CN113030900A - Dynamic matching reflection coefficient scaling measurement method and device based on surface element distribution - Google Patents
Dynamic matching reflection coefficient scaling measurement method and device based on surface element distribution Download PDFInfo
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Abstract
The application relates to a bin distribution-based dynamic matching reflection coefficient scaling measurement method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring RCS surface element distribution information of the metal scaling model at an incident wave angle by acquiring a metal scaling model RCS measurement value of the metal scaling model and a coating scaling model RCS measurement value of the coating scaling model, and according to the metal scaling model RCS measurement value, the coating scaling model RCS measurement value and first reflection coefficient information of a coating material of the coating scaling model; and acquiring second reflection coefficient information of the coating material of the target prototype, and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and the preset geometric scaling factor. The method is beneficial to improving the inversion accuracy, enlarges the application range of the scale measurement, and provides an effective solution for the material dispersion problem of the terahertz frequency band scale measurement.
Description
Technical Field
The present application relates to the field of radar technologies, and in particular, to a method and an apparatus for measuring a bin distribution dynamic matching reflection coefficient scaling, a computer device, and a storage medium.
Background
In recent years, along with the increase of the demand for measuring the scale of an oversized target, higher requirements are put on a frequency band and a scale inversion method for scale measurement. In order to realize the scale measurement of a large scale factor, the scale frequency is increased to a terahertz frequency band, the dispersion characteristic of a material is not neglected, the condition of the classical electromagnetic similarity cannot be met, and how to invert the scale measurement result of the terahertz frequency band to obtain the RCS of a target prototype becomes a problem to be solved urgently.
For the problem of material dispersion in scaling measurement, the existing solution mainly includes: relaxing the similarity law method, undetermined physical scaling factor method and finding alternative materials. In the existing method, a method for searching for alternative materials relates to selection or synthesis of alternative materials, and the realization period is long, the success rate is not high, and the method is difficult; the effectiveness of the relaxation similarity law method is limited to high frequency, far field and backscattering, and the error is obviously increased when the relaxation similarity law method is used for a complex target; the undetermined physical scaling factor method has good effect when being used for two materials with similar change trends of the reflection coefficients, and when the change trends of the reflection coefficients of the two materials are different, the method has larger errors. The method for broadening the similarity law and the undetermined physical scaling factor has the defect that the scaling inversion method fails due to the fact that the complex change trend of the coating material under various influence factors is not fully considered. The existing method ignores the influence of a plurality of influence factors (such as thickness, loss tangent, angle and the like) of the reflection coefficient of the material on the scaling relation, and has low inversion accuracy on the scaling of a material coating target with complicated reflection coefficient change and a target with a complicated structure.
Disclosure of Invention
In view of the above, it is desirable to provide a bin distribution-based dynamic matching reflection coefficient scaling measurement method, apparatus, computer device, and storage medium capable of improving the inversion accuracy of the target prototype RCS values inverted from the scaling model RCS measurement values.
A bin distribution based dynamic matching reflection coefficient scaling measurement method, the method comprising:
acquiring a metal reduction model RCS measurement value of the metal reduction model and a coating reduction model RCS measurement value of the coating reduction model; the coating scale model is formed by coating materials on the surface of the metal scale model, the number of the coating scale models can be one or more, and the coating material of each coating scale model is different;
acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident radar wave and the normal direction of the surface element of the coating scaling model;
obtaining RCS surface element distribution information of the metal scaling model under the incident wave angle according to the RCS measured value of the metal scaling model, the RCS measured value of the coating scaling model and the first reflection coefficient information;
and acquiring second reflection coefficient information of the coating material of the target prototype, and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and a preset geometric scaling factor.
In one embodiment, the method further comprises the following steps: acquiring a metal reduction model RCS measurement value of the metal reduction model and a coating reduction model RCS measurement value of the coating reduction model; the coating reduction model is used for coating the surface of the metal reduction model with a material, the number of the coating reduction models is the number of the surface elements minus 1, and the coating material of each coating reduction model is different; the surface elements are classified according to the normal direction, and the surface elements with the same normal direction are of one class.
In one embodiment, the method further comprises the following steps: acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident radar wave and the normal direction of the surface element of the coating scaling model; the first reflection coefficient information changes along with the change of the size of an included angle formed by incident radar waves and different surface elements of the coating scaling model.
In one embodiment, the method further comprises the following steps: acquiring the metal scaling model RCS measured value, the coating scaling model RCS measured value and the first reflection coefficient information;
substituting the metal reduction model RCS measurement, the coating reduction model RCS measurement, and the first reflection coefficient information into the following equation:
wherein the content of the first and second substances,
representing the RCS measurements of the 1 st 1 … m-1 st coating scale model,
representing the incident angle of the incident radar wave;
representing the metal shrinkage model RCS measurement; m represents the number of categories of the bin; thetajJ 1 … m represents the angle formed by the incident wave and the normal direction of the j-th surface element, and Rk(θj) Where k is 1 … m-1, j is 1 … m, and the angle formed by the incident wave and the normal direction of the jth surface element of the kth coating scale model is thetajThe reflection coefficient of time;
indicating the bin normal is
Stacking all surface elements and RCS;
solving to obtain RCS surface element distribution information of the metal scaling model under the incident wave angle
In one embodiment, the method further comprises the following steps: acquiring second reflection coefficient information of the target prototype coating material;
substituting the second reflection coefficient information, the bin distribution information and a preset geometric scaling factor into the following formula:
wherein the content of the first and second substances,
an RCS value, R, representing the target prototypeprot(θj) J 1 … m represents the included angle formed by the incident wave and the normal direction of the j-th surface element of the target prototype as thetajThe time reflection coefficient s is a preset geometric scaling factor;
and calculating a result according to a formula to obtain the RCS value of the target prototype.
In one embodiment, the method further comprises the following steps: when the surface elements of the coating scale model are divided into two types, acquiring the RCS measured value of the metal scale model, the RCS measured value of the coating scale model and first reflection coefficient information of a coating material of the coating scale model;
substituting the metal reduction model RCS measurement, the coating reduction model RCS measurement, and the first reflection coefficient information into the following equation:
solving to obtain RCS surface element distribution information of the metal scaling model under the incident wave angle
In one embodiment, the method further comprises the following steps: acquiring second reflection coefficient information of the target prototype coating material;
substituting the second reflection coefficient information, the bin distribution information and a preset geometric scaling factor into the following formula:
and calculating a result according to a formula to obtain the RCS value of the target prototype.
A bin distribution based dynamically matched reflectance scaling measurement apparatus, the apparatus comprising:
the system comprises a scaling model RCS measured value acquisition module, a scaling model RCS measuring value acquisition module and a scaling model RCS measuring value acquisition module, wherein the scaling model RCS measured value acquisition module is used for acquiring a metal scaling model RCS measured value of a metal scaling model and a coating scaling model RCS measured value of a coating scaling model; the coating scaling model is used for coating the surface of the metal scaling model with a material, and the coating material of each coating scaling model is different;
the first reflection coefficient information acquisition module is used for acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident wave of the radar and the normal direction of the surface element of the coating scaling model;
the RCS surface element distribution information acquisition module is used for acquiring RCS surface element distribution information of the metal scaling model under the incident wave angle according to the RCS measured value of the metal scaling model, the RCS measured value of the coating scaling model and the first reflection coefficient information;
and the RCS value acquisition module of the target prototype is used for acquiring second reflection coefficient information of the coating material of the target prototype and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and a preset geometric scaling factor.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a metal reduction model RCS measurement value of the metal reduction model and a coating reduction model RCS measurement value of the coating reduction model; the coating scale model is formed by coating materials on the surface of the metal scale model, the number of the coating scale models can be one or more, and the coating material of each coating scale model is different;
acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident radar wave and the normal direction of the surface element of the coating scaling model;
obtaining RCS surface element distribution information of the metal scaling model under the incident wave angle according to the RCS measured value of the metal scaling model, the RCS measured value of the coating scaling model and the first reflection coefficient information;
and acquiring second reflection coefficient information of the coating material of the target prototype, and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and a preset geometric scaling factor.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a metal reduction model RCS measurement value of the metal reduction model and a coating reduction model RCS measurement value of the coating reduction model; the coating scale model is formed by coating materials on the surface of the metal scale model, the number of the coating scale models can be one or more, and the coating material of each coating scale model is different;
acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident radar wave and the normal direction of the surface element of the coating scaling model;
obtaining RCS surface element distribution information of the metal scaling model under the incident wave angle according to the RCS measured value of the metal scaling model, the RCS measured value of the coating scaling model and the first reflection coefficient information;
and acquiring second reflection coefficient information of the coating material of the target prototype, and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and a preset geometric scaling factor.
According to the dynamic matching reflection coefficient scaling measurement method and device based on surface element distribution, computer equipment and storage medium, RCS surface element distribution information of the metal scaling model under the incident wave angle is obtained by obtaining a metal scaling model RCS measurement value of the metal scaling model and a coating scaling model RCS measurement value of the coating scaling model, and according to the metal scaling model RCS measurement value, the coating scaling model RCS measurement value and first reflection coefficient information of a coating material of the coating scaling model; and acquiring second reflection coefficient information of the coating material of the target prototype, and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and the preset geometric scaling factor. According to the method, reflection coefficients are dynamically matched based on an included angle between an incident wave and a surface element normal direction, weight distribution of reflection coefficient contributions at different angles is realized, the reflection coefficients are replaced according to the similarity of a scaling model and the surface element distribution of a prototype, and finally inversion from the scaling model to a target prototype is realized. For two materials with different reflection coefficient change trends, the method is favorable for improving inversion accuracy, enlarges the application range of the scale measurement, provides an effective solution for the problem of material dispersion of the terahertz frequency band scale measurement, does not increase the complexity of a system and an algorithm, and has high stability, good universality and better practicability.
Drawings
FIG. 1 is a diagram illustrating an exemplary scenario for an embodiment of a bin distribution-based method for dynamically matching reflectance scaling measurements;
FIG. 2 is a schematic diagram of the invention in one embodiment;
FIG. 3 is a bin distribution based dynamic matching reflectance scaling measurement method in one embodiment;
FIG. 4 is a top view of a SLICY model in one embodiment;
fig. 5 is a schematic diagram of an embodiment of the SLICY model when the observation angle is 270 ° and θ is 45 °;
FIG. 6 is a graphical representation of the scaled model A and the reflection coefficient of the prototype coating material in one embodiment;
FIG. 7 is a graphical representation of the RCS results of a prototype resulting from inversion of the scaled model A in one embodiment;
FIG. 8 is a graphical representation of the scaled model B and the reflection coefficient of the prototype coating material in one embodiment;
FIG. 9 is a graphical representation of the RCS results of a prototype resulting from inversion of the scaled model B in one embodiment;
FIG. 10 is a block diagram of an embodiment of a bin distribution based dynamic matching reflectance scale measurement apparatus;
FIG. 11 is a diagram illustrating an internal structure of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The bin distribution-based dynamic matching reflection coefficient scaling measurement method can be applied to the following application environments. The method comprises the steps that a bin distribution-based dynamic matching reflection coefficient scaling measurement method is executed through a terminal, RCS bin distribution information of a metal scaling model under an incident wave angle is obtained by obtaining a metal scaling model RCS measurement value of the metal scaling model and a coating scaling model RCS measurement value of a coating scaling model, and according to the metal scaling model RCS measurement value, the coating scaling model RCS measurement value and first reflection coefficient information of a coating material of the coating scaling model; and acquiring second reflection coefficient information of the coating material of the target prototype, and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and the preset geometric scaling factor. The terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
In one embodiment, as shown in fig. 1, there is provided a bin distribution-based dynamic matching reflection coefficient scaling measurement method, comprising the steps of:
and 102, acquiring a metal reduction model RCS measured value of the metal reduction model and a coating reduction model RCS measured value of the coating reduction model.
The invention mainly solves the problem that when the material parameter is dispersed in the measurement frequency band (or the material parameter of the reduction model is inconsistent with the material parameter of the prototype) in the radar scattering cross section (RCS) reduction measurement, the RCS measurement value of the reduction model is used for inverting the RCS of the target prototype.
Scaling measurement is one of the effective methods to obtain the target RCS. When the RCS measurement of a certain full-size target cannot be realized, the scale model of the full-size target can be measured, and the RCS of the target prototype is inverted according to the scale relation. The precondition of scale measurement is that a model measurement system similar to the prototype measurement system is constructed, corresponding physical quantities in two similar electromagnetic systems need to meet a certain relationship, and the electromagnetic similarity is the summary of the relationship. When the material parameters do not change along with the frequency, the condition of the electromagnetic similarity is simplified, and the scaling measurement can be realized only by ensuring that the geometric scaling factor and the wavelength scaling factor of the model electromagnetic system are the same. With the increase of the size of the prototype of the target to be measured (such as an oversize aircraft carrier), the scaling factor becomes larger, and the frequency of scaling measurement rises to the terahertz frequency band. Terahertz (THz) waves generally refer to electromagnetic waves with the frequency between 0.1THz and 10THz, the dispersion characteristics of materials in the frequency band gradually appear, the same material presents different material parameters (such as dielectric constant) in the microwave frequency band and the Terahertz frequency band, and the condition of electromagnetic similarity cannot be met. For the scale measurement of an oversized target raised to a terahertz frequency band, in order to enable the scale measurement result of the terahertz frequency band to be capable of performing high-precision inversion of the RCS of a target prototype, the influence on the scale relation of the RCS when material parameters change needs to be considered, and further, an RCS scale inversion method when a scale model and prototype material parameters are different is provided.
Figure 2 shows a schematic diagram of the invention (the object model here is a cylinder as an example),
indicating the normal, theta, of different bins1、θ2Respectively represent incident angles of
And the included angle between the incident wave and the surface element with different directions. Theta corresponding to different incident angles1、θ2The values of the corresponding reflection coefficients are different. The complex RCS of each coated facet can be viewed as the modulation of the reflectance of the coating material on the complex RCS of the corresponding metal facet, and therefore, by applying the RCS measurements of the scaled model and the scaled model of the metal and the material reflectance, the facet distributions at different angles can be obtained.
The coating reduction model is a metal reduction model with material coating on the surface, and the coating reduction model can be one or more, and the coating material of each coating reduction model is different.
And 104, acquiring first reflection coefficient information of the coating material according to an included angle formed by the incident radar wave and the normal direction of the surface element of the coating scaling model.
Theta corresponding to different incident angles1、θ2The values of the corresponding reflection coefficients are different.
And step 106, obtaining RCS surface element distribution information of the metal scaling model under the incident wave angle according to the RCS measured value of the metal scaling model, the RCS measured value of the coating scaling model and the first reflection coefficient information.
Under the same observation angle, the scaling models with similar shapes have the same distribution proportion with different normal surface elements of the prototype, and the similarity is an important theoretical basis for reversely coating the target prototype. Therefore, RCS surface element distribution information of the metal scaling model under the incident wave angle is obtained and can be used for inversion of a coating target prototype.
And 108, acquiring second reflection coefficient information of the coating material of the target prototype, and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and the preset geometric scaling factor.
The RCS value of an intermediate model is obtained by utilizing the surface element distribution of a metal scaling model under different angles and dynamically matching the material reflection coefficient of the prototype, the intermediate model is a hypothetical model, the intermediate model and the target prototype meet the scaling condition of the electromagnetic similarity, and the RCS of the target prototype can be inverted through the intermediate model and the RCS scaling relation of the electromagnetic similarity.
According to the method for measuring the dynamic matching reflection coefficient scaling based on the bin distribution, RCS bin distribution information of the metal scaling model under the incident wave angle is obtained by obtaining a metal scaling model RCS measured value of the metal scaling model and a coating scaling model RCS measured value of a coating scaling model, and according to the metal scaling model RCS measured value, the coating scaling model RCS measured value and first reflection coefficient information of a coating material of the coating scaling model; and acquiring second reflection coefficient information of the coating material of the target prototype, and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and the preset geometric scaling factor. According to the method, reflection coefficients are dynamically matched based on an included angle between an incident wave and a surface element normal direction, weight distribution of reflection coefficient contributions at different angles is realized, the reflection coefficients are replaced according to the similarity of a scaling model and the surface element distribution of a prototype, and finally inversion from the scaling model to a target prototype is realized. For two materials with different reflection coefficient change trends, the method is favorable for improving inversion accuracy, enlarges the application range of the scale measurement, provides an effective solution for the problem of material dispersion of the terahertz frequency band scale measurement, does not increase the complexity of a system and an algorithm, and has high stability, good universality and better practicability.
In one embodiment, the method further comprises the following steps: acquiring a metal reduction model RCS measurement value of the metal reduction model and a coating reduction model RCS measurement value of the coating reduction model; the coating reduction model is to coat the surface of the metal reduction model with materials, the number of the coating reduction models is the number of the surface elements minus 1, and the coating materials of each coating reduction model are different; the surface elements are classified according to the normal direction, and the surface elements with the same normal direction are classified into one type.
In the case of multi-bin, if the number of bin classes is m, then m-1 RCS values for the scaled model are required.
In one embodiment, the method further comprises the following steps: acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident radar wave and a normal direction of a surface element of the coating scaling model; the first reflection coefficient information changes along with the change of the size of an included angle formed by the incident wave of the radar and different surface elements of the coating scaling model.
In one embodiment, the method further comprises the following steps: acquiring a metal scaling model RCS measured value, a coating scaling model RCS measured value and first reflection coefficient information; substituting the metal reduction model RCS measurement, the coating reduction model RCS measurement, and the first reflection coefficient information into the following equation:
wherein the content of the first and second substances,
representing the RCS measurements of the 1 st 1 … m-1 st coating scale model,
representing the incident angle of the incident radar wave;
representing the RCS measurement value of a metal shrinkage model; m represents the number of categories of the bin; thetajJ-1 … m represents the angle formed by the incident wave and the normal direction of the j-th surface element,
Rk(θj) Where k is 1 … m-1, j is 1 … m, and the angle formed by the incident wave and the normal direction of the jth surface element of the kth coating scale model is thetajThe reflection coefficient of time;
indicating the bin normal is
Stacking all surface elements and RCS;
the RCS surface element distribution information of the metal scaling model under the incident wave angle is obtained through solving
In one embodiment, the method further comprises the following steps: acquiring second reflection coefficient information of the target prototype coating material; substituting the second reflection coefficient information, the bin distribution information and the preset geometric scaling factor into the following formula:
wherein the content of the first and second substances,
RCS value, R, representing a prototype of the objectprot(θj) J 1 … m represents the included angle formed by the incident wave and the normal direction of the j-th surface element of the target prototype as thetajThe time reflection coefficient s is a preset geometric scaling factor;
and (5) calculating a result according to a formula to obtain the RCS value of the target prototype.
In one embodiment, the method further comprises the following steps: when the surface elements of the coating scale model are divided into two types, acquiring a metal scale model RCS measured value, a coating scale model RCS measured value and first reflection coefficient information of a coating material of the coating scale model;
substituting the metal reduction model RCS measurement, the coating reduction model RCS measurement, and the first reflection coefficient information into the following equation:
the RCS surface element distribution information of the metal scaling model under the incident wave angle is obtained through solving
In one embodiment, the method further comprises the following steps: acquiring second reflection coefficient information of the target prototype coating material; substituting the second reflection coefficient information, the bin distribution information and the preset geometric scaling factor into the following formula:
and (5) calculating a result according to a formula to obtain the RCS value of the target prototype.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In an embodiment, as shown in fig. 3, a bin distribution-based dynamic matching reflection coefficient scaling measurement method is provided, where the model is a two-bin distribution model, and the processing procedure may be divided into three parts: the method comprises the following steps of firstly, mainly carrying out RCS measurement, respectively measuring a coating scaling model and a metal scaling model, and obtaining a complex RCS value of the corresponding models; secondly, dynamically acquiring distribution of normal surface elements under different observation angles by combining the reflection coefficient of the model material; and thirdly, introducing the reflection coefficient of the prototype material and inverting the geometric scaling factor to obtain the RCS of the coating prototype.
In another embodiment, the simulation experiment designs a scaling model A and a target prototype which have different parameters of coating materials and the same change trend of the reflection coefficient of the materials.
The target prototype selected by the simulation experiment is a SLICY model, the model top view is shown in fig. 4, and the model with an observation angle of phi 270 degrees and theta 45 degrees is shown in fig. 5. The observation angle of scaling inversion is 270 deg., theta is 0-180 deg.. The parameters of the scaled model a and the target prototype are detailed in table 1.
TABLE 1 simulation parameters for scaled model A and target prototype
The scaled model a and the prototype coating material reflection coefficient are shown in fig. 6. It can be seen from the figure that the two curves have the same trend, which corresponds to the case of different material parameters but the same trend of the reflection coefficient. The RCS result of the prototype obtained by inversion of the scaling model A by adopting a dynamic matching reflection coefficient method is shown in FIG. 7, the inversion result is consistent with the simulation calculation result of the prototype, and the effectiveness of the method is proved.
In another embodiment, the simulation experiment designs a scaling model B and a target prototype, wherein the scaling model B and the target prototype have different parameters of coating materials and different change trends of the reflection coefficients of the materials.
The simulation model is still a SLICY model, the change trend of the reflection coefficient of the coating material of the scaling model B is different from that of the prototype, and the parameters of the scaling model B and the target prototype are detailed in a table 2.
TABLE 2 simulation parameters for scaled model B and target prototype
The scaled model B and the prototype coating material reflection coefficient are shown in fig. 8. It can be seen from the figure that the reflectance variation trends of the two materials are significantly different. Therefore, the reflection coefficient has a complex variation trend under the action of a plurality of influence factors, and the complex variation trend is also a key factor influencing scaling inversion. The prototype result obtained by inversion of the scaling model B is shown in FIG. 9, the prototype RCS result obtained by inversion by the proposed method and the undetermined physical scaling factor method is shown in the figure, and the effectiveness of the method is proved by comparing the prototype RCS result with the prototype RCS obtained by simulation calculation.
In addition, the inversion result of the undetermined physical scaling factor method is compared with the inversion result of the undetermined physical scaling factor method, so that the scaling inversion accuracy of the bin distribution-based dynamic matching reflection coefficient method is obviously improved compared with the undetermined physical scaling factor method.
In one embodiment, as shown in fig. 10, there is provided a bin distribution-based dynamic matching reflection coefficient scale measurement apparatus, including: a scaling model RCS measurement value obtaining module 1002, a first reflection coefficient information obtaining module 1004, an RCS surface element distribution information obtaining module 1006, and an RCS value obtaining module 1008 of a target prototype, wherein:
a scaling model RCS measurement value obtaining module 1002, configured to obtain a metal scaling model RCS measurement value of a metal scaling model and a coating scaling model RCS measurement value of a coating scaling model; the coating scale model is one or more than one metal scale model, and the coating material of each coating scale model is different;
the first reflection coefficient information acquisition module 1004 is used for acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident wave of the radar and a normal direction of a surface element of the coating scaling model;
the RCS surface element distribution information obtaining module 1006 is configured to obtain RCS surface element distribution information of the metal scaling model at an incident wave angle according to the RCS measurement value of the metal scaling model, the RCS measurement value of the coating scaling model, and the first reflection coefficient information;
and the RCS value obtaining module 1008 of the target prototype is configured to obtain second reflection coefficient information of the coating material of the target prototype, and obtain the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information, and the preset geometric scaling factor.
The RCS bin distribution information obtaining module 1006 is further configured to obtain a metal scaling model RCS measurement value, a coating scaling model RCS measurement value, and first reflection coefficient information;
substituting the metal reduction model RCS measurement, the coating reduction model RCS measurement, and the first reflection coefficient information into the following equation:
wherein the content of the first and second substances,
representing the RCS measurements of the 1 st 1 … m-1 st coating scale model,
representing the incident angle of the incident radar wave;
representing the RCS measurement value of a metal shrinkage model; m represents the number of categories of the bin; thetajJ 1 … m represents the angle formed by the incident wave and the normal direction of the j-th surface element, and Rk(θj) Where k is 1 … m-1, j is 1 … m, and the angle formed by the incident wave and the normal direction of the jth surface element of the kth coating scale model is thetajThe reflection coefficient of time;
indicating the bin normal is
Stacking all surface elements and RCS;
the RCS surface element distribution information of the metal scaling model under the incident wave angle is obtained through solving
The RCS value acquisition module 1008 of the target prototype is further configured to acquire second reflectance information of the coating material of the target prototype;
substituting the second reflection coefficient information, the bin distribution information and the preset geometric scaling factor into the following formula:
wherein the content of the first and second substances,
RCS value, R, representing a prototype of the objectprot(θj) J 1 … m represents the included angle formed by the incident wave and the normal direction of the j-th surface element of the target prototype as thetajThe time reflection coefficient s is a preset geometric scaling factor;
and (5) calculating a result according to a formula to obtain the RCS value of the target prototype.
The RCS bin distribution information obtaining module 1006 is further configured to obtain a metal reduction model RCS measurement value, a coating reduction model RCS measurement value, and first reflection coefficient information of a coating material coating the reduction model when the bins coated with the reduction model are classified into two types;
substituting the metal reduction model RCS measurement, the coating reduction model RCS measurement, and the first reflection coefficient information into the following equation:
the RCS surface element distribution information of the metal scaling model under the incident wave angle is obtained through solving
The RCS value acquisition module 1008 of the target prototype is further configured to acquire second reflectance information of the coating material of the target prototype;
substituting the second reflection coefficient information, the bin distribution information and the preset geometric scaling factor into the following formula:
and (5) calculating a result according to a formula to obtain the RCS value of the target prototype.
For specific definition of the apparatus for measuring dynamic matching reflection coefficient scale based on binning distribution, reference may be made to the above definition of the method for measuring dynamic matching reflection coefficient scale based on binning distribution, which is not described herein again. The modules in the bin distribution-based dynamic matching reflection coefficient scale measurement device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 11. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a bin distribution based dynamic matching reflectance scaling measurement method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 11 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A bin distribution-based dynamic matching reflection coefficient scaling measurement method is characterized by comprising the following steps:
acquiring a metal reduction model RCS measurement value of the metal reduction model and a coating reduction model RCS measurement value of the coating reduction model; the coating scale model is formed by coating materials on the surface of the metal scale model, the number of the coating scale models can be one or more, and the coating material of each coating scale model is different;
acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident radar wave and the normal direction of the surface element of the coating scaling model;
obtaining RCS surface element distribution information of the metal scaling model under the incident wave angle according to the RCS measured value of the metal scaling model, the RCS measured value of the coating scaling model and the first reflection coefficient information;
and acquiring second reflection coefficient information of the coating material of the target prototype, and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and a preset geometric scaling factor.
2. The method of claim 1, wherein the obtaining a metal reduction model RCS measurement of a metal reduction model and a coating reduction model RCS measurement of a coating reduction model; the coating reduction model is a metal coating reduction model, the surface of which is coated with materials, the coating reduction model can be one or more, and the coating material of each coating reduction model is different, and comprises the following steps:
acquiring a metal reduction model RCS measurement value of the metal reduction model and a coating reduction model RCS measurement value of the coating reduction model; the coating reduction model is used for coating the surface of the metal reduction model with a material, the number of the coating reduction models is the number of the surface elements minus 1, and the coating material of each coating reduction model is different; the surface elements are classified according to the normal direction, and the surface elements with the same normal direction are of one class.
3. The method of claim 2, wherein obtaining the first reflection coefficient information of the coating material according to an included angle formed by an incident radar wave and a normal direction of the coating scale model surface element comprises:
acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident radar wave and the normal direction of the surface element of the coating scaling model; the first reflection coefficient information changes along with the change of the size of an included angle formed by incident radar waves and different surface elements of the coating scaling model.
4. The method according to claim 3, wherein obtaining RCS bin distribution information of the metal scale model at the incident wave angle according to the metal scale model RCS measurement value, the coating scale model RCS measurement value and the first reflection coefficient information comprises:
acquiring the metal scaling model RCS measured value, the coating scaling model RCS measured value and the first reflection coefficient information;
substituting the metal reduction model RCS measurement, the coating reduction model RCS measurement, and the first reflection coefficient information into the following equation:
wherein the content of the first and second substances,
representing the RCS measurements of the 1 st.. m-1 coating scale model,
representing the incident angle of the incident radar wave;
representing the metal shrinkage model RCS measurement; m represents the number of categories of the bin; thetajJ 1.. m denotes an angle formed by an incident wave and a normal direction of a j-th surface element, and R isk(θj) And k is 1.. m-1, j is 1.. m represents that an included angle formed by an incident wave and the normal direction of the j-type surface element of the k-th coating scaling model is thetajThe reflection coefficient of time;
indicating the bin normal is
Stacking all surface elements and RCS;
solving to obtain RCS surface element distribution information of the metal scaling model under the incident wave angle
5. The method according to claim 4, wherein the obtaining second reflection coefficient information of the target prototype coating material, and obtaining the RCS value of the target prototype according to the second reflection coefficient information, the bin distribution information and the preset geometric scaling factor comprises:
acquiring second reflection coefficient information of the target prototype coating material;
substituting the second reflection coefficient information, the bin distribution information and a preset geometric scaling factor into the following formula:
wherein the content of the first and second substances,
an RCS value, R, representing the target prototypeprot(θj) J 1.. m represents that an included angle formed by an incident wave and the normal direction of the jth surface element of the target prototype is thetajThe time reflection coefficient s is a preset geometric scaling factor;
and calculating a result according to a formula to obtain the RCS value of the target prototype.
6. The method according to claim 5, wherein obtaining RCS bin distribution information of the metal scale model at the incident wave angle according to the metal scale model RCS measurement value, the coating scale model RCS measurement value and the first reflection coefficient information comprises:
when the surface elements of the coating scale model are divided into two types, acquiring the RCS measured value of the metal scale model, the RCS measured value of the coating scale model and first reflection coefficient information of a coating material of the coating scale model;
substituting the metal reduction model RCS measurement, the coating reduction model RCS measurement, and the first reflection coefficient information into the following equation:
solving to obtain RCS surface element distribution information of the metal scaling model under the incident wave angle
7. The method according to claim 6, wherein the obtaining second reflection coefficient information of the target prototype coating material, and obtaining the RCS value of the target prototype according to the second reflection coefficient information, the bin distribution information and the preset geometric scaling factor comprises:
acquiring second reflection coefficient information of the target prototype coating material;
substituting the second reflection coefficient information, the bin distribution information and a preset geometric scaling factor into the following formula:
and calculating a result according to a formula to obtain the RCS value of the target prototype.
8. A bin distribution based dynamically matched reflectance scaling measurement apparatus, the apparatus comprising:
the system comprises a scaling model RCS measured value acquisition module, a scaling model RCS measuring value acquisition module and a scaling model RCS measuring value acquisition module, wherein the scaling model RCS measured value acquisition module is used for acquiring a metal scaling model RCS measured value of a metal scaling model and a coating scaling model RCS measured value of a coating scaling model; the coating scaling model is used for coating the surface of the metal scaling model with a material, and the coating material of each coating scaling model is different;
the first reflection coefficient information acquisition module is used for acquiring first reflection coefficient information of the coating material according to an included angle formed by an incident wave of the radar and the normal direction of the surface element of the coating scaling model;
the RCS surface element distribution information acquisition module is used for acquiring RCS surface element distribution information of the metal scaling model under the incident wave angle according to the RCS measured value of the metal scaling model, the RCS measured value of the coating scaling model and the first reflection coefficient information;
and the RCS value acquisition module of the target prototype is used for acquiring second reflection coefficient information of the coating material of the target prototype and acquiring the RCS value of the target prototype according to the second reflection coefficient information, the surface element distribution information and a preset geometric scaling factor.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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Inventor after: Zeng Yang Inventor after: Pang Shuang Inventor after: Yang Qi Inventor after: Deng Bin Inventor after: Wang Hongqiang Inventor before: Zeng Yang Inventor before: Feng Shuang Inventor before: Yang Qi Inventor before: Deng Bin Inventor before: Wang Hongqiang |