frequency f, the initial number of genetic iterations G, and the initial crossover probability P. c Initial mutation probability P m Initial population size N p And the position step size step, where the position step size step = 0.5, representing half a wavelength.

[0067] In one feasible implementation, step S20 may further include steps S22 to S25:

[0068] Step S22: Construct an initial population based on antenna data. The initial population includes multiple individuals, each of which consists of two genes, each representing the transmitting antenna and the receiving antenna, respectively.

[0069] Each individual in the initial population constructed based on antenna data has two genes, with gene dimensions N. t and N r N t This indicates that the gene can represent N. t The position information of each transmitting element, N r This indicates that the gene can represent N. r The location information of each transmitting element. This includes the location information of the transmitting elements of the transmitting antenna and the location information of the receiving elements of the receiving antenna.

[0070] Step S23: Based on the position information of the transmitting antenna array elements and the position information of the receiving antenna array elements corresponding to each individual, the corresponding individuals are encoded to generate an intermediate population. Each individual in the intermediate population carries a target gene, which is the encoded position information of the transmitting antenna array elements and the encoded position information of the receiving antenna array elements.

[0071] At this point, based on the position information of the transmitting antenna array elements and the position information of the receiving antenna array elements corresponding to each individual, the genes of the individual are encoded so that the real values ​​on the corresponding gene dimension can represent the encoded position information of the transmitting antenna array elements and the position information of the receiving antenna array elements. Individuals carrying the encoded array element position information are then grouped into an intermediate population.

[0072] Taking the gene representing the position information of the horizontally transmitting array elements as an example, the process of encoding the gene is shown in Formula 1:

[0073]

[0074] Among them, P tx Indicates the position information of the transmitting array elements in the horizontal direction; F tx The gene dimension is N. t Gene sequence; randi([0,nTXs],N t ,NP) represents generating an N t A ×NP matrix, where the values ​​in the matrix are random integers from 0 to nTXs, and each integer is multiplied by step; sort means sorting in ascending order, ceil means rounding up, and floor means rounding down.

[0075] Taking a gene that receives array element position information in the horizontal direction as an example, the process of encoding the gene is shown in Formula 2:

[0076]

[0077] Among them, P rx Indicates the position information of the receiving array elements in the horizontal direction; F rx The gene dimension is N. r Gene sequence; randi([0,nTXs],N r ,NP) represents generating an N r A ×NP matrix, where the values ​​in the matrix are random integers from 0 to nTXs, and each integer is multiplied by step; sort means sorting in ascending order, ceil means rounding up, and floor means rounding down.

[0078] It should be noted that the position information P of the launch array elements in the elevation direction ty and receive array element position information P ry The calculation process is the same as that of Formula 1 and Formula 2, the only difference being the different parameters substituted.

[0079] Step S24: Decode the target gene of each individual in the intermediate population to obtain the actual physical position of the array element corresponding to each individual.

[0080] Step S25: Substitute the actual physical position of the array element into the preset virtual array element calculation formula to obtain the virtual array element corresponding to each individual.

[0081] The target gene of each individual in the intermediate population is decoded into the actual physical location of the array element, based on the N obtained from the decoding of each individual. t The position of each launch element and N r The virtual array element is calculated based on the position of each receiving array element. Formulas 3 and 4 for the virtual array element are as follows:

[0082]

[0083] The actual physical position of the array element is the horizontal position P of the transmitting array element in Formula 3. txi and the position P of the receiving array element rxj And the position P of the launch array element in the elevation direction in Formula 4. tyi and the position P of the receiving array element ryj F ry The gene dimension is N. r The gene sequence, F ty The gene dimension is N. t The gene sequence, X n This represents the positions of n virtual array elements.

[0084] Step S30: Calculate the individual fitness of individuals based on virtual array elements, and perform preset crossover and preset mutation operations on individuals based on their individual fitness to obtain the optimized array element positions.

[0085] In one feasible implementation, step S30 may further include steps S31 to S36:

[0086] Step S31: Substitute the wavelength and aperture of the virtual array element into the preset angle resolution calculation formula to obtain the virtual angle resolution. Determine whether the virtual angle resolution is greater than the target antenna angle resolution reflected by the antenna data corresponding to the virtual array element. The target antenna angle resolution is the target horizontal antenna angle resolution or the target elevation antenna angle resolution.

[0087] Step S32: If the virtual angular resolution is greater than the target antenna angular resolution, then set the individual fitness of the individual corresponding to the virtual angular resolution to 0.

[0088] Step S33: If the virtual angular resolution is less than or equal to the target antenna angular resolution, substitute the virtual element position and wavelength in the virtual array element into the preset radiation pattern calculation formula to obtain a multi-angle radiation pattern, and obtain the target sidelobe value in the multi-angle radiation pattern. Determine the individual fitness of the individual corresponding to the virtual angular resolution based on the target sidelobe value.

[0089] It should be noted that the preset angle resolution calculation formula in this embodiment is shown in Formula 4.

[0090]

[0091] Where ant_size is the virtual element aperture, λ is the wavelength, and res is the virtual angular resolution.

[0092] Substituting the wavelength and aperture of the virtual array element corresponding to each individual into Formula 4 as shown above, we can obtain the virtual angular resolution corresponding to the individual. At this point, we determine whether the calculated virtual angular resolution is greater than the target antenna angular resolution reflected by the antenna data corresponding to that individual.

[0093] If the calculated virtual angular resolution is determined to be greater than the corresponding target antenna angular resolution, then the individual fitness of the corresponding individual is directly set to 0.

[0094] If the calculated virtual angular resolution is equal to or less than the corresponding target antenna angular resolution, then the virtual element position and wavelength of the virtual element corresponding to the individual need to be substituted into the preset radiation pattern calculation formula shown below, i.e., Formula 5, to calculate the multi-angle radiation pattern.

[0095]

[0096] Where, F(θ) i For multi-angle orientation diagrams, X n This represents the virtual array element position.

[0097] Based on the multi-angle radiation pattern calculated above, the highest sidelobe value (i.e., the target sidelobe value) is determined, and the opposite value of the highest sidelobe value is determined as the individual fitness of the corresponding individual. The judgment process described above can be shown in Formula 6.

[0098]

[0099] Wherein, -max(MSLL) i ) represents the inverse value of the highest sidelobe value, and res_threshold represents the angular resolution of the target antenna.

[0100] It should be noted that the target antenna angular resolution in this embodiment is set based on the actual antenna application requirements.

[0101] Step S34: Using a preset selection strategy, obtain the fitness of a target individual whose fitness is greater than the preset individual fitness, and use the individual corresponding to the fitness of the target individual as the parent.

[0102] The preset selection strategy in this embodiment is roulette wheel selection, which determines the likelihood of offspring retention based on the proportion of each individual's fitness. For a given individual, the higher its fitness, the greater its chance of being selected. If an individual's fitness is fit... i If the population size is NP, then the probability pi of it being selected is expressed as:

[0103]

[0104] Step S35: Randomly pair individuals from the parent generation and cross them to generate offspring individuals.

[0105] With crossover probability P C Choose an odd-numbered individual and pair it with its next even-numbered individual (i.e., its parent). For each selected combination, randomly generate two N-dimensional individuals. t and N r The two 01 sequences correspond to two dimensions of N for an odd number of individuals. t and N r If a value in the 01 sequence is 1, then the value of the corresponding gene sequence in odd-numbered individuals is swapped with the value of the corresponding gene sequence in even-numbered individuals to generate offspring individuals.

[0106] Step S36: Randomly mutate the offspring individuals to obtain the target parameters, and replace the real values ​​on the genes of the corresponding offspring individuals with the target parameters to obtain the optimized array element positions.

[0107] Iterate through each offspring individual, generating a random number r in the interval [0,1]. If r <P m If the offspring is selected as the mutated individual, the real values ​​of the original two gene sequences of the offspring are replaced with parameters from the randomly generated value range to obtain the optimized array element positions.

[0108] In this embodiment, step S35 is a preset crossover operation, and step S36 is a preset mutation operation.

[0109] In one feasible implementation, steps S37 to S39 may be included after step S36:

[0110] Step S37: Determine the current number of genetic iterations and check whether the current number of genetic iterations is greater than the initial number of genetic iterations.

[0111] Step S38: If the current number of genetic iterations is greater than the initial number of genetic iterations, then execute the step of forming an L-shaped antenna array from the linear arrays corresponding to the optimized array element positions.

[0112] Step S39: If the current genetic iteration is less than or equal to the initial genetic iteration number, then execute the step of calculating the individual fitness of the individual based on the virtual array elements.

[0113] The current number of genetic iterations is the number of times steps S20 and S30 have been executed. If the current number of genetic iterations is greater than the initial number of genetic iterations, the verification is considered successful, and the optimized array element positions output at the current time do not exhibit overfitting. If the current number of genetic iterations is less than or equal to the initial number of genetic iterations, it is considered that steps S20 and S30 need to be executed again based on the current optimized array element positions to ensure that the output antenna array exhibits low sidelobe performance at multiple angles.

[0114] It should be noted that step S30 is based on the position P of the transmitting array element. txi and the position P of the receiving array element rxj The position optimization of the receiving and transmitting array elements in the horizontal and elevation directions, respectively, is an independent operation that needs to be performed separately.

[0115] Step S40: The linear arrays corresponding to the optimized array element positions are arranged into an L-shaped antenna array.

[0116] The optimized horizontal receiver and transmitter positions obtained through steps S20 and S30 are combined into an L-shaped antenna array.

[0117] The specific steps for forming an L-shaped antenna array are as follows: the horizontal receiving elements and the elevation receiving elements share the same first element. The horizontal receiving elements are arranged horizontally with the position of the first element as the origin, and the elevation receiving elements are arranged with the position of the first element as the origin, thus forming an L-shaped array. The transmitting elements can be arranged in the same way to form an L-shaped area array.

[0118] Example physical antenna location as follows Figure 2 As shown, the example virtual antenna position is as follows: Figure 3 As shown, where, Figure 2 In this context, tx represents the transmitting antenna and rx represents the receiving antenna.

[0119] In this embodiment, by calculating individual fitness, the optimization process is prevented from overfitting to low sidelobes, so that the optimized array has better overall performance. At the same time, the linear arrays corresponding to the optimized array element positions are formed to make full use of the length and width of the limited space, so as to maximize the horizontal and pitch apertures and achieve better angular resolution.

[0120] This application provides a terminal device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the antenna array optimization method in Embodiment 1 above.

[0121] The following is for reference. Figure 4 The diagram illustrates a structural schematic of a terminal device suitable for implementing embodiments of this application. The terminal device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0122] like Figure 4 As shown, the terminal device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the terminal device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows terminal devices to communicate wirelessly or wiredly with other devices to exchange data. Although terminal devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0123] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0124] The terminal device provided in this application employs the antenna array optimization method described in the above embodiments, addressing the technical problem of how to fully utilize space while improving angular resolution and effectively suppressing sidelobes in antenna array design under limited space conditions. Compared with the prior art, the beneficial effects of the terminal device provided in this application are the same as those of the antenna array optimization method provided in the above embodiments, and other technical features of this terminal device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0125] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0126] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0127] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the antenna array optimization method in the above embodiments.

[0128] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0129] The aforementioned computer-readable storage medium may be included in the terminal device; or it may exist independently and not assembled into the terminal device.

[0130] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by a terminal device, the terminal device: acquires antenna data of each antenna included in the antenna array to be optimized; encodes each individual in the initial population reflected by the antenna data to obtain an intermediate population; decodes and calculates the intermediate population to obtain virtual array elements for each individual; calculates the individual fitness of the individual based on the virtual array elements; performs preset crossover and preset mutation operations on the individuals based on the individual fitness to obtain the optimized array element positions; and assembles the linear arrays corresponding to the optimized array element positions into an L-shaped antenna array.

[0131] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0133] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0134] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described antenna array optimization method. This addresses the technical problem of how to fully utilize space while improving angular resolution and effectively suppressing sidelobes in antenna array design under limited space conditions. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the antenna array optimization method provided in the above embodiments, and will not be repeated here.

[0135] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. An antenna array optimization method, characterized in that, The antenna array optimization method includes: Obtain antenna data for each antenna included in the antenna array to be optimized; Encode each individual in the initial population reflected by the antenna data to obtain an intermediate population, and decode the intermediate population to obtain the virtual array element of each individual. The individual fitness of the individual is calculated based on the virtual array element, and the individual is subjected to a preset crossover operation and a preset mutation operation based on the individual fitness to obtain the optimized array element position; The optimized array element positions are used to form an L-shaped antenna array from the linear arrays. The antenna includes a transmitting antenna and a receiving antenna, and the step of acquiring antenna data of each antenna included in the antenna array to be optimized includes: The number of transmitting and receiving antennas on the antenna array to be optimized is determined based on the number of channels. Based on the element dimensions reflected by the number of channels, the horizontal spacing between the target transmitting and receiving antennas in the horizontal direction, and the elevation spacing between the target transmitting and receiving antennas in the elevation direction are determined. The physical apertures of the target transmitting horizontal antenna and the target receiving horizontal antenna in the horizontal direction are determined using a preset space size, as well as the physical apertures of the target receiving transmitting antenna and the target receiving elevation antenna in the elevation direction; and, The target horizontal antenna angle resolution in the horizontal direction and the target elevation antenna angle resolution in the elevation direction are determined by using preset application requirements. The step of calculating the individual fitness of the individual based on the virtual array elements includes: Substitute the wavelength and aperture of the virtual array element into the preset angle resolution calculation formula to obtain the virtual angle resolution. Determine whether the virtual angle resolution is greater than the target antenna angle resolution reflected by the antenna data corresponding to the virtual array element. The target antenna angle resolution is the target horizontal antenna angle resolution or the target elevation antenna angle resolution. If the virtual angular resolution is greater than the target antenna angular resolution, then the individual fitness of the individual corresponding to the virtual angular resolution is set to 0; If the virtual angular resolution is less than or equal to the target antenna angular resolution, the virtual element positions and wavelengths in the virtual array elements are substituted into the preset radiation pattern calculation formula to obtain a multi-angle radiation pattern, and the target sidelobe value in the multi-angle radiation pattern is obtained. Based on the target sidelobe value, the individual fitness of the individual corresponding to the virtual angular resolution is determined.

2. The antenna array optimization method as described in claim 1, characterized in that, Before the step of encoding each individual in the initial population reflected by the antenna data to obtain the intermediate population, the method further includes: The genetic parameters in the preset genetic algorithm are initialized to obtain the working frequency, initial number of genetic iterations, initial crossover probability, initial mutation probability, initial population size, and position step size.

3. The antenna array optimization method as described in claim 2, characterized in that, The step of encoding each individual in the initial population reflected by the antenna data to obtain the intermediate population includes: An initial population is constructed based on the antenna data, wherein the initial population includes multiple individuals, each individual is composed of two genes, each gene representing the transmitting antenna and the receiving antenna respectively; Based on the position information of the transmitting antenna array elements and the position information of the receiving antenna array elements corresponding to each individual, the corresponding individuals are encoded to generate the intermediate population. Each individual in the intermediate population carries a target gene, which is the encoded position information of the transmitting antenna array elements and the encoded position information of the receiving antenna array elements.

4. The antenna array optimization method as described in claim 3, characterized in that, The step of decoding the intermediate population to obtain the virtual array element of each individual includes: Decode the target gene of each individual in the intermediate population to obtain the actual physical position of the array element corresponding to each individual; Substituting the actual physical positions of the array elements into the preset virtual array element calculation formula yields the virtual array elements corresponding to each individual.

5. The antenna array optimization method as described in claim 4, characterized in that, The step of performing preset crossover and preset mutation operations on the individuals based on their fitness to obtain the optimized array element positions includes: Using a preset selection strategy, a target individual fitness is obtained whose individual fitness is greater than the preset individual fitness, and the individual corresponding to the target individual fitness is taken as the parent. Randomly pair individuals from the parent generation and cross them to generate offspring individuals; The offspring individuals are randomly mutated to obtain target parameters. The real values ​​on the genes of the individuals corresponding to the offspring individuals are replaced with the target parameters to obtain the optimized array element positions.

6. The antenna array optimization method as described in claim 5, characterized in that, Following the step of obtaining the optimized array element positions, the following is also included: Determine the current number of genetic iterations, and determine whether the current number of genetic iterations is greater than the initial number of genetic iterations; If the current number of genetic iterations is greater than the initial number of genetic iterations, then the step of forming an L-shaped antenna array from the linear arrays corresponding to the optimized array element positions is executed. If the current genetic iteration is less than or equal to the initial genetic iteration number, then the step of calculating the individual fitness of the individual based on the virtual array elements is executed.

7. A terminal device, characterized in that, The terminal device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the antenna array optimization method as described in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the antenna array optimization method as described in any one of claims 1 to 6.

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