Antenna performance fluctuation interval analysis method based on adaptive sampling and related equipment

Abstract

The invention relates to the technical field of antenna performance uncertainty analysis, in particular to an antenna performance fluctuation interval analysis method based on adaptive sampling and related equipment, and the method comprises the steps: obtaining an uncertainty space based on the type of an antenna system and an uncertain factor fluctuation interval, and uniformly sampling uncertain factors to obtain initial sample points; calculating the response performance of each sample antenna, determining a performance interval, and screening contribution sample points to form a set according to the performance interval; and increasing contribution sample points in the uncertainty space by using global and local sample point increasing algorithms, and updating the set. And calculating the response performance of the antenna by using the updated set to obtain a new performance interval. And iteratively calculating a relative error of adjacent performance indexes according to the performance interval of the new set, and when the relative error conforms to a convergence criterion, taking the performance interval conforming to the criterion as a fluctuation interval analysis result.

Description

Technical Field

[0001] The present invention relates to the technical field of antenna performance uncertainty analysis, and in particular to an antenna performance fluctuation interval analysis method based on adaptive sampling and related equipment. Background Art

[0002] Antenna systems (such as array antennas and mesh reflector antennas) are subject to a variety of uncertainties, including manufacturing errors, structural deformation, environmental load fluctuations, and electromagnetic excitation signal fluctuations. These uncertainties can cause antenna performance to deviate from its ideal design values, leading to performance degradation (e.g., reduced surface accuracy, pointing deviations, and reduced detection range). To ensure the reliability of antenna performance, it is necessary to study the impact of these multiple sources of uncertainty on the final antenna performance and estimate the fluctuation range of performance indicators.

[0003] For the interval analysis problem of the above-mentioned antenna performance indicators, the current methods can be divided into three types: Monte Carlo sampling-based strategies, interval mathematics-based strategies, and sampling-based interval analysis methods. The Monte Carlo sampling-based strategy estimates the upper and lower bounds of performance indicators by performing massive sampling in the uncertainty space. This method assumes that the uncertainty factors obey a certain probability distribution, and then randomly generates a large number of sample points based on these distributions. Antenna electromagnetic simulation is performed on each sample point to obtain the corresponding performance indicator value, and finally the fluctuation range is determined by counting these performance indicator values. However, for antenna electromagnetic simulation, which is relatively time-consuming for a single analysis, accurate analysis accuracy requires a large number of sampling times (generally around 10 5 This results in a computationally intensive and often unacceptable workload, limiting its practical application. Interval mathematics-based strategies combine interval mathematical operations with antenna electromagnetic analysis formulas to transfer the interval fluctuations of variables to the electromagnetic performance interval. This method represents uncertainty factors as interval variables and then uses interval mathematical operations to calculate the antenna electromagnetic analysis formulas, obtaining the interval range of performance indicators. However, there are two drawbacks. First, the magnetic analysis of antenna performance must be simplified into explicit expressions; otherwise, it cannot be combined with interval mathematics. However, such simplification often results in missing computational details (e.g., failure to consider mutual coupling effects between array elements), which in turn distorts the performance calculation results. Second, interval mathematics generally leads to interval expansion of the calculated results, i.e., expansion beyond the true interval range, resulting in reduced feasibility of the calculated results. Sampling-based interval analysis methods gradually approach the upper and lower bounds of the system performance interval by sampling within the uncertainty space. This method performs sampling at each iteration and updates the interval estimate of the performance indicator based on the sampling results. However, the sampling at each iteration does not take into account the location of existing sample points, resulting in repeated sampling in local areas and low computational efficiency. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned prior art and provide an antenna performance fluctuation interval analysis method and related equipment based on adaptive sampling to solve the technical problem of low efficiency of interval sampling calculation.

[0005] The purpose of the present invention is achieved by the following technical solutions: In a first aspect, the present invention provides a method for analyzing antenna performance fluctuation intervals based on adaptive sampling, comprising: Obtain uncertainty space based on antenna types of the antenna system and fluctuation ranges of various types of uncertainty factors; Uniformly sampling the uncertainty factors in the uncertainty space to obtain initial sample points, calculating the antenna response performance for each initial sample point, and obtaining a performance range for all samples; screening all current contributing sample points based on the performance range to obtain a contributing sample point set; Using the global sample addition algorithm and the local sample addition algorithm to add several contributing sample points in the uncertainty space, and update the contributing sample point set; The antenna response performance is calculated using the updated contribution sample point set, and the performance range of all updated samples is obtained; According to the performance interval of the updated contribution sample point set, the relative errors of adjacent performance indicators are iteratively calculated. After the relative errors meet the convergence criterion, the performance interval that meets the convergence criterion is used as the corresponding fluctuation interval analysis result.

[0006] As a further improvement of the present invention, the uncertainty space is obtained according to the antenna type of the antenna system and the fluctuation range of each type of uncertainty factor, specifically including: Classify the uncertainties in the antenna system according to antenna type and antenna characteristics, and obtain the corresponding uncertainty fluctuation range for each type; Normalize all uncertainties in the uncertainty fluctuation range to form a standard range; 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.

[0007] As a further improvement of the present invention, the uncertainty 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, specifically including: According to the initial sample points, the uncertainty space is divided into multiple Voronoi polyhedra using the Voronoi diagram method; Calculate the antenna response performance for each sample point corresponding to the Voronoi polyhedron, and calculate the upper and lower bounds of the performance of all current samples to obtain the performance range of all samples; The contribution sample points of the upper and lower performance bounds in the Voronoi polyhedron are identified respectively to obtain a set of contribution sample points.

[0008] As a further improvement of the present invention, the upper and lower performance bounds of all current samples are calculated, specifically including:

[0009] Where, is the upper bound of the antenna’s response performance, is the lower bound of the antenna’s response performance, is the dimension of the Voronoi polyhedron, is the performance index of the d-th dimension corresponding to the i-th sample point.

[0010] As a further improvement of the present invention, the contributing sample point screening process is as follows:

[0011]

[0012] Where, is the upper bound of the performance of the d-th dimension antenna, is the performance lower bound of the d-th dimension antenna, The j-th contributing sample point in the d-th dimension.

[0013] As a further improvement of the present invention, the global sample addition algorithm is:

[0014] Where, The sample points added by the global sample addition algorithm, For alternative sample points, is the sample point in the contributing sample point set.

[0015] As a further improvement of the present invention, the local sample addition algorithm includes:

[0016] Where, For the In the polyhedron where the contributing sample points are located, add The added point index of a local sample; For alternative sample points; is a sample point in the contributing sample point set; For the kWithin the Voronoi region of contributing sample points, the added q local sample points, is the Euclidean distance.

[0017] As a further improvement of the present invention, the convergence criterion is:

[0018]

[0019] Where, For the In the iteration step The absolute value of the error in the upper or lower bound of each degree of freedom, is the convergence threshold, To find the mean of the absolute values ​​of the errors over all degrees of freedom, is the dth performance upper bound or the dth performance lower bound in the hth iteration, is the d-th upper bound or the d-th lower bound of the performance in the h-1-th iteration.

[0020] In a second aspect, the present invention provides an antenna performance fluctuation interval analysis system based on adaptive sampling, which is used to implement the above-mentioned antenna performance fluctuation interval analysis method based on adaptive sampling, including: The initial fluctuation range confirmation module obtains the uncertainty space according to the antenna type of the antenna system and the fluctuation range of each type of uncertainty factors; The sample acquisition module uniformly samples the uncertainty 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; and screens all current contributing sample points according to the performance range to obtain a contributing sample point set; The sample addition module uses the global sample addition algorithm and the local sample addition algorithm to add a number of contributing sample points in the uncertainty space and update the contributing sample point set; The interval update module calculates the antenna response performance using the updated contribution sample point set and obtains the performance interval of all updated samples; The interval analysis module iteratively calculates the relative errors 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 used as the corresponding fluctuation interval analysis result.

[0021] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to perform the above-mentioned antenna performance fluctuation interval analysis method based on adaptive sampling.

[0022] In a fourth aspect, the present invention provides a computing device, comprising: 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 executing the above-mentioned antenna performance fluctuation interval analysis method based on adaptive sampling.

[0023] The beneficial effects of the present invention are as follows: the present invention provides an antenna performance fluctuation interval analysis method based on adaptive sampling, which mainly ensures the efficiency of antenna performance interval analysis. Through the contribution sample point screening mechanism, redundant samples with little impact on performance are eliminated, the initial sampling scale is significantly reduced, and the computational waste of uniform sampling in the entire space is avoided. The relative error of the performance index is iteratively calculated to ensure the stability of the results. When the error meets the convergence criterion, the performance interval can accurately characterize the limit boundary of the antenna response, avoiding the risk of conservative estimation or missed judgment. By considering global sampling and local sampling at the current sample position, the calculation of useless samples can be avoided while ensuring accuracy, significantly improving the efficiency of interval analysis. For multidisciplinary analysis of antennas in actual engineering, where single simulation analysis is extremely time-consuming, this method has obvious practical significance. At the same time, the method of the present invention is not limited to the antenna type, and can be applied to array antennas and reflector antennas. This method can directly use the original analysis model without modification. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] 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, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 This is a specific implementation flow chart of the adaptive interval performance analysis method proposed in the present invention.

[0026] Figure 2 It is a schematic diagram of the global point addition and local point addition process in the same iteration step.

[0027] Figure 3 The figure shows the comparison of the effects of the method of the present invention in the electrical performance interval analysis of a linear array example (uncertainty level 0.03).

[0028] Figure 4 It is the error convergence curve of the method of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose and technical solution of the present invention clearer and easier to understand, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0030] The technical solutions of the present invention will be described clearly and completely below with reference to the accompanying drawings and specific embodiments. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0031] Example 1 like Figures 1 to 4 As shown, this embodiment provides an antenna performance fluctuation interval analysis method based on adaptive sampling, which can efficiently and accurately obtain interval boundary information for antenna structure and electromagnetic performance. Unlike existing Monte Carlo methods or interval mathematics methods, the main advantage and feature of this method is the invention of an adaptive point addition strategy. This criterion minimizes the sampling and calculation of invalid samples at the upper and lower bounds of the antenna performance interval, thereby obtaining accurate performance indicator interval information while ensuring computational efficiency. The following is a specific implementation method.

[0032] The uncertainty space is obtained based on the antenna type of the antenna system and the fluctuation range of various types of uncertain factors (such as the amplitude and phase of the excitation of the array antenna unit, the length of the cable net antenna, etc.). Specifically, the uncertainties in the antenna system are classified according to the antenna type and antenna characteristics, and the fluctuation range of the uncertainties corresponding to each type is obtained; Normalize all uncertainties in the uncertainty fluctuation range to form a standard range; 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.

[0033] The uncertainty 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; all current contributing sample points are screened according to the performance range to obtain a contributing sample point set.

[0034] Specifically, according to the initial sample points, the uncertainty space is divided into multiple Voronoi polyhedra using the Voronoi diagram method; Calculate the antenna response performance for each sample point corresponding to the Voronoi polyhedron, and calculate the upper and lower bounds of the performance of all current samples to obtain the performance range of all samples; Furthermore, the upper and lower bounds of performance for all current samples are calculated, including:

[0035] Where, is the upper bound of the antenna’s response performance, is the lower bound of the antenna’s response performance, is the dimension of the Voronoi polyhedron, is the performance index of the d-th dimension corresponding to the i-th sample point.

[0036] The contribution sample points of the upper and lower bounds of the performance in the Voronoi polyhedron are identified respectively, and a set of contribution sample points is obtained.

[0037] Furthermore, the contributing sample point screening process is as follows:

[0038]

[0039] Where, is the upper bound of the performance of the d-th dimension antenna, is the performance lower bound of the d-th dimension antenna, For the j-th contributing sample point in the d-th dimension. The sample points corresponding to the values ​​are the points that contribute to the current upper / lower bounds. Obviously, samples other than the above values ​​do not contribute to the current bounds.

[0040] The global sample adding algorithm and the local sample adding algorithm are used to add several contributing sample points in the uncertainty space and update the contributing sample point set.

[0041] In this embodiment, first determine the number of global samples that need to be increased in each iteration step. , and the number of local samples .

[0042] The global sample addition algorithm is used to iteratively increase sample points. The global sample addition algorithm is:

[0043] Where, The sample points added by the global sample addition algorithm, For alternative sample points, is the sample point in the contributing sample point set. The value corresponds to the candidate point with the largest distance from the existing sample point, which will be added as the new global sample point in this iteration step.

[0044] Set the number of iterations to After iteration, all global sample points of the current iteration step are obtained.

[0045] Similarly, determine the number of local samples to be added in each iteration step, and add sample points according to the local sample addition algorithm. The local sample addition algorithm includes:

[0046] Where, For the In the polyhedron where the contributing sample points are located, add The added point index of a local sample; For alternative sample points; is a sample point in the contributing sample point set; For the k Within the Voronoi region of contributing sample points, the added q local sample points, is the Euclidean distance.

[0047] This embodiment considers the positions of all previously calculated points each time a point is added. The global point addition strategy uses the point farthest from the currently calculated point location, aiming to evenly distribute the points throughout the space. Local point addition relies on the polyhedron containing the contributing points after Voronoi decomposition, thus avoiding human interference. This local point addition strategy ensures that newly added points are evenly distributed within the polyhedron, avoiding clumping. This prevents added points from being too close to existing points.

[0048] The antenna response performance is calculated using the updated contribution sample point set, and the performance range of all updated samples is obtained; According to the performance interval of the updated contribution sample point set, the relative errors of adjacent performance indicators are iteratively calculated. After the relative errors meet the convergence criterion, the performance interval that meets the convergence criterion is used as the corresponding fluctuation interval analysis result.

[0049] The convergence criterion in this embodiment is:

[0050]

[0051] Where, For the In the iteration step The absolute value of the error in the upper or lower bound of each degree of freedom, is the convergence threshold, To find the mean of the absolute values ​​of the errors over all degrees of freedom, is the dth performance upper bound or the dth performance lower bound in the hth iteration, is the d-th upper bound or the d-th lower bound of the performance in the h-1-th iteration.

[0052] When the relative error does not meet the convergence criterion, continue to add new sample points until the convergence criterion is met.

[0053] The convergence threshold in this embodiment is generally a small positive number, such as 0.01, etc. The specific value can be adjusted according to the actual analysis object and performance indicators.

[0054] Example 2 This embodiment further explains the antenna performance fluctuation interval analysis method based on adaptive sampling provided in Example 1 in conjunction with an application example.

[0055] This simulation experiment is based on the method proposed by the present invention (see Figure 1 ), consider the electrical performance (far-field pattern) analysis of a linear array antenna (see the antenna schematic Figure 2 The antenna and its uncertainty parameters are as follows: The number of elements is 10; the operating frequency is 5.8 GHz. The uncertainty variable is the excitation amplitude of the antenna element; the uncertainty levels (i.e., the ratio of the fluctuation level to the nominal value) are 0.01, 0.03, and 0.05. The electrical performance indicators analyzed are maximum radiated power (Pmax), sidelobe level (SLL), and half-power beamwidth (BW).

[0056] The specific implementation steps are: Step 1: Start antenna performance interval analysis; Step 2: Classify the uncertainties in the system according to antenna type and characteristics, and obtain the upper and lower bounds of the fluctuations of each type of factor; Specifically, the uncertainties in the system are classified according to the antenna type and characteristics. For example, for a mesh reflector antenna, the error may exist in the length of the front cable net, the length of the rear cable net, the length of the adjustment cable, and the position of the boundary point. The error type can be classified into kind.

[0057] Obtain the upper and lower bounds of fluctuations for various factors. For example, for a mesh reflector antenna, the cable length error interval can be modeled using the cable cutting process and historical data from the cable cutting machine. For an array antenna, the unit excitation amplitude and phase errors can be modeled using sample measurements.

[0058] Step 3: Normalize all uncertainty factors and perform initial sampling based on the Latin hypercube method in the standard space composed of them.

[0059] Step 3a: Different types of uncertainty factors in the antenna system generally have different dimensions and magnitudes. The fluctuation range of each type of factor (such as the elastic modulus of the material) needs to be calculated. Normalized to and constitute the standard uncertainty space, where . For a factor before normalization , which is in The corresponding value of is:

[0060] According to the error variables in the specific antenna The size of Determine the number of initial sample points; Based on Latin hypercube sampling, in the standard uncertainty space ( Sampling is carried out in the dimensional space), and the sample set is recorded as S.

[0061] Step 4: Divide the entire uncertainty space according to the Voronoi diagram method.

[0062] Based on the current sample set , perform Voronoi partitioning on the standard uncertainty space, and divide the uncertainty space into several Voronoi polyhedrons, each of which contains a current sample point.

[0063] 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 indicator .

[0064] Step 5a: For the current sample set For all samples in the , calculate the corresponding antenna performance indicators (surface accuracy, radiation pattern, gain, etc.). This part of the calculation requires the use of commercial software or related simulation calculation programs.

[0065] Step 5b: Calculate the upper and lower bounds of the current performance indicator. The specific calculation method is as follows:

[0066] in, is the upper bound (U) or lower bound (L) of the antenna performance index, which can be a scalar or vector (dimension is ), which is described here in general terms as a vector. Specifically, if the radiation pattern of the antenna in space is an indicator, the number of angles of the entire spherical space discretized during electromagnetic calculations is Antenna performance indicators For the The sample point corresponding to Indicators of dimensions, or For the The upper or lower bound of the indicator of the dimension, operation For all sample points (the first iteration step is ) Find the maximum or minimum value among the dimension indicators.

[0067] Step 6: Determine the upper bound of the performance index for all current sample points / Lower bound of current performance indicator Whether there is contribution, determine the set of sample points that contribute to the upper bound / lower bound respectively / .

[0068] The “ / ” involved in this embodiment represents “or”, that is, the process of solving the upper bound and the lower bound of performance in one embodiment of the present invention is basically the same. When solving the upper bound, the contributing sample point set is , and the lower bound is .

[0069] For the upper bound solution process, satisfy , all Values ​​form a set ; Similarly, for the lower bound solution process, satisfy All Values ​​form a set In the above collection The sample points corresponding to the values ​​are the points that contribute to the current upper / lower bounds. Obviously, samples other than the above values ​​do not contribute to the current bounds.

[0070] Step 7: According to the global addition index proposed by the present invention, global samples are added to the entire uncertainty space. and add it to the sample set ; Step 7a: Determine the number of global samples to increase at each iteration , and the number of local samples ; Step 7b: The present invention proposes a new global sampling criterion , as shown below:

[0071] in For alternative sample points, The current sample set A sample in . According to the above formula, the maximum The value corresponds to the candidate point with the largest distance from the existing sample point, which will be added as the new global sample point in this iteration step.

[0072] Step 7c: Repeat step 7b for a total of times, and obtain all global sample points of this iteration step.

[0073] Step 8: According to the local point addition index proposed by the present invention, for the set / The Voronoi diagram polyhedron corresponding to each contributing sample in Add local sample points and add all newly added local samples to the sample set .

[0074] Step 8a: Determine the number of local samples to be added at each iteration ; Step 8b: The present invention proposes a new local sampling criterion , as shown below:

[0075] in, For the The first contributing sample in the polyhedron This indicator can achieve uniform sampling within the polyhedron and avoid sample aggregation and waste of computing resources.

[0076] Step 9: Based on all current samples, update the upper bound of the performance indicator using step 5 / Nether , use step 6 to calculate the corresponding contributing sample set / ; Step 10: For the current upper bound / lower bound calculation, calculate the relative error of the performance indicators of two adjacent steps respectively.

[0077] Specifically, the convergence criterion is defined as follows,

[0078] in, For the In the iteration step The absolute value of the error of the upper / lower bound of each degree of freedom, operation To find the mean of the absolute values ​​of the errors over all degrees of freedom.

[0079] It can be seen that if the mean Less than the given tolerance When , the iteration ends and the current interval analysis result is considered to be converged; otherwise, it is necessary to return to step 7 and continue to add new sample points. Generally, a small positive number is used, such as 0.01. The specific value can be adjusted according to the actual analysis object and performance indicators.

[0080] Step 11: If the relative error converges, the analysis process ends and the upper / lower bound of the performance indicator is obtained accurately; otherwise, return to step 7.

[0081] like Figure 2 The figure shows a schematic diagram of the global and local point addition processes within the same iteration step. This shows that the proposed method not only enables global search, maximizing the distribution of samples across the uncertainty space, but also encrypts samples near contributing points (key points), enabling exploration of key regions. This balancing of global exploration and local deep exploration improves computational efficiency.

[0082] For the 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 mathematics (IA). The specific data are shown in Table 1.

[0083] Table 1 Performance comparison of this method with Monte Carlo simulation (MCS) and interval mathematics (IA)

[0084] This simulation conducted theoretical experiments for different uncertainty levels in the excitation amplitude of radiating elements. Under all three uncertainty levels, compared to interval mathematics methods, the fluctuation range and interval width of the method corresponding to one embodiment of the present invention are significantly closer to the MCS method (which generally considers its results to be exact due to its large sample size), while requiring only approximately 1% of the number of calculation samples.

[0085] at the same time Figure 3 The comparison of radiation patterns when the uncertainty level is 0.03 is given. It can be seen that the method proposed in one embodiment of the present invention can better approximate the exact solution in both the main lobe area and the side lobe area, verifying the accuracy of the method. Figure 4 The iterative curve in shows that this scheme has better convergence characteristics.

[0086] Example 3 An embodiment of the present invention provides an antenna performance fluctuation interval analysis system based on adaptive sampling, including: The initial fluctuation range confirmation module obtains the uncertainty space according to the antenna type of the antenna system and the fluctuation range of each type of uncertainty factors; The sample acquisition module uniformly samples the uncertainty 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; and screens all current contributing sample points according to the performance range to obtain a contributing sample point set; The sample addition module uses the global sample addition algorithm and the local sample addition algorithm to add a number of contributing sample points in the uncertainty space and update the contributing sample point set; The interval update module calculates the antenna response performance using the updated contribution sample point set and obtains the performance interval of all updated samples; The interval analysis module iteratively calculates the relative errors 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 used as the corresponding fluctuation interval analysis result.

[0087] Example 4 In another embodiment of the present invention, a computer-readable storage medium is provided as a storage component within a terminal device, whose primary function is to store programs and data. It should be noted that the computer-readable storage medium herein encompasses not only the terminal device's built-in storage component but also any supported expansion storage components. Essentially, it is a tangible medium capable of containing or storing programs that can be accessed by, or run in conjunction with, an instruction execution system, device, or component.

[0088] The storage medium provides a storage area for the terminal's operating system and stores one or more instructions suitable for the processor to load and execute. These instructions can constitute one or more computer programs containing program codes.

[0089] In particular, examples (a non-exclusive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable magnetic disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any reasonable combination of the foregoing.

[0090] The storage medium may also include a data signal transmitted as part of a baseband portion or carrier wave, which carries readable program code. Such a transmitted data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any reasonable combination of the two. In addition, computer-readable storage media may also refer to other readable media other than traditional readable storage media, which are capable of sending, transmitting, or transmitting programs for use by or in conjunction with an instruction execution system, device, or component. The program code on the storage medium may be transmitted via any suitable medium, including but not limited to wireless, wired, optical cable, or any reasonable combination thereof.

[0091] The program code used to implement the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as "C." The program code can be executed entirely on the user's computing device, partially on the user's device as a standalone software package, partially distributed across the user's device and a remote computing device, or entirely on a remote computing 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 wide area network (WAN), or connected to an external computing device via the Internet through an Internet service provider (ISP).

[0092] The processor can load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the antenna performance fluctuation interval analysis method based on adaptive sampling described in Example 1.

Claims

1. A method for analyzing antenna performance fluctuation intervals based on adaptive sampling, characterized in that: include: Obtain uncertainty space based on antenna types of the antenna system and fluctuation ranges of various types of uncertainty factors; Uniformly sampling the uncertainty factors in the uncertainty space to obtain initial sample points, calculating the antenna response performance for each initial sample point, and obtaining a performance range for all samples; screening all current contributing sample points based on the performance range to obtain a contributing sample point set; Using the global sample addition algorithm and the local sample addition algorithm to add several contributing sample points in the uncertainty space, and update the contributing sample point set; The antenna response performance is calculated using the updated contribution sample point set, and the performance range of all updated samples is obtained; According to the performance interval of the updated contribution sample point set, the relative errors of adjacent performance indicators are iteratively calculated. After the relative errors meet the convergence criterion, the performance interval that meets the convergence criterion is used as the corresponding fluctuation interval analysis result.

2. The antenna performance fluctuation interval analysis method based on adaptive sampling according to claim 1, characterized in that: The uncertainty space is obtained based on the antenna type of the antenna system and the fluctuation range of each type of uncertainty factor, including: Classify the uncertainties in the antenna system according to antenna type and antenna characteristics, and obtain the corresponding uncertainty fluctuation range for each type; Normalize all uncertainties in the uncertainty fluctuation range to form a standard range; 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.

3. The antenna performance fluctuation interval analysis method based on adaptive sampling according to claim 1, characterized in that: The uncertainty 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, including: According to the initial sample points, the uncertainty space is divided into multiple Voronoi polyhedra using the Voronoi diagram method; Calculate the antenna response performance for each sample point corresponding to the Voronoi polyhedron, and calculate the upper and lower bounds of the performance of all current samples to obtain the performance range of all samples; The contribution sample points of the upper and lower performance bounds in the Voronoi polyhedron are identified respectively to obtain a set of contribution sample points.

4. The antenna performance fluctuation interval analysis method based on adaptive sampling according to claim 3 is characterized in that: Calculate the upper and lower bounds of performance for all current samples, including: Where, is the upper bound of the antenna’s response performance, is the lower bound of the antenna’s response performance, is the dimension of the Voronoi polyhedron, is the performance index of the d-th dimension corresponding to the i-th sample point.

5. The antenna performance fluctuation interval analysis method based on adaptive sampling according to claim 3, characterized in that: The contribution sample point screening process is as follows: Where, is the upper bound of the performance of the d-th dimension antenna, is the performance lower bound of the d-th dimension antenna, The j-th contributing sample point in the d-th dimension.

6. The antenna performance fluctuation interval analysis method based on adaptive sampling according to claim 1, characterized in that: The global sample addition algorithm is: Where, The sample points added by the global sample addition algorithm, For alternative sample points, is the sample point in the contributing sample point set.

7. The antenna performance fluctuation interval analysis method based on adaptive sampling according to claim 1, characterized in that: The local sample addition algorithm includes: Where, For the In the polyhedron where the contributing sample points are located, add The added point index of a local sample; For alternative sample points; is a sample point in the contributing sample point set; For the k Within the Voronoi region of contributing sample points, the added q local sample points, is the Euclidean distance.

8. The antenna performance fluctuation interval analysis method based on adaptive sampling according to claim 1, characterized in that: The convergence criterion is: Where, For the In the iteration step The absolute value of the error in the upper or lower bound of each degree of freedom, is the convergence threshold, To find the mean of the absolute values ​​of the errors over all degrees of freedom, is the dth performance upper bound or the dth performance lower bound in the hth iteration, is the d-th upper bound or the d-th lower bound of the performance in the h-1-th iteration.

9. An antenna performance fluctuation interval analysis system based on adaptive sampling, used to implement the antenna performance fluctuation interval analysis method based on adaptive sampling according to any one of claims 1 to 8, characterized in that: include: The initial fluctuation range confirmation module obtains the uncertainty space according to the antenna type of the antenna system and the fluctuation range of each type of uncertainty factors; The sample acquisition module uniformly samples the uncertainty 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; and screens all current contributing sample points according to the performance range to obtain a contributing sample point set; The sample addition module uses the global sample addition algorithm and the local sample addition algorithm to add a number of contributing sample points in the uncertainty space and update the contributing sample point set; The interval update module calculates the antenna response performance using the updated contribution sample point set and obtains the performance interval of all updated samples; The interval analysis module iteratively calculates the relative errors 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 used as the corresponding fluctuation interval analysis result.

10. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the antenna performance fluctuation interval analysis method based on adaptive sampling according to any one of claims 1 to 8.

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