Ray tracing material electromagnetic parameter correction method and device

Abstract

This application discloses a method and apparatus for correcting electromagnetic parameters of materials using ray tracing, addressing the problem of insufficient accuracy in material parameter correction caused by random selection of measurement points in traditional methods. The method includes: selecting multiple candidate points in a communication environment, randomly measuring one of them as a starting point; correcting the material parameters using the actual channel information of all measured points; simulating the channel information of all candidate points based on the corrected parameters; selecting the point with the lowest average channel similarity to the measured points from the unmeasured points as the next measurement point; repeating the above correction and point selection steps, iteratively optimizing until a preset number of measurements is reached. This application significantly improves the accuracy of electromagnetic parameter correction and the reliability of ray tracing simulation by dynamically optimizing the measurement point positions, enabling limited measurement resources to acquire more discriminative channel information.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method and apparatus for correcting the electromagnetic parameters of a ray-tracing material. Background Technology

[0002] Ray tracing is a key technology in the planning and optimization of wireless communication systems. It uses a 3D environmental model and the electromagnetic parameters of object materials to simulate information such as channel propagation path fading coefficient, transmission delay, and propagation angle of a communication system in a real-world environment.

[0003] To improve the simulation accuracy of ray tracing, it is necessary to select several measurement points in the actual communication environment and measure the actual channel propagation information at these points to correct the electromagnetic parameters of the surface materials of each object in the ray-traced 3D model. However, traditional methods for correcting electromagnetic parameters of materials typically only consider randomly and uniformly sampling several measurement points from the environment, without optimizing the selection of measurement point locations. A significant drawback of this approach is that randomly selected measurement points may be concentrated in areas with similar channel characteristics, resulting in high similarity of channel information at different measurement points. This makes the channel information obtained based on these measurement points insufficient and lacking in diversity, making it impossible to comprehensively and accurately infer and correct the electromagnetic parameters of the materials in the environment model. Ultimately, this leads to poor material parameter correction results, limiting the reliability of ray tracing simulation in practical applications.

[0004] Therefore, optimizing the selection strategy of channel measurement points so that limited measurement resources can acquire the most discriminative channel information, thereby completing the correction of material electromagnetic parameters more efficiently and accurately, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] This application proposes a method and apparatus for correcting electromagnetic parameters of materials through ray tracing, in order to solve the problem of insufficient accuracy in material parameter correction caused by improper selection of measurement points in the prior art.

[0006] To achieve the above objectives, the technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a method for correcting electromagnetic parameters of materials using ray tracing. This method includes the following steps: randomly selecting multiple location points in a communication environment as a set of candidate points for channel measurement; using one of these location points as the current measurement point; measuring the power delay spectrum (PDP) of that point; using the measured PDPs of all points in the already measured point set; correcting the electromagnetic parameters of each material using a ray tracing environment model to obtain the material parameter correction results; based on the material parameter correction results; using ray tracing simulation to obtain the simulated PDPs of all candidate points for channel measurement under the current material parameters; selecting a location point from the candidate points that has not yet been measured, which has the largest average similarity metric distance to the simulated PDPs of the already measured point set, as a new measurement point; and measuring the PDP of that point; repeating the steps for correcting the electromagnetic parameters of each material, iteratively optimizing the material parameters and the measurement point positions until the number of measured points reaches a preset threshold.

[0007] Furthermore, in the above method, the correction of electromagnetic parameters of each material includes: setting the search range of dielectric constant of each material in the environmental model; using ray tracing to simulate the simulated PDP of the measurement point under each combination of material parameters; calculating the similarity metric distance between the measured PDP and each simulated PDP; and selecting the material parameter combination that minimizes the similarity metric distance as the material parameter correction result.

[0008] Furthermore, in the above method, the calculation of the similarity metric distance includes: normalizing the path power of the measured PDP and the simulated PDP respectively; performing Gaussian weighted merging of the normalized power of the simulated PDP on the path delay of the measured PDP to obtain the merged power distribution; and calculating the KL divergence between the normalized power distribution of the measured PDP and the merged power distribution as the similarity metric distance.

[0009] Furthermore, in the above method, the Gaussian weighted variance is obtained by dividing the path delay variance of the measured PDP by a preset variance adjustment coefficient, wherein the path delay variance of the measured PDP is calculated based on the delay values ​​of all paths of the measured PDP.

[0010] Furthermore, in the above method, the process of selecting the next channel measurement point includes: for each candidate point that has not yet been measured, calculating the similarity metric distance between its simulated PDP and the simulated PDP of each measured point; calculating the average of the similarity metric distance between the candidate point and all measured points; and selecting the candidate point with the largest average value as the next channel measurement point.

[0011] Furthermore, in the above method, the step of Gaussian weighted merging of the normalized power of the simulated PDP on the path delay of the measured PDP includes: for each path of the measured PDP, taking its delay as the center, and summing the normalized power of all paths of the simulated PDP according to the difference between their respective delays and the center, as the merged power on that measurement path.

[0012] Secondly, embodiments of this application provide a ray-tracing material electromagnetic parameter correction device, which includes: a measurement point selection module for randomly selecting a set of candidate points in a communication environment and selecting measurement points according to an optimization strategy; a channel measurement module for measuring actual channel information at the selected measurement points; a parameter correction module for correcting the electromagnetic parameters of each material in the environment model based on the actual channel information of the measured points; a simulation module for simulating the channel information of each candidate point through ray tracing based on the current material parameters; and an optimization control module for iteratively controlling the measurement point selection, channel measurement, parameter correction, and simulation processes until a stopping condition is met.

[0013] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described in the first aspect above.

[0014] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the first aspects above.

[0015] The above-mentioned at least one technical solution adopted in the embodiments of this application can achieve the following beneficial effects: by dynamically iteratively optimizing the selection position of the channel measurement point in the process of realizing material parameter correction, the accuracy of material parameter correction is improved and the actual reliability of ray tracing simulation is enhanced. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating an embodiment of the method of this application; Figure 2 This is a flowchart of an embodiment that further optimizes the material parameter correction step of the method in this application; Figure 3 This is a flowchart of an embodiment that further optimizes the point selection step of the method in this application; Figure 4This is a structural diagram of the device in this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0019] Example 1 Figure 1 The diagram shows a flowchart of a ray-tracing method for correcting electromagnetic parameters of materials according to an embodiment of this application. The core concept of this method is to correct the material parameters by measuring the PDP at several points, and then, based on the corrected material parameters, use ray tracing technology to generate simulated PDPs for the measured points and other candidate points. Then, select the position point with the lowest PDP similarity to the measured point from the candidate points as the next measurement point. This enables iterative optimization of the material parameters and the measurement point position, and allows the channel information at different measurement points to have the highest possible distinguishability.

[0020] The method in this embodiment specifically includes the following steps: Step 110: Initialize the measurement points and measure the actual channel information.

[0021] In step 110, multiple location points are randomly selected in the communication environment as a set of candidate points for channel measurement. One of the location points is taken as the current measurement point, and the power delay spectrum (PDP) of that point is measured.

[0022] Specifically, randomly and uniformly selected from the communication environment Location points As candidate points for channel measurement. Then, from all Randomly select a location from the candidate points for channel measurement. As the starting measurement point, the actual power delay spectrum (PDP) at that location is measured using channel measurement equipment, denoted as ,in Location point Total number of channel paths below and The first Measure the path's delay and power. Add this point to the "Set of Measured Points". Initially .

[0023] Step 120: Correct the material electromagnetic parameters based on the current set of measured points.

[0024] In step 120, the electromagnetic parameters of each material are corrected using the measurement PDP of all points in the measured point set and the ray tracing environment model, and the material parameter correction results are obtained.

[0025] Specifically, it includes the following sub-steps: Step 121: Set the material dielectric constant search range. This involves setting the search range for the composite dielectric constants of the various materials included in the ray tracing environment model. For example, to obtain the dielectric constants of the materials included in the environment model... Various materials (such as concrete, glass, vegetation, etc.), and for the first The real part of the complex permittivity of a certain material With the imaginary part Set the search range set separately and ,in .

[0026] Step 122: Simulate and calculate the similarity metric. Simulate the set of measured points in the environment model using ray tracing. Each point in every possible combination of material parameters The simulation of the PDP is shown below. The initial condition (only one measurement point) is used. Let's take the example of simulation location points as an example: Combination of complex permittivity of different materials The following PDP is

[0027] in This represents the total number of channel paths in the simulated PDP. and Each of its first The delay and power of each path. = 1~ .

[0028] Then, the measurement PDP is calculated. With simulated PDP The similarity metric distance between two PDPs is used to measure their similarity. The smaller the similarity metric distance, the higher the similarity between the two PDPs, meaning the higher the similarity of their channel information.

[0029] The specific method for calculating the similarity metric distance is as follows: First, the measurement of PDP and simulated PDP The path power is normalized respectively. That is, the path power is obtained. Normalized power and Normalized power ,in For path indexing.

[0030] Then, the normalized power of the simulated PDP is Gaussian-weighted and combined over the path delay of the measured PDP to obtain the combined power distribution. Specifically, for the measured PDP... The path, its delay As the center, the normalized power of all paths in the simulated PDP is calculated according to their respective time delays. ( The difference between the center and the target center is Gaussian weighted and summed to obtain the combined power along the measurement path. .

[0031] Among them, the Gaussian weighted variance By measuring the path delay variance of the PDP Divide by a preset variance adjustment factor To obtain, that is ,in To measure the variance of the path delays across all paths of the PDP, the normalized power distribution of the measured PDP is then calculated. Combined power distribution with simulated PDP The KL divergence between the two points is used as the final similarity metric, distance. The formula for calculating the KL divergence is: ,in This is the sum of all combined power.

[0032] Step 123: Determine the optimal material parameters. This involves considering all combinations of complex permittivity for different materials. Search in the middle to find what works and The combination of material parameters that has the minimum similarity metric distance (i.e., the highest similarity). This serves as the result of the material parameter correction in this iteration.

[0033] Step 130: Optimize the selection of the next channel measurement point.

[0034] In step 130, based on the initial material parameter correction results, the simulated PDP of all channel measurement candidate points under the current material parameters is obtained by ray tracing simulation. From the candidate points that have not yet been measured, a point with the largest average similarity metric distance to the simulated PDP of the already measured point set is selected as a new measurement point, and the PDP of this point is measured.

[0035] The specific process is as follows: First, the corrected material parameters obtained in step 120 are used... Simulate all through ray tracing One candidate point The simulated PDP, denoted as Then, for each that has not yet been measured (i.e., is not in the set) Candidate points (in the middle) Calculate its simulated PDP With the set of measured points Each point in Simulated PDP The similarity metric between points is measured by distance (calculated using the same method as in step 122), and the average of these distances is calculated. From the unmeasured candidate points, the point that matches the set of measured points is searched and selected. Location points with the highest average PDP similarity metric distance (i.e., the lowest average similarity) This location will be used as the next channel measurement point. This location can be determined using the following formula:

[0036] The symbol "\" represents the set difference operation. Represents a set The number of elements in the middle.

[0037] This strategy aims to select the point where the channel information is most distinguishable from the existing information, thereby providing richer information in the next correction.

[0038] Step 140: Measure the new points and iteratively optimize.

[0039] In step 140, the PDP at that point is measured, and the steps for correcting the electromagnetic parameters of each material are repeated. The material parameters and the position of the measurement point are iteratively optimized until the number of measured points reaches a preset threshold.

[0040] The next channel measurement point selected by the measurement optimization The actual PDP is shown below, and this point is added to the set of measured points. The process then returns to step 120, but by this time the set of measured points has already been determined. It contains multiple points. When step 120 is executed again, the material parameter correction is based on the set. All measured PDPs and corresponding simulated PDPs are compared, with the goal of searching for the material parameter combination that minimizes the average similarity metric distance between the measured PDPs and the ray-tracing simulated PDPs for all measured points. Then, step 130 is executed again to select new measurement points. This process is repeated iteratively to optimize the material parameters and measurement point positions until the number of measured points reaches a preset number. ( ).

[0041] Through the above iterative process, the method proposed in this application dynamically combines channel measurement information to optimize the location of subsequent measurement points, breaking the limitations of traditional random selection. This enables limited measurement resources to efficiently cover regions with diverse channel characteristics, thereby significantly improving the accuracy and efficiency of material electromagnetic parameter correction.

[0042] Example 2 As a further optimized method embodiment based on Embodiment 1, this paper focuses on demonstrating how to concretize the detailed similarity calculation and optimized point selection process described in the claims into an executable flow containing more decomposition steps, especially the average distance calculation logic when processing multiple measured points.

[0043] Figure 2 The method of the illustrated embodiment, within the framework of Embodiment 1, further optimizes step 120 (material parameter correction) as follows: Step 220: Material parameter correction for multiple measurement points. When the set of measured points... Contains multiple points (e.g., at the k-th iteration) When correcting material parameters, the matching degree of all points must be considered simultaneously. Step 220 further includes steps 221-223: Step 221: Parameter combination traversal simulation.

[0044] For each combination of complex permittivity of materials within the preset search range Using ray tracing to simulate the set of measured points Each position point The simulated PDP under this combination is denoted as .

[0045] Step 222: Calculate the combined average similarity.

[0046] For the current combination of material parameters being traversed Calculate the set The similarity metric for all points is the average distance. Specifically: for each point... Following the method described in step 122 of Example 1 (normalization, Gaussian weighted merging, calculation of KL divergence), the actual measured PDP is calculated. Compared with the current simulation PDP The similarity metric between them is distance. Then, calculate the average distance to all points: This average value reflects the current combination of material parameters. The overall fit to all measured channel information.

[0047] Step 223: Determine the optimal combination.

[0048] Iterate through all possible combinations of material parameters Choose the distance metric that makes the average similarity measure equal. The smallest combination is taken as the optimal material parameters after this iteration. .Right now, .

[0049] Example 3 Figure 3 The method of the illustrated embodiment, within the framework of Embodiment 1, further optimizes step 130 (optimizing point selection) as follows: Step 230: Evaluation of candidate points based on global optimal parameters.

[0050] Using the optimal material parameters obtained in step 220 All candidate points are evaluated to select the next measurement point. Step 230 further includes steps 231-233: Step 231: Global simulation.

[0051] Using ray tracing, based on Simulate all Channel measurement candidate points The PDP, obtained .

[0052] Step 232: Calculate the discrimination of the candidate points.

[0053] For each that has not yet been measured (i.e.) (Candidate points) Evaluate its relationship with the currently measured set of points. All points exhibit "dissimilarity" or "discriminability," further including steps 232a~b: Step 232a: Calculate the point-to-point distance.

[0054] For each measured point Calculate each candidate point that has not yet been measured. Simulated PDP With the measured points Simulated PDP The similarity metric between them is distance. Note that this calculation is for the KL divergence between simulated PDPs, and no actual measurement is required. The formula is similar to step 122, but the input is simulated data.

[0055] Step 232b: Calculate the average distance.

[0056] Calculate candidate points Average similarity metric distance between all measured points: The larger the average distance, the greater the average difference between the simulated channel characteristics of the candidate point and the characteristics of the existing set of measured points, i.e., the higher the discrimination.

[0057] Step 233: Select the optimal candidate point.

[0058] Among all unmeasured candidate points, select the average distance. The largest point will be used as the next measurement point. .Right now, .

[0059] The subsequent steps (measurement, adding to the set, and determining whether the preset number has been reached) are the same as step 140 in Embodiment 1. The average distance calculation and decision-making process in the multi-measurement point scenario of this embodiment further refines the method of this application.

[0060] Example 4 This application also provides a ray-tracing material electromagnetic parameter correction device. This device is used to execute the methods described in the foregoing embodiments, and its core lies in achieving iterative optimization of measurement point selection and material parameter correction through the collaborative work of modules. Figure 4 This is a schematic diagram of the structure of a ray tracing material electromagnetic parameter correction device provided in one embodiment of this application.

[0061] The ray-tracing material electromagnetic parameter correction device includes a measurement point selection module 41, a channel measurement module 42, a parameter correction module 43, a simulation module 44, and an optimization control module 45.

[0062] The measurement point selection module is used to randomly select a set of candidate points in the communication environment and select measurement points according to an optimization strategy. Specifically, in the initialization phase, the module randomly and uniformly selects N location points from the target communication scenario (such as an indoor or outdoor area) to form an initial set of candidate points. During the iteration process, the module receives instructions from the optimization control module and, based on the current optimal material parameters and simulation results, executes the optimization algorithm described in step 130 of Embodiment 1 or step 230 of Embodiment 3 to determine the next optimal measurement position from the candidate points.

[0063] The channel measurement module is used to measure actual channel information at selected measurement points. This module typically includes or is connected to standard channel sounding equipment, enabling measurement at locations specified by the measurement point selection module. or On top of that, perform actual channel measurements to obtain actual channel information such as the power delay spectrum (PDP) at that point. .

[0064] The simulation module is used to simulate the channel information of each candidate point using ray tracing based on the current material parameters. This module integrates a ray tracing simulation engine and a 3D environment model. When the parameter correction module outputs a set of material parameters (such as a combination of complex permittivity),... When performing ray tracing calculations, the simulation module can quickly perform ray tracing calculations on one or more specified location points (such as measured points or all candidate points) and generate corresponding simulation PDPs. .

[0065] The parameter correction module is used to correct the electromagnetic parameters of each material in the environmental model based on the actual channel information of the measured points. This module is the core of material parameter optimization. In a further optimized embodiment of this application, the parameter correction module further includes a parameter search unit, a similarity calculation unit, and a parameter determination unit.

[0066] Its working process includes: First, based on the environmental model, a search is performed within the preset range of electromagnetic parameters for each material. This function can be executed by its internal parameter search unit. Second, for each combination of material parameters to be evaluated, the simulation module is invoked to obtain the simulated PDP for the corresponding point, and its internal similarity calculation unit calculates the similarity metric distance between the simulated PDP and the measured PDP. Finally, its internal parameter determination unit selects the material parameter combination with the smallest similarity metric distance from all searched combinations as the correction result for the current iteration. The specific work of the similarity calculation unit includes: normalizing the PDP path power; performing a cross-path power merging operation to merge the simulated PDP power onto the time delay axis of the measured PDP; and calculating the KL divergence between the normalized power distribution and the merged power distribution as the final similarity metric.

[0067] The optimization control module iteratively controls the measurement point selection, channel measurement, parameter correction, and simulation processes until a stopping condition is met. This module is the "brain" of the device, responsible for coordinating the orderly operation of each module. It controls the start, execution, and termination of the iterative cycle. For example, it first instructs the measurement point selection module and the channel measurement module to complete the measurement of the initial point; then it instructs the parameter correction module to perform the first parameter correction; next, it instructs the simulation module to perform a global simulation based on the new parameters, and instructs the measurement point selection module to calculate and determine the next measurement point; after that, it instructs the channel measurement module to perform a new point measurement, and restarts the parameter correction module for a new round of correction. This cycle continues until the number of measured points reaches a preset threshold, or other stopping conditions are met.

[0068] The device can be integrated into a channel simulation platform as software, or it can be used as part of a hardware device for automated and intelligent channel measurement and model correction tasks.

[0069] Example 4 This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the ray-tracing material electromagnetic parameter correction method as described in any of Embodiments 1-3, or loads any of the processing modules in Embodiment 4. This electronic device can be a server, workstation, laptop computer, or dedicated channel measurement and analysis instrument, etc.

[0070] Furthermore, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the ray-tracing material electromagnetic parameter correction method as described in any one of embodiments 1 to 3.

[0071] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0072] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical, technical, and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0073] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for correcting electromagnetic parameters of a material during ray tracing, characterized in that, Includes the following steps: In the communication environment, multiple locations are randomly selected as a set of candidate points for channel measurement. One of the locations is taken as the current measurement point, and the power delay spectrum (PDP) of that point is measured. Using the measurement PDP of all points in the measured point set, the electromagnetic parameters of each material are corrected using the ray tracing environment model, and the material parameter correction results are obtained. Based on the initial material parameter correction results, ray tracing simulation is used to obtain the simulated PDP of all channel measurement candidate points under the current material parameters. From the candidate points that have not yet been measured, a point with the largest average similarity metric distance to the simulated PDP of the set of measured points is selected as the new measurement point. Measure the PDP at this point, repeat the steps of correcting the electromagnetic parameters of each material, and iteratively optimize the material parameters and the position of the measurement point until the number of measured points reaches the preset threshold.

2. The method according to claim 1, characterized in that, The correction of electromagnetic parameters for each material includes: Define the search range for the dielectric constants of each material in the environmental model; The simulation PDP of the measurement points under each combination of material parameters was simulated using ray tracing. Calculate the similarity metric distance between the measured PDP and each simulated PDP; The combination of material parameters that minimizes the similarity metric distance is selected as the material parameter correction result.

3. The method according to claim 2, characterized in that, The calculation of the similarity metric distance includes: The path power of the measured PDP and the simulated PDP are normalized respectively; The normalized power of the simulated PDP is combined with the path delay of the measured PDP using a Gaussian weighted method to obtain the combined power distribution. The KL divergence between the normalized power distribution of the measured PDP and the combined power distribution is calculated as a similarity metric distance.

4. The method according to claim 3, characterized in that, The Gaussian-weighted variance is obtained by dividing the path delay variance of the measured PDP by a preset variance adjustment coefficient, wherein the path delay variance of the measured PDP is calculated based on the delay values ​​of all paths of the measured PDP.

5. The method according to claim 1, characterized in that, The process of selecting the next channel measurement point includes: For each candidate point that has not yet been measured, calculate the similarity metric distance between its simulated PDP and the simulated PDP of each measured point; Calculate the average of the similarity metric distances between the candidate point and all measured points; The candidate point with the largest average value is selected as the next channel measurement point.

6. The method according to claim 3, characterized in that, The step of Gaussian weighted merging of the normalized power of the simulated PDP on the path delay of the measured PDP includes: for each path of the measured PDP, taking its delay as the center, and summing the normalized power of all paths of the simulated PDP according to the difference between their respective delays and the center, as the merged power on that measurement path.

7. A ray-tracing material electromagnetic parameter correction device, used to implement the method described in any one of claims 1 to 6, characterized in that, include: The measurement point selection module is used to randomly select a set of candidate points in the communication environment and select measurement points according to the optimization strategy. The channel measurement module is used to measure actual channel information at selected measurement points; The parameter correction module is used to correct the electromagnetic parameters of each material in the environmental model based on the actual channel information of the measured points. The simulation module is used to simulate the channel information of each candidate point by ray tracing based on the current material parameters; The optimized control module is used to iteratively control the selection of measurement points, channel measurement, parameter correction, and simulation process until the stopping condition is met.

8. The apparatus according to claim 7, characterized in that, The parameter correction module includes: The parameter search unit is used to search within the preset range of electromagnetic parameters of each material; The similarity calculation unit is used to calculate the similarity metric distance between the measured PDP and the simulated PDP; The parameter determination unit is used to select the material parameter combination with the smallest similarity metric distance as the correction result of the current iteration.

9. The apparatus according to claim 8, characterized in that, The similarity calculation unit is specifically used for: Normalize the PDP path power; Perform cross-path power combining; Calculate the KL divergence as a similarity measure.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

11. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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