Antenna array optimization method and device, equipment and storage medium

Abstract

The invention provides an antenna array optimization method and device, equipment and a storage medium, and the method comprises the steps: constructing an antenna array model, and initializing the parameters of the antenna array model; constructing a fitness function based on the antenna array model; calculating an optimal solution of the antenna array model based on the fitness function and an electric eel foraging optimization algorithm; and deriving a target antenna array based on the optimal solution. According to the technical scheme provided by the invention, the exploration capability and the utilization efficiency are improved through the electric eel foraging optimization algorithm, the optimal solution is finally obtained, and the sparse array layout corresponding to the optimal solution is the optimal arrangement mode of the antenna array. Meanwhile, the constructed fitness function enables the optimized array to show high direction finding performance, can adapt to the characteristics of agricultural machinery navigation, and improves the precision and reliability of agricultural machinery navigation.

Description

Technical Field

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

[0002] With the rapid development of national science and technology, antenna arrays have gradually been applied to the field of agricultural machinery navigation, playing an increasingly important and irreplaceable role. The construction of efficient antenna arrays makes challenging tasks such as large-scale unmanned farmland operations possible. In addition, these antennas also play a key role in precision agriculture, agricultural automation, farmland monitoring, smart irrigation and energy.

[0003] In agricultural production, accurate positioning and navigation systems are essential to improving the operating efficiency and accuracy of agricultural machinery. Due to the high mobility of agricultural machinery navigation systems, which brings variability and uncertainty, traditional antenna array design methods cannot fully meet the needs of agricultural machinery navigation, affecting positioning accuracy, signal stability and system robustness. Therefore, it is very necessary and urgent to study new and efficient antenna array optimization methods to meet the needs of agricultural machinery navigation and improve the stability and efficiency of the system. In order to solve this problem, it is necessary to design a more intelligent and adaptable antenna array optimization algorithm to adapt to the characteristics of agricultural machinery navigation and improve the stability and efficiency of the system. Summary of the invention

[0004] The present invention provides an antenna array optimization method, device, equipment and storage medium, which are used to solve the defect that the antenna array optimization method in the prior art is not suitable for agricultural machinery navigation, and realize the antenna array optimization method is suitable for agricultural machinery navigation.

[0005] The present invention provides an antenna array optimization method, comprising the following steps: Constructing an antenna array model and initializing parameters of the antenna array model; Constructing a fitness function based on the antenna array model; Calculating the optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm; A target antenna array is derived based on the optimal solution.

[0006] According to an antenna array optimization method provided by the present invention, the optimal solution of the antenna array model is calculated based on the fitness function and the electric eel foraging optimization algorithm, including: Initializing the position information of each electric eel and setting the parameters of the electric eel foraging optimization algorithm; wherein the parameters of the electric eel foraging optimization algorithm include the population number and the maximum number of iterations; In each iteration process, based on the fitness function and the electric eel foraging optimization algorithm, the position information and fitness value of each electric eel after the electric eel performs interaction behavior, resting behavior, hunting behavior, migration behavior, and exploration and development behavior are calculated, and the optimal position of each electric eel is updated based on the position information and fitness value of each electric eel; After each iteration is completed, it is determined whether the current number of iterations has reached the maximum number of iterations. If so, the optimal position of each electric eel is output.

[0007] According to an antenna array optimization method provided by the present invention, the location information and fitness value of each electric eel after performing interactive behavior, resting behavior, hunting behavior and migration behavior, as well as exploration and development behavior are calculated based on the fitness function and the electric eel foraging optimization algorithm, including: When the electric eel performs migration behavior, the migration behavior is performed based on the Levy flight mechanism.

[0008] According to an antenna array optimization method provided by the present invention, when the electric eel performs migration behavior, the migration behavior is performed based on the Levy flight mechanism, including: When the electric eel performs migration behavior, based on the migration formula Migration behavior; represents any location in the hunting area, A function representing the Levy flight mechanism, and They represent the position and velocity of the i-th electric eel in the t-th iteration respectively.

[0009] According to an antenna array optimization method provided by the present invention, the location information and fitness value of each electric eel after performing interactive behavior, resting behavior, hunting behavior and migration behavior, as well as exploration and development behavior are calculated based on the fitness function and the electric eel foraging optimization algorithm, including: When the electric eel performs exploration and exploitation behaviors, the electric eel is controlled to perform exploitation behaviors or exploration behaviors based on energy factors.

[0010] According to an antenna array optimization method provided by the present invention, the expression of the fitness function is: ; ; Where F represents the fitness function, MSLL represents the maximum level of the lobes other than the main lobe, and Respectively represent the maximum level values ​​of lobes other than the main lobe in azimuth and elevation directions; represents the average emissivity; represents the total power in the non-target area; represents the array sparsity ratio, represent weight coefficients, and .

[0011] According to an antenna array optimization method provided by the present invention, the step of constructing an antenna array model and initializing parameters of the antenna array model includes: Determine the coverage area of ​​the antenna array, and divide the coverage area into a plurality of rectangular grids, wherein each rectangular grid represents a potential antenna position; Set the array structure, number of array elements, minimum element spacing and element sparsity, as well as boundary constraints of the antenna array; Allocating coordinate information to each rectangular grid, and randomly setting the antenna position according to the array element sparsity rate; The antenna array is initialized according to the array element sparsity rate and the array element minimum spacing.

[0012] The present invention also provides an antenna array optimization device, comprising: A first building module is configured to build an antenna array model and initialize parameters of the antenna array model; A second building module is configured to build a fitness function based on the antenna array model; A calculation module, configured to calculate an optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm; A derivation module is configured to derive a target antenna array based on the optimal solution.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the antenna array optimization method described above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the antenna array optimization method described in any one of the above is implemented.

[0015] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the antenna array optimization method described above is implemented.

[0016] The antenna array optimization method, device, equipment and storage medium provided by the present invention first construct an antenna array model and initialize the parameters of the antenna array model, then establish a fitness function, evaluate and screen through the electric eel foraging optimization algorithm, and obtain the optimal solution of the antenna array model. During the optimization process of the electric eel foraging optimization algorithm, the position of the electric eel is adaptively adjusted as the number of iterations increases to improve the exploration ability and utilization efficiency of the algorithm, and finally the optimal solution is obtained. The sparse array layout corresponding to the optimal solution is the best arrangement of the antenna array. The fitness function constructed by the present invention enables the optimized array to exhibit a higher direction-finding performance, can adapt to the characteristics of agricultural machinery navigation, and improve the accuracy and reliability of agricultural machinery navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the antenna array optimization method provided by the present invention.

[0019] Figure 2 It is a schematic diagram of an antenna array model in an embodiment provided by the present invention.

[0020] Figure 3 It is the omnidirectional radiation pattern of the array in the embodiment provided by the present invention.

[0021] Figure 4 It is the antenna array pattern in the embodiment provided by the present invention.

[0022] Figure 5 It is a schematic diagram of simulation results in the embodiment provided by the present invention.

[0023] Figure 6 It is a structural schematic diagram of the antenna array optimization device provided by the present invention.

[0024] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In agricultural production, accurate positioning and navigation systems are essential to improve the operating efficiency and accuracy of agricultural machinery. Optimized antenna arrays can significantly improve the positioning accuracy of navigation systems, enabling agricultural machinery to achieve accurate route planning and automatic driving in field operations. This high-precision navigation is of great significance for realizing large-scale unmanned farmland operations, reducing human intervention and improving production efficiency. In terms of farmland monitoring and intelligent irrigation, optimized antenna arrays can monitor soil moisture, meteorological conditions and crop growth in real time, and transmit this data to the equipment of farmers or agricultural experts. With this data, farmers can make more scientific agricultural decisions, optimize irrigation plans, and improve crop yields and quality while reducing resource waste. In the field of agricultural automation, optimized antenna arrays can be used for agricultural robot control, inter-device communication and production monitoring. They realize data exchange and collaboration between agricultural equipment, monitor the production process in real time, and remotely control agricultural equipment, thereby realizing intelligent agriculture and farm automation, improving production efficiency and product quality, and reducing production costs. Optimized antenna arrays also play an important role in a variety of agricultural applications. For example, in precision seeding, antenna array optimization can ensure that the seeding machine moves accurately along the predetermined route and improves the uniformity of seeding density and distribution. In terms of pest and disease monitoring and prevention, optimized antenna arrays can support the efficient operation of drones and other monitoring equipment, acquire and transmit pest and disease data in real time, and help farmers take timely prevention and control measures. In harvesting and field management, optimized antenna arrays can improve the path planning accuracy of harvesters and other agricultural machinery, reduce omissions and overlapping operations, and improve harvesting efficiency and the yield rate of agricultural products. In addition, in animal husbandry, optimized antenna arrays can be used for livestock positioning and monitoring. Through precise positioning and data transmission, it helps ranchers manage livestock more effectively and monitor their health status and activity trajectories.

[0027] In the energy sector, optimized antenna arrays play an important role. In the monitoring of solar and wind power stations, optimized antenna arrays can achieve real-time monitoring of solar power stations and wind power stations. Antenna arrays can transmit data such as equipment operating status and environmental conditions, helping managers optimize energy collection and equipment maintenance, and improve energy production efficiency. Real-time monitoring and data transmission capabilities help to promptly detect and resolve equipment failures and ensure the stable operation of energy stations. In smart grids, optimized antenna arrays can be used to transmit power equipment status, load conditions, and grid operation data. Through real-time monitoring and data transmission, power distribution can be optimized, the stability and reliability of the grid can be improved, and the development of smart grids can be supported. The optimized design of antenna arrays ensures efficient and reliable data transmission, enabling grid management to adjust power loads in real time, balance supply and demand, and prevent grid overload or power waste.

[0028] Due to the high mobility of agricultural machinery navigation systems, which brings variability and uncertainty, traditional antenna array design methods cannot fully meet the needs of agricultural machinery navigation, affecting positioning accuracy, signal stability and system robustness. Therefore, it is very necessary and urgent to study new and efficient antenna array optimization methods to meet the needs of agricultural machinery navigation and improve the stability and efficiency of the system. In order to solve this problem, it is necessary to design a more intelligent and adaptable antenna array optimization algorithm to adapt to the characteristics of agricultural machinery navigation and improve the stability and efficiency of the system.

[0029] Therefore, the present invention provides antenna array optimization based on the electric eel foraging optimization algorithm, which has significant advantages over traditional array optimization methods. The improved electric eel foraging optimization algorithm has a strong global search capability, which can find the optimal solution in the entire search space and avoid falling into the local optimum. This advantage enables it to perform well in dealing with high-dimensional, multi-peak and nonlinear complex optimization problems, while traditional methods usually rely on local search strategies, which are easy to fall into local optimal solutions and difficult to find global optimal solutions. By simulating the foraging behavior of electric eels, including interactions, resting, hunting and migration, the optimization process can perform both extensive global searches and fine local optimizations. In the interactive stage, the interactive strategy of the electric eel foraging optimization algorithm is used to preliminarily optimize the position of the antenna array elements, and potential high fitness areas are found through global search. In the rest stage, the results of the interactive stage are locally optimized to further improve the performance of the array. In the hunting stage, the array scheme optimized in the rest stage is more finely optimized locally to avoid falling into the local optimum. In the migration phase, the Levy flight mechanism is used to search to avoid falling into the local optimum and enhance the global search capability. The optimal solution is updated and iterated until the maximum number of iterations or the convergence criterion is reached.

[0030] In contrast, traditional methods often optimize all parameters at once, with high computational complexity, low efficiency, and a relatively rigid optimization process, which is difficult to flexibly adjust according to actual conditions. The improved electric eel foraging optimization algorithm combines the advantages of global search and local optimization, significantly improving its optimization accuracy, convergence speed, robustness, and stability. Through efficient global search and fine local optimization, the improved electric eel foraging optimization algorithm can more accurately find the optimal array solution and significantly improve the optimization accuracy. At the same time, this method reduces the computational complexity, accelerates the convergence speed, and makes the overall optimization process faster. In addition, the diversified search mechanism and flexible adjustment strategy of the improved electric eel foraging optimization algorithm improve the robustness and stability of the optimization process, and can better adapt to different initial conditions and optimization requirements.

[0031] Combine the following Figure 1-Figure 7 The antenna array optimization method, device, equipment and storage medium of the present invention are described.

[0032] Figure 1 FIG. 1 is a flow chart of an antenna array optimization method according to an exemplary embodiment. Figure 1 As shown, in an exemplary embodiment, the antenna array optimization method includes steps 110 to 140, which are described in detail as follows.

[0033] Step 110: construct an antenna array model and initialize the parameters of the antenna array model.

[0034] In an embodiment of the present invention, an antenna array is a system composed of two or more single antenna units that are fed and arranged in space according to certain requirements. The antenna units are independent or interrelated to enhance and improve the directivity and intensity of the radiation field. The antenna radiating units that constitute the antenna array are called array elements. Through the coordinated work of multiple antenna units, the antenna array can focus electromagnetic waves in a specific direction, thereby improving the directivity of the signal. At the same time, the antenna array can increase the intensity of the radiation field, making the received signal clearer and more stable.

[0035] In the embodiment of the present invention, an antenna array model is constructed and the parameters thereof are initialized to facilitate subsequent optimal solution.

[0036] In an exemplary embodiment of the present invention, step 110 of constructing an antenna array model and initializing parameters of the antenna array model includes the following steps, which are described in detail as follows.

[0037] Determine the coverage area of ​​the antenna array, and divide the coverage area into a plurality of rectangular grids, wherein each rectangular grid represents a potential antenna position; Set the array structure, number of array elements, minimum element spacing and element sparsity, as well as boundary constraints of the antenna array; Allocating coordinate information to each rectangular grid, and randomly setting the antenna position according to the array element sparsity rate; The antenna array is initialized according to the array element sparsity rate and the array element minimum spacing.

[0038] In the embodiment of the present invention, the coverage area of ​​the antenna array is determined on the yoz plane and is divided into regular rectangular grids, as shown in FIG. Figure 2 As shown, each rectangular grid represents a potential antenna position; the number of array elements is set to N×M, the minimum array element spacing is d, the array element sparsity rate is S, the size of the array structure, and the boundary constraint is [L,U], where L represents Low and U represents Up, which are the lower and upper bounds of the search space respectively. Coordinates are assigned to each rectangular grid, and the antenna position is randomly set according to the array element sparsity rate.

[0039] The antenna array is binary-encoded, and the matrix element 0 represents that the corresponding rectangular grid has no array element, and 1 represents that the corresponding rectangular grid has array elements. The antenna array is initialized according to the array element sparsity rate and the minimum array element spacing. The antenna array is represented as follows: ; Among them, C represents the antenna array, Indicates the antenna placement at the (N, M) position. A value of 1 indicates that an antenna is placed at this position, and a value of 0 indicates that no antenna is placed at this position.

[0040] Step 120: construct a fitness function based on the antenna array model.

[0041] In an embodiment of the present invention, a fitness function is constructed to calculate the fitness value when the electric eel obtains new position information during the optimization iteration process based on the electric eel foraging optimization algorithm, so as to measure the quality of the new position information obtained by each electric eel.

[0042] In an exemplary embodiment of the present invention, the fitness function is expressed as: ; ; Where F represents the fitness function, MSLL represents the maximum level of the lobes other than the main lobe, and Respectively represent the maximum level values ​​of lobes other than the main lobe in azimuth and elevation directions; represents the average emissivity; represents the total power in the non-target area; represents the array sparsity ratio, represent weight coefficients, and .

[0043] In the embodiment of the present invention, in the antenna radiation pattern, for any antenna, in most cases, the direction of its E plane or H plane is Figure 1 Generally petal-shaped, such as Figure 3 As shown, the lobe with the maximum radiation direction is called the main lobe, and the remaining lobes are called side lobes.

[0044] In the embodiment of the present invention, the fitness function realizes the fast and efficient optimization of the sparse phased array by comprehensively considering multiple factors such as antenna radiation intensity, effective coverage area, average power level and total power in non-target areas. This diversified consideration method not only improves the accuracy and efficiency of agricultural machinery navigation, but also enhances the system's adaptability to environmental changes, ensuring stable navigation performance under different operating conditions.

[0045] Step 130, calculating the optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm.

[0046] In an embodiment of the present invention, the electric eel foraging optimization algorithm (EEFO) is a heuristic optimization algorithm based on the foraging behavior of electric eels in nature, which is used to solve complex optimization problems. The electric eel foraging optimization algorithm randomly generates a certain number of electric eel individuals in the search space, and each electric eel individual represents a potential solution. The electric eel foraging optimization algorithm simulates the process of electric eels releasing electric pulses to perceive the surrounding environment, and updates the electric eel individuals through randomness and local search strategies. According to the feedback of the electrical signal, it gradually approaches the optimal solution, and optimizes the objective function by adjusting the position and state of the electric eel individuals.

[0047] In an exemplary embodiment of the present invention, the step 130 of calculating the optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm includes the following steps, which are described in detail as follows.

[0048] Initializing the position information of each electric eel and setting the parameters of the electric eel foraging optimization algorithm; wherein the parameters of the electric eel foraging optimization algorithm include the population number and the maximum number of iterations; In each iteration process, based on the fitness function and the electric eel foraging optimization algorithm, the position information and fitness value of each electric eel after the electric eel performs interaction behavior, resting behavior, hunting behavior, migration behavior, and exploration and development behavior are calculated, and the optimal position of each electric eel is updated based on the position information and fitness value of each electric eel; After each iteration is completed, it is determined whether the current number of iterations has reached the maximum number of iterations. If so, the optimal position of each electric eel is output.

[0049] In the embodiment of the present invention, the maximum number of iterations in the electric eel foraging optimization algorithm is set 、Electric eel population and the location of the electric eel ( ). According to the boundary constraints and array element sparsity, the position information of the electric eel is initialized. The initial position information table is: , where L and U are the lower and upper bounds of the search space respectively, and 𝑟 is a random vector in the range of (0,1).

[0050] During each iteration, each electric eel performed interactive behavior, resting behavior, hunting behavior, and migratory behavior. The relevant descriptions of each behavior are as follows.

[0051] In the embodiment of the present invention, when performing interactive behaviors, the electric eels interact with each other to explore the search space globally, and adjust their positions according to the population center and other randomly selected positions, which is specifically expressed as follows: ; in, represents the candidate position of the ith electric eel after interaction at the tth iteration, is the position of a randomly selected electric eel, As the activity area center, is a randomly selected position in the activity area, is a random vector used to adjust the swimming direction and step length of the electric eel. is the selection probability, which is a random value between (0, 1).

[0052] In the embodiment of the present invention, when performing resting behavior, the electric eel explores space in a specific area so as to more intensively develop the local search space, which helps to refine the search in a promising area. According to the following formula: ; in, represents the resting position of the i-th electric eel at time t+1, Indicates the center of the current rest area, which can be regarded as the middle position of the current electric eel group. is the scale factor of the rest area, which is used to control the size of the offset and measure the degree to which the electric eel deviates from the center. represents the position of the best solution the electric eel has found so far.

[0053] In the embodiment of the present invention, when the electric eel finds prey during hunting, it continuously communicates and cooperates with its companions through the discharge of low-electric organs, and concentrates on the area where the best solution (prey) is located by surrounding the prey and adjusting its position to enhance the local search ability. The electric eel surrounds the prey and adjusts its position to concentrate on the area where the best solution (prey) is located, enhancing the local search ability, and the prey position is updated according to the following formula: ; in, Indicates the new prey position after the hunting behavior at time t+1. This position is based on the currently known best solution (prey) position, with a certain offset Get, is the scale factor of the hunting area, controlling the amplitude of position adjustment during hunting. Makes the hunting area smaller over time, a constant between (0,1).

[0054] In the embodiment of the present invention, when the electric eel finds prey during the migration behavior, the electric eel migrates from the resting area to the hunting area. In the embodiment of the present invention, the Levy flight mechanism is used to describe the migration behavior, and the Levy flight mechanism avoids falling into the local optimum. The migration behavior can be expressed by the following formula: ; in, and is a random number between (0,1), Represents any position within the hunting area. This position is obtained through hunting behavior and represents the possible movement position of the electric eel during the hunting process. and They represent the position and velocity of the i-th electric eel in the t-th iteration respectively.

[0055] The function L of the Levy flight mechanism is used to guide the electric eel to jump over a large area in the hunting area to avoid falling into the local optimum. The function L of the Levy flight mechanism is expressed as follows: ; Among them, u and v are random variables that obey the standard normal distribution, is the scaling parameter, , is the Gamma function, is a distribution parameter, which usually takes a value in the range of [1,2].

[0056] When performing exploration and exploitation behaviors, the energy factor E(t) is used to determine when the electric eel is exploring or exploiting, ensuring a balance between exploration and exploitation during the optimization process.

[0057] in, In the early stage of the algorithm, the larger energy factor encourages the electric eel to explore globally and find potential high fitness areas in the entire search space. As the number of iterations increases, the energy factor gradually decreases, and the electric eel is more inclined to conduct local development in known better areas, refine the search, and find the optimal solution.

[0058] After performing the above-mentioned behaviors, the electric eel obtains a new position, which is the optimal position for the current number of iterations. The fitness value of each electric eel in the new position is calculated by the fitness function mentioned above. The electric eel senses the position of the prey through low discharge, so it can adjust its position at any time. If the electric eel senses the approach of the prey during foraging, it will move to the candidate position, otherwise, the electric eel will stay at the current position. Therefore, the position of the electric eel is updated according to its latest fitness value and position, which can be expressed as: ; in, It represents the new position of the electric eel after a series of actions. Indicates the fitness value corresponding to x. That is, the fitness of the new position is compared with the current position. If the fitness of the new position is greater than the fitness of the current position, the position of the electric eel is updated to the new position as its optimal position. Otherwise, it is not updated and the current position is still its optimal position.

[0059] Determine whether the current number of iterations has reached the set maximum number of iterations. If not, set the number of iterations t=t+1, and perform a series of behaviors such as interaction and rest again until the current number of iterations reaches the set maximum number of iterations.

[0060] Step 140: derive a target antenna array based on the optimal solution.

[0061] In the embodiment of the present invention, if the current number of iterations reaches the set maximum number of iterations, the optimal antenna array arrangement scheme is derived based on the optimal position of each electric eel and output to obtain the target antenna array. Under the premise of meeting specific design constraints, the optimal performance is ensured.

[0062] In an exemplary embodiment of the present invention, a simulation experiment is carried out based on the technical solution provided by the present invention. The processor selected for the simulation experiment is 13th Gen Intel(R) Core(TM) i5-13500HX 2.50 GHz, the memory is 16G, the hard disk is 1.5T, the operating system is Windows 11 Home Chinese Edition, and the programming environment is MATLAB R2020a. The specific simulation settings are as follows: Consider a small-scale antenna array with an array size of 10*10, a rectangular array with an array element sparsity of 40%, and a minimum array element spacing of at least , the unit feeding is equal amplitude and in-phase excitation. Algorithm parameter setting: Maximum number of iterations , the electric eel population size is , the upper and lower bounds of the search space are [0,10], and the scales of the rest area and hunting area are preset to , Levy flight distribution parameter .

[0063] Figure 3 and Figure 4 The array omnidirectional radiation pattern and antenna array pattern in the embodiment are shown. Among them, the maximum sidelobe level achieved by the 10*10 array is -17.9dB. Figure 5 The convergence curve comparison of the 10*10 array under the present invention and other prior arts is shown. Figure 5 It can be seen from the simulation results shown that the technical solution provided by the present invention can find a high-quality solution and achieve convergence in only 100 iterations, which is superior to other existing optimization technologies in terms of both solution quality and convergence speed.

[0064] The technical solution proposed in the present invention focuses on the optimization of antenna sparse arrays. First, an antenna array model is constructed and the antenna array is encoded. Then, a fitness function is established. The electric eel foraging optimization algorithm is used for evaluation and screening to obtain the optimal solution of the antenna array model. In the process of optimizing the electric eel foraging optimization algorithm, the position of the electric eel is adaptively adjusted as the number of iterations increases to improve the exploration ability and utilization efficiency of the algorithm. Finally, the optimal solution is obtained, and the sparse array layout corresponding to the optimal solution is determined as the optimal arrangement of the antenna array. The present invention emphasizes the constraints such as the directivity, coverage, signal strength and interference level of the antenna, and constructs a multi-objective fitness function. Under the premise of ensuring the same number of array elements and the minimum spacing between array elements, the optimized array shows a higher direction finding performance, thereby improving the accuracy and reliability of agricultural machinery navigation. In addition, in the specific implementation process, the present invention comprehensively considers a variety of influencing factors to make the optimization results more comprehensive and accurate. By adaptively adjusting the position of the electric eel at different iteration stages, the effectiveness and stability in the optimization process are guaranteed. The introduction of the multi-objective fitness function enables the present invention to achieve global optimization under multiple constraints, thereby further improving the performance and reliability of the antenna array. The present invention provides a new idea and method for the design and application of wireless communication arrays, and has broad application prospects. By improving the direction finding performance of antenna arrays, it not only improves the accuracy of agricultural machinery navigation, but also plays an important role in other fields that require precise signal processing and high-reliability communication.

[0065] The antenna array optimization device provided by the present invention is described below. The antenna array optimization device described below and the antenna array optimization method described above can be referred to each other. It should be noted that the device provided in the following embodiment and the method provided in the above embodiment belong to the same concept, and the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment, which will not be repeated here.

[0066] In an exemplary embodiment of the present invention, see Figure 6 , Figure 6 An antenna array optimization device according to an exemplary embodiment includes the following modules: A first construction module 610 is configured to construct an antenna array model and initialize parameters of the antenna array model; A second construction module 620 is configured to construct a fitness function based on the antenna array model; A calculation module 630 is configured to calculate an optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm; The derivation module 640 is configured to derive a target antenna array based on the optimal solution.

[0067] In an exemplary embodiment of the present invention, the calculation module 630 includes: A first initialization submodule is configured to initialize the position information of each electric eel and set the parameters of the electric eel foraging optimization algorithm; wherein the parameters of the electric eel foraging optimization algorithm include the population number and the maximum number of iterations; A calculation submodule is configured to calculate, in each iteration process, the position information and fitness value of each electric eel after performing interactive behavior, resting behavior, hunting behavior and migration behavior, as well as exploration and development behavior based on the fitness function and the electric eel foraging optimization algorithm, and update the optimal position of each electric eel based on the position information and the fitness value of each electric eel; The judgment submodule is configured to judge whether the current number of iterations reaches the maximum number of iterations after each iteration is completed, and if so, output the optimal position of each electric eel.

[0068] In an exemplary embodiment of the present invention, the computing submodule includes: The migration unit is configured to perform the migration behavior based on the Levy flight mechanism when the electric eel performs the migration behavior.

[0069] In an exemplary embodiment of the present invention, the migration unit comprises: Migration subunit, including when the electric eel performs migration behavior, based on the migration formula Migration behavior; represents any location in the hunting area, A function representing the Levy flight mechanism, and They represent the position and velocity of the i-th electric eel in the t-th iteration respectively.

[0070] In an exemplary embodiment of the present invention, the computing submodule includes: The development and exploration unit is configured to control the electric eel to perform development behavior or exploration behavior based on the energy factor when the electric eel performs exploration and exploitation behavior.

[0071] In an exemplary embodiment of the present invention, the fitness function is expressed as: ; ; Where F represents the fitness function, MSLL represents the maximum level of the lobes other than the main lobe, and Respectively represent the maximum level values ​​of lobes other than the main lobe in azimuth and elevation directions; represents the average emissivity; represents the total power in the non-target area; represents the array sparsity ratio, represent weight coefficients, and .

[0072] In an exemplary embodiment of the present invention, the first building block 610 includes: A determination submodule is configured to determine a coverage area of ​​the antenna array and divide the coverage area into a plurality of rectangular grids, wherein each rectangular grid represents a potential antenna position; A setting submodule, configured to set the array structure, the number of array elements, the minimum spacing between array elements and the sparseness rate of array elements, and the boundary constraints of the antenna array; A setting submodule, configured to assign coordinate information to each of the rectangular grids, and randomly set an antenna position according to the array element sparsity rate; The second initialization submodule is configured to initialize the antenna array according to the array element sparsity rate and the array element minimum spacing.

[0073] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7 As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communication interface 720 and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the antenna array optimization method, which includes: constructing an antenna array model and initializing the parameters of the antenna array model; Constructing a fitness function based on the antenna array model; Calculating the optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm; A target antenna array is derived based on the optimal solution.

[0074] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0075] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the antenna array optimization method provided by the above methods, the method includes: constructing an antenna array model, and initializing the parameters of the antenna array model; Constructing a fitness function based on the antenna array model; Calculating the optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm; A target antenna array is derived based on the optimal solution.

[0076] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to perform the antenna array optimization method provided by the above methods, the method comprising: constructing an antenna array model, and initializing parameters of the antenna array model; Constructing a fitness function based on the antenna array model; Calculating the optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm; A target antenna array is derived based on the optimal solution.

[0077] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0078] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An antenna array optimization method, characterized in that: include: Constructing an antenna array model and initializing parameters of the antenna array model; Constructing a fitness function based on the antenna array model; Calculating the optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm; A target antenna array is derived based on the optimal solution.

2. The antenna array optimization method according to claim 1, characterized in that: The calculating the optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm comprises: Initializing the position information of each electric eel and setting the parameters of the electric eel foraging optimization algorithm; wherein the parameters of the electric eel foraging optimization algorithm include the population number and the maximum number of iterations; In each iteration process, based on the fitness function and the electric eel foraging optimization algorithm, the position information and fitness value of each electric eel after the electric eel performs interaction behavior, resting behavior, hunting behavior, migration behavior, and exploration and development behavior are calculated, and the optimal position of each electric eel is updated based on the position information and fitness value of each electric eel; After each iteration is completed, it is determined whether the current number of iterations has reached the maximum number of iterations. If so, the optimal position of each electric eel is output.

3. The antenna array optimization method according to claim 2, characterized in that: The method of calculating the position information and fitness value of each electric eel after performing interaction behavior, resting behavior, hunting behavior, migration behavior, and exploration and development behavior based on the fitness function and the electric eel foraging optimization algorithm includes: When the electric eel performs migration behavior, the migration behavior is performed based on the Levy flight mechanism.

4. The antenna array optimization method according to claim 3, characterized in that: When the electric eel performs the migration behavior, the migration behavior is performed based on the Levy flight mechanism, including: When the electric eel performs migration behavior, based on the migration formula Migration behavior; represents any location in the hunting area, A function representing the Levy flight mechanism, and They represent the position and velocity of the i-th electric eel in the t-th iteration respectively.

5. The antenna array optimization method according to claim 2, characterized in that: The method of calculating the position information and fitness value of each electric eel after performing interaction behavior, resting behavior, hunting behavior, migration behavior, and exploration and development behavior based on the fitness function and the electric eel foraging optimization algorithm includes: When the electric eel performs exploration and exploitation behaviors, the electric eel is controlled to perform exploitation behaviors or exploration behaviors based on energy factors.

6. The antenna array optimization method according to claim 1, characterized in that: The expression of the fitness function is: ； ； Where F represents the fitness function, MSLL represents the maximum level of the lobes other than the main lobe, and Respectively represent the maximum level values ​​of lobes other than the main lobe in azimuth and elevation directions; represents the average emissivity; represents the total power in the non-target area; represents the array sparsity ratio, represent weight coefficients, and .

7. The antenna array optimization method according to any one of claims 1 to 6, characterized in that: The step of constructing an antenna array model and initializing parameters of the antenna array model includes: Determine the coverage area of ​​the antenna array, and divide the coverage area into a plurality of rectangular grids, wherein each rectangular grid represents a potential antenna position; Set the array structure, number of array elements, minimum element spacing and element sparsity, as well as boundary constraints of the antenna array; Allocating coordinate information to each rectangular grid, and randomly setting the antenna position according to the array element sparsity rate; The antenna array is initialized according to the array element sparsity rate and the array element minimum spacing.

8. An antenna array optimization device, characterized in that: include: A first building module is configured to build an antenna array model and initialize parameters of the antenna array model; A second building module is configured to build a fitness function based on the antenna array model; A calculation module, configured to calculate an optimal solution of the antenna array model based on the fitness function and the electric eel foraging optimization algorithm; A derivation module is configured to derive a target antenna array based on the optimal solution.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the antenna array optimization method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the antenna array optimization method according to any one of claims 1 to 7 is implemented.

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