Pure phase array directional diagram synthesis method and system based on improved bat algorithm

Abstract

The invention discloses a pure phase array directional diagram synthesis method and system based on an improved bat algorithm. According to the scheme, an array model constructed by a plurality of antenna units is set; constructing an optimization objective function of the array model; the objective function is minimized according to a bat algorithm, and optimization training of the array model is completed; and according to the array model obtained through optimization, pure phase array pattern synthesis is carried out. According to the embodiment of the invention, an initial phase distribution is created by using a parabolic function aiming at pure phase symmetrical directional diagram synthesis, the distribution has the characteristic of being approximate to a flat-topped mode, so that the optimization process is accelerated, in addition, excitation distribution is expressed in a polynomial form, the optimization problem is converted into optimization of polynomial coefficients, and the optimization efficiency is improved. Therefore, antenna pattern integration is realized, and the method can be widely applied to the technical field of computers.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a pure phase array pattern synthesis method and system based on an improved bat algorithm. Background Art

[0002] Phased array radar changes the shape of the antenna pattern by controlling the excitation amplitude and excitation phase of the antenna unit, giving the phased array radar more flexible spatial filtering and space-time processing capabilities, which is called antenna pattern synthesis. Although excellent antenna pattern synthesis can be achieved by simultaneously weighting and adjusting the amplitude and phase, amplitude weighting usually requires adding amplitude attenuators to the array, which not only increases the complexity of antenna design but also increases costs. In contrast, phase weighting can achieve similar effects only through the phase shifter of the phased array antenna, and provides benefits such as maximizing sensitivity and power efficiency. Therefore, the phase weighting method has high practical value in engineering applications.

[0003] The goal of pure phase antenna pattern synthesis is to form the desired antenna pattern by optimizing the excitation phase while maintaining the same excitation amplitude of the antenna unit. However, this process is essentially a nonlinear and non-convex optimization problem, because the feasible domain corresponds to the intersection of a circle, and its global optimal solution is still unknown.

[0004] In order to achieve the pure phase pattern synthesis of the antenna, the researchers initially relaxed the amplitude constraint into a convex constraint and applied the alternating optimization algorithm to solve the pure phase antenna pattern synthesis problem. However, due to the convex relaxation, the consistency of the amplitude is difficult to guarantee and it is difficult to apply to the actual system. The evolutionary algorithm is based on Darwin's theory of evolution, simulating natural selection, survival of the fittest, and evolving the optimal solution to the problem through multiple generations of inheritance, mutation, crossover, and replication. In recent years, evolutionary algorithms have attracted widespread attention due to their high efficiency in dealing with complex non-convex problems, including genetic algorithms (GA, Genetic Algorithm), particle swarm optimization algorithms (PSO, Particle Swarm Optimization) and bat algorithms (BA, Bat Alorithm). Therefore, the application of evolutionary algorithms to solve the pure phase pattern synthesis problem has great advantages.

[0005] In the field of phase-only array pattern synthesis, the following problems exist:

[0006] 1. Most existing optimization algorithms directly optimize the phase of antenna units. As the array size and the number of antenna units increase, the concentration of local optimal solutions becomes more intensive and complex. This complexity makes it easier for the optimization algorithm to fall into the local optimal solution. In order to obtain a satisfactory solution, more experiments are needed, which further increases the amount of calculation.

[0007] 2. Some improvements to the optimization algorithm focus on relaxing the optimization problem, which results in the optimization result not being a strictly constant amplitude and is difficult to directly apply to actual systems. Summary of the invention

[0008] The main purpose of the embodiment of the present invention is to propose a pure phase array pattern synthesis method and system based on an improved bat algorithm, which can realize antenna pattern synthesis and accelerate the optimization process.

[0009] To achieve the above object, an embodiment of the present invention provides a pure phase array pattern synthesis method based on an improved bat algorithm, comprising the following steps:

[0010] Setting up an array model constructed from multiple antenna elements;

[0011] Constructing an optimization objective function of the array model;

[0012] Minimizing the objective function according to the bat algorithm to complete the optimization training of the array model;

[0013] Based on the optimized array model, pure phase array pattern synthesis is performed.

[0014] In some embodiments, the setting of an array model constructed by a plurality of antenna units comprises the following steps:

[0015] Determine the number of antenna units included in a uniform linear array; determine the spacing between the antenna units of the uniform linear array; determine the excitation amplitude and excitation phase of each antenna unit;

[0016] The expression for constructing the array far-field pattern is:

[0017]

[0018] Where θ is the scanning angle, k is the wave number, n represents the nth antenna unit, N represents the total number of antenna units, Φ n represents the excitation phase of the nth antenna unit; j represents the imaginary unit; d represents the antenna unit spacing.

[0019] In some embodiments, constructing the optimization objective function of the array model comprises the following steps:

[0020] The selection of orthogonal basis functions is determined according to the shapes of basis functions, and the phase distribution of the array is expanded using the M-order orthogonal basis functions to obtain the expression of the target phase distribution of the array;

[0021] An initial phase distribution is generated by using a quadratic polynomial, and the initial phase distribution preliminarily presents a flat-top pattern characteristic;

[0022] According to the initial phase distribution, adjusting and optimizing the expression of the target phase distribution;

[0023] A loss function of the array model is constructed to optimize the objective function of the array model.

[0024] In some embodiments, the expression of the target phase distribution is: Among them, Φ n represents the target phase distribution; ΔΦ represents the value range of the polynomial; M represents the order of the orthogonal basis function; m represents the mth order basis function; k m represents the corresponding weight real coefficient; f m represents an m-order polynomial; d n represents the distance from the nth array element to the center of the array element; D represents the total length of the array;

[0025] The initial phase distribution A n The expression is: Where ΔA is the range constant of the initial phase;

[0026] The expression of the loss function is:

[0027] Where c represents the loss function; H represents the number of sampling points in the main lobe area; F(θ h ) represents the directional pattern; S represents the number of sampling points of the maximum value in the sidelobe area; θ h represents the sampling angle of the main lobe area; P d () represents the desired pattern; w1 and w2 represent weight coefficients; θ s The sampling angle representing the maximum value of the sidelobe area.

[0028] In some embodiments, the minimizing the objective function according to the bat algorithm to complete the optimization training of the array model includes the following steps:

[0029] Introducing nonlinear decreasing inertia weight To adjust the velocity term coefficient, the inertia weight is defined as: in, represents the inertia weight; w min and w max is a constant; t represents time t; T represents the maximum number of iterations of the algorithm;

[0030] The chaotic mapping is used to replace the update of the emission rate in the standard bat algorithm. The sine mapping is used to make the emission rate in the standard bat algorithm become a chaotic number between 0 and 1. The update formula of the sine mapping is defined as: t+1 =a(r t )2 sin(πr t ), where a represents the mapping amplitude constant; r t represents the firing rate at time t;

[0031] The array model is optimized and trained according to the improved bat algorithm.

[0032] In some embodiments, the optimization training of the array model is completed according to the improved bat algorithm, comprising the following steps:

[0033] Configure population size and maximum number of iterations;

[0034] Randomly initialize the initial position of each bat in the population;

[0035] Initialize the loudness, firing rate, and frequency of pulses fired at each location for each bat when searching for prey;

[0036] When the maximum number of iterations is not reached, according to formula f i =f min +(f max -f min )β updates and adjusts the frequency of bats transmitting pulses at various locations, and according to the formula and Update and adjust the position and speed of each bat; among them, f i represents the frequency of the transmitted pulse of the i-th bat; f min Represents the minimum frequency; f max represents the maximum frequency; β represents a random vector that obeys a uniform distribution; represents the position of bat i at time t; represents the speed of bat i at time t; represents the inertia weight; x * Represents the global optimal solution of the current iteration;

[0037] When the random number is less than the current emission rate, according to the formula x new =x old +εA t Generate a new local solution from the optimal solution, and generate a new solution by random flight until the random number is not less than the current emission rate; where x new represents the new local solution; x old represents the old optimal solution; ε represents a random number; A t Represents the average loudness of all bats in the current iteration;

[0038] According to the new local solution, the loudness and emission rate of the transmitted pulse are updated, and the frequency of the current transmitted pulse is calculated to obtain the optimal solution for the frequency of the current transmitted pulse until the maximum number of iterations is reached.

[0039] In some embodiments, the updated expression of the loudness of the transmitted pulse is:

[0040] The updated expression of the emission rate of the emission pulse is:

[0041] in, represents the loudness of the pulse emitted by the i-th bat at time t; α and γ represent constants; represents the initial firing rate; represents the firing rate of the i-th bat at time t+1.

[0042] Another aspect of the embodiment of the present invention further provides a pure phase array pattern synthesis system based on an improved bat algorithm, comprising:

[0043] The first module is used to set an array model constructed by multiple antenna units;

[0044] The second module is used to construct an optimization objective function of the array model;

[0045] The third module is used to minimize the objective function according to the bat algorithm to complete the optimization training of the array model;

[0046] The fourth module is used to synthesize the pure phase array pattern according to the optimized array model.

[0047] To achieve the above objective, another aspect of an embodiment of the present invention provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0048] To achieve the above objective, another aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0049] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0050] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a method and system for synthesizing pure phase array patterns based on an improved bat algorithm, wherein the scheme sets an array model constructed by multiple antenna units; constructs an optimization objective function of the array model; minimizes the objective function according to the bat algorithm to complete the optimization training of the array model; and performs pure phase array pattern synthesis according to the optimized array model. The embodiments of the present invention use a parabolic function to create an initial phase distribution for pure phase symmetric pattern synthesis, which has the characteristics of an approximate flat-top mode, thereby accelerating the optimization process. In addition, the embodiments of the present invention express the excitation distribution in a polynomial form, and convert the optimization problem into the optimization of polynomial coefficients, thereby realizing antenna pattern synthesis. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of an implementation environment provided by an embodiment of the present invention;

[0052] Figure 2 is a flow chart of the overall steps provided by an embodiment of the present invention;

[0053] Figure 3 It is a flowchart of specific implementation steps provided by an embodiment of the present invention;

[0054] Figure 4 is a schematic diagram of equivalent phase distribution of six Chebyshev polynomials provided in an embodiment of the present invention;

[0055] Figure 5 The phase distribution diagram and the corresponding array pattern obtained by the quadratic polynomial provided in the embodiment of the present invention;

[0056] Figure 6 is a flowchart of iterative steps after improving the bat algorithm provided by an embodiment of the present invention;

[0057] Figure 7 is a schematic diagram of a loss function convergence curve provided by an embodiment of the present invention; wherein, Figure 7 (a) represents the loss function convergence curve of standard BA, N = 12; Figure 7 (b) represents the loss function convergence curve of improved BA, N = 12; Figure 7 (c) represents the loss function convergence curve of standard BA, N = 32; Figure 7 (d) represents the loss function convergence curve of improved BA, N = 32;

[0058] Figure 8 is a directional pattern synthesis result and a corresponding phase schematic diagram provided by an embodiment of the present invention;

[0059] Fig. 9It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention. They are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the attached claims.

[0061] It is understood that the terms "first", "second", etc. used in the present invention may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0062] The terms "at least one", "multiple", "each", "any", etc. used in the present invention, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0064] Before describing the embodiments of the present invention in detail, some related technologies involved in the embodiments of the present invention are first described as follows:

[0065] Phased array radar: phase-controlled electronically scanned array radar, a radar array composed of a large number of identical radiating units, each radiating unit is independently controlled by wave control and phase shifters in phase and amplitude, and can obtain accurate and predictable radiation patterns and beam pointing. When the radar is working, the transmitter distributes power to each antenna unit through the feeder network, radiates energy through a large number of independent antenna units, and performs power synthesis in space to form the required beam pointing.

[0066] Pure phase pattern synthesis: The antenna pattern shape is changed by controlling only the excitation phase of the antenna unit. The distributed feature representation of the data is discovered by combining low-level features to form more abstract high-level representation attribute categories or features.

[0067] Bat Algorithm: An optimization algorithm. Bats use echolocation when searching for prey. Bats emit short pulses while roaming, and once they detect approaching prey, they increase their pulse emission rate and pulse frequency. The Bat Algorithm idealizes the echolocation behavior of bats and is used to solve specific optimization problems.

[0068] The embodiment of the present invention provides a method and system for synthesizing a pure phase array pattern based on an improved bat algorithm, and relates to the field of computer technology. The method for synthesizing a pure phase array pattern based on an improved bat algorithm provided in an embodiment of the present invention can be applied to a terminal, can be applied to a server, or can be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements a method for synthesizing a pure phase array pattern based on an improved bat algorithm, etc., but is not limited to the above forms.

[0069] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0070] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to a network wirelessly or wired to complete data transmission and exchange.

[0071] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0072] In addition, the server 101 can also be a node server in the blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0073] The terminal 102 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal 102 may also be a vehicle-mounted terminal of various device types described above, but is not limited thereto. The terminal 102 and the server 101 may be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0074] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a pure phase array pattern synthesis method based on an improved bat algorithm. The following is explained using the application of the pure phase array pattern synthesis method based on the improved bat algorithm in the server 101 as an example. It can be understood that the method can also be applied to the terminal 102.

[0075] Reference Figure 2 , Figure 2 The flowchart of the method for synthesizing pure phase array patterns based on the improved bat algorithm applied to the server provided in the embodiment of the present invention, the execution subject of the method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method may include the following steps:

[0076] Setting up an array model constructed from multiple antenna elements;

[0077] Constructing an optimization objective function of the array model;

[0078] Minimizing the objective function according to the bat algorithm to complete the optimization training of the array model;

[0079] Based on the optimized array model, pure phase array pattern synthesis is performed.

[0080] In some embodiments, the setting of an array model constructed by a plurality of antenna units comprises the following steps:

[0081] Determine the number of antenna units included in a uniform linear array; determine the spacing between the antenna units of the uniform linear array; determine the excitation amplitude and excitation phase of each antenna unit;

[0082] The expression for constructing the array far-field pattern is:

[0083]

[0084] Where θ is the scanning angle, k is the wave number, n represents the nth antenna unit, N represents the total number of antenna units, Φ n represents the excitation phase of the nth antenna unit; j represents the imaginary unit; d represents the antenna unit spacing.

[0085] In some embodiments, constructing the optimization objective function of the array model comprises the following steps:

[0086] The selection of orthogonal basis functions is determined according to the shapes of basis functions, and the phase distribution of the array is expanded using the M-order orthogonal basis functions to obtain the expression of the target phase distribution of the array;

[0087] An initial phase distribution is generated by using a quadratic polynomial, and the initial phase distribution preliminarily presents a flat-top pattern characteristic;

[0088] According to the initial phase distribution, adjusting and optimizing the expression of the target phase distribution;

[0089] A loss function of the array model is constructed to optimize the objective function of the array model.

[0090] In some embodiments, the expression of the target phase distribution is: Among them, Φ n represents the target phase distribution; ΔΦ represents the value range of the polynomial; M represents the order of the orthogonal basis function; m represents the mth order basis function; k m represents the corresponding weight real coefficient; f m represents an m-order polynomial; d n represents the distance from the nth array element to the center of the array element; D represents the total length of the array;

[0091] The initial phase distribution A n The expression is: Where ΔA is the range constant of the initial phase;

[0092] The expression of the loss function is:

[0093] Where c represents the loss function; H represents the number of sampling points in the main lobe area; F(θ h ) represents the directional pattern; S represents the number of sampling points of the maximum value in the sidelobe area; θ h represents the sampling angle of the main lobe area; P d () represents the desired pattern; w1 and w2 represent weight coefficients; θ s The sampling angle representing the maximum value of the sidelobe area.

[0094] In some embodiments, the minimizing the objective function according to the bat algorithm to complete the optimization training of the array model includes the following steps:

[0095] Introducing nonlinear decreasing inertia weight To adjust the velocity term coefficient, the inertia weight is defined as: in, represents the inertia weight; w min and w max is a constant; t represents time t; T represents the maximum number of iterations of the algorithm;

[0096] The update of the emission rate in the standard Bat Algorithm (BA) is replaced by a chaotic map, and a sine map is used to make the emission rate in the standard Bat Algorithm (BA) become a chaotic number between 0 and 1; wherein the update formula of the sine map is defined as: t+1 =a(r t ) 2 sin(πr t ), where a represents the mapping amplitude constant; r t represents the firing rate at time t;

[0097] The array model is optimized and trained according to the improved bat algorithm.

[0098] In some embodiments, the optimization training of the array model is completed according to the improved bat algorithm, comprising the following steps:

[0099] Configure population size and maximum number of iterations;

[0100] Randomly initialize the initial position of each bat in the population;

[0101] Initialize the loudness, firing rate, and frequency of pulses fired at each location for each bat when searching for prey;

[0102] When the maximum number of iterations is not reached, according to formula f i =f min +(f max -f min )β updates and adjusts the frequency of bats transmitting pulses at various locations, and according to the formula and Update and adjust the position and speed of each bat; among them, f i represents the frequency of the transmitted pulse of the i-th bat; f min Represents the minimum frequency; f max represents the maximum frequency; β represents a random vector that obeys a uniform distribution; represents the position of bat i at time t; represents the speed of bat i at time t; represents the inertia weight; x * Represents the global optimal solution of the current iteration;

[0103] When the random number rand is less than the current emission rate, according to the formula x new =x old +εA t Generate a new local solution from the optimal solution, and generate a new solution by random flight until the random number rand is not less than the current emission rate; where x new represents the new local solution; x old represents the old optimal solution; ε represents a random number; A t Represents the average loudness of all bats in the current iteration;

[0104] According to the new local solution, the loudness and emission rate of the transmitted pulse are updated, and the frequency of the current transmitted pulse is calculated to obtain the optimal solution for the frequency of the current transmitted pulse until the maximum number of iterations is reached.

[0105] In some embodiments, the updated expression of the loudness of the transmitted pulse is:

[0106] The updated expression of the emission rate of the emission pulse is:

[0107] in, represents the loudness of the pulse emitted by the i-th bat at time t; α and γ represent constants; represents the initial firing rate; represents the firing rate of the i-th bat at time t+1.

[0108] The following describes the specific implementation process of the present invention in detail by taking a specific application scenario as an example:

[0109] refer to Figure 3The specific implementation steps of the present invention include:

[0110] [1] Set up the array model;

[0111] Consider a uniform linear array consisting of N units, where the spacing between antenna units in the array is d and the excitation amplitude of the nth unit is I n , the excitation phase is Φ n Assuming that the antenna elements are isotropic, the array far-field pattern can be expressed as:

[0112]

[0113] Wherein, θ is the scanning angle, k=2π / λ is the wave number, and λ is the wavelength.

[0114] [2] Construct the optimization objective function;

[0115] Based on the condition that the transmitting component needs to saturate output in engineering applications, that is, I n =1,n=1,...,N, only the excitation phase Φ n In the previous synthesis of array patterns, Φ n Optimization is performed to obtain the desired array pattern. However, as the number of array elements increases, the complexity of the calculation will rise sharply. Since any function can be represented by a linear combination of orthogonal basis functions, the phase distribution can also be described by the weighted sum of appropriate basis functions. Using this method, even an array consisting of hundreds of elements can have its phase distribution described by a few basis functions, and subsequent optimization can be performed on the weights of the basis functions, which significantly reduces the number of unknowns and speeds up the optimization. The choice of orthogonal basis function depends on the shape of the basis function. For example, for a symmetric pattern, a symmetric phase distribution is required, so only even-order basis functions are considered. The phase distribution of the array is expanded using M-order orthogonal basis functions, which can be expressed as

[0116]

[0117] Among them, f m (2d n / D) represents the m-order polynomial, ΔΦ is the value range of the polynomial, k m ∈[-1,1] is the corresponding weight real coefficient, d n is the distance from the nth array element to the center of the array element, and D is the total length of the array. Taking the Chebyshev polynomial as an example, take the first M = 6th order polynomial, set ΔΦ = 30 and expand it to [-30°, 30°], and get the phase distribution as follows Figure 4 shown.

[0118] In the optimization of the symmetric desired pattern, the optimized initial phase can be sampled from the symmetric distribution. Therefore, a quadratic polynomial is used to generate an initial phase distribution, which initially exhibits the characteristics of a flat-top pattern. Optimization based on this helps speed up the entire optimization process. The initial phase is defined as

[0119]

[0120] Where ΔA is the range constant of the initial phase. Taking the uniform linear array with N=32 and d=0.5λ as an example, when ΔA=400°, the initial phase distribution and the formed directional pattern of the array are as follows: Figure 5 As shown, Figure 5 In , the horizontal axis is the antenna element number; the vertical axis is the excitation phase (°).

[0121] 2. Horizontal axis: scanning angle θ (°) Vertical axis: antenna pattern (dB). Obviously, the phase distribution obtained by the quadratic polynomial is suitable as the initial phase for the synthesis of pure phase-symmetric patterns. By adjusting the value of ΔA, the width of the main lobe can be easily adjusted to meet the needs.

[0122] In summary, the phase Φ n Can be rewritten as

[0123]

[0124] For the pure phase pattern synthesis of N array elements, it is only necessary to search in the M-dimensional Chebyshev phase space, so that the solution of Φ n ,n=1,...,N is converted to solve the polynomial coefficient k m , k = 1, ..., M. By solving the polynomial coefficient k m , the desired symmetrical pattern can be obtained.

[0125] Construct the following loss function:

[0126]

[0127] Where w1 and w2 are weight coefficients, which are used to measure the difference between the synthesis result and the expected result of the main lobe fluctuation and side lobe level respectively; θ h ,h=1,...,H is the sampling angle of the main lobe area,θ s ,s=1,...,S is the sampling angle of the maximum value in the sidelobe area, P d (θ) is the desired directional pattern. The desired directional pattern can be obtained by minimizing equation (5).

[0128] [3], the bat algorithm minimizes the loss function;

[0129] When searching for prey, bats use echolocation. They emit short pulses while roaming, and once they detect approaching prey, they increase their pulse emission rate and pulse frequency. The bat algorithm idealizes the echolocation behavior of bats and uses it to solve specific optimization problems.

[0130] In standard BA, assume that there are G bats in the L-dimensional search space, and bat i, i = 1, ..., G is at position x i At speed v i Random flight, with frequency f i , Loudness A i The pulses can be used to search for prey, and the frequency of the pulses can be automatically adjusted according to the proximity of the target. i , Loudness A i and the emission rate r i At time t, the position of bat i is and speed Updated to:

[0131] f i =f min +(f max -f min )β (6)

[0132]

[0133] Among them, [f min ,f max ] is the frequency range of pulses emitted by each bat, β∈[0,1] is a random vector that obeys uniform distribution, x * is the global optimal solution of the current iteration.

[0134] BA has the ability to perform global and local searches at the same time, and can automatically transition from global search to local search by adjusting relevant parameters. In the local search phase, the algorithm will randomly walk around the current optimal solution and generate a new solution for each bat locally:

[0135] x new =x old +εA t (9)

[0136] Among them, ε∈[-1,1] is a random number, A t is the average loudness of all bats in the current iteration.

[0137] In addition, as the iterations progress, the loudness A of the transmitted pulses i and the emission rate r i must also be updated accordingly. Assuming that loudness generally decreases after prey is detected and firing rate increases after prey is detected, we can set:

[0138]

[0139]

[0140] Among them, α and γ are constants, and the initial loudness Usually it can be [1,2], the initial emission rate r i 0 Typically this can be [0,1].

[0141] This method makes some improvements to the standard BA. First, a nonlinear decreasing inertia weight w is introduced t To adjust the velocity term coefficient, the inertia weight of this method Defined as

[0142]

[0143] Among them, w min and w max is a constant, and T is the maximum number of iterations of the algorithm. After adding the inertia weight, the speed update formula (7) becomes

[0144]

[0145] Secondly, this method uses chaotic mapping to replace the update of the emission rate r in the standard BA to improve the performance of BA. In the standard BA, the emission rate changes monotonically in [0,1]. This method uses sine mapping to make it a chaotic number between 0 and 1. The sine mapping update formula is defined as

[0146] r t+1 =a(r t ) 2 sin(πr t ) (14)

[0147] In the embodiment of the present invention, after improving the bat algorithm, the corresponding training iteration process is shown in FIG. Figure 6 shown.

[0148] [4] The results of the present invention show:

[0149] (1) Convergence performance:

[0150] For comparison, the algorithm of the present invention and the standard BA are used for array pattern synthesis. The desired pattern is set to have a flat-top main lobe at θ∈[-16°, 16°] and a side lobe of no more than -14dB at θ∈[-90°, -20°]∪[20°, 90°]. For N=12 and N=32, the three algorithms are tested 100 times respectively. The convergence results of the loss function are shown in Figure 7 As shown, Figure 7 middle, Figure 7The horizontal axis of (a) is the number of iterations, and the vertical axis is the loss function c; Figure 7 The horizontal axis of (b) is the number of iterations, and the vertical axis is the loss function c; Figure 7 The horizontal axis of (c) is the number of iterations, and the vertical axis is the loss function c; Figure 7 The horizontal axis of (d) is the number of iterations, and the vertical axis is the loss function c. It can be seen that the standard BA falls into different local optimal solutions in each trial, and the convergence curves of multiple trials are relatively scattered. In contrast, the improved BA convergence curves are relatively densely distributed, and the convergence values ​​of the loss function are relatively close; on the other hand, with the increase in the number of array elements, the dispersion of the standard BA convergence curve increases, while the improved BA can also converge relatively well with the increase in the number of array elements.

[0151] (2) Synthesis results of directional diagram:

[0152] For the desired directional pattern in (1) and N=32, the improved BA of the present invention, the standard BA and the standard PSO are tested 100 times respectively. The optimal directional pattern synthesis result and its corresponding phase are as follows: Figure 8 As shown, Figure 8 In the figure, the horizontal axis of the left figure is the scanning angle θ (°); the vertical axis is the antenna pattern (dB); Figure 8 The horizontal axis of the middle right figure is the antenna unit number, and the vertical axis is the excitation phase (°). It can be seen that the improved BA of the present invention is superior to the other two algorithms in terms of the maximum fluctuation of the main lobe and the maximum level of the side lobe.

[0153] In summary, the present invention has the following characteristics:

[0154] The present invention improves the bat algorithm and proposes an optimization method for symmetrical antenna pattern synthesis in combination with series expansion, which shows excellent performance in main lobe ripple control, side lobe suppression, convergence curve concentration and calculation time consumption. The present invention is based on an optimization model of series expansion and initial phase distribution: to improve the optimization efficiency, series expansion is applied to reduce the number of optimization variables; the phase is given an initial value with a flat-top pattern characteristic to further improve the efficiency. The present invention makes two improvements based on the standard bat algorithm, introducing inertia weights and chaotic mapping to improve the problem of the algorithm falling into local optimality.

[0155] Compared with the prior art, the present invention has the following advantages:

[0156] 1. The present invention utilizes series expansion to reduce the number of optimization variables and the complexity of the optimization problem.

[0157] 2. The initial value setting can be adjusted according to the desired directional diagram to improve optimization efficiency while avoiding premature optimization.

[0158] 3. Compared with the standard BA, the present invention introduces an inertia weight in the speed update, and then uses chaotic mapping to update the emission rate, thereby improving the comprehensive performance of the array pattern.

[0159] Another aspect of the embodiment of the present invention further provides a pure phase array pattern synthesis system based on an improved bat algorithm, comprising:

[0160] The first module is used to set an array model constructed by multiple antenna units;

[0161] The second module is used to construct an optimization objective function of the array model;

[0162] The third module is used to minimize the objective function according to the bat algorithm to complete the optimization training of the array model;

[0163] The fourth module is used to synthesize the pure phase array pattern according to the optimized array model.

[0164] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0165] The embodiment of the present invention further provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned pure phase array pattern synthesis method based on the improved bat algorithm when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0166] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0167] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0168] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0169] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the pure phase array pattern synthesis method based on the improved bat algorithm of the embodiment of the present invention;

[0170] Input / output interface 903, used to implement information input and output;

[0171] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0172] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0173] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0174] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned pure phase array pattern synthesis method based on the improved bat algorithm.

[0175] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0176] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0177] It should be noted that in various specific embodiments of the present invention, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present invention needs to obtain the user's sensitive personal information, it will obtain the user's separate permission or consent through a pop-up window or jump to a confirmation page, and after clearly obtaining the user's separate permission or consent, it will obtain the necessary user-related data for the normal operation of the embodiment of the present invention.

[0178] The embodiments described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Those skilled in the art can appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0179] Those skilled in the art will appreciate that the technical solutions shown in the figures do not limit the embodiments of the present invention and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0180] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0181] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0182] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0183] It should be understood that in the present invention, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can represent: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0184] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0185] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0187] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including multiple instructions for 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 methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store programs.

[0188] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the embodiments of the present invention is not limited thereby. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the embodiments of the present invention shall be within the scope of the rights of the embodiments of the present invention.

Claims

1. A pure phase array pattern synthesis method based on an improved bat algorithm, characterized in that: The following steps are involved: Setting up an array model constructed from multiple antenna elements; Constructing an optimization objective function of the array model; Minimizing the objective function according to the bat algorithm to complete the optimization training of the array model; Based on the optimized array model, pure phase array pattern synthesis is performed.

2. The method for synthesizing pure phase array patterns based on an improved bat algorithm according to claim 1, characterized in that: The arrangement of an array model constructed by a plurality of antenna units comprises the following steps: Determine the number of antenna units included in a uniform linear array; determine the spacing between the antenna units of the uniform linear array; determine the excitation amplitude and excitation phase of each antenna unit; The expression for constructing the array far-field pattern is: Where θ is the scanning angle, k is the wave number, n represents the nth antenna unit, N represents the total number of antenna units, Φ n represents the excitation phase of the nth antenna unit; j represents the imaginary unit; d represents the antenna unit spacing.

3. The method for synthesizing pure phase array patterns based on an improved bat algorithm according to claim 1, characterized in that: The optimization objective function of constructing the array model comprises the following steps: The selection of orthogonal basis functions is determined according to the shapes of basis functions, and the phase distribution of the array is expanded using the M-order orthogonal basis functions to obtain the expression of the target phase distribution of the array; An initial phase distribution is generated by using a quadratic polynomial, and the initial phase distribution preliminarily presents a flat-top pattern characteristic; According to the initial phase distribution, adjusting and optimizing the expression of the target phase distribution; A loss function of the array model is constructed to optimize the objective function of the array model.

4. The method for synthesizing pure phase array patterns based on an improved bat algorithm according to claim 3, characterized in that: The expression of the target phase distribution is: Among them, Φ n represents the target phase distribution; ΔΦ represents the value range of the polynomial; M represents the order of the orthogonal basis function; m represents the mth order basis function; k m represents the corresponding weight real coefficient; f m represents an m-order polynomial; d n represents the distance from the nth array element to the center of the array element; D represents the total length of the array; The initial phase distribution A n The expression is: Wherein, ΔA is the range constant of the initial phase; the expression of the loss function is: Where c represents the loss function; H represents the number of sampling points in the main lobe area; F(θ h ) represents the directional pattern; S represents the number of sampling points of the maximum value in the sidelobe area; θ h represents the sampling angle of the main lobe area; P d () represents the desired pattern; w1 and w2 represent weight coefficients; θ s The sampling angle representing the maximum value of the sidelobe area.

5. The method for synthesizing pure phase array patterns based on an improved bat algorithm according to claim 1, characterized in that: The method of minimizing the objective function according to the bat algorithm to complete the optimization training of the array model includes the following steps: Introducing nonlinear decreasing inertia weight To adjust the velocity term coefficient, the inertia weight is defined as: in, represents the inertia weight; w min and w max is a constant; t represents time t; T represents the maximum number of iterations of the algorithm; The chaotic mapping is used to replace the update of the emission rate in the standard bat algorithm. The sine mapping is used to make the emission rate in the standard bat algorithm become a chaotic number between 0 and 1. The update formula of the sine mapping is defined as: t+1 =a(r t ) 2 sin(πr t ), where a represents the mapping amplitude constant; r t represents the firing rate at time t; The array model is optimized and trained according to the improved bat algorithm.

6. The method for synthesizing pure phase array patterns based on an improved bat algorithm according to claim 5, characterized in that: The optimization training of the array model is completed according to the improved bat algorithm, comprising the following steps: Configure population size and maximum number of iterations; Randomly initialize the initial position of each bat in the population; Initialize the loudness, firing rate, and frequency of pulses fired at each location for each bat when searching for prey; When the maximum number of iterations is not reached, according to formula f i =f min +(f max -f min )β updates and adjusts the frequency of bats transmitting pulses at various locations, and according to the formula and Update and adjust the position and speed of each bat; among them, f i represents the frequency of the transmitted pulse of the i-th bat; f min Represents the minimum frequency; f max represents the maximum frequency; β represents a random vector that obeys a uniform distribution; represents the position of bat i at time t; represents the speed of bat i at time t; represents the inertia weight; x * Represents the global optimal solution of the current iteration; When the random number is less than the current emission rate, according to the formula x new =x old +εA t Generate a new local solution from the optimal solution, and generate a new solution by random flight until the random number is not less than the current emission rate; where x new represents the new local solution; x old represents the old optimal solution; ε represents a random number; A t Represents the average loudness of all bats in the current iteration; According to the new local solution, the loudness and emission rate of the transmitted pulse are updated, and the frequency of the current transmitted pulse is calculated to obtain the optimal solution for the frequency of the current transmitted pulse until the maximum number of iterations is reached.

7. The method for synthesizing pure phase array patterns based on an improved bat algorithm according to claim 6, characterized in that: The updated expression of the loudness of the transmitted pulse is: The updated expression of the emission rate of the emission pulse is: in, represents the loudness of the pulse emitted by the i-th bat at time t; α and γ represent constants; represents the initial firing rate; represents the firing rate of the i-th bat at time t+1.

8. A pure phase array pattern synthesis system based on an improved bat algorithm, characterized in that: include: The first module is used to set an array model constructed by multiple antenna units; The second module is used to construct an optimization objective function of the array model; The third module is used to minimize the objective function according to the bat algorithm to complete the optimization training of the array model; The fourth module is used to synthesize the pure phase array pattern according to the optimized array model.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 7.

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