Sparse array configuration method, device and equipment based on improved particle swarm optimization

Abstract

The invention relates to the technical field of array antennas, and provides a sparse array configuration method based on an improved particle swarm algorithm, and the method comprises the steps: building a sparse array population set which comprises a plurality of sparse array populations, and each sparse array population is composed of a plurality of particle array elements; and obtaining a fitness function corresponding to each sparse array population. A target sparse array configuration is obtained based on an improved particle swarm algorithm of a symbiotic strategy, the position parameter sets corresponding to the multiple sparse array populations and the fitness function, the symbiotic strategy is used for updating the position parameter sets, and therefore the particle swarm algorithm is improved through the symbiotic strategy; in the subsequent updating process of the positions of the particle array elements of the sparse array population, the mutual influence relation between the sparse array populations can be considered, local optimum is avoided, and the optimization effect of the antenna sparse array configuration is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of array antennas, and in particular to a sparse array configuration method, device and equipment based on an improved particle swarm algorithm. Background Art

[0002] With the continuous innovation of antenna array technology, the importance of non-uniform array design has become increasingly prominent. Non-uniform array design aims to achieve performance improvements by optimizing the antenna array layout, adjusting the number of array elements, or changing the spacing between array elements. However, due to the inherent nonlinear optimization characteristics of antenna array layout, array configuration optimization has been a hot research topic for the past few decades, especially the research on optimization techniques for sparse array configurations. Specifically, how to ensure that the sparse array's radiation pattern has the minimum maximum sidelobe level for sparse array configurations is the most important issue that needs to be addressed in antenna array configuration.

[0003] In the prior art, the optimization of antenna sparse array configuration can be achieved through different intelligent optimization algorithms, such as: a sparse array configuration method based on a genetic algorithm, a sparse array configuration method based on an ant colony algorithm, a sparse array configuration method based on a differential evolution algorithm, and a sparse array configuration method based on a particle swarm algorithm, so that the sparse array radiation pattern has the minimum maximum sidelobe level.

[0004] However, the existing technology has the problems of large computational complexity and easy falling into local optimality, which has a poor optimization effect on the antenna sparse array configuration and cannot effectively make the sparse array's radiation pattern have the minimum maximum sidelobe level. Summary of the Invention

[0005] Based on this, it is necessary to provide a sparse array configuration method, device and equipment based on an improved particle swarm algorithm to address the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a sparse array configuration method based on an improved particle swarm optimization algorithm, the method comprising:

[0007] Establishing a sparse array population set, wherein the sparse array population set includes: a plurality of sparse array populations, each of which is composed of a plurality of particle array elements;

[0008] Obtaining the fitness function corresponding to each of the sparse array populations;

[0009] The target sparse array configuration is obtained based on an improved particle swarm algorithm of a symbiotic strategy, position parameter sets corresponding to a plurality of the sparse array populations, and the fitness function; wherein the symbiotic strategy is used to update the position parameter set.

[0010] In a second aspect, an embodiment of the present invention provides a sparse array configuration device based on an improved particle swarm optimization algorithm, comprising:

[0011] A population set establishing module is used to establish a sparse array population set, wherein the sparse array population set includes: a plurality of sparse array populations, and the sparse array population is composed of a plurality of particle array elements;

[0012] A fitness function acquisition module, used to obtain the fitness function corresponding to each of the sparse array populations;

[0013] The target sparse array configuration acquisition module is used to obtain the target sparse array configuration based on the improved particle swarm algorithm of the symbiotic strategy, the position parameter sets corresponding to the multiple sparse array populations, and the fitness function; wherein the symbiotic strategy is used to update the position parameter set.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the sparse array configuration method based on the improved particle swarm algorithm described in the first aspect.

[0015] The technical solution provided by the embodiment of the present invention has the following advantages compared with the prior art:

[0016] An embodiment of the present invention provides a sparse array configuration method based on an improved particle swarm algorithm. This method establishes a sparse array population set, wherein the sparse array population set includes: multiple sparse array populations, and the sparse array population is composed of multiple particle array elements. The fitness function corresponding to each sparse array population is obtained. Based on the improved particle swarm algorithm of the symbiotic strategy, the position parameter sets corresponding to the multiple sparse array populations, and the fitness function, the target sparse array configuration is obtained, wherein the symbiotic strategy is used to update the position parameter set. In this way, the particle swarm algorithm improved by the symbiotic strategy can take into account the mutual influence relationship between the sparse array populations in the subsequent update process of the positions of the particle array elements of the sparse array population, avoid falling into the local optimum, and improve the optimization effect of the antenna sparse array configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 A schematic diagram of a flow chart of a sparse array configuration method based on an improved particle swarm optimization algorithm provided in an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the comparison results of an optimal fitness value experiment for a sparse linear array provided by an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of a Monte Carlo-based fitness value comparison experiment result of a sparse linear array provided in an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of a directional pattern comparison experiment result of a sparse linear array provided by an embodiment of the present invention;

[0023] Figure 5 A schematic diagram of a target sparse array configuration of a sparse linear array provided in an embodiment of the present invention;

[0024] Figure 6 A schematic diagram of the comparison results of an optimal fitness value experiment for a sparse circular array provided in an embodiment of the present invention;

[0025] Figure 7 A schematic diagram of a Monte Carlo-based fitness value comparison experiment result of a sparse circular array provided in an embodiment of the present invention;

[0026] Figure 8 A schematic diagram of a directional pattern comparison experiment result of a sparse circular array provided in an embodiment of the present invention;

[0027] Figure 9 A schematic diagram of a target sparse array configuration of a sparse circular array provided in an embodiment of the present invention;

[0028] Figure 10 A schematic structural diagram of a sparse array configuration device based on an improved particle swarm algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the solutions of the present invention will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features therein can be combined with each other.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all the embodiments.

[0031] In one embodiment, Figure 1 As shown, Figure 1 A schematic flow chart of a sparse array configuration method based on an improved particle swarm optimization algorithm provided in an embodiment of the present invention specifically includes the following steps:

[0032] S11: Create a sparse array population set.

[0033] The sparse array population set includes: multiple sparse array populations, each of which is composed of multiple particle array elements. A sparse array population refers to a system in which a portion of particle array elements are arranged in a full array by setting preset parameters and preset positions, while particle array elements in the remaining positions are in an inactive state, i.e., no particle array elements exist. The preset parameters may include, but are not limited to, the number of sparse particle array elements, the spacing between particle array elements, the radius of the sparse array, etc. The present invention does not specifically limit this, and those skilled in the art may set them according to actual circumstances.

[0034] Furthermore, for the sparse array population set P, a matrix can be established to store multiple sparse array populations p. pi T , for a sparse array population p pi T Composed of multiple particle array elements x iM Composition, x iM ∈{0,1}, where when x iM When x is 1, it indicates that the particle array is currently enabled. On the contrary, when x is iM When it is 0, it indicates that the particle array element is currently in the closed state, that is, there is no particle array element. Further, the sparse array population set P can be expressed as:

[0035] P={p p1 T ,p p2 T ,p p3 T ,...p pi T ,...p pNP T},

[0036] Among them, p pirepresents the i-th sparse array population in the sparse array population set, i=1,2,3...NP, NP represents the total number of sparse array populations, for example, NP=30.

[0037] p pi ={x i1 , x i2 , x i3 ,....x iM}

[0038] Among them, x iM Represents the state of the Mth particle array element in the i-th sparse array population.

[0039] Based on this, it should be noted that due to x iM Represents the state of the Mth particle array element in the i-th sparse array population, that is, for p po The array configuration corresponding to the i-th sparse array population in the sparse array population set can be determined.

[0040] Optionally, based on the above embodiment, in some embodiments of the present invention, in order to ensure that the apertures of all sparse array populations are equal and to enhance the fairness between configurations that are relatively corresponding among sparse array populations, after executing S11, the following step is further included:

[0041] The plurality of sparse array populations are constrained according to preset fixed activation positions, wherein the preset fixed activation positions refer to positions of particle array elements with activation states determined among corresponding positions in the plurality of sparse array populations.

[0042] Optionally, based on the above embodiment, in some embodiments of the present invention, since the sparse array population includes a sparse linear array population and a sparse circular array population, one implementation method for constraining multiple sparse array populations according to preset fixed activation positions may be:

[0043] For a sparse linear array population, the preset fixed enabled positions are determined to be the head and tail positions of the sparse linear array, that is, there must be enabled particle array elements at the head and tail positions of the array.

[0044] For a sparse circular array population, it is sufficient to determine the preset fixed enabled position as a randomly selected position of the sparse circular array, that is, for a sparse circular array population, randomly select a preset number of positions as preset fixed enabled positions, and there must be an enabled particle array element at the preset fixed enabled position.

[0045] For example, the randomly selected preset number of positions may be the 1st, 16th, 31st, and 46th positions, but are not limited thereto. The present invention does not specifically limit this, and those skilled in the art may set it according to actual conditions.

[0046] Specifically, for the sparse array, a sparse array population set is established, and the sparse array population set includes: multiple sparse array populations, and the sparse array population is composed of multiple particle array elements.

[0047] S12: Obtain the fitness function corresponding to each sparse array population.

[0048] Among them, the fitness function is used to calculate the maximum sidelobe level corresponding to each sparse array population. It should be noted that for a sparse array, the maximum sidelobe level can be used to measure whether the sparse array configuration corresponding to each current sparse array population is optimal. That is, when the maximum sidelobe level is the lowest, it means that the sparse array configuration corresponding to the sparse array population at this time is relatively optimal.

[0049] Specifically, after establishing a sparse array population set including a plurality of sparse array populations, the fitness function corresponding to each sparse array population is obtained.

[0050] Optionally, based on the above embodiment, in some embodiments of the present invention, an implementation of S12 may be:

[0051] S121: Obtain the directional pattern function corresponding to each sparse array population.

[0052] Among them, the pattern function can describe the relationship between the antenna radiation characteristics corresponding to the sparse array and the change of the spatial direction coordinates, and the fitness function is further obtained according to the pattern function.

[0053] Specifically, for each sparse array population, a directional pattern function corresponding to each sparse array population is obtained.

[0054] Optionally, based on the above embodiment, in some embodiments of the present invention, the sparse array population includes: a sparse linear array population and a sparse circular array population. Based on this, the directional pattern function corresponding to the sparse linear array population can be defined by the following expression:

[0055]

[0056] Among them, m represents the mth particle array element, λ represents the wavelength, and d m represents the distance between the position of the mth particle array element and the position of the first particle array element, θ0 represents the maximum pointing direction of the beam pitch, and f m It is related to the state of the particle array element at the mth particle array element position. That is, when the particle array element state at the mth particle array element position is enabled, f m =1, on the contrary, f m =0, that is, the particle array element at the mth particle array element position is not enabled.

[0057] The pattern function corresponding to the sparse circular array population can be defined by the following expression:

[0058]

[0059] Among them, R m represents the angle between the mth array element and the center of the circle and the x-axis, R represents the radius of the sparse circular array, θ0 represents the maximum pointing direction of the beam, Represents the maximum direction of the azimuth.

[0060] It should be noted that when calculating the directional pattern of a sparse circular array, only the directional pattern in the azimuth plane of the sparse circular array is considered.

[0061] S122: Calculate the maximum sidelobe level of each sparse array population according to the pattern function and the position parameter set.

[0062] Among them, the position parameter set refers to the position information of multiple particle array elements included in each sparse array population, that is, the configuration of the sparse array population. The position parameter set can be, for example, the positions of multiple particle array elements, the distance between each particle array element, and whether the current particle array element is enabled, but is not limited to this. The present invention does not specifically limit it, and those skilled in the art can set it according to actual conditions.

[0063] S123: Determine the fitness function of each sparse array population according to the maximum sidelobe level of each sparse array population.

[0064] Specifically, after obtaining the pattern function and the position parameter set, the maximum sidelobe level of each sparse array population is calculated. Further, the fitness function of each sparse array population is determined according to the maximum sidelobe level of each sparse array population.

[0065] Optionally, based on the above embodiment, in some embodiments of the present invention, the sparse array population includes: a sparse linear array population and a sparse circular array population. Based on this, the fitness function corresponding to the sparse linear array population can be defined by the following expression:

[0066] Fitness(i)=MSLL(i)(i=1,2,3...NP)

[0067]

[0068] Where MSLL(i) represents the maximum sidelobe level corresponding to the i-th sparse linear array population, S represents the area of ​​the sidelobe, represents the normalized directional pattern corresponding to the i-th sparse linear array population. Indicates the maximum value of the pattern function corresponding to the i-th sparse linear array population.

[0069] The fitness function corresponding to the sparse circular array population can be defined by the following expression:

[0070] Fitness(i)=MSLL(i)(i=1,2,3...NP)

[0071]

[0072] Among them, MSLL(i) represents the maximum sidelobe level corresponding to the i-th sparse circular array population, Represents the normalized directional pattern corresponding to the i-th sparse circular array population. Represents the maximum value of the pattern function corresponding to the i-th sparse circular array population.

[0073] S13: Based on the improved particle swarm optimization algorithm of the symbiotic strategy, the position parameter sets corresponding to multiple sparse array populations and the fitness function, the target sparse array configuration is obtained.

[0074] Among them, the symbiotic strategy is used to update the position parameter set. The symbiotic strategy is constructed based on the mutually dependent symbiotic relationship between kingfishers and otters.

[0075] Specifically, when otters hunt in the water, they stir up the sediment at the bottom, startling schools of fish and forcing them to swim to the surface. This allows kingfishers to more easily find and prey on these fish. At the same time, the kingfishers may miss some fish during their hunt, which are then caught by the otters.

[0076] Based on this, there is mutual influence between different populations of sparse arrays. Therefore, the particle swarm algorithm is improved based on the symbiosis strategy. In the subsequent update process of the position of the particle array elements of the sparse array population, the mutual influence between the sparse array populations can be taken into account, avoiding falling into the local optimum and improving performance.

[0077] Specifically, after obtaining the fitness function corresponding to each sparse array population, the target sparse array configuration is obtained based on the improved particle swarm algorithm of the symbiotic strategy, the position parameter sets corresponding to the multiple sparse array populations, and the fitness function.

[0078] Optionally, based on the above embodiment, in some embodiments of the present invention, an implementation of S13 may be:

[0079] S131: For the current iteration operation of the improved particle swarm algorithm, determine the fitness value of each sparse array population according to the position parameter set of the sparse array population and the fitness function.

[0080] The current iteration operation refers to a cyclic iteration algorithm for the improved particle swarm algorithm. For example, assuming that the preset number of iterations of the improved particle swarm algorithm is 500, the multiple execution steps of the improved particle swarm algorithm need to be repeatedly executed 500 times. When the preset number of iterations is 500, the final result is output. However, this is not limited to this, and the present invention is not specifically limited thereto. Those skilled in the art can set it according to actual circumstances.

[0081] The fitness value refers to the parameter used to subsequently determine the target sparse array configuration.

[0082] S132: Determine, among multiple fitness values, the sparse array configuration of the sparse array population corresponding to the minimum fitness value as the first sparse array configuration of the current iterative operation.

[0083] Specifically, for the current iterative operation of the improved particle swarm algorithm, the fitness value of each sparse array population is determined according to the position parameter set of the sparse array population and the fitness function, and the fitness values ​​corresponding to the multiple sparse array populations are obtained. Among the multiple fitness values, the numerical values ​​of the multiple fitness values ​​are compared, and the sparse array configuration of the sparse array population corresponding to the minimum fitness value is determined as the first sparse array configuration of the current iterative operation.

[0084] S133: Determine the position update function of each sparse array population based on the symbiotic strategy and the speed update function.

[0085] The velocity update function is used to update the velocity of each sparse array population, that is, to update the velocity of the plurality of particle array elements included in each sparse array population.

[0086] Optionally, based on the above embodiment, in some embodiments of the present invention, an implementation of S133 may be:

[0087] S1331: Obtain the speed update function of each sparse array population.

[0088] Specifically, for each sparse array population, a speed update function for updating a plurality of particle array elements included in each sparse array population is obtained.

[0089] Optionally, based on the above embodiment, in some embodiments of the present invention, for the sparse array population set P={p p1 T ,p p2 T ,p p3 T ,...p pi T ...p pNP T}, in the current k-th iteration operation of the improved particle swarm algorithm, for each sparse array population, the velocity matrix of multiple sparse array populations is set to V = {v1 T ,v2 T ,v3 T ,.v i T ..v NP T}, then for the k+1th iteration, the velocity update function can be defined by the following expression:

[0090]

[0091] Among them, p bi (k) indicates that the i-th sparse array population obtains the optimal sparse array configuration after k iterations, g b represents the global optimal sparse array configuration obtained after all iterative operations, w represents the inertia weight, c1 and c2 represent the learning factors. For example, for w = 0.5, c1 = 1.49445, c2 = 1.49445, but not limited to this. The present invention is not specifically limited thereto, and those skilled in the art can set it according to actual conditions. α =(γ1,γ2,γ3...γ M ), where each element in the vector obeys a uniform distribution, i.e., γ i ~U(0,1),γ b =(γ1,γ2,γ3...γ M ), where each element in the vector obeys a uniform distribution, i.e., γ j ~U(0,1).

[0092] S1332: Obtain the location update factor corresponding to the symbiotic strategy.

[0093] The position update factor is used to affect the position information of multiple particle array elements included in each sparse array population. The position update factor includes: a first position update factor and a second position update factor. The first position update factor can be defined by the following expression:

[0094] β=e -2J

[0095] Where J is the ratio of the current iteration number to the preset number of iterations.

[0096] The second position update factor can be defined by the following expression:

[0097] α=(2y1-1,2y2-1,...2y M -1)

[0098] Where Y=(y1,y2,...y M), each element obeys a uniform distribution, that is, y j ~U(0,1).

[0099] S1333: Determine a position update function of the sparse array population according to the position update factor and the speed update function.

[0100] Specifically, after obtaining the position update factor and the speed update function, the position update function of the sparse array population is determined according to the position update factor and the speed update function.

[0101] Optionally, based on the above embodiment, in some embodiments of the present invention, the location update function may be defined by the following expression:

[0102]

[0103] Among them, p Pn =p pl1 , p pl1 represents a sparse array population randomly selected from multiple sparse array populations, p pm =p pl2 , p pl2 Represents a sparse array population randomly selected from multiple sparse array populations.

[0104] It should be noted that the updated particle position is constrained, with the upper limit of the position being 1 and the lower limit being 0. If the position exceeds the upper limit, it is set to 1, and if it is below the lower limit, it is set to 0. If it is within the limit, it is rounded to either 0 or 1.

[0105] S134: Update the sparse array population position parameter set according to the position update function and the speed update function, and return to execute the current iterative operation for the improved particle swarm algorithm to obtain the second sparse array configuration, and determine the first initial target sparse array configuration according to the first sparse array configuration and the second sparse array configuration.

[0106] Specifically, after obtaining the position update function and the speed update function, the positions of the multiple particle array elements included in the sparse array population are updated, thereby updating the sparse array population position parameter set. After the sparse array population position parameter set is updated, the current iterative operation of the improved particle swarm algorithm is returned to be executed, and the fitness value of each sparse array population is determined according to the position parameter set of the sparse array population and the fitness function. Among the multiple fitness values, the sparse array configuration of the sparse array population corresponding to the minimum fitness value is determined to be the second sparse array configuration of the current iterative operation. Further, the fitness value of the first sparse array configuration and the fitness value of the second sparse array configuration are compared to determine that the sparse array configuration with the minimum fitness value is the first initial target sparse array configuration.

[0107] S135: Update the sparse array population position parameter set according to the position update function and the speed update function, and return to execute the current iterative operation for the improved particle swarm algorithm to obtain the third sparse array configuration. According to the first initial target sparse array configuration and the third sparse array configuration, determine the second initial target sparse array configuration until the preset number of iterations is met and obtain the target sparse array configuration.

[0108] Specifically, after determining the first initial target sparse array configuration, the sparse array population position parameter set is updated according to the position update function and the speed update function, and the current iterative operation for the improved particle swarm algorithm is returned to be executed. The fitness value of each sparse array population is determined according to the position parameter set of the sparse array population and the fitness function. Among the multiple fitness values, the sparse array configuration of the sparse array population corresponding to the minimum fitness value is determined as the third sparse array configuration of the current iterative operation. According to the first initial target sparse array configuration and the third sparse array configuration, the second initial target sparse array configuration is determined until the preset number of iterations is met and the target sparse array configuration is obtained.

[0109] In this way, the sparse array configuration method based on the improved particle swarm algorithm provided in this embodiment establishes a sparse array population set, wherein the sparse array population set includes: multiple sparse array populations, and the sparse array population is composed of multiple particle array elements. The fitness function corresponding to each sparse array population is obtained. Based on the improved particle swarm algorithm of the symbiotic strategy, the position parameter sets corresponding to the multiple sparse array populations, and the fitness function, the target sparse array configuration is obtained, wherein the symbiotic strategy is used to update the position parameter set. In this way, the particle swarm algorithm improved by the symbiotic strategy can take into account the mutual influence relationship between the sparse array populations in the subsequent update process of the positions of the particle array elements of the sparse array population, avoid falling into the local optimum, and improve the optimization effect of the antenna sparse array configuration.

[0110] Optionally, based on the above embodiment, in some embodiments of the present invention, before executing S133, the following steps are further included:

[0111] S21: Obtain the target probability of symbiosis between multiple sparse array populations according to the target probability formula. The target probability formula can be defined by the following expression:

[0112] P E =P Emax -j(P Emax -P Emin )

[0113] Among them, P Emax and P EminThe maximum target probability and the minimum target probability of symbiosis can be set according to the actual situation. For example, P Emax =0.5, P Emin =0, but not limited thereto, the present invention has no specific limitation, and those skilled in the art can set it according to actual conditions.

[0114] S22: Determine whether symbiosis occurs based on the target probability and the random probability value.

[0115] The random probability value is a random variable between 0 and 1, for example, the random probability value P r It can be 0.4 obtained randomly, but is not limited thereto. The present invention does not specifically limit this, and those skilled in the art can set it according to actual conditions.

[0116] Alternatively, one way to determine whether symbiosis occurs based on the target probability and the random probability value is to calculate the target probability P E , random probability value P r , compare the random probability value P r With 1-P E The size of P r >1-P E When symbiosis occurs, it is determined that

[0117] S23: When it is determined that symbiosis occurs, determining a position update function for each sparse array population based on the symbiosis strategy and the speed update function.

[0118] Specifically, according to the comparison of the target probability and the random probability value, when it is determined that symbiosis occurs, the position update function of each sparse array population is determined based on the symbiosis strategy and the speed update function.

[0119] Optionally, based on the above embodiment, in some embodiments of the present invention, when it is determined that symbiosis does not occur, a target sparse array configuration is obtained by a sparse array configuration method based on a standard particle swarm algorithm.

[0120] In this way, this embodiment can change the disturbance when the positions of the plurality of particle array elements included in each sparse array population are updated by judging whether symbiosis occurs.

[0121] Optionally, based on the above embodiment, in some embodiments of the present invention, after updating the sparse array population position parameter set according to the position update function and the speed update function, the method further includes:

[0122] S31: Obtain the number of particle array elements in the updated sparse array population that are in an enabled state.

[0123] S32: According to the number of particle array elements and the initial number of particle array elements in the enabled state in the initial sparse array population, adjust the particle array elements in the enabled state in the updated sparse array population.

[0124] The initial sparse array population refers to each sparse array population included in the sparse array population set established initially.

[0125] Optionally, based on the above embodiment, in some embodiments of the present invention, an implementation of S32 may be:

[0126] S321: If the number of particle array elements is greater than the initial number of particle array elements in the initial sparse array population that are enabled, the particle array elements in the current sparse array population that are enabled and greater than the initial number of particle array elements are disabled according to a preset method.

[0127] Specifically, the number of particle array elements in the updated sparse array population that are enabled is obtained. A determination is made as to whether the number of particle array elements is greater than the initial number of particle array elements in the initial sparse array population that are enabled. If the number of particle array elements is greater than the initial number of particle array elements in the initial sparse array population that are enabled, the particle array elements in the current sparse array population that are enabled in a state greater than the initial number of particle array elements are disabled according to a preset method.

[0128] Optionally, based on the above embodiment, in some embodiments of the present invention, since the sparse array population includes: a sparse linear array population and a sparse circular array population, based on this, when the sparse array population is a sparse linear array population, one implementation method for disabling the particle array elements in the current sparse array population that are in an enabled state and have a number greater than the initial number of particle array elements according to a preset method may be:

[0129] It is determined that there are enabled particle array elements at the head and tail positions of the current sparse array population, and the enabled particle array elements in the current sparse array population that are greater than the initial number of particle array elements are randomly turned off between the head and tail positions, that is, the redundant enabled particle array elements are eliminated.

[0130] Optionally, based on the above embodiments, in some embodiments of the present invention, when the sparse array population is a sparse circular array population, one implementation method for shutting down the particle array elements in the current sparse array population that are in an enabled state greater than the initial number of particle array elements according to a preset method may be: randomly selecting excess particle array elements in the enabled state except for the preset fixed enabled positions and eliminating them.

[0131] Optionally, based on the above embodiment, in some embodiments of the present invention, another implementation of S32 may be:

[0132] S3221: If the number of particle array elements is less than the initial number of particle array elements in the initial sparse array population that are in an enabled state, the particle array elements in the current sparse array population that are less than the initial number of particle array elements are enabled according to a preset method.

[0133] Optionally, based on the above embodiments, in some embodiments of the present invention, when the sparse array population is a sparse linear array population, it is determined that there are enabled particle array elements at the head and tail positions of the current sparse array population, and particle array elements in the current sparse array population that are less than the initial number of particle array elements are randomly enabled between the head and tail positions.

[0134] Optionally, based on the above embodiment, in some embodiments of the present invention, when the sparse array population is a sparse circular array population, particle array elements smaller than are randomly selected from preset fixed activation positions for activation.

[0135] In this way, it can be ensured that after the multiple particle array elements included in the sparse array population are updated, they have the same sparse rate as the initial sparse array population, that is, there are the same number of particle array elements in the enabled state.

[0136] Optionally, based on the above embodiments, in some embodiments of the present invention, in order to verify that the present invention can improve the optimization effect of the antenna sparse array configuration, the present invention is compared with the sparse array configuration method based on the genetic algorithm and the sparse array configuration method based on the particle swarm algorithm. The experimental results are as follows: Figures 2 to 9 As shown, Figures 2 to 4 Schematic diagram of the comparison of experimental results of sparse linear array, according to Figures 2 to 3 It can be seen that under the same conditions, for example, a linear array is composed of 60 particle array elements arranged at equal intervals of half the wavelength, and all particle array elements have equal amplitude omnidirectional characteristics. A sparse linear array refers to an array composed of 60 particle array elements, in which 30 particle array elements are determined to be enabled among the 60 particle array elements, with the upper limit of the particle position being 1 and the lower limit of the particle position being 0. Set the wavelength to 1m, the antenna beam pointing to 0°, w = 0.5, c1 = 1.49445, c2 = 1.49445, P Emax =0.5, P Emin = 0, the preset number of iterations is 500 times. Compared with other methods, the present invention obtains the lowest optimal fitness value of the target sparse array configuration. Figure 4 It can be seen that compared with other methods, the sidelobe level of the directional pattern synthesized by the target sparse array configuration obtained by the present invention is the lowest, indicating that the performance of the present invention is the best compared with other methods. Figure 5 A schematic diagram of a target sparse array configuration of a sparse linear array provided by an embodiment of the present invention, wherein the first position and the last position of the target sparse array configuration both have particle array elements in an enabled state. Figures 6 to 8Schematic diagram of the comparison of experimental results of sparse circular array, according to Figures 6 and 7 It can be seen that under the same conditions, for example, a uniform circular array is composed of 60 particle array elements evenly distributed. Set the wavelength to 1m, R to 5 times the wavelength, the main beam azimuth pointing angle to 180°, the pitch angle pointing angle to 45°, the distance between the particle array elements to about half the wavelength, and all particle array elements have equal amplitude omnidirectional characteristics. The sparse circular array refers to an array composed of 60 particle array elements, in which 30 particle array elements are determined to be enabled among the 60 particle array elements. Compared with other methods, the present invention obtains the lowest optimal fitness value of the target sparse array configuration. Figure 8 It can be seen that compared with other methods, the sidelobe level of the directional pattern synthesized by the target sparse array configuration obtained by the present invention is the lowest, indicating that the performance of the present invention is the best compared with other methods. Figure 9 A schematic diagram of a target sparse array configuration of a sparse circular array provided in an embodiment of the present invention.

[0137] It should be understood that although Figures 1 to 9 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figures 1 to 9 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The order of execution of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0138] In one embodiment, Figure 10 As shown, a sparse array configuration device based on an improved particle swarm algorithm is provided, comprising: a population set establishing module 10 , a fitness function acquiring module 11 and a target sparse array configuration acquiring module 12 .

[0139] The population set establishing module is used to establish a sparse array population set, wherein the sparse array population set includes: a plurality of sparse array populations, and the sparse array population is composed of a plurality of particle array elements;

[0140] A fitness function acquisition module, used to obtain the fitness function corresponding to each of the sparse array populations;

[0141] The target sparse array configuration acquisition module is used to obtain the target sparse array configuration based on the improved particle swarm algorithm of the symbiotic strategy, the position parameter sets corresponding to the multiple sparse array populations, and the fitness function; wherein the symbiotic strategy is used to update the position parameter set.

[0142] In this way, this embodiment establishes a sparse array population set through a population set establishment module, wherein the sparse array population set includes: multiple sparse array populations, and the sparse array population is composed of multiple particle array elements. The fitness function acquisition module obtains the fitness function corresponding to each sparse array population. The target sparse array configuration acquisition module obtains the target sparse array configuration based on the improved particle swarm algorithm of the symbiotic strategy, the position parameter sets corresponding to the multiple sparse array populations, and the fitness function; wherein the symbiotic strategy is used to update the position parameter set, so that the particle swarm algorithm improved by the symbiotic strategy can take into account the mutual influence relationship between the sparse array populations in the subsequent update process of the position of the particle array elements of the sparse array population, avoid falling into the local optimum, and improve the optimization effect of the antenna sparse array configuration.

[0143] Regarding the specific definition of the sparse array configuration device based on the improved particle swarm algorithm, please refer to the definition of the sparse array configuration method based on the improved particle swarm algorithm above, which will not be repeated here. Each module in the above-mentioned server can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0144] An embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the sparse array configuration method based on the improved particle swarm algorithm provided in the embodiment of the present invention can be implemented. For example, when the processor executes the computer program, the sparse array configuration method based on the improved particle swarm algorithm provided in the embodiment of the present invention can be implemented. Figures 1 to 9 The technical solutions of any of the method embodiments shown have similar implementation principles and technical effects, which will not be described in detail here.

[0145] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM).

[0146] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A sparse array configuration method based on an improved particle swarm optimization algorithm, characterized in that: include: Establishing a sparse array population set, wherein the sparse array population set includes: a plurality of sparse array populations, each of which is composed of a plurality of particle array elements; Obtaining the fitness function corresponding to each of the sparse array populations; The target sparse array configuration is obtained based on an improved particle swarm algorithm of a symbiotic strategy, position parameter sets corresponding to a plurality of the sparse array populations, and the fitness function; wherein the symbiotic strategy is used to update the position parameter set.

2. The method according to claim 1, characterized in that The obtaining of the fitness function corresponding to each of the sparse array populations includes: Obtaining a directional pattern function corresponding to each of the sparse array populations; Calculating the maximum sidelobe level of each of the sparse array populations according to the pattern function and the position parameter set; The fitness function of each of the sparse array populations is determined according to the maximum sidelobe level of each of the sparse array populations.

3. The method according to claim 2, characterized in that The sparse array population includes: a sparse linear array population and a sparse circular array population. The directional pattern function corresponding to the sparse linear array population can be defined by the following expression: Among them, m represents the mth particle array element, λ represents the wavelength, and d m represents the distance between the position of the mth particle array element and the position of the first particle array element, θ0 represents the maximum pointing direction of the beam pitch, and f m It is related to the particle array element state at the mth particle array element position. When the particle array element state at the mth particle array element position is enabled, f m =1, on the contrary, the particle array element at the mth particle array element position is not enabled, f m =0; The pattern function corresponding to the sparse circular array population can be defined by the following expression: Among them, R m represents the angle between the mth array element and the center of the circle and the x-axis, R represents the radius of the sparse circular array, θ0 represents the maximum pointing direction of the beam pitch, represents the maximum pointing direction in azimuth, and the directional pattern function corresponding to the sparse circular array population is the directional pattern of the sparse circular array in the azimuth plane; The fitness function corresponding to the sparse linear array population can be defined by the following expression: Fitness(i)=MSLL(i) (i=1,2,3...NP) Where MSLL(i) represents the maximum sidelobe level corresponding to the i-th sparse linear array population, S represents the area of ​​the sidelobe, represents the normalized directional pattern corresponding to the i-th sparse linear array population; represents the maximum value of the pattern function corresponding to the i-th sparse linear array population; The fitness function corresponding to the sparse circular array population can be defined by the following expression: Fitness(i)=MSLL(i) (i=1,2,3...NP) Among them, MSLL(i) represents the maximum sidelobe level corresponding to the i-th sparse circular array population, represents the normalized directional pattern corresponding to the i-th sparse circular array population, Represents the maximum value of the pattern function corresponding to the i-th sparse circular array population.

4. The method according to claim 3, characterized in that The improved particle swarm algorithm based on the symbiotic strategy, the position parameter sets corresponding to the plurality of sparse array populations, and the fitness function are used to obtain a target sparse array configuration, including: For a current iterative operation of the improved particle swarm algorithm, determining the fitness value of each of the sparse array populations according to the position parameter set of the sparse array population and the fitness function; Among the multiple fitness values, determining the sparse array configuration of the sparse array population corresponding to the minimum fitness value as the first sparse array configuration of the current iterative operation; Determining a position update function for each of the sparse array populations based on the symbiotic strategy and the speed update function; updating the position parameter set of the sparse array population according to the position update function and the speed update function, returning to execute the current iteration operation of the improved particle swarm algorithm to obtain a second sparse array configuration, and determining a first initial target sparse array configuration according to the first sparse array configuration and the second sparse array configuration; According to the position update function and the speed update function, the position parameter set of the sparse array population is updated, and the current iterative operation of the improved particle swarm algorithm is returned to be executed to obtain a third sparse array configuration. According to the first initial target sparse array configuration and the third sparse array configuration, a second initial target sparse array configuration is determined until a preset number of iterations is met to obtain a target sparse array configuration.

5. The method according to claim 4, characterized in that The determining of the position update function of each of the sparse array populations based on the symbiotic strategy and the speed update function includes: Obtaining a velocity update function for each of the sparse array populations; Obtaining a location update factor corresponding to the symbiotic strategy; A position update function of the sparse array population is determined according to the position update factor and the speed update function.

6. The method according to claim 5, characterized in that The location update factor includes: a first location update factor and a second location update factor. The first location update factor can be defined by the following expression: β＝e -2J Where J is the ratio of the current iteration number to the preset number of iterations; The second location update factor may be defined by the following expression: α=(2y1-1,2y2-1,...2y M -1) Where Y=(y1,y2,...y M ), each element obeys a uniform distribution, that is, y j ~U(0,1); The position update function can be defined by the following expression: Among them, p pn =p pl1 , p pl1 represents a sparse array population randomly selected from the multiple sparse array populations, p pm =p pl2 , p pl2 represents a sparse array population randomly selected from the multiple sparse array populations.

7. The method according to claim 4, characterized in that Before determining the position update function of each of the sparse array populations based on the symbiotic strategy and the speed update function, the method further includes: Obtaining target probabilities of symbiosis between the plurality of sparse array populations according to a target probability formula; determining whether symbiosis occurs according to the target probability and the random probability value; When it is determined that symbiosis occurs, a position update function of each of the sparse array populations is determined based on the symbiosis strategy and the speed update function.

8. The method according to claim 4, characterized in that After updating the position parameter set of the sparse array population according to the position update function and the speed update function, the method further includes: Obtaining the updated number of particle array elements in the sparse array population that are in an enabled state; According to the number of particle array elements and the initial number of particle array elements in an enabled state in the initial sparse array population, the particle array elements in the enabled state in the updated sparse array population are adjusted.

9. A sparse array configuration device based on an improved particle swarm algorithm, characterized in that: include: A population set establishing module is used to establish a sparse array population set, wherein the sparse array population set includes: a plurality of sparse array populations, and the sparse array population is composed of a plurality of particle array elements; A fitness function acquisition module, used to obtain the fitness function corresponding to each of the sparse array populations; The target sparse array configuration acquisition module is used to obtain the target sparse array configuration based on the improved particle swarm algorithm of the symbiotic strategy, the position parameter sets corresponding to the multiple sparse array populations, and the fitness function; wherein the symbiotic strategy is used to update the position parameter set.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the sparse array configuration method based on the improved particle swarm optimization algorithm according to any one of claims 1 to 8 are implemented.

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