Method and device for confirming and measuring uncertainty of electromagnetic simulation system

Abstract

The invention provides an uncertainty confirmation measurement method and device for an electromagnetic simulation system. The method comprises the following steps: acquiring electromagnetic simulation response data of a target obtained by the electromagnetic simulation system; sampling electromagnetic simulation response data to obtain an electromagnetic simulation data sample; according to an input parameter sample corresponding to the electromagnetic simulation data sample, obtaining a real electromagnetic data sample of the target under the input parameter sample; respectively calculating Mahalanobis distance cumulative distribution functions of the real electromagnetic data sample and the electromagnetic simulation data sample; and determining a comprehensive measurement index of the electromagnetic simulation system according to the Mahalanobis distance cumulative distribution function. According to the scheme, the problem of strong correlation of electromagnetic system response in a space field or a time field is well solved, so that the multi-dimensional random vector with uncertainty output by an electromagnetic simulation system is converted into the one-dimensional random variable, and the problem that an existing method can only be used for confirmation measurement of deterministic system response output is solved; and a technical basis is provided for subsequent model confirmation work.

Description

Technical Field

[0001] The present invention relates to the field of electromagnetic simulation technology, and in particular to an uncertainty confirmation measurement method and device for an electromagnetic simulation system. Background Art

[0002] In the study of radar performance, simulation calculations are widely used to obtain the target electromagnetic scattering characteristic data RCS data. However, as the research becomes more and more in-depth, the research objects become more and more complex, and various uncertainties of electromagnetic simulation systems also emerge. The accuracy and confidence of simulation response prediction are also increasingly prominent. Therefore, it is necessary to use model confirmation measurement methods to evaluate and verify electromagnetic simulation systems.

[0003] Model confirmation measurement is a method used to quantitatively compare the degree of consistency between simulation calculations and test results. At present, the confirmation measurement method in the field of electromagnetics is mainly the feature selection confirmation (FSV) method, but this method relies on the professional experience of engineers and has a large degree of subjectivity and arbitrariness. Moreover, this method is mainly used for the confirmation measurement of deterministic simulation results and is not suitable for simulation model outputs that consider uncertainty. At the same time, traditional model confirmation measurement methods cannot consider the uncertainty in simulation calculations; for the problem of continuous multi-point response of electromagnetic simulation output in spatial fields or time fields, the correlation between responses cannot be considered. Therefore, it is urgent to provide an uncertainty confirmation measurement method and device for electromagnetic simulation systems. Summary of the invention

[0004] The present invention provides an uncertainty confirmation measurement method and device for an electromagnetic simulation system. The method converts a multi-dimensional random vector with uncertainty output by the electromagnetic simulation system into a one-dimensional random variable, solving the problem that existing confirmation measurement methods can only be used for confirmation measurement of deterministic system response outputs, and providing a technical basis for subsequent model confirmation work.

[0005] In a first aspect, the present invention provides an uncertainty confirmation measurement method for an electromagnetic simulation system, comprising:

[0006] Acquiring electromagnetic simulation response data of the target obtained by the electromagnetic simulation system;

[0007] Sampling the electromagnetic simulation response data to obtain electromagnetic simulation data samples;

[0008] According to the input parameter sample corresponding to the electromagnetic simulation data sample, obtaining the real electromagnetic data sample of the target under the input parameter sample;

[0009] Calculating the Mahalanobis distance cumulative distribution function of the real electromagnetic data sample and the electromagnetic simulation data sample respectively;

[0010] According to the Mahalanobis distance cumulative distribution function, a comprehensive metric index of the electromagnetic simulation system is determined.

[0011] Optionally, sampling and acquiring electromagnetic simulation data samples from the electromagnetic simulation response data includes:

[0012] Determining a verification domain of the electromagnetic simulation system;

[0013] Sampling and obtaining input parameter samples of the electromagnetic simulation system;

[0014] The electromagnetic simulation response data in the verification domain output by the input parameter sample is used as the electromagnetic simulation data sample.

[0015] Optionally, the obtaining of a real electromagnetic data sample of the target under the input parameter sample includes:

[0016] In the verification domain, the real electromagnetic data sample corresponding to the input parameter sample is obtained through physical measurement.

[0017] Optionally, respectively calculating the Mahalanobis distance cumulative distribution function of the real electromagnetic data sample and the electromagnetic simulation data sample includes:

[0018] Calculating the electromagnetic simulation data sample to obtain a first mean vector and a first covariance matrix;

[0019] Obtaining a first Mahalanobis distance of each electromagnetic simulation data sample according to the first mean vector, the first covariance matrix and the electromagnetic simulation data sample;

[0020] Based on the first Mahalanobis distance, generating a first Mahalanobis distance cumulative distribution function;

[0021] and,

[0022] Calculating the real electromagnetic data samples to obtain a second mean vector and a second covariance matrix;

[0023] Obtaining a second Mahalanobis distance of each of the real electromagnetic data samples according to the second mean vector, the second covariance matrix and the real electromagnetic data samples;

[0024] Based on the second Mahalanobis distance, a second Mahalanobis distance cumulative distribution function is generated.

[0025] Optionally, the Mahalanobis distance cumulative distribution function includes a first Mahalanobis distance cumulative distribution function corresponding to the electromagnetic simulation data sample and a second Mahalanobis distance cumulative distribution function corresponding to the real electromagnetic data sample;

[0026] Determining the comprehensive metric index of the electromagnetic simulation system according to the Mahalanobis distance cumulative distribution function includes:

[0027] The area of ​​a region formed by the first Mahalanobis distance cumulative distribution function and the second Mahalanobis distance cumulative distribution function is calculated to obtain the comprehensive metric.

[0028] Optionally, it also includes:

[0029] The comprehensive metric indicators of different electromagnetic simulation systems are ranked, and the electromagnetic simulation system corresponding to the minimum comprehensive metric indicator is determined as the preferred electromagnetic simulation system.

[0030] In a second aspect, the present invention further provides an uncertainty confirmation measurement device for an electromagnetic simulation system, comprising:

[0031] A simulation module, used to obtain electromagnetic simulation response data of a target obtained by an electromagnetic simulation system; and to sample electromagnetic simulation data samples from the electromagnetic simulation response data;

[0032] An acquisition module, configured to acquire, according to an input parameter sample corresponding to the electromagnetic simulation data sample, a real electromagnetic data sample of the target under the input parameter sample;

[0033] The confirmation metric module is used to calculate the Mahalanobis distance cumulative distribution function of the real electromagnetic data sample and the electromagnetic simulation data sample respectively; and determine the comprehensive metric index of the electromagnetic simulation system according to the Mahalanobis distance cumulative distribution function.

[0034] In a third aspect, the present invention further provides a computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the uncertainty confirmation measurement method of the electromagnetic simulation system described in any one of the above items is implemented.

[0035] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute any of the uncertainty confirmation measurement methods for an electromagnetic simulation system described above.

[0036] In a fifth aspect, an embodiment of the present invention further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the method described in any first aspect of this specification.

[0037] The present invention provides an uncertainty confirmation measurement method and device for an electromagnetic simulation system. After obtaining the electromagnetic simulation response data for a target output by the electromagnetic simulation system, in order to consider random uncertainty, the method adopts a sampling method to obtain electromagnetic simulation data samples, and obtains real electromagnetic data samples corresponding to the electromagnetic simulation data samples through physical observation; then, the Mahalanobis distance cumulative distribution function of the electromagnetic simulation data sample and the real electromagnetic data sample is calculated respectively, and then the comprehensive measurement index of the electromagnetic simulation system in the global verification domain is determined based on the Mahalanobis distance cumulative distribution function. In this way, the comprehensive measurement index obtained by the present invention can realize the evaluation of any electromagnetic simulation system. The method provided by the present invention not only well handles the problem of strong correlation of the electromagnetic system response in the spatial field or time field, but also converts the multi-dimensional random vector with uncertainty in the verification domain of the electromagnetic simulation system output into a one-dimensional random variable, which solves the problem that the traditional feature selection evaluation method in the electromagnetic field can only be used for the confirmation measurement of the deterministic system response output, and provides a technical basis for subsequent model confirmation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is a flow chart of an uncertainty confirmation measurement method for an electromagnetic simulation system provided by an embodiment of the present invention;

[0040] Figure 2 is an actual observed RCS curve considering experimental uncertainty provided by an embodiment of the present invention;

[0041] Figure 3 is a comparison diagram of a simulated RCS curve output by an electromagnetic simulation system A provided by an embodiment of the present invention and an actually observed RCS curve;

[0042] Figure 4 is a comparison diagram of a simulated RCS curve output by an electromagnetic simulation system B provided by an embodiment of the present invention and an actually observed RCS curve;

[0043] Figure 5 is a comparison diagram of the actual observed RCS curve and the Mahalanobis distance cumulative distribution function of the output response of the electromagnetic simulation system A provided by an embodiment of the present invention;

[0044] Figure 6is a comparison diagram of the actual observed RCS curve and the Mahalanobis distance cumulative distribution function of the output response of the electromagnetic simulation system B provided by an embodiment of the present invention;

[0045] Figure 7 is a hardware architecture diagram of a computing device provided by an embodiment of the present invention;

[0046] Figure 8 It is a structural diagram of an uncertainty confirmation measurement device for an electromagnetic simulation system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0048] At present, common confirmation measurement methods include: hypothesis testing method, Bayesian factor method, frequentist method and area measurement method. Among them, hypothesis testing method only determines whether the model is accurate, but cannot quantitatively evaluate the accuracy of candidate models. The Bayesian factor method is derived from the Bayesian hypothesis test and relies heavily on the prior knowledge of analysts. Frequentist methods represented by the frequency index method are mainly based on comparing the mean or certain specific statistical features (such as maximum value) of model output and experimental data, so they cannot capture the overall statistical distribution of model output. The area measurement method quantitatively describes the model deviation by quantifying the area difference between the distribution function curve of the model output and the CDF curve of the experimental data, but this method is only applicable to single-output models.

[0049] For the uncertainty confirmation measurement problem of electromagnetic simulation system, the confirmation measurement indicators used for model evaluation are required to meet the following characteristics: 1) Quantitativeness: Model confirmation indicators should be a quantitative evaluation of the accuracy of mathematical models in predicting real physical systems, which is a basic characteristic of model confirmation indicators. 2) Objectivity: When different analysts use model confirmation indicators to evaluate model accuracy, the evaluation results obtained should be the same when the model output response and the corresponding test data are given. 3) Consideration of uncertainty and response correlation: Model confirmation indicators should consider model and test errors caused by various uncertain factors, that is, model errors caused by random factors and human cognitive limitations, parameter errors, random errors generated when reading and measuring test data, and correlation between multi-dimensional response quantities. 4) Globality: Model confirmation indicators should have the ability to quantitatively evaluate the accuracy of electromagnetic simulation models in the entire field verification domain.

[0050] At present, the confirmation measurement method used in the field of electromagnetics is mainly the feature selection confirmation (FSV) method. This method usually compares data by engineers by viewing graphical displays, which is highly subjective and arbitrary. In addition, the traditional method does not consider the problem of continuous multi-point response of electromagnetic simulation output in the spatial field or time field, nor does it consider the correlation between responses. Therefore, in order to solve the above problems, the present invention proposes an uncertainty confirmation measurement method for electromagnetic simulation system.

[0051] Please refer to Figure 1 , an embodiment of the present invention provides an uncertainty confirmation measurement method for an electromagnetic simulation system, comprising:

[0052] Step 100, acquiring electromagnetic simulation response data of a target obtained by an electromagnetic simulation system;

[0053] Step 102, acquiring electromagnetic simulation data samples from the electromagnetic simulation response data;

[0054] Step 104, obtaining a real electromagnetic data sample of the target under the input parameter sample according to the input parameter sample corresponding to the electromagnetic simulation data sample;

[0055] Step 106, respectively calculating the Mahalanobis distance cumulative distribution function of the real electromagnetic data sample and the electromagnetic simulation data sample;

[0056] Step 108: Determine a comprehensive metric of the electromagnetic simulation system based on the Mahalanobis distance cumulative distribution function.

[0057] In the present invention, after obtaining the electromagnetic simulation response data for the target output by the electromagnetic simulation system, in order to consider random uncertainty, a sampling method is used to obtain electromagnetic simulation data samples, and a real electromagnetic data sample corresponding to the electromagnetic simulation data sample is obtained through physical observation; then the Mahalanobis distance cumulative distribution function of the electromagnetic simulation data sample and the real electromagnetic data sample is calculated respectively, and then the comprehensive measurement index of the electromagnetic simulation system in the global verification domain is determined based on the Mahalanobis distance cumulative distribution function. In this way, the comprehensive measurement index obtained by the present invention can realize the evaluation of any electromagnetic simulation system. The method provided by the present method not only well handles the problem of strong correlation of the electromagnetic system response in the spatial field or time field, but also converts the multi-dimensional random vector with uncertainty in the verification domain of the electromagnetic simulation system output into a one-dimensional random variable, which solves the problem that the traditional feature selection evaluation method in the electromagnetic field can only be used for the confirmation measurement of the deterministic system response output, and provides a technical basis for subsequent model confirmation work.

[0058] Described below Figure 1 How the various steps are performed.

[0059] In step 100, several electromagnetic simulation systems may be used to obtain electromagnetic simulation response data of the same target. Since the electromagnetic simulation response data obtained by different electromagnetic simulation systems are different, it is necessary to select the optimal electromagnetic simulation system through a confirmation measurement method.

[0060] In step 102, electromagnetic simulation data samples are obtained from electromagnetic simulation response data sampling, including:

[0061] Determine the verification domain of the electromagnetic simulation system;

[0062] Sampling to obtain input parameter samples of the electromagnetic simulation system;

[0063] The electromagnetic simulation response data in the verification domain output by the input parameter sample is used as the electromagnetic simulation data sample.

[0064] It should be noted that the output response type of the electromagnetic simulation system (i.e., the electromagnetic simulation model) is the type of electromagnetic simulation response data, such as electric field strength, magnetic field strength, scattering cross section (RCS), absorption power, radiation power, etc. The electromagnetic simulation response data of the target is the performance in the verification domain (time field or space field), where the length of the verification domain represents the length of the field. For example, if the electromagnetic simulation response data is an RCS curve from 0° to 180° with a step length of 2°, the verification domain can be expressed as {x1, x2…, x n}, length n = 180 / 2 = 90. It should be noted that each input parameter sample in the verification domain x1, x2..., x n The electromagnetic simulation response data below is a group of electromagnetic simulation data samples, and the electromagnetic simulation data samples include n response data.

[0065] Specifically, the Monte Carlo sampling method can be used to obtain electromagnetic simulation data samples, respectively in the verification domain x1, x2..., x n Under this condition, p groups of electromagnetic simulation data samples of p input parameter samples considering random uncertainty are obtained through electromagnetic simulation system simulation. is the electromagnetic simulation data sample obtained from the i-th input parameter sample; p groups of electromagnetic simulation data samples are represented by Y m (X), X represents the input sample parameters:

[0066]

[0067] in, Indicates that all input parameter samples are in the validation domain x n The response data under has p response data, which is a p×1 column vector, and n is the length of the verification domain; is the response data of the pth input parameter sample in each verification domain, which is a 1×n row vector.

[0068] In the present invention, in order to consider the uncertainty of the input parameters of the electromagnetic scattering simulation model, the input parameter samples are obtained by a sampling method, which not only reduces the amount of data but also retains the characteristic information of the input parameter samples, thereby improving the efficiency of the confirmation measurement while ensuring accurate evaluation of the electromagnetic simulation system.

[0069] In step 104, a real electromagnetic data sample of the target under the input parameter sample is obtained, including:

[0070] In the verification domain, the real electromagnetic data samples corresponding to the input parameter samples are obtained through physical measurements.

[0071] Specifically, as described in the previous example, q sets of real electromagnetic data samples are represented as Y e (X):

[0072]

[0073] in, Indicates that all input parameter samples are in the validation domain x n The response data under has q response data, which is a q×1 column vector, and n is the length of the verification domain; is the response data of the qth input parameter sample in each verification domain, which is a 1×n row vector, p=q.

[0074] For step 106, the Mahalanobis distance cumulative distribution function of the real electromagnetic data sample and the electromagnetic simulation data sample is calculated respectively, including:

[0075] Calculating the electromagnetic simulation data samples to obtain a first mean vector and a first covariance matrix;

[0076] Obtaining a first Mahalanobis distance of each electromagnetic simulation data sample according to the first mean vector, the first covariance matrix and the electromagnetic simulation data sample;

[0077] Based on the first Mahalanobis distance, generating a first Mahalanobis distance cumulative distribution function;

[0078] and,

[0079] Calculating the real electromagnetic data samples to obtain a second mean vector and a second covariance matrix;

[0080] Obtaining a second Mahalanobis distance of each real electromagnetic data sample according to the second mean vector, the second covariance matrix and the real electromagnetic data sample;

[0081] Based on the second Mahalanobis distance, a second Mahalanobis distance cumulative distribution function is generated.

[0082] Specifically, the first Mahalanobis distance is determined by the following formula:

[0083]

[0084] in, is the electromagnetic simulation data sample of the i-th input parameter sample The Mahalanobis distance to the first mean vector μ; Σ is the first covariance matrix; similarly, the calculation formula of the second Mahalanobis distance is the same as this formula.

[0085] Specifically, based on the first Mahalanobis distance, generating a first Mahalanobis distance cumulative distribution function includes: calculating the first Mahalanobis distance cumulative distribution function of the output response of the electromagnetic simulation system according to all data sets of the first Mahalanobis distance;

[0086] Based on the second Mahalanobis distance, a second Mahalanobis distance cumulative distribution function is generated, including: calculating the second Mahalanobis distance cumulative distribution function of the output response of the electromagnetic simulation system according to all the data sets of the second Mahalanobis distance.

[0087] In a preferred embodiment, the Mahalanobis distance cumulative distribution function includes a first Mahalanobis distance cumulative distribution function corresponding to the electromagnetic simulation data sample and a second Mahalanobis distance cumulative distribution function corresponding to the real electromagnetic data sample;

[0088] In step 108, the comprehensive metric index of the electromagnetic simulation system is determined according to the Mahalanobis distance cumulative distribution function, including:

[0089] The area of ​​the region formed by the first Mahalanobis distance cumulative distribution function and the second Mahalanobis distance cumulative distribution function is calculated to obtain a comprehensive measurement index.

[0090] Specifically, the comprehensive metric of the electromagnetic simulation system is calculated by the following formula:

[0091]

[0092] Among them, F m (MD) is the first Mahalanobis distance cumulative distribution function; S e (MD) is the second Mahalanobis distance cumulative distribution function.

[0093] In the present invention, by introducing the Mahalanobis distance into the uncertainty confirmation measurement problem of the electromagnetic simulation system, the multi-dimensional random vector with uncertainty in the verification domain of the electromagnetic simulation system output is converted into a one-dimensional random variable, thereby realizing the dimensionality reduction function and better solving the problem that the traditional FSV method can only be used for deterministic measurement. At the same time, the measurement result obtained based on the Mahalanobis distance is more reasonable and intuitive, providing a technical basis for subsequent model confirmation work.

[0094] In the present invention, the Mahalanobis distance not only extracts the correlation information between the output responses under the verification domain, but also reflects the closeness between each output sample and the mean vector, so that the final measurement result is more accurate and not affected by sample anomalies. In addition, since the Mahalanobis distance is a distance indicator constructed by standardizing the output variables, its value is not affected by the dimensions of different output variables.

[0095] After step 108, the method further includes:

[0096] The comprehensive metric indicators of different electromagnetic simulation systems are ranked, and the electromagnetic simulation system corresponding to the minimum comprehensive metric indicator is determined as the preferred electromagnetic simulation system.

[0097] It should be noted that the smaller the comprehensiveness metric is, the better the performance of the electromagnetic simulation system is, and the closer its simulation data is to the real electromagnetic data.

[0098] The present invention takes into account the uncertainties of simulation and experiment in radar performance research, and provides an uncertainty confirmation measurement method for an electromagnetic simulation system. The method introduces the Mahalanobis distance to realize the consideration of the correlation of system response in the space field or time field, eliminates the influence of the correlation between variables on the final measurement result, evaluates the difference between the multi-dimensional correlation response data of simulation and physical observation, obtains a comprehensive quantitative index of the difference between simulation and real physical observation, and further realizes the evaluation of different electromagnetic simulation systems.

[0099] In a specific implementation, taking the electromagnetic simulation system of a dielectric sphere as an example, the uncertainty confirmation measurement method for the electromagnetic simulation system includes:

[0100] Step 1: The output responses of electromagnetic simulation system A and electromagnetic simulation system B are both radar scattering area RCS from 0° to 180° with a step length of 2°. The data of radar scattering area at different angles constitute the response data in the field space verification domain; the verification domain is {x1, x2…, x n}, length n = 90;

[0101] Step 2: Considering random uncertainty, the Monte Carlo sampling method is used to obtain electromagnetic simulation data samples and real electromagnetic data samples, including:

[0102] 1) The electromagnetic simulation system includes three random uncertainty input parameters: relative dielectric constant, sphere density and operating frequency. The corresponding uncertainty descriptions are shown in Table 1.

[0103] Table 1

[0104]

[0105] 2) Considering the uncertainty of the experiment, sampling is performed under the input parameter distribution of the set correct model and the known correct model is input for calculation, and 5000 sets of RCS data samples of simulation output in the verification domain are obtained. The RCS data samples are used as real electromagnetic data samples (i.e., the actual observed RCS curve), as shown in Figure 2 As shown in the figure, sampling is performed under the uncertainty input parameter distribution of the set electromagnetic simulation system A and input into the electromagnetic simulation system A for calculation, and 5000 sets of simulated RCS data samples output by the electromagnetic simulation system A in the verification domain are obtained. The comparison diagram of the uncertainty simulated RCS curve of the simulated output of the electromagnetic simulation system A and the actual observed RCS curve is obtained (as shown in the figure). Figure 3 ); Similarly, 5000 sets of simulated RCS data samples output by electromagnetic simulation system B in the verification domain are obtained, and the comparison diagram of the uncertainty simulated RCS curve output by electromagnetic simulation system B and the actual observed RCS curve is obtained (as shown in Figure 4 shown);

[0106] Step 3: Using Mahalanobis distance to calculate the Mahalanobis distance cumulative distribution function of the output response of the electromagnetic simulation model, including:

[0107] First, the simulated RCS data sample Y output by the electromagnetic simulation system A obtained by sampling in step 2 m (X), calculate its mean vector μ=(μ1,μ2,…μ n ) and the covariance matrix ∑, through the formula Calculate the simulated RCS data samples for each dimension Mahalanobis distance to the mean vector μ Then the first Mahalanobis distance cumulative distribution function F output by the electromagnetic simulation system A is obtained m (MD);

[0108] Similarly, according to the real electromagnetic data samples obtained in step 2, the second Mahalanobis distance cumulative distribution function S is obtained. e (MD);

[0109] Similarly, according to the simulated RCS data samples obtained by the electromagnetic simulation system B in step 2, the first Mahalanobis distance cumulative distribution function is obtained;

[0110] Step 4: For the electromagnetic simulation system A, plot the first Mahalanobis distance cumulative distribution function F obtained by step 3 m (MD) and the second Mahalanobis distance cumulative distribution function S e (MD), such as Figure 5 As shown. The area measurement method is used to calculate the comprehensive measurement index of the uncertainty output of the electromagnetic simulation system A in the global verification domain, and the comprehensive measurement index is 0.1896;

[0111] Step 5: For the electromagnetic simulation system B, plot the first Mahalanobis distance cumulative distribution function F obtained by step 3 m (MD) and the second Mahalanobis distance cumulative distribution function S e (MD), such as Figure 6 As shown. The area measurement method is used to calculate the comprehensive measurement index of the uncertainty output of the electromagnetic simulation system B in the global verification domain, and the comprehensive measurement index is 0.2639;

[0112] Step 6: 0.1896<0.2639, so the electromagnetic simulation system A is better than the electromagnetic simulation system B.

[0113] It should be noted that in Figure 3 and Figure 4 In the example, the test response is the actual observed RCS curve, and the simulation response is the simulated RCS curve output by the electromagnetic simulation system; Figure 5 and Figure 6 In the figure, the experiment is the Mahalanobis distance cumulative distribution function of the actual observed RCS curve, and the simulation is the Mahalanobis distance cumulative distribution function of the output response of the electromagnetic simulation system.

[0114] like Figure 7 , Figure 8 As shown, an embodiment of the present invention provides an uncertainty confirmation measurement device for an electromagnetic simulation system. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 7 As shown, a hardware architecture diagram of a computing device where an uncertainty confirmation measurement device of an electromagnetic simulation system provided by an embodiment of the present invention is located is shown. Figure 7 In addition to the processor, memory, network interface, and non-volatile memory shown in the figure, the computing device in which the device is located in the embodiment may also generally include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 8 As shown, as a device in a logical sense, the CPU of the computing device in which it is located reads the corresponding computer program in the non-volatile memory into the memory and runs it. This embodiment provides an uncertainty confirmation measurement device for an electromagnetic simulation system, including:

[0115] The simulation module 800 is used to obtain electromagnetic simulation response data of the target obtained by the electromagnetic simulation system; and to sample the electromagnetic simulation response data to obtain electromagnetic simulation data samples;

[0116] An acquisition module 802 is used to acquire a real electromagnetic data sample of the target under the input parameter sample according to the input parameter sample corresponding to the electromagnetic simulation data sample;

[0117] The confirmation metric module 804 is used to calculate the Mahalanobis distance cumulative distribution function of the real electromagnetic data sample and the electromagnetic simulation data sample respectively; and determine the comprehensive metric index of the electromagnetic simulation system according to the Mahalanobis distance cumulative distribution function.

[0118] In some specific implementations, the simulation module 800 may be used to execute the above steps 100 and 102 , the acquisition module 802 may be used to execute the above step 104 , and the confirmation metric module 804 may be used to execute the above steps 106 and 108 .

[0119] In some specific implementations, the simulation module 800 is further configured to perform the following operations:

[0120] Determine the verification domain of the electromagnetic simulation system;

[0121] Sampling to obtain input parameter samples of the electromagnetic simulation system;

[0122] The electromagnetic simulation response data in the verification domain output by the input parameter sample is used as the electromagnetic simulation data sample.

[0123] In some specific implementations, the acquisition module 802 is further configured to perform the following operations:

[0124] In the verification domain, the real electromagnetic data samples corresponding to the input parameter samples are obtained through physical measurements.

[0125] In some specific implementations, the confirmation metric module 804 is further configured to perform the following operations:

[0126] Calculating the electromagnetic simulation data samples to obtain a first mean vector and a first covariance matrix;

[0127] Obtaining a first Mahalanobis distance of each electromagnetic simulation data sample according to the first mean vector, the first covariance matrix and the electromagnetic simulation data sample;

[0128] Based on the first Mahalanobis distance, generating a first Mahalanobis distance cumulative distribution function;

[0129] and,

[0130] Calculating the real electromagnetic data samples to obtain a second mean vector and a second covariance matrix;

[0131] Obtaining a second Mahalanobis distance of each real electromagnetic data sample according to the second mean vector, the second covariance matrix and the real electromagnetic data sample;

[0132] Based on the second Mahalanobis distance, a second Mahalanobis distance cumulative distribution function is generated.

[0133] In some specific implementations, the confirmation metric module 804 is further configured to perform the following operations:

[0134] The area of ​​the region formed by the first Mahalanobis distance cumulative distribution function and the second Mahalanobis distance cumulative distribution function is calculated to obtain a comprehensive measurement index.

[0135] In some specific implementations, the confirmation metric module 804 is further configured to perform the following operations:

[0136] The comprehensive metric indicators of different electromagnetic simulation systems are ranked, and the electromagnetic simulation system corresponding to the minimum comprehensive metric indicator is determined as the preferred electromagnetic simulation system.

[0137] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on an uncertainty confirmation measurement device for an electromagnetic simulation system. In other embodiments of the present invention, an uncertainty confirmation measurement device for an electromagnetic simulation system may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0138] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0139] An embodiment of the present invention further provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, an uncertainty confirmation measurement method for an electromagnetic simulation system in any embodiment of the present invention is implemented.

[0140] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes an uncertainty confirmation measurement method for an electromagnetic simulation system in any embodiment of the present invention.

[0141] An embodiment of the present application also provides a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes an uncertainty confirmation measurement method for an electromagnetic simulation system described in any of the above embodiments.

[0142] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0143] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0144] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0145] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0146] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0147] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical factors in the process, method, article or device including the elements.

[0148] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for measuring uncertainty in an electromagnetic simulation system, characterized in that: include: Acquiring electromagnetic simulation response data of the target obtained by the electromagnetic simulation system; Sampling the electromagnetic simulation response data to obtain electromagnetic simulation data samples; According to the input parameter sample corresponding to the electromagnetic simulation data sample, obtaining the real electromagnetic data sample of the target under the input parameter sample; Calculating the Mahalanobis distance cumulative distribution function of the real electromagnetic data sample and the electromagnetic simulation data sample respectively; According to the Mahalanobis distance cumulative distribution function, a comprehensive metric index of the electromagnetic simulation system is determined.

2. The method according to claim 1, characterized in that The step of sampling the electromagnetic simulation response data to obtain an electromagnetic simulation data sample comprises: Determining a verification domain of the electromagnetic simulation system; Sampling and obtaining input parameter samples of the electromagnetic simulation system; The electromagnetic simulation response data in the verification domain output by the input parameter sample is used as the electromagnetic simulation data sample.

3. The method according to claim 2, characterized in that The obtaining of a real electromagnetic data sample of the target under the input parameter sample comprises: In the verification domain, the real electromagnetic data sample corresponding to the input parameter sample is obtained through physical measurement.

4. The method according to claim 1, characterized in that: Calculating the Mahalanobis distance cumulative distribution function of the real electromagnetic data sample and the electromagnetic simulation data sample respectively, including: Calculating the electromagnetic simulation data sample to obtain a first mean vector and a first covariance matrix; Obtaining a first Mahalanobis distance of each electromagnetic simulation data sample according to the first mean vector, the first covariance matrix and the electromagnetic simulation data sample; Based on the first Mahalanobis distance, generating a first Mahalanobis distance cumulative distribution function; and, Calculating the real electromagnetic data samples to obtain a second mean vector and a second covariance matrix; Obtaining a second Mahalanobis distance of each of the real electromagnetic data samples according to the second mean vector, the second covariance matrix and the real electromagnetic data samples; Based on the second Mahalanobis distance, a second Mahalanobis distance cumulative distribution function is generated.

5. The method according to claim 1, characterized in that The Mahalanobis distance cumulative distribution function includes a first Mahalanobis distance cumulative distribution function corresponding to the electromagnetic simulation data sample and a second Mahalanobis distance cumulative distribution function corresponding to the real electromagnetic data sample; Determining the comprehensive metric index of the electromagnetic simulation system according to the Mahalanobis distance cumulative distribution function includes: The area of ​​a region formed by the first Mahalanobis distance cumulative distribution function and the second Mahalanobis distance cumulative distribution function is calculated to obtain the comprehensive metric.

6. The method according to any one of claims 1 to 5, characterized in that: Also includes: The comprehensive metric indicators of different electromagnetic simulation systems are ranked, and the electromagnetic simulation system corresponding to the minimum comprehensive metric indicator is determined as the preferred electromagnetic simulation system.

7. An uncertainty confirmation measurement device for an electromagnetic simulation system, characterized in that: include: A simulation module, used for acquiring electromagnetic simulation response data of a target obtained by an electromagnetic simulation system; and acquiring electromagnetic simulation data samples from the electromagnetic simulation response data; An acquisition module, configured to acquire, according to an input parameter sample corresponding to the electromagnetic simulation data sample, a real electromagnetic data sample of the target under the input parameter sample; The confirmation metric module is used to calculate the Mahalanobis distance cumulative distribution function of the real electromagnetic data sample and the electromagnetic simulation data sample respectively; and determine the comprehensive metric index of the electromagnetic simulation system according to the Mahalanobis distance cumulative distribution function.

8. A computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.

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