Antenna array optimization method, terminal device and storage medium

Abstract

The invention discloses an antenna array optimization method, terminal equipment and a storage medium, and relates to the technical field of antennae, and the method comprises the steps: obtaining the antenna data of each antenna included in a to-be-optimized antenna array, carrying out the coding of each individual in an initial population reflected by the antenna data, obtaining an intermediate population, and carrying out the coding of each individual in the initial population, and performing decoding calculation on the intermediate population to obtain a virtual array element of each individual, calculating individual fitness of the individuals according to the virtual array elements, performing preset crossover operation and preset mutation operation on the individuals according to the individual fitness to obtain optimized array element positions, and forming an L-shaped antenna array by linear arrays corresponding to the optimized array element positions. The optimization process is prevented from being over-fitted to a low side lobe by calculating individual fitness, and meanwhile, linear arrays corresponding to the positions of the optimized array elements form an L-shaped antenna array, so that the length and the width of a limited space are fully utilized, horizontal and pitching apertures are maximized, and better angular resolution is realized.

Description

Technical Field

[0001] The present application relates to the field of antenna technology, and in particular to an antenna array optimization method, a terminal device, and a storage medium. Background Art

[0002] Antenna technology has been widely used in many high-tech fields such as radar, communication, wireless positioning, astronomical observation and electronic countermeasures. This technology achieves directional transmission and reception of signals by adjusting the arrangement of multiple antenna elements and the signal phase relationship, significantly improving the angular resolution, signal gain and anti-interference ability of the device.

[0003] The traditional sparse array design method reduces the physical size by reducing the number of array elements, thereby reducing hardware costs and simplifying the system architecture, which has certain advantages in situations where resources are limited or space is limited. However, this design method has significant defects, namely that the increase in the spacing between array elements often leads to higher sidelobe levels, resulting in strong sidelobe effects, interfering with the main beam signal, and reducing signal detection accuracy. Especially in high-precision scenarios such as radar and communications, the unbalanced performance of the sidelobes seriously affects the overall performance of the equipment.

[0004] Although genetic algorithms, as a global optimization algorithm, have shown their advantages in antenna array optimization design and can find approximate 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 ignoring the performance at other angles, resulting in uneven sidelobe levels at multiple angles. This optimization process lacks systematic verification of multiple angles, making it impossible for the final designed array to maintain balanced sidelobe performance at multiple angles.

[0005] Therefore, for antenna array design under limited space conditions, how to fully utilize the space while improving the angular resolution and effectively suppressing the side lobes has become a problem that needs to be solved urgently in the field of antenna array optimization design.

[0006] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0007] The main purpose of this application is to provide an antenna array optimization method, terminal equipment and storage medium, aiming to solve the technical problem of how to improve the angular resolution and effectively suppress the side lobes while fully utilizing the space in antenna array design under limited space conditions.

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

[0009] Acquire antenna data of 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 perform decoding calculation on the intermediate population to obtain a virtual array element of each individual;

[0011] Calculating the 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, and obtaining an optimized array element position;

[0012] The linear arrays corresponding to the optimized array element positions are formed into an L-shaped antenna array.

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

[0014] Determine the number of transmitting antennas and the number of receiving antennas on the antenna array to be optimized according to the number of channels, and determine the target transmitting antenna horizontal spacing and the target receiving antenna horizontal spacing in the horizontal direction, and the target transmitting antenna pitch spacing and the target receiving antenna pitch spacing in the elevation direction according to the array element size reflected by the number of channels; and, determine the target transmitting horizontal antenna physical aperture and the target receiving horizontal antenna physical aperture in the horizontal direction, and determine the target receiving transmitting antenna physical aperture and the target receiving elevation antenna physical aperture in the elevation direction using a preset space size; and, determine the target horizontal antenna angular resolution in the horizontal direction using preset application requirements, and determine a target pitch antenna angular resolution in the elevation direction that is less than.

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

[0016] Initialize the genetic parameters in the preset genetic algorithm to obtain the working frequency, initial genetic iteration number, initial crossover probability, initial mutation probability, initial population number and position step length.

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

[0018] An initial population is constructed according to the antenna data, wherein the initial population includes a plurality of individuals, each of which is composed of two genes, and 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;

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

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

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

[0022] 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 body.

[0023] In one embodiment, the step of calculating the individual fitness of the individual according to the virtual array element comprises:

[0024] Substituting the wavelength and the aperture of the virtual array element in the virtual array element into the preset angle resolution calculation formula to obtain the virtual angle resolution, and judging whether the virtual angle resolution is greater than the target antenna angle resolution reflected by the antenna data corresponding to the virtual array element, wherein the target antenna angle resolution is the target horizontal antenna angle resolution or the target elevation antenna angle resolution;

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

[0026] If the virtual angular resolution is less than or equal to the target antenna angular resolution, the virtual array element position and wavelength in the virtual array element 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. The individual fitness of the individual corresponding to the virtual angular resolution is determined according to the target sidelobe value.

[0027] In one embodiment, the step of performing a preset crossover operation and a preset mutation operation on the individual according to the individual fitness to obtain an optimized array element position includes:

[0028] Use the preset selection strategy to obtain the target individual fitness whose individual fitness is greater than the preset individual fitness, and use the individual corresponding to the target individual fitness as the parent;

[0029] Randomly pair individuals from the parent generation for crossover to generate offspring individuals;

[0030] The offspring individuals are randomly mutated to obtain the target parameters, and 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.

[0031] In one embodiment, after the step of obtaining the optimized array element position, the step further includes:

[0032] Determine the current genetic iteration number, and judge whether the current genetic iteration number is greater than the initial genetic iteration number;

[0033] If the current genetic iteration number is greater than the initial genetic iteration number, executing the step of forming an L-shaped antenna array from the linear array corresponding to the optimized array element position;

[0034] If the current genetic iteration is less than or equal to the initial genetic iteration number, the step of calculating the individual fitness of the individual according to the virtual array element is performed.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a terminal device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the antenna array optimization method as described above.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which 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, the steps of the antenna array optimization method described above are implemented.

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

[0038] It is proposed to 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, and decode the intermediate population to obtain a virtual array element for each individual, calculate the individual fitness of the individual according to the virtual array element, perform preset crossover operations and preset mutation operations on the individual according to the individual fitness, obtain the optimized array element position, and form the linear array corresponding to the optimized array element position into an L-shaped antenna array.

[0039] This application avoids overfitting of the optimization process to low side lobes by calculating individual fitness, 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 into an L-shaped antenna array to fully utilize the length and width of the limited space, maximize the horizontal and pitch apertures, and achieve better angular resolution. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A schematic diagram of a flow chart provided for an embodiment of an antenna array optimization method of the present application;

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

[0044] Figure 3 is a schematic diagram of the virtual antenna position;

[0045] Figure 4 Schematic diagram of the device structure of the hardware operating environment involved in the antenna array optimization method in the embodiment of the present application.

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

[0047] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The main solution of the embodiment of the present application is: obtaining antenna data of each antenna included 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 and calculating the intermediate population to obtain a virtual array element for each individual, calculating the individual fitness of the individual according to the virtual array element, performing preset crossover operations and preset mutation operations on the individual according to the individual fitness to obtain an optimized array element position, and forming an L-shaped antenna array with the linear array corresponding to the optimized array element position.

[0050] Since the traditional sparse array design method reduces the physical size by reducing the number of array elements, thereby reducing hardware costs and simplifying the system architecture, this has certain advantages in situations where resources are limited or space is limited. However, this design method has significant defects, namely that the increase in the array element spacing often leads to higher sidelobe levels, resulting in strong sidelobe effects, interfering with the main beam signal, and reducing signal detection accuracy. Especially in high-precision scenarios such as radar and communications, the unbalanced performance of the sidelobes seriously affects the overall performance of the equipment.

[0051] Although genetic algorithms, as a global optimization algorithm, have shown their advantages in antenna array optimization design and can find approximate 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 ignoring the performance at other angles, resulting in uneven sidelobe levels at multiple angles. This optimization process lacks systematic verification of multiple angles, making it impossible for the final designed array to maintain balanced sidelobe performance at multiple angles.

[0052] Therefore, for antenna array design under limited space conditions, how to fully utilize the space while improving the angular resolution and effectively suppressing the side lobes has become a problem that needs to be solved urgently in the field of antenna array optimization design.

[0053] The present application provides a solution, which avoids overfitting of the optimization process to low side lobes by calculating individual fitness, 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 into an L-shaped antenna array to make full use of the length and width of the limited space, maximize the horizontal and pitch apertures, and achieve better angular resolution.

[0054] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a terminal device, etc. capable of realizing the above functions. The following takes the terminal device as an example to illustrate this embodiment and the following embodiments.

[0055] Based on this, the embodiment of the present application provides an antenna array optimization method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the antenna array optimization method of the present application.

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

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

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

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

[0060] In this embodiment, the number of transmitting antennas N of the transmitting antennas set on the antenna array to be optimized is determined according to the number of channels on the device on which the antenna array to be optimized is placed. t and the number of receiving antennas N r .

[0061] Because one channel can correspond to one antenna element, the number of channels is equal to the number of antenna elements. Therefore, the number of channels on the device can reflect the size of the element. At this time, the minimum horizontal spacing d of the transmitting antenna in the horizontal direction of the antenna array to be optimized can be determined according to the element size. tx (i.e., target transmitting antenna horizontal spacing) and minimum receiving antenna horizontal spacing d rx (i.e., the horizontal spacing of the target receiving antennas), and the minimum elevation spacing d of the transmitting antennas in the elevation direction ty (i.e., target transmit antenna pitch spacing) and minimum receive antenna pitch spacing d ry (i.e. the target receiving antenna elevation distance).

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

[0063] At the same time, according to the application requirements of the antenna array installed on the device (i.e., the preset application requirements), 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 angle resolution), and the minimum elevation antenna angle resolution res in the elevation direction y (i.e. target elevation antenna angle resolution).

[0064] Step S20, encoding each individual in the initial population reflected by the antenna data to obtain an intermediate population, and decoding and calculating the intermediate population to obtain a virtual array element of each individual.

[0065] In a feasible implementation manner, step S20 further includes step S21:

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

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

[0068] Step S22, constructing an initial population according to the antenna data, wherein the initial population includes a plurality of individuals, each individual is composed of two genes, and each gene represents a transmitting antenna and a receiving antenna respectively.

[0069] Each individual in the initial population constructed based on antenna data has two genes, and the gene dimensions are N t and N r , N t Indicates that the gene can express N t The position information of the transmitting array elements, N r Indicates that the gene can express N r The genes include the position information of the transmitting array element of the transmitting antenna and the position information of the receiving array element of the receiving antenna.

[0070] Step S23, based on the transmitting antenna array element position information and the receiving antenna array element position information corresponding to each individual, encode the corresponding individuals to generate an intermediate population, wherein each individual in the intermediate population carries the target gene, and the target gene is the encoded transmitting antenna array element position information and the encoded receiving antenna array element position information.

[0071] At this time, based on the transmitting antenna array element position information and receiving antenna array element position information corresponding to each individual, the gene of the individual is encoded so that the real value on the corresponding gene dimension can represent the encoded transmitting antenna array element position information and receiving antenna array element position information, and the individuals carrying the encoded array element position information are formed into an intermediate population.

[0072] Taking the gene of the horizontal transmission element position information 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 element in the horizontal direction; F tx Indicates that the gene dimension is N t Gene sequence; randi([0,nTXs],N t ,NP) represents the generation of an N t ×NP matrix, the values ​​in the matrix are random integers from 0 to nTXs, and then each integer is multiplied by step; sort means sorting from small to large, ceil means rounding up, and floor means rounding down.

[0075] Taking the gene of the receiving 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 receiving array element position information in the horizontal direction; F rx Indicates that the gene dimension is N r Gene sequence; randi([0,nTXs],N r ,NP) represents the generation of an N r ×NP matrix, the values ​​in the matrix are random integers from 0 to nTXs, and then each integer is multiplied by step; sort means sorting from small to large, ceil means rounding up, and floor means rounding down.

[0078] It should be noted that the transmitting array element position information P in the pitch direction ty and receiving array element position information P ry The calculation process is the same as that of Formula 1 and Formula 2, the only difference is that the parameters are different.

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

[0080] Step S25 , substituting the actual array element physical position into the preset virtual array element calculation formula to obtain the virtual array element corresponding to each body.

[0081] Decode the target gene of each individual in the intermediate population into the actual physical position of the array element, and use the N obtained by decoding each individual t The transmit element positions and N r The virtual array element is calculated from the receiving array element position. Formula 3 and Formula 4 of the virtual array element are as follows:

[0082]

[0083] The actual physical position of the array element is the horizontal position of the transmitting array element in formula 3: txi and the receiving element position P rxj , and the transmitting array element position P in the pitch direction in formula 4 tyi and the receiving element position P ryj , F ry Indicates that the gene dimension is N r The gene sequence of F ty Indicates that the gene dimension is N t The gene sequence of X n Indicates the positions of n virtual array elements.

[0084] Step S30, calculating the individual fitness of the individuals according to the virtual array elements, performing a preset crossover operation and a preset mutation operation on the individuals according to the individual fitness, and obtaining an optimized array element position.

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

[0086] Step S31, substituting the wavelength and the virtual array element aperture in the virtual array element into a preset angle resolution calculation formula to obtain a virtual angle resolution, and determining whether the virtual angle resolution is greater than a target antenna angle resolution reflected by antenna data corresponding to the virtual array element, wherein the target antenna angle resolution is a target horizontal antenna angle resolution or a target elevation antenna angle resolution.

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

[0088] Step S33, if the virtual angular resolution is less than or equal to the target antenna angular resolution, the virtual array element position and wavelength in the virtual array element 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, and the individual fitness of the individual corresponding to the virtual angular resolution is determined according to 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] Among them, ant_size is the virtual array element aperture, λ is the wavelength, and res is the virtual angular resolution.

[0092] Substituting the wavelength and the virtual array element aperture of each individual into Formula 4 shown above, the virtual angular resolution corresponding to the individual can be obtained. At this time, it is determined whether the calculated virtual angular resolution is greater than the target antenna angular resolution reflected by the antenna data corresponding to the individual.

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

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

[0095]

[0096] Among them, F(θ) i is a multi-angle directional diagram, X n is the virtual array element position.

[0097] Based on the multi-angle directional diagram calculated as above, the highest sidelobe value (ie, 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 as described above can be shown in Formula 6.

[0098]

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

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

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

[0102] The preset selection strategy in this embodiment is roulette, that is, the probability of retaining the offspring is determined according to the proportion of the individual fitness of each individual. For an individual, the greater its individual fitness, the greater its chance of being selected. i , the population size is NP, then the probability pi of it being selected is expressed as:

[0103]

[0104] Step S35, randomly pairing individuals from the parent generation for crossover to generate offspring individuals.

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

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

[0107] Traverse each offspring individual and generate a random number r in the interval [0,1]. If r <P m , then the offspring individual is selected as the mutated individual, and the parameters in the randomly generated value range are used to replace the real values ​​of the original two gene sequences of the offspring individual to obtain the optimized array element position.

[0108] Among them, step S35 is the preset crossover operation in this embodiment, and step S36 is the preset mutation operation in this embodiment.

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

[0110] Step S37, determining the current genetic iteration number, and judging whether the current genetic iteration number is greater than the initial genetic iteration number.

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

[0112] Step S39: If the current genetic iteration is less than or equal to the initial genetic iteration number, the step of calculating the individual fitness of the individual according to the virtual array element is executed.

[0113] The current number of genetic iterations is the number of executions of step S20 and step S30. If the current number of genetic iterations is greater than the initial number of genetic iterations, it is considered that the verification is passed, and the currently output optimized array element position does not have 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 step S30 need to be executed again based on the currently optimized array element position, so as to ensure the low sidelobe performance of the output antenna array at multiple angles.

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

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

[0116] The two linear arrays of receiving array element positions and transmitting array element positions in the horizontal direction and the two linear arrays of receiving array element positions and transmitting array element positions in the elevation direction obtained through step S20 and step S30 are used to form an L-shaped antenna array.

[0117] The specific steps of forming an L-shaped antenna array are as follows: the receiving array element position in the horizontal direction and the receiving array element position in the elevation direction share the first array element, wherein the receiving array element position in the horizontal direction is arranged horizontally with the position of the first array element as the origin, and the receiving array element position in the elevation direction is arranged with the position of the first array element as the origin, thereby forming an L-shaped array. Similarly, the transmitting array element position can also form an L-shaped array.

[0118] Example physical antenna locations are Figure 2 As shown, the example virtual antenna position is as follows Figure 3 As shown, Figure 2 Here, tx represents the transmitting antenna and rx represents the receiving antenna.

[0119] In this embodiment, the individual fitness is calculated to avoid overfitting of the optimization process to the low side lobes, so that the optimized array has a better overall performance. At the same time, the linear array corresponding to the optimized array element position is formed to make full use of the length and width of the limited space, maximize the horizontal and pitch apertures, and achieve better angular resolution.

[0120] The present 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the antenna array optimization method in the above-mentioned embodiment one.

[0121] Reference below Figure 4 , which shows a schematic diagram of the structure of a terminal device suitable for implementing the embodiment of the present application. The terminal device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The terminal device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0122] like Figure 4 As shown, the terminal device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the terminal device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the terminal device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a terminal device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0123] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0124] The terminal device provided by the present application adopts the antenna array optimization method in the above embodiment, and can solve the technical problem of how to improve the angular resolution and effectively suppress the side lobes while making full use of the space for the antenna array design under limited space conditions. Compared with the prior art, the beneficial effects of the terminal device provided by the present application are the same as the beneficial effects of the antenna array optimization method provided by the above embodiment, and the other technical features in the terminal device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0125] It should be understood that the various parts disclosed in this application can be implemented by 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 one or more embodiments or examples in a suitable manner.

[0126] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0127] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, and the computer-readable program instructions are used to execute the antenna array optimization method in the above-mentioned embodiment.

[0128] The computer-readable storage medium provided in the present 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 of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. 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 combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0129] The computer-readable storage medium may be included in the terminal device; or may exist independently without being installed in the terminal device.

[0130] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the terminal device, the terminal device: obtains 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, and decodes and calculates the intermediate population to obtain a virtual array element for each individual, calculates the individual fitness of the individual according to the virtual array element, performs a preset crossover operation and a preset mutation operation on the individual according to the individual fitness, obtains an optimized array element position, and composes the linear array corresponding to the optimized array element position into an L-shaped antenna array.

[0131] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0133] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0134] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned antenna array optimization method, and can solve the technical problem of how to improve the angular resolution and effectively suppress the side lobes while making full use of the space for the 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 the beneficial effects of the antenna array optimization method provided in the above-mentioned embodiment, which will not be repeated here.

[0135] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An antenna array optimization method, characterized in that: The antenna array optimization method comprises: Acquire antenna data of each antenna included in the antenna array to be optimized; Encoding each individual in the initial population reflected by the antenna data to obtain an intermediate population, and decoding and calculating the intermediate population to obtain a virtual array element of each individual; Calculating the individual fitness of the individual according to the virtual array element, and performing a preset crossover operation and a preset mutation operation on the individual according to the individual fitness to obtain an optimized array element position; The linear arrays corresponding to the optimized array element positions are formed into an L-shaped antenna array.

2. The antenna array optimization method according to claim 1, characterized in that: The antenna includes a transmitting antenna and a receiving antenna, and the step of obtaining antenna data of each antenna included in the antenna array to be optimized includes: Determine the number of transmitting antennas and the number of receiving antennas on the antenna array to be optimized according to the number of channels, and determine the target horizontal spacing of transmitting antennas and the target horizontal spacing of receiving antennas in the horizontal direction, and the target pitch spacing of transmitting antennas and the target pitch spacing of receiving antennas in the elevation direction according to the array element size reflected by the number of channels; and, Determine the target transmitting horizontal antenna physical aperture and the target receiving horizontal antenna physical aperture in the horizontal direction and determine the target receiving transmitting antenna physical aperture and the target receiving elevation antenna physical aperture in the elevation direction using the preset space size; and, The target horizontal antenna angle resolution in the horizontal direction is determined using the preset application requirements, and the target elevation antenna angle resolution in the elevation direction is determined.

3. The antenna array optimization method according to claim 2, characterized in that: Before the step of encoding each individual in the array element position reflected by the antenna data to obtain the intermediate population, the step further includes: Initialize the genetic parameters in the preset genetic algorithm to obtain the working frequency, initial genetic iteration number, initial crossover probability, initial mutation probability, initial population number and position step length.

4. The antenna array optimization method according to claim 3, characterized in that: The step of encoding each individual in the initial population reflected by the antenna data to obtain an intermediate population comprises: Constructing an initial population according to the antenna data, wherein the initial population includes a plurality of individuals, each individual is composed of two genes, and each gene represents a transmitting antenna and a receiving antenna respectively; Based on the transmitting antenna array element position information and the receiving antenna array element position information respectively corresponding to each of the individuals, the corresponding individuals are encoded to generate the intermediate population, wherein each of the individuals in the intermediate population carries a target gene, and the target gene is the encoded transmitting antenna array element position information and the encoded receiving antenna array element position information.

5. The antenna array optimization method according to claim 4, characterized in that: The step of performing decoding calculation on the intermediate population to obtain a virtual array element of each individual comprises: Decoding the target gene of each individual in the intermediate population to obtain the actual array element physical position corresponding to each individual; Substitute the actual array element physical position into a preset virtual array element calculation formula to obtain a virtual array element corresponding to each of the individuals.

6. The antenna array optimization method according to claim 5, characterized in that: The step of calculating the individual fitness of the individual according to the virtual array element comprises: Substituting the wavelength and the virtual array element aperture in the virtual array element into a preset angle resolution calculation formula to obtain a virtual angle resolution, and determining whether the virtual angle resolution is greater than a target antenna angle resolution reflected by the antenna data corresponding to the virtual array element, wherein the target antenna angle resolution is the target horizontal antenna angle resolution or the target elevation antenna angle resolution; If the virtual angle resolution is greater than the target antenna angle resolution, the individual fitness of the individual corresponding to the virtual angle resolution is set to 0; If the virtual angular resolution is less than or equal to the target antenna angular resolution, the virtual array element position and wavelength in the virtual array element 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, and the individual fitness of the individual corresponding to the virtual angular resolution is determined according to the target sidelobe value.

7. The antenna array optimization method according to claim 6, characterized in that: The step of performing a preset crossover operation and a preset mutation operation on the individual according to the individual fitness to obtain an optimized array element position comprises: Using a preset selection strategy, obtaining a target individual fitness whose individual fitness is greater than a preset individual fitness, and taking an individual corresponding to the target individual fitness as a parent; Randomly pair individuals from the parent generation for crossover to generate offspring individuals; The offspring individuals are randomly mutated to obtain target parameters, and 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.

8. The antenna array optimization method according to claim 7, characterized in that: After the step of obtaining the optimized array element position, the following step is further included: Determine the current genetic iteration number, and judge whether the current genetic iteration number is greater than the initial genetic iteration number; If the current genetic iteration number is greater than the initial genetic iteration number, executing the step of forming an L-shaped antenna array from the linear array corresponding to the optimized array element position; If the current genetic iteration is less than or equal to the initial genetic iteration number, the step of calculating the individual fitness of the individual according to the virtual array element is performed.

9. A terminal device, characterized in that: The terminal device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the antenna array optimization method according to any one of claims 1 to 8.

10. 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, the steps of the antenna array optimization method according to any one of claims 1 to 8 are implemented.

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