Adaptive sampling based antenna performance fluctuation interval analysis method and related device

Abstract

The present application relates to the technical field of antenna performance uncertainty analysis, and particularly relates to an antenna performance fluctuation interval analysis method based on adaptive sampling and related equipment, wherein an uncertainty space is obtained based on an antenna system type and a fluctuation interval of uncertain factors, initial sample points are obtained by uniformly sampling the uncertain factors, the performance of each sample antenna is calculated and a performance interval is determined, and a set of contribution sample points is formed according to the performance interval. The global and local sample point algorithm is used to increase the contribution sample points in the uncertainty space, and the set is updated. The performance of the antenna response is calculated using the updated set, and a new performance interval is obtained. The relative error of adjacent performance indicators is iteratively calculated according to the new set performance interval, and when the relative error meets the convergence criterion, the performance interval meeting the criterion is taken as the fluctuation interval analysis result.

Description

Technical Field

[0001] This invention relates to the field of antenna performance uncertainty analysis technology, specifically to an antenna performance fluctuation range analysis method and related equipment based on adaptive sampling. Background Technology

[0002] Antenna systems (array antennas, mesh reflector antennas, etc.) are subject to various uncertainties, such as manufacturing errors, structural deformation, environmental load fluctuations, and electromagnetic excitation signal fluctuations. These uncertainties obviously cause antenna performance to deviate from its ideal design values, resulting in performance degradation (e.g., decreased surface accuracy, pointing deviation, shortened detection range). To ensure the reliability of antenna performance during service, it is necessary to study the impact of multiple uncertainties on the final antenna performance and estimate the fluctuation range of performance indicators.

[0003] For the interval analysis problem of the aforementioned antenna performance indicators, current methods can be divided into three categories: Monte Carlo sampling-based strategies, interval mathematics-based strategies, and sampling-based interval analysis methods. Monte Carlo sampling-based strategies estimate the upper and lower bounds of performance indicators by performing massive sampling within the uncertainty space. This method assumes that the uncertainty factors follow a certain probability distribution, then randomly generates a large number of sample points based on these distributions, performs antenna electromagnetic simulation on each sample point, obtains the corresponding performance indicator value, and finally determines its fluctuation range by statistically analyzing these performance indicator values. However, for antenna electromagnetic simulation, which is time-consuming even in a single analysis, accurate analysis requires a large number of sampling times (typically 10). 5 (Above). This results in a huge computational load required for this method, often unacceptable, limiting its application in practical engineering. The interval mathematics-based strategy combines interval mathematical operations with antenna electromagnetic analysis formulas to transfer the interval fluctuations of variables to the interval of electromagnetic performance. This method represents uncertainties as interval variables and then uses the operational rules of interval mathematics to calculate the antenna electromagnetic analysis formulas to obtain the interval range of performance indicators. However, it has two shortcomings: First, the antenna performance magnetic analysis must be simplified to an explicit expression; otherwise, it cannot be combined with interval mathematics. However, this simplification often leads to the loss of details in the calculation process (such as the inability to consider the mutual coupling effect between array elements), resulting in distorted performance calculation results. Second, interval mathematics generally leads to the interval expansion phenomenon of the calculation results, i.e., the range is larger than the true interval, resulting in a decrease in the feasibility of the calculation results. The sampling-based interval analysis method gradually approaches the upper and lower bounds of the system performance interval by sampling step by step within the uncertainty space. This method samples in each iteration step and updates the interval estimate of the performance indicators based on the sampling results. However, its sampling in each iteration step does not consider the position of existing sample points, which will cause repeated sampling in local areas, resulting in low computational efficiency. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an antenna performance fluctuation range analysis method and related equipment based on adaptive sampling, which addresses the shortcomings of the prior art and solves the technical problem of low efficiency in interval sampling calculation.

[0005] The objective of this invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a method for analyzing antenna performance fluctuation ranges based on adaptive sampling, comprising:

[0007] The uncertainty space is obtained based on the antenna type of the antenna system and the fluctuation range of each type of uncertainty factor;

[0008] Uncertain factors in the uncertainty space are uniformly sampled to obtain initial sample points. The antenna response performance is calculated for each initial sample point, and the performance range of all samples is obtained. Based on the performance range, all contributing sample points are selected to obtain the set of contributing sample points.

[0009] The global sample addition algorithm and the local sample addition algorithm are used to add several contributing sample points in the uncertainty space and update the set of contributing sample points.

[0010] The antenna response performance is calculated using the updated set of contributing sample points, and the performance range of all updated samples is obtained.

[0011] Based on the performance range of the updated contribution sample point set, the relative error of adjacent performance indicators is calculated iteratively. After the relative error meets the convergence criterion, the performance range that meets the convergence criterion is taken as the corresponding fluctuation range analysis result.

[0012] As a further improvement of the present invention, the uncertainty space is obtained based on the antenna type of the antenna system and the fluctuation range of each type of uncertainty factor, specifically including:

[0013] Uncertainties in the antenna system are classified according to antenna type and antenna characteristics, and the fluctuation range of uncertainties corresponding to each type is obtained.

[0014] All uncertain factors within the fluctuation range of uncertain factors are normalized to form a standard range;

[0015] The standard space is used as the uncertainty space, and Latin hypercube sampling is used to uniformly sample in the standard space to obtain the initial sample points.

[0016] As a further improvement of the present invention, the uncertainties in the uncertainty space are uniformly sampled to obtain initial sample points. The antenna response performance is calculated for each initial sample point, and the performance range of all samples is obtained, specifically including:

[0017] Based on the initial sample points, the uncertainty space is divided into multiple Voronoi polyhedra using the Voronoi diagram method;

[0018] Calculate the antenna response performance for each sample point corresponding to each Voronoi polyhedron, and calculate the upper and lower bounds of the performance for all samples to obtain the performance range for all samples.

[0019] Contributing sample points for the upper and lower bounds of performance within the Voronoi polyhedron are identified respectively, resulting in a set of contributing sample points.

[0020] As a further improvement to the present invention, the calculation of the upper and lower bounds of the performance of all current samples specifically includes:

[0021]

[0022] In the formula, This represents the upper bound of the antenna's response performance. This is the lower bound of the antenna's response performance. Let V be the dimension of the Voronoi polyhedron. Let be the performance metric for the d-th dimension corresponding to the i-th sample point.

[0023] As a further improvement to the present invention, the process for selecting contribution sample points is as follows:

[0024]

[0025]

[0026] In the formula, This is the upper bound of the performance of the antenna in the d-th dimension. This is the lower bound for the performance of the antenna in the d-th dimension. It is the j-th contributing sample point in the d-th dimension.

[0027] As a further improvement of the present invention, the global sample increment algorithm is as follows:

[0028]

[0029] In the formula, The sample points added by the global sample addition algorithm. As candidate sample points, The sample points in the contributing sample point set.

[0030] As a further improvement of the present invention, the local sample increment algorithm includes:

[0031]

[0032] In the formula, For the first Add the first contribution sample point to the polyhedron containing the polyhedron. Additional indicators for a local sample; These are candidate sample points; The sample points in the contributing sample point set; For the first k Within the Voronoi region of the contributing sample points, the first [number] [sample] has been added. q Local sample points, It is a Euclidean distance.

[0033] As a further improvement of the present invention, the convergence criterion is as follows:

[0034]

[0035]

[0036] In the formula, For the first In the iterative step, the first The absolute value of the error of the upper or lower bound of each degree of freedom. The convergence threshold, To obtain the mean of the absolute values ​​of the errors across all degrees of freedom, For the h-th iteration, the d-th upper bound or the d-th lower bound of performance. This represents the upper or lower bound of the performance in the (h-1)th iteration.

[0037] Secondly, the present invention provides an antenna performance fluctuation range analysis system based on adaptive sampling, used to implement the above-mentioned antenna performance fluctuation range analysis method based on adaptive sampling, including:

[0038] The initial fluctuation range is determined by the module, and the uncertainty space is obtained based on the antenna type of the antenna system and the fluctuation range of each type of uncertainty factor.

[0039] The sample acquisition module uniformly samples the uncertain factors in the uncertainty space to obtain initial sample points, calculates the antenna response performance for each initial sample point, and obtains the performance range of all samples; based on the performance range, it filters out all current contributing sample points to obtain a set of contributing sample points.

[0040] The sample addition module uses global and local sample addition algorithms to add several contributing sample points in the uncertainty space and update the set of contributing sample points.

[0041] The interval update module uses the updated set of contributing sample points to calculate the antenna response performance and obtains the performance interval of all samples after the update.

[0042] The interval analysis module iteratively calculates the relative error of adjacent performance indicators based on the performance interval of the updated contribution sample point set. After the relative error meets the convergence criterion, the performance interval that meets the convergence criterion is taken as the corresponding fluctuation interval analysis result.

[0043] Thirdly, the present invention provides a computer-readable storage medium for storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the above-described method for analyzing antenna performance fluctuation ranges based on adaptive sampling.

[0044] Fourthly, the present invention provides a computing device, comprising:

[0045] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for performing the above-described adaptive sampling-based antenna performance fluctuation range analysis method.

[0046] The beneficial effects of this invention are as follows: This invention provides an antenna performance fluctuation range analysis method based on adaptive sampling, mainly ensuring the high efficiency of antenna performance range analysis. Through a contribution sample point screening mechanism, redundant samples with little impact on performance are eliminated, significantly reducing the initial sampling scale and avoiding the computational waste of uniform sampling across the entire space. Iterative calculation of the relative error of performance indicators ensures the stability of the results. When the error meets the convergence criterion, the performance range can accurately characterize the limit boundary of the antenna response, avoiding the risk of conservative estimation or omission. By considering global and local sampling at the current sample position, the calculation of useless samples can be avoided while ensuring accuracy, significantly improving the efficiency of range analysis. For multidisciplinary antenna analysis, where a single simulation analysis is extremely time-consuming in practical engineering, this method has significant practical value. Furthermore, the method of this invention is not limited to antenna type and can be applied to array antennas as well as reflector antennas. This method can directly utilize the original analysis model without modification. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the specific implementation of the adaptive interval performance analysis method proposed in this invention.

[0049] Figure 2 This is a schematic diagram of the global and local point addition process within the same iteration step.

[0050] Figure 3 This is a comparison of the effectiveness of the method of the present invention in the electrical performance range analysis of a linear array example (uncertainty level 0.03).

[0051] Figure 4 This is the error convergence curve of the method of the present invention. Detailed Implementation

[0052] To make the objectives and technical solutions of this invention clearer and easier to understand, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0053] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. The described embodiments are only some embodiments of the present invention, and not all embodiments.

[0054] Example 1

[0055] like Figures 1 to 4 As shown, this embodiment provides an antenna performance fluctuation range analysis method based on adaptive sampling, which can efficiently and accurately obtain range boundary information of antenna structure / electromagnetic performance. Unlike existing Monte Carlo methods or range mathematics methods, the main advantage and feature of this method is the invention of an adaptive point-addition strategy. This criterion can minimize the sampling and calculation of invalid samples at the upper / lower bounds of the antenna performance range, obtaining accurate performance index range information while ensuring computational efficiency. The specific implementation method is as follows.

[0056] The uncertainty space is obtained based on the antenna type of the antenna system and the fluctuation range of various uncertainties (such as the amplitude and phase of the excitation of array antenna elements, the cable length of cable net antennas, etc.).

[0057] Specifically, uncertainties in the antenna system are classified according to antenna type and antenna characteristics, and the fluctuation range of uncertainties corresponding to each type is obtained;

[0058] All uncertain factors within the fluctuation range of uncertain factors are normalized to form a standard range;

[0059] The standard space is used as the uncertainty space, and Latin hypercube sampling is used to uniformly sample in the standard space to obtain the initial sample points.

[0060] Uncertain factors in the uncertainty space are uniformly sampled to obtain initial sample points. The antenna response performance is calculated for each initial sample point, and the performance range of all samples is obtained. Based on the performance range, all contributing sample points are selected to obtain the set of contributing sample points.

[0061] Specifically, based on the initial sample points, the uncertainty space is divided into multiple Voronoi polyhedra using the Voronoi diagram method;

[0062] Calculate the antenna response performance for each sample point corresponding to each Voronoi polyhedron, and calculate the upper and lower bounds of the performance for all samples to obtain the performance range for all samples.

[0063] Furthermore, calculate the upper and lower bounds of the performance for all current samples, specifically including:

[0064]

[0065] In the formula, This represents the upper bound of the antenna's response performance. This is the lower bound of the antenna's response performance. Let V be the dimension of the Voronoi polyhedron. Let be the performance metric for the d-th dimension corresponding to the i-th sample point.

[0066] Contributing sample points for the upper and lower bounds of performance within the Voronoi polyhedron are identified respectively, resulting in a set of contributing sample points.

[0067] Furthermore, the process for selecting contributing sample points is as follows:

[0068]

[0069]

[0070] In the formula, This is the upper bound of the performance of the antenna in the d-th dimension. This is the lower bound for the performance of the antenna in the d-th dimension. For the j-th contributing sample point in the d-th dimension. (In the set) The sample points corresponding to the values ​​are those that contribute to the current upper / lower bound. Obviously, samples other than the values ​​mentioned above do not contribute to the current boundary.

[0071] The global sample addition algorithm and the local sample addition algorithm are used to add several contributing sample points in the uncertainty space and update the set of contributing sample points.

[0072] In this embodiment, it is first determined that the number of global samples needs to be increased in each iteration step. and local sample number .

[0073] The number of sample points is increased iteratively using a global sample increment algorithm. The global sample increment algorithm is as follows:

[0074]

[0075] In the formula, The sample points added by the global sample addition algorithm. As candidate sample points, The sample points in the contributing sample point set. Maximum. The value corresponds to the candidate point with the largest distance from the existing sample points, and will be added as a new global sample point in this iteration step.

[0076] Set the number of iterations to After several iterations, all global sample points for the current iteration step are obtained.

[0077] Similarly, determine the number of local samples to be added in each iteration step, and add sample points according to the local sample increment algorithm. The local sample increment algorithm includes:

[0078]

[0079] In the formula, For the first Add the first contribution sample point to the polyhedron containing the polyhedron. Additional indicators for a local sample; These are candidate sample points; The sample points in the contributing sample point set; For the first k Within the Voronoi region of the contributing sample points, the first [number] [sample] has been added. q Local sample points, It is a Euclidean distance.

[0080] In this embodiment, the positions of all currently calculated points are considered each time a point is added. The global point addition strategy is to add the point furthest from the current point position, aiming to evenly distribute the points across the entire space. The local point addition strategy relies on the polyhedron containing the contributing points after Voronoi subdivision, thus avoiding human interference. This local point addition strategy also ensures that the newly added points are relatively evenly distributed within the polyhedron, preventing clustering. Furthermore, it avoids the added point being too close to the existing point position.

[0081] The antenna response performance is calculated using the updated set of contributing sample points, and the performance range of all updated samples is obtained.

[0082] Based on the performance range of the updated contribution sample point set, the relative error of adjacent performance indicators is calculated iteratively. After the relative error meets the convergence criterion, the performance range that meets the convergence criterion is taken as the corresponding fluctuation range analysis result.

[0083] The convergence criterion in this embodiment is:

[0084]

[0085]

[0086] In the formula, For the first In the iterative step, the first The absolute value of the error of the upper or lower bound of each degree of freedom. The convergence threshold, To obtain the mean of the absolute values ​​of the errors across all degrees of freedom, For the h-th iteration, the d-th upper bound or the d-th lower bound of performance. This represents the upper or lower bound of the performance in the (h-1)th iteration.

[0087] When the relative error does not meet the convergence criterion, continue to add new sample points until the convergence criterion is met.

[0088] In this embodiment, the convergence threshold is generally a small positive number, such as 0.01. The specific value can be adjusted according to the actual analysis object and performance indicators.

[0089] Example 2

[0090] This embodiment further explains the antenna performance fluctuation range analysis method based on adaptive sampling provided in Embodiment 1, with the help of application examples.

[0091] This simulation experiment is based on the method proposed in this invention (see details in the attached diagram). Figure 1 Consider the electrical performance (far-field pattern) analysis of a linear array antenna (see antenna schematic diagram). Figure 2The antenna and its uncertainty parameters are as follows: First, the number of elements is 10; the operating frequency is 5.8 GHz. The uncertainty variable is the excitation amplitude of the antenna unit; the uncertainty level (i.e., the proportion of the fluctuation level to the nominal value) is 0.01, 0.03, and 0.05. The electrical performance indicators analyzed are the maximum radiated power (Pmax), sidelobe level (SLL), and half-power beamwidth (BW).

[0092] The specific implementation steps are as follows:

[0093] Step 1: Begin antenna performance range analysis;

[0094] Step 2: Classify the uncertainties in the system according to the antenna type and characteristics, and obtain the upper and lower bounds of the fluctuations of each type of factor;

[0095] Specifically, uncertainties in the system are categorized based on antenna type and characteristics. For example, for a mesh reflector antenna, errors may exist in the length of the front mesh, the length of the rear mesh, the length of the adjustment cable, and the position of boundary points. These errors can be categorized according to their type. kind.

[0096] Obtain the upper and lower bounds of fluctuations for various factors. For example, for a mesh reflector antenna, the cutting process of the cable cutting machine and historical data can be used to model the interval boundaries of the cable length error; for the element excitation amplitude and phase error of an array antenna, the measurement of the sample can be used to model them.

[0097] Step 3: Normalize all uncertainties and perform initial sampling in the standard space they constitute based on the Latin hypersolution method.

[0098] Step 3a: Different types of uncertainty factors in an antenna system generally have different dimensions and magnitudes. It is necessary to define the fluctuation range of each type of factor (e.g., the elastic modulus of the material). Normalization to And constitutes a standard uncertainty space, in which Regarding a certain factor before normalization , its in The corresponding value is:

[0099]

[0100] Based on the specific antenna error variables The size, according to Determine the initial number of sample points; based on Latin hypercube sampling, in the standard uncertainty space ( Sampling is carried out within a 3D space, and the sample set is denoted as S.

[0101] Step 4: Divide the entire uncertainty space according to the Voronoi diagram method.

[0102] Based on the current sample set The standard uncertainty space is partitioned into several Voronoi polyhedra, each containing a current sample point.

[0103] Step 5: For all current samples, calculate the corresponding antenna structure / electromagnetic performance indicators (surface accuracy, radiation pattern, gain, etc.) according to the specific project requirements, and calculate the upper bound of the current performance indicators. / Lower bound of current performance metric .

[0104] Step 5a: For the current sample set For all samples, calculate their corresponding antenna performance parameters (surface accuracy, radiation pattern, and gain, etc.). This part of the calculation requires the use of commercial software or relevant simulation programs.

[0105] Step 5b; Calculate the upper / lower bound of the current performance metric. The specific calculation method is as follows:

[0106]

[0107] in, The upper bound (U) or lower bound (L) of the antenna performance index fluctuation can be a scalar or a vector (dimensional ). Here, a generalized description is given using vectors. Specifically, if the radiation pattern of the antenna in space is taken as an indicator, then the number of discrete angles across the entire spherical space during electromagnetic calculations is... Antenna performance specifications For the first The corresponding sample point of the th sample point Indicators in each dimension or For the first Upper or lower bounds for indicators in each dimension, operation For all 1 sample point (the first iteration step is ) ) Find the maximum or minimum value among the various dimensional indicators.

[0108] Step 6: Determine the upper bound of the performance index for each of the current sample points. / Lower bound of current performance metric Whether a contribution is made, determine the set of sample points that contribute to the upper / lower bound, respectively. / .

[0109] In this embodiment, " / " represents "or", meaning that the process of solving for the upper and lower bounds of performance is basically the same in one embodiment of the present invention. When calculating the upper bound, the set of contributing sample points is... The set at the lower bound is .

[0110] Regarding the solution process for the upper bound, it satisfies all Values ​​constitute a set Similarly, regarding the solution process for the lower bound, it satisfies... All Values ​​constitute a set In the above set The sample points corresponding to the values ​​are those that contribute to the current upper / lower bound. Obviously, samples other than the values ​​mentioned above do not contribute to the current boundary.

[0111] Step 7: Based on the global addition index proposed in this invention, add global samples to the entire uncertainty space. One, and add it to the sample set. ;

[0112] Step 7a: Determine the number of global samples to be increased in each iteration step. and local sample number ;

[0113] Step 7b: This invention proposes a new global sampling criterion. The details are as follows:

[0114]

[0115] in As candidate sample points, For the current sample set A sample in the dataset. Calculated using the above formula, the maximum... The value corresponds to the candidate point with the largest distance from the existing sample points, and will be added as a new global sample point in this iteration step.

[0116] Step 7c: Repeat step 7b for a total of Next, obtain all global sample points for this iteration step.

[0117] Step 8: Based on the local addition index proposed in this invention, for the set / The Voronoi diagram polyhedron corresponding to each contributing sample Add local sample points Each new local sample is added to the sample set. .

[0118] Step 8a: Determine the number of local samples to be increased in each iteration step ;

[0119] Step 8b: This invention proposes a new local sampling criterion. The details are as follows:

[0120]

[0121] in, In the first The first contributing sample is located in the polyhedron of the first contributing sample. This metric allows for uniform sampling within a polyhedron, avoiding sample clustering and wasting computational resources.

[0122] Step 9: Based on all current samples, update the upper bound of the performance metric using the method from Step 5. / Lower Boundary Step 6 is used to calculate the corresponding set of contributing samples. / ;

[0123] Step 10: For the current upper / lower bound calculation, calculate the relative error of the performance index between two adjacent steps.

[0124] Specifically, the convergence criterion is defined as follows:

[0125]

[0126] in, For the first In the iterative step, the first The absolute value of the error of the upper / lower bound of each degree of freedom, operation To obtain the mean of the absolute values ​​of the errors across all degrees of freedom.

[0127] It can be seen that if the mean Less than the given tolerance When the iteration ends, the current interval analysis result is considered converged; otherwise, it is necessary to return to step 7 and continue to add new sample points. Generally, a small positive number is chosen, such as 0.01, and the specific value can be adjusted according to the actual analysis object and performance indicators.

[0128] Step 11: If the relative error converges, the analysis process ends, and it is considered that the upper / lower bound of the accuracy performance index has been obtained; otherwise, return to step 7.

[0129] like Figure 2The diagram illustrates the global and local point addition processes within the same iteration step. It is evident that the method proposed in this invention can achieve global search, distributing samples across the uncertainty space as widely as possible; simultaneously, it can also encrypt samples near contribution points (key points), enabling exploration of critical regions. By balancing global exploration and local depth mining, computational efficiency is improved.

[0130] For this linear array antenna, the performance of the method proposed in one embodiment of the present invention was compared with Monte Carlo simulation (MCS) and interval mathematical method (IA), and the specific data are shown in Table 1.

[0131] Table 1 compares the performance of this method with Monte Carlo simulation (MCS) and interval mathematical methods (IA).

[0132]

[0133] This simulation presents theoretical experiments for different levels of uncertainty in the excitation amplitude of radiating elements. Under the three levels of uncertainty, compared to interval mathematical methods, the fluctuation range and interval width of the method in one embodiment of this invention are significantly closer to the MCS method (because its sample size is large enough, its result is generally considered an exact solution), but the required number of computational samples is only about 1% of that of the MCS method.

[0134] at the same time Figure 3 A comparison of radiation patterns at an uncertainty level of 0.03 is presented, demonstrating that the method proposed in one embodiment of the present invention can better approximate the accurate solution in both the main lobe and side lobe regions, verifying the accuracy of the method. Furthermore, from... Figure 4 The iterative curves in the figure reveal the superior convergence characteristics of this scheme.

[0135] Example 3

[0136] One embodiment of the present invention provides an antenna performance fluctuation range analysis system based on adaptive sampling. It includes:

[0137] The initial fluctuation range is determined by the module, and the uncertainty space is obtained based on the antenna type of the antenna system and the fluctuation range of each type of uncertainty factor.

[0138] The sample acquisition module uniformly samples the uncertain factors in the uncertainty space to obtain initial sample points, calculates the antenna response performance for each initial sample point, and obtains the performance range of all samples; based on the performance range, it filters out all current contributing sample points to obtain a set of contributing sample points.

[0139] The sample addition module uses global and local sample addition algorithms to add several contributing sample points in the uncertainty space and update the set of contributing sample points.

[0140] The interval update module uses the updated set of contributing sample points to calculate the antenna response performance and obtains the performance interval of all samples after the update.

[0141] The interval analysis module iteratively calculates the relative error of adjacent performance indicators based on the performance interval of the updated contribution sample point set. After the relative error meets the convergence criterion, the performance interval that meets the convergence criterion is taken as the corresponding fluctuation interval analysis result.

[0142] Example 4

[0143] In another embodiment of the present invention, a computer-readable storage medium is provided as a storage component within a terminal device, the main function of which is to store programs and data. It should be noted that the computer-readable storage medium here includes not only the built-in storage component of the terminal device but also extended storage components supported by the device. Essentially, it is a tangible medium capable of containing or storing programs that can be invoked by or operated in conjunction with an instruction execution system, device, or apparatus.

[0144] This storage medium provides a storage area for the terminal's operating system, and also stores one or more instructions suitable for the processor to load and execute. These instructions can constitute one or more computer programs containing program code.

[0145] Specifically, examples of computer-readable storage media (a non-exclusive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), optical fiber, portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any reasonable combination of the above types.

[0146] The storage medium may also include data signals propagated as part of a baseband portion or carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any reasonable combination of both. Furthermore, computer-readable storage medium may also refer to other readable media besides conventional readable storage media, capable of sending, propagating, or transmitting programs for use or operation by an instruction execution system, apparatus, or device. The program code on the storage medium can be transmitted via any suitable medium, including but not limited to wireless, wired, optical fiber, or any reasonable combination thereof.

[0147] The program code used to implement the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C. The execution modes of the program code include: running entirely on the user's computing device, running partially on the user's device as a standalone software package, running partially distributed between the user's device and a remote computing device, or running entirely on a remote computing device or device server. When a remote computing device is involved, the device can be connected to the user's computing device via any type of network such as a local area network (LAN) or a wide area network (WAN), or connected to the external computing device via the Internet through an Internet service provider (ISP).

[0148] The processor is capable of loading and executing one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the antenna performance fluctuation range analysis method based on adaptive sampling described in Example 1.

Claims

1. A method for analyzing an antenna performance fluctuation interval based on adaptive sampling, characterized in that, The method comprises the following steps: obtaining an uncertainty space according to the antenna type of the antenna system and the fluctuation interval of each type of uncertain factor; uniformly sampling the uncertain factors in the uncertainty space to obtain initial sample points, calculating the antenna response performance of each initial sample point, and obtaining the performance interval of all samples; adding a plurality of contribution sample points in the uncertainty space by using a global sample point adding algorithm and a local sample point adding algorithm, and updating the contribution sample point set; calculating the antenna response performance by using the updated contribution sample point set, and obtaining the performance interval of all samples after updating; iteratively calculating the relative error of adjacent performance indicators according to the performance interval of the updated contribution sample point set, and taking the performance interval meeting the convergence criterion as the corresponding fluctuation interval analysis result when the relative error meets the convergence criterion; the global sample point adding algorithm comprises the following steps: wherein a sample point added by the global sample enrichment algorithm, a candidate sample point, a sample point in the set of contributing sample points; the local sample point adding algorithm comprises the following steps: In the formula, For the first Add the first contribution sample point to the polyhedron containing the polyhedron. Additional indicators for a local sample; These are candidate sample points; The sample points in the contributing sample point set; For the first k Within the Voronoi region of the contributing sample points, the first [number] [sample] has been added. q Local sample points, It is a Euclidean distance.

2. The method of claim 1, wherein, obtaining an uncertainty space according to the antenna type of the antenna system and the fluctuation interval of each type of uncertain factor, specifically comprising the following steps: classifying the uncertain factors in the antenna system according to the antenna type and the antenna characteristics, and obtaining the fluctuation interval of each type of uncertain factor; normalizing all uncertain factors in the uncertain factor fluctuation interval to form a standard interval; taking the standard space as the uncertainty space, and uniformly sampling in the standard space by using Latin hypercube sampling to obtain initial sample points.

3. The adaptive sampling based antenna performance fluctuation range analysis method of claim 1, wherein, uniformly sampling the uncertain factors in the uncertainty space to obtain initial sample points, calculating the antenna response performance of each initial sample point, and obtaining the performance interval of all samples, specifically comprising the following steps: dividing the uncertainty space into a plurality of Voronoi polyhedrons by using a Voronoi graph method according to the initial sample points; calculating the response performance of the antenna for the sample points corresponding to each Voronoi polyhedron, and calculating the performance upper bound and the performance lower bound of all samples to obtain the performance interval of all samples; respectively identifying the contribution sample points of the performance upper bound and the performance lower bound in the Voronoi polyhedrons to obtain the contribution sample point set.

4. The adaptive sampling based antenna performance fluctuation range analysis method of claim 3, wherein, calculating the performance upper bound and the performance lower bound of all samples, specifically comprising the following steps: wherein, is an upper bound of the response performance of the antenna, is a lower bound of the response performance of the antenna, is a Voronoi polyhedron dimension, is a performance indicator of the dth dimension corresponding to the ith sample point.

5. The adaptive sampling based antenna performance fluctuation range analysis method of claim 3, wherein, the contribution sample point screening process comprises the following steps: wherein is an upper bound on the performance of the dthdimensional antenna, is a lower bound on the performance of the dthdimensional antenna, is the jthcontribution sample point in the dthdimension.

6. The method of claim 1, wherein, the convergence criterion comprises the following steps: wherein is the number of iterations, is the number of degrees of freedom, is the absolute value of the error upper or lower bound on the dth degree of freedom in the hth iteration, is the convergence threshold, is the mean of the absolute values of the errors on all degrees of freedom, is the dth performance upper or lower bound in the hth iteration, is the dth performance upper or lower bound in the h-1th iteration.

7. An adaptive sampling based antenna performance fluctuation interval analysis system for implementing the adaptive sampling based antenna performance fluctuation interval analysis method according to any one of claims 1 to 6, characterized in that, The method comprises the following steps: an initial fluctuation interval determination module for obtaining an uncertainty space according to the antenna type of the antenna system and the fluctuation interval of each type of uncertain factor; a sample obtaining module for uniformly sampling the uncertain factors in the uncertainty space to obtain initial sample points, calculating the antenna response performance of each initial sample point, and obtaining the performance interval of all samples; the contribution sample points of all samples are screened according to the performance interval to obtain a contribution sample point set; a sample point adding module for adding a plurality of contribution sample points in the uncertainty space by using a global sample point adding algorithm and a local sample point adding algorithm, and updating the contribution sample point set; an interval updating module for calculating the antenna response performance by using the updated contribution sample point set, and obtaining the performance interval of all samples after updating; The interval analysis module iteratively calculates relative errors of adjacent performance indicators according to performance intervals of the updated contribution sample point set, and regards the performance interval meeting the convergence criterion as the analysis result of the corresponding fluctuation interval after the relative error meets the convergence criterion.

8. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions that when executed by a computer cause the computer to perform a method of any of claims 1-7. The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the adaptive sampling based antenna performance fluctuation interval analysis method according to any one of claims 1 to 6.

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