CN115356703A - Surface element distribution-based rough target RCS scaling measurement method and device - Google Patents
Surface element distribution-based rough target RCS scaling measurement method and device Download PDFInfo
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Abstract
The application relates to a method and a device for measuring RCS (Radar Cross section) scaling of a rough target based on bin distribution. The method comprises the following steps: selecting a plurality of prior rough models with different surface roughness according to rough targets to be estimated in a preset roughness interval, respectively obtaining complex radar scattering cross sections of the prior rough models, partitioning surface elements of the prior rough models according to surface element deflection angles, respectively counting surface element occupation ratios and cosine values of surface element deflection angle mean values in the intervals, constructing linear equation sets according to the complex radar scattering cross sections of the prior rough models, the surface element occupation ratios in the intervals and the cosine values of the surface element deflection angle mean values, obtaining weight factors of the intervals through the linear equation sets, and finally carrying out inversion according to the weight factors, preset scaling factors and surface element parameters of the rough targets to be estimated to obtain the radar scattering cross sections of the rough targets to be estimated. By adopting the method, the RCS inversion accuracy of large-size targets with rough metal surfaces in the terahertz frequency band can be improved.
Description
Technical Field
The application relates to the technical field of target radar scattering cross section scale measurement, in particular to a method and a device for measuring RCS (radar cross section scale) of a rough target based on surface element distribution.
Background
Scaling model measurement is one of the effective methods to obtain the target prototype RCS. The scale measurement is realized by constructing a model electromagnetic system similar to the prototype electromagnetic system according to the electromagnetic similarity, performing RCS measurement on the scale model by using the model electromagnetic system, and performing RCS inversion on the target prototype according to the electromagnetic similarity. The electromagnetic wave wavelength, the geometric dimension of each part of the target, the material parameter and the like in the model measurement system need to be subjected to proportional change strictly according to electromagnetic similarity conditions, when the material parameter does not change along with the frequency (such as a metal target), the condition of the electromagnetic similarity is simplified, and the scaling measurement can be realized only by ensuring that the geometric dimension scaling factor and the wavelength scaling factor of the model electromagnetic system are the same. In recent years, with the increase of the demand for large-size target detection, RCS scale measurement systems and scale inversion methods face new problems. For the scale measurement of a large-size target in an X-band (such as an aircraft carrier) in a darkroom, a required geometric scale factor may reach more than one hundred times, and the measurement frequency of a model system will rise to a terahertz frequency band, which poses a new challenge to the RCS scale measurement.
Terahertz (THz) waves generally refer to electromagnetic waves with a frequency of 0.1 THz to 10 THz, and compared with microwaves, terahertz waves are shorter in wavelength, easy to realize large scale factors, and have advantages in scale measurement of large-size targets. However, the wavelength of the terahertz wave is in the same order of magnitude as the microstructure of the target surface, the terahertz wave is sensitive to the microstructure and the rough fluctuation of the target surface, the microwave frequency band can be regarded as a metal target with a smooth surface, and the influence of the surface roughness on the scattering property of the target in the terahertz frequency band cannot be ignored. For RCS scaling measurement of an oversized target which rises to a terahertz frequency band, the current model processing precision cannot realize strict geometric scaling of the surface roughness of the model, and the condition of electromagnetic similarity cannot be met, so that higher requirements are provided for the scaling measurement of the terahertz frequency band. In order to enable the RCS scaling measurement result of the terahertz frequency band to invert the RCS of the target prototype with high precision, the influence of the surface roughness on the RCS scaling relationship needs to be considered, and therefore how to implement the RCS scaling inversion of the terahertz frequency band rough metal target becomes an urgent problem to be solved.
Disclosure of Invention
Based on this, it is necessary to provide a method for RCS scaling measurement of rough targets based on surface element distribution, which can obtain radar scattering cross section of large-size targets with rough metal on the surface.
A bin distribution based method for RCS scaling measurement of rough objects, the method comprising:
selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval, and respectively obtaining a complex radar scattering cross section of each prior rough model;
acquiring a surface element deflection angle of each prior rough model, partitioning the surface elements of each prior rough model according to the surface element deflection angle, and respectively counting the surface element occupation ratio in each interval and the cosine value of the surface element deflection angle mean value;
constructing a linear equation set according to the complex radar scattering cross section of each prior rough model, the surface element proportion in each interval and the cosine value of the surface element deflection angle mean value, and solving according to the linear equation set to obtain the weight factor of each interval;
and carrying out inversion according to the weight factor, a preset scaling factor and the surface element parameters of the rough target to be estimated to obtain the radar scattering cross section of the rough target to be estimated.
In one embodiment, the complex radar cross section of the prior coarse model is obtained by means of simulation calculation or RCS measurement.
In one embodiment, the bin bias angle of each prior rough model is an included angle between a rough surface bin normal vector and a smooth surface bin normal vector of each prior rough model.
In one embodiment, partitioning the bins of each of the a priori coarse models according to the bin bias angles includes:
and carrying out logarithmic transformation on the surface element deflection angles, and dividing the surface element deflection angles into preset intervals at equal intervals according to intervals covered by transformation results.
In one embodiment, the cosine value of the mean deviation angle of the elements in each interval is calculated by the following formula:
in the above formulae, subscriptspNumbering prior coarse models, subscripts, to distinguish different coarsenessqIs the number of the deflection angle interval, indicates different deflection angle intervals,
is shown aspA priori rough model is distributed inqThe number of bins within a bin interval,
indicating bin bias angles within the interval
Of the average value of (a).
In one embodiment, a linear equation set is constructed according to the complex radar scattering cross section of each prior coarse model, the bin fraction in each interval, and the cosine value of the mean value of the bin deflection angles as follows:
in the above-mentioned formula,
is shown aspA priori rough model is distributed inqThe area element ratio in the area element interval,
representing the cosine of the mean of the bin bias angles in each bin,
representing each of said a priori coarse models at an observation angle of
The complex radar cross-section of time,
representing the weight factor for each interval that needs to be solved.
In one embodiment, the obtaining of the radar scattering cross section of the rough target to be estimated by performing inversion according to the weight factor, a preset scaling factor and the bin parameter of the rough target to be estimated includes:
substituting the weight factor, a preset scaling factor and the surface element parameters of the rough target to be estimated into the following formula, and solving to obtain a complex radar scattering cross section of the rough target to be estimated;
then obtaining a radar scattering cross section of the rough target to be estimated according to the complex radar scattering cross section;
in the above-mentioned formula, the first and second,
representing the bin ratio of different interval bins of the rough object to be estimated,
representing the cosine value of the surface element deflection angle mean value of the rough object to be estimated,
which represents a weight factor, is given by the weight factor,srepresents a scaling factor; and the bin occupation ratios of bins in different intervals of the rough target to be estimated and the cosine values of the bin deflection angle mean values are bin parameters of the rough target to be estimated.
In one embodiment, the number of the prior coarse models is selected to be consistent with the number of the binning areas of each prior coarse model.
In one embodiment, the roughness parameter of the rough object to be estimated meets the following requirements:
the relative length of the rough target to be estimated is fixed, and the root mean square height is changed, wherein the root mean square height
Has a value range of
Correlation length
In that
Selecting a certain fixed value within the range;
or the correlation length of the rough target to be estimated changes along with the variation of the root mean square height, wherein the root mean square height
Has a value range of
Correlation length
。
In one embodiment, the surface of the rough object to be estimated is made of metal.
A bin distribution based rough target RCS scaling measurement apparatus, the apparatus comprising:
the system comprises a prior model RCS obtaining module, a rough model calculating module and a rough model calculating module, wherein the prior model RCS obtaining module is used for selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval and respectively obtaining a complex radar scattering cross section of each prior rough model;
a prior model surface element parameter acquisition module, configured to acquire a surface element deflection angle of each prior coarse model, partition the surface element of each prior coarse model according to the surface element deflection angle, and count a surface element proportion and a cosine value of a surface element deflection angle mean value in each interval respectively;
the weight factor solving module is used for constructing a linear equation set according to the complex radar scattering cross section of each prior rough model, the surface element occupation ratio in each interval and the cosine value of the surface element deflection angle mean value, and solving according to the linear equation set to obtain the weight factor of each interval;
and the RCS acquisition module is used for carrying out inversion according to the weight factor, a preset scaling factor and surface element parameters of the rough target to be estimated so as to obtain the radar scattering cross section of the rough target to be estimated.
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:
selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval, and respectively obtaining a complex radar scattering cross section of each prior rough model;
acquiring a surface element deflection angle of each prior rough model, partitioning the surface elements of each prior rough model according to the surface element deflection angle, and respectively counting the surface element occupation ratio in each interval and the cosine value of the surface element deflection angle mean value;
constructing a linear equation set according to the complex radar scattering cross section of each prior rough model, the surface element proportion in each interval and the cosine value of the surface element deflection angle mean value, and solving according to the linear equation set to obtain the weight factor of each interval;
and carrying out inversion according to the weight factor, a preset scaling factor and the surface element parameters of the rough target to be estimated to obtain the radar scattering cross section of the rough target to be estimated.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval, and respectively obtaining a complex radar scattering cross section of each prior rough model;
acquiring a surface element deflection angle of each prior rough model, partitioning a surface element of each prior rough model according to the surface element deflection angle, and respectively counting the surface element occupation ratio in each interval and the cosine value of the surface element deflection angle mean value;
constructing a linear equation set according to the complex radar scattering cross section of each prior rough model, the surface element proportion in each interval and the cosine value of the surface element deflection angle mean value, and solving according to the linear equation set to obtain the weight factor of each interval;
and carrying out inversion according to the weight factor, a preset scaling factor and the surface element parameters of the rough target to be estimated to obtain the radar scattering cross section of the rough target to be estimated.
According to the RCS scaling measurement method and device for the rough target based on surface element distribution, a plurality of prior rough models with different surface roughness are selected according to the rough target to be estimated in a preset roughness interval, complex radar scattering cross sections of the prior rough models are obtained respectively, surface elements of the prior rough models are partitioned according to surface element deflection angles, surface element occupation ratios in intervals and cosine values of surface element deflection angle mean values are counted respectively, linear equation sets are constructed according to the complex radar scattering cross sections of the prior rough models, the surface element occupation ratios in the intervals and the cosine values of the surface element deflection angle mean values, weight factors of the intervals are obtained through solving of the linear equation sets, and finally, inversion is carried out according to the weight factors, the preset scaling factors and surface element parameters of the rough target to be estimated to obtain the radar scattering cross sections of the rough target to be estimated. By adopting the method, the RCS inversion accuracy of large-size targets with rough metal surfaces in the terahertz frequency band can be improved.
Drawings
FIG. 1 is a schematic flow chart of a RCS scaling measurement method for a rough target based on bin distribution in one embodiment;
FIG. 2 is a schematic diagram of a rough plate bin bias angle in one embodiment;
FIG. 3 is a schematic flow chart of an RCS scaling measurement method for rough targets based on bin distribution in another embodiment;
FIG. 4 is a schematic diagram of distribution ratio of the surface elements of the rough plate in a simulation experiment;
FIG. 5 is a graphical illustration of KL divergence of the coarse plate bin distribution in a simulation experiment;
FIG. 6 is a schematic diagram of the same frequency band, same size and different roughness of the rough flat RCS inversion result in a simulation experiment, wherein FIG. 6 (a) is the root mean square height 0.08
FIG. 6 (b) shows the root mean square height of 0.11
FIG. 6 (c) shows the root mean square height of 0.14
FIG. 6 (d) shows the RMS height of 0.16
FIG. 6 (e) shows the RMS height of 0.2
；
FIG. 7 is a schematic diagram of the results of the RCS inversion of a coarse flat panel satisfying the statistical scaling relationship in a simulation experiment, where FIG. 7 (a) is the RMS height of 0.1
FIG. 7 (b) shows the root mean square height of 0.12
FIG. 7 (c) shows the height of the root mean square of 0.15
；
FIG. 8 is a block diagram of an embodiment of a device for RCS scaling measurement of rough targets based on bin distribution;
FIG. 9 is a diagram illustrating an internal structure of a computer device according to an 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.
Aiming at the problems that in the prior art, the research on the RCS scaling relation of a terahertz frequency band rough target is less, the derivation related to the scaling relation of a rough sea surface is established on the basis of a strict geometric similarity condition, and because rough fluctuation has randomness, the strict geometric similarity relation between a scaling model and a prototype is difficult to meet. In the application, the influence of randomness of rough fluctuation is considered, a scaling inversion method of a rough target is researched based on statistical parameters of roughness, the limit condition of 'strict geometric similarity' of electromagnetic similarity is broken through to a certain extent, only the roughness parameters of a scaling model and a prototype are required to meet the scaling relation in the statistical sense, and the rough fluctuation of the surface of the scaling model and the surface of a target prototype is not required to meet the strict geometric similarity, as shown in fig. 1, the RCS scaling measurement method of the rough target based on surface element distribution is provided, and the method comprises the following steps:
s100, selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval, and respectively obtaining a complex radar scattering cross section of each prior rough model;
step S110, acquiring a surface element deflection angle of each prior rough model, partitioning the surface elements of each prior rough model according to the surface element deflection angle, and respectively counting the surface element proportion in each interval and the cosine value of the surface element deflection angle mean value;
step S120, constructing a linear equation set according to the complex radar scattering cross section of each prior rough model, the surface element proportion in each interval and the cosine value of the surface element deflection angle mean value, and solving according to the linear equation set to obtain a weight factor of each interval;
and S130, performing inversion according to the weight factor, the preset scaling factor and the surface element parameters of the rough target to be estimated to obtain the radar scattering cross section of the rough target to be estimated.
In step S100, each prior rough model is a scaling model of the rough object to be estimated. Selecting a model with known RCS (radar-cross section) and same shape and size and different surface roughness as a prior rough model in a preset roughness interval, namely selecting the prior rough model with the same parameters except for different roughness. And the observation angle of the prior rough model is obtained by means of simulation calculation or RCS actual measurement
Complex radar cross section value of time
。
In this embodiment, the rough target surface to be subjected to RCS estimation is made of metal.
In step S110, the surface of the prior rough model is decomposed into a plurality of surface elements based on the concept of surface elements, and an included angle between a normal vector of the rough surface element of the prior rough model and a normal vector of the smooth surface element is calculated as a declination angle of each surface element.
Specifically, as shown in fig. 2 (in the figure, the prior roughness model is a flat plate, for example, and the roughness fluctuation satisfies a gaussian distribution). In the drawings
Representing the vector of the incident wave
With the smooth flat plate vector
When the surface element of the smooth flat plate is modulated by the roughness parameter, the surface element normal vector direction changes,
farnet for rough plate surface element
With the normal vector of the smooth flat surface element
The included angle of (a) is also the declination angle. Different roughness parameters correspond to different bin deflection angle distributions. For a specific surface element, the single-station RCS of the surface element changes in a nonlinear way along with the observation angle. For a certain rough target whole, under a fixed observation angle, the scattering fields of the surface elements with different directions contributing to the rough target whole are different.
And then, carrying out partition statistics on the surface elements of each prior rough model, wherein the height fluctuation of the rough surface is random, the deflection angle of the surface element is also a random variable, the single-station RCS of the surface element is the largest in the normal vector direction of the surface element, the single-station RCS of the surface element is specular reflection at the moment, and when the observation angle deviates from the normal vector direction of the surface element, the single-station RCS of the surface element is decreased in a nonlinear way along with the increase of the deflection angle, so that a nonlinear partition mode is adopted on the surface element partition.
Specifically, vertex coordinates of the surface element and normal vector information of the surface element are obtained according to a source file of a prior rough model designed through simulation, and surface element deflection angle information of the rough plate is obtained based on the vertex coordinates and the normal vector information of the surface element. Second face element declination
Carrying out logarithmic transformation to obtain
Then to
The covered interval is divided into preset intervalsqEach interval is used for respectively counting the surface element ratio of each interval
Inner surface of the sectionDeviation angle
Mean value of
And cosine value thereof
。
Further, the cosine value of the mean value of the element deflection angles in each interval is calculated by adopting the following formula:
in the formula (1), subscriptspNumbering prior roughness models, subscripts, to distinguish different roughness prior roughness modelsqIs the number of the deflection angle interval, indicates different deflection angle intervals,
is shown aspA priori rough model is distributed inqThe number of surface elements in the surface element interval,
indicating bin bias angles within the interval
Of the average value of (a).
Next, in step S120, the weighting factors between the different bin deflection angles are solved, which mainly involve the selection of the a priori rough model and the construction of the linear equation set. The difference between the rough models in a specific roughness interval and the change of RCS along with the roughness parameter are considered simultaneously when the prior rough model is selected, and the weighting factor solved by the constructed linear equation set has good estimation capability and high RCS inversion accuracy. The method provides a KL divergence-based difference measurement criterion and determines the applicable roughness range of the method, wherein the KL divergence-based difference measurement criterion is specifically expressed as follows: within a specific roughness interval, KL divergence among different rough models obtained based on surface element deflection angle distribution has a good linear variation trend along with roughness parameters.
In the method, a linear equation set is required to be constructed in a specific roughness interval, a weight factor is solved, and a target prototype RCS is inverted, and the method is summarized and obtained according to the difference measurement criterion based on KL divergence and the change trend of the RCS along with the roughness, and is suitable for the following two types of roughness parameter intervals: the relative length of the rough object to be estimated is fixed, and the root mean square height is changed, wherein the root mean square height
Has a value range of
Correlation length of
In that
A fixed value is selected within the range.
Or the correlation length of the rough object to be estimated varies with the RMS height, which varies
Has a value range of
Correlation length
。
Specifically, a linear equation set is constructed according to the complex radar scattering cross section of each prior coarse model, the bin fraction in each interval and the cosine value of the bin deflection angle mean value, and the linear equation set comprises:
in the formula (2), the first and second groups of the chemical reaction are represented by the following formula,
is shown aspThe prior coarse model is distributed atqThe area element ratio in the area element interval,
representing the cosine of the mean of the bin bias angles in each bin,
representing each prior coarse model at an observation angle of
The complex radar cross-section of time,
representing the weight factor of each interval that needs to be solved. The observation angle can be found by using the formula (2) as
Weight factor of different bin intervals
。
In step S130, the weighting factor, the preset scaling factor and the bin parameter of the rough target to be estimated are substituted into formula (3), and the complex radar scattering cross section of the rough target to be estimated is obtained by solving:
in the formula (3), the first and second groups,
representing the bin fraction of different interval bins of the rough object to be estimated,
representing the cosine of the mean of the bin deflection angles of the rough object to be estimated,
which represents a weight factor, is given by the weight factor,sindicating the scaling factor. The bin occupation ratios of bins in different sections of the rough target to be estimated and the cosine values of the mean values of the bin deflection angles are known bin parameters of the rough target to be estimated.
And finally, substituting the obtained complex radar scattering cross section into a formula (4) to obtain the radar scattering cross section of the rough target to be estimated.
In this embodiment, for the convenience of solving the formula (2), the number of the selected prior coarse models is consistent with the number of the bins of each prior coarse model.
In this embodiment, the method may also be implemented according to the steps shown in the flowchart shown in fig. 3.
The method can not only invert the target prototype RCS with the roughness parameter meeting the scaling relation, but also estimate the RCS of other roughness models with the same electrical size in a certain roughness interval, thereby providing an efficient solution for estimating the RCS of the rough target.
The method is also verified by a simulation experiment, and the specific contents of the simulation experiment are as follows:
designing a series of rough metal flat plate models with different roughness in a specific roughness interval, and firstly, acquiring RCS (Radar Cross section) of the corresponding models by utilizing electromagnetic simulation calculation software; secondly, extracting vertex coordinate information of the surface element according to a source file of the rough model, calculating the normal vector and the deflection angle distribution of the surface element, and obtaining the relevant parameters of the surface element distribution required by the invention after nonlinear transformation and partition processing; then, constructing a linear equation set through a formula (2) to solve the weight factors of different surface element intervals in the roughness interval; and finally, substituting the weighting factor, the scaling factor and the related bin parameters of the rough target prototype into equations (3) - (4) to obtain the RCS of the target prototype.
The rough flat plate designed by simulation experiment has rough fluctuation with root mean square height in the range of
Correlation length of
Take a fixed value
The simulation calculation frequency for the scaled model is 220GHz and the simulation calculation frequency for the prototype is 110GHz, and the parameters of the a priori rough plates used to construct the system of linear equations are listed in Table 1. As shown in fig. 4, the area element ratio of different intervals after the surface element deflection angle of the prior rough flat plate is subjected to nonlinear processing is shown, and the horizontal axis is the value of the center of the interval. As shown in fig. 5, the bin bias angle distribution for the a priori rough plate numbers 2-5 is relative to the KL dispersion value for rough plate number 1,
expressed as a root mean square height of
Has a roughness model and a root mean square height ofQThe KL divergence of the rough model of (1) can be seen from FIG. 5 that the KL divergence of the prior rough flat plate in the roughness interval has a good linear relation, and the obtained weighting factor has high-precision RCS inversion capability. In order to test the effectiveness of the method, the RCS of the rough flat plate with the same frequency band, the same size and different roughness and the RCS of the prototype flat plate meeting the statistical scaling relation are respectively inverted by the weight factor, and then the RCS inversion result of the corresponding rough flat plate is given.
TABLE 1 correlation parameters of a priori rough plates
(1) Same-frequency-band, same-size and different-roughness rough flat plate RCS inversion
In order to verify the predictive capability of the proposed method for different roughness models, a series of plates with different roughness are designed in the part, the parameters of the rough plate are listed in table 2, and the binning angles of the rough plate are transformed and partitioned in the same manner as described above to obtain the relevant parameters of the binning distribution of the rough plate. Substituting the weighting factor of the interval, the scaling factor between the prototype and the model (the scaling factor with the same frequency and size is 1) and the bin distribution parameters of the target to be solved into a formula (3) and a formula (4) to obtain the RCS inversion result of the corresponding rough flat plate, as shown in FIG. 6, wherein the root mean square height of the rough flat plate in FIG. 6 (a) is 0.08 of the root mean square height
FIG. 6 (b) shows the root mean square height of 0.11
FIG. 6 (c) shows the root mean square height of 0.14
FIG. 6 (d) shows the root mean square height of 0.16
FIG. 6 (e) shows the height of the root mean square of 0.2
. By comparing the RCS inversion result of the rough flat plate with the simulation calculation result, the method can be used for accurately estimating the RCS of other flat plates with different roughness in a certain angle range, and the effectiveness of the method is proved.
TABLE 2 correlation parameters for different roughness plates
(2) Rough flat RCS inversion satisfying statistical scaling relationship
In order to verify the RCS inversion capability of the method for the rough plate meeting the statistical scaling relationship, the rough plate meeting the statistical scaling relationship is designed in the RCS inversion capability part, parameters of the rough plate are listed in a table 3, and the surface element deflection angles of the rough plate are transformed and partitioned in the same manner to obtain related parameters of surface element distribution of the rough plate. The size of the group of rough plates is 2 times of that of the prior rough plate, the electromagnetic wave wavelength calculated by simulation is 2 times of that of the prior rough plate, and the geometric scaling factor is 2. Substituting the weighting factor of the interval, the scaling factor between the prototype and the model and the bin distribution parameter of the target to be solved into the formula (3) and the formula (4) to obtain the RCS inversion result of the corresponding rough flat plate, as shown in fig. 7, wherein fig. 7 (a) is the root mean square height of 0.1
FIG. 7 (b) shows the root mean square height of 0.12
FIG. 7 (c) shows the root mean square height of 0.15
. By comparing the RCS inversion result of the rough plate with the simulation calculation result, the method can invert the RCS of the rough plate meeting the statistical scaling relation at high precision within a certain angle range, and the effectiveness of the method is proved.
The results of the simulation experiments show that the method can be used for inverting the RCS of the rough target meeting the statistical scaling relation and the RCS of other roughness targets with the same frequency band and the same size in a pre-estimated corresponding roughness interval at high precision in a specific roughness interval, an effective solution is provided for the RCS scaling measurement and high-precision inversion of the rough target, the requirement of the RCS scaling measurement of the terahertz frequency band on the surface roughness of the model is reduced, and the application range of the scaling measurement is expanded.
According to the RCS scaling measurement method for the rough target based on the surface element distribution, the rough target is regarded as a series of randomly fluctuating surface element sets, surface element distribution information of a micro-level is introduced from the surface element distribution, the change rule of the surface element distribution along with roughness parameters is analyzed, the scattering characteristic of the whole rough target is researched on the basis, and the relation between the micro surface element and the macro scattering characteristic is established. The method is a core idea through the method and is an important breakthrough for RCS scaling measurement of a rough target. Compared with a method for analyzing the scattering characteristics of the rough target based on the scattering coefficient, the method introduces micro surface element information, and is expected to improve the inversion accuracy of the RCS of the rough target.
The first key step in the method is how to perform the partition statistics on the bins of the rough object. Because the height fluctuation of the rough target surface is random, the surface element deflection angle is also a random variable, the single-station RCS of the surface element is the largest in the normal vector direction, the reflection is specular reflection at the moment, and when the observation angle deviates from the surface element normal vector direction, the single-station RCS of the surface element is in nonlinear decline along with the increase of the deflection angle. According to the method, the bin deflection angles are subjected to nonlinear transformation and then partitioned, so that nonlinear partition processing of the bins is realized, and the contribution of the bins with different deflection angles to the whole RCS of the rough target can be better represented.
Usually, two statistical variables of root mean square height and correlation length are adopted for describing the rough surface of the target, but in the terahertz frequency band, the fluctuation characteristics of the rough target cannot be accurately represented only by the two statistical variables, so that the fluctuation characteristics of the rough target are further represented by introducing surface element distribution in the method. In order to describe and measure the difference of roughness fluctuation among different rough targets from a surface element distribution level, KL divergence is introduced to measure the difference of surface element distribution, a surface element distribution difference measurement criterion based on the KL divergence is provided, the application range of the method is summarized on the basis, and a foundation is laid for the method.
The key of the scaling inversion method is that the RCS of a rough target is inverted by using a weighting factor in a specific roughness interval, comprehensive analysis is carried out on distribution rules of different roughness surface elements and RCS variation trends, and the RCS of different roughness models can be represented by linear superposition of surface elements in different deflection angle intervals in the specific roughness interval, so that the weighting factor of different surface element intervals is solved by adopting a mode of constructing a linear equation set, and the RCS of other rough targets is estimated by using the weighting factor, which is the key for realizing the scaling inversion by the method.
The statistical analysis is carried out on the surface element distribution of the rough model, and the surface element distribution of the rough target prototype meeting the statistical scaling relation and the surface element distribution of the scaling model have similarity, so that the high-precision inversion of the RCS of the rough target prototype is realized based on the weight factors of different surface element deflection angle intervals in two roughness intervals meeting the statistical scaling relation.
According to the method, RCS scaling inversion is carried out based on the surface element deflection angle distribution of the rough target, micro surface element distribution information is introduced, and high-precision inversion of the RCS of the rough target can be achieved. And the rough fluctuation of the surfaces of the target prototype and the scaling model is not required to meet strict geometric similarity, only the roughness parameters (root-mean-square height and related length) are required to meet the scaling relation in a statistical sense, so that the requirement on the surface roughness of the scaling model is reduced, the application range of scaling measurement is expanded, and an effective solution is provided for the scaling measurement of the terahertz frequency band rough target.
The method can not only invert RCS of a rough target (the roughness parameter and the model size are scaled according to the same scale factor) meeting the scaling relation of statistical significance, but also estimate RCS of other roughness models (the model size and the measurement frequency are unchanged, only the surface roughness is changed) in the same roughness interval, and provides a high-efficiency and high-precision estimation method for acquiring the RCS of the rough target.
By combining a specific scale measurement system, the method is easy to realize, high in stability and good in universality, and has good practicability without increasing the complexity of the system and an algorithm while obtaining better scale measurement and inversion results.
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 one embodiment, as shown in fig. 8, there is provided a bin distribution-based RCS scale measurement apparatus for rough targets, comprising: a prior model RCS obtaining module 200, a prior model surface element parameter obtaining module 210, a weighting factor solving module 220 and a rough target RCS to be estimated obtaining module 230, wherein:
the prior model RCS obtaining module 200 is used for selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval and respectively obtaining a complex radar scattering cross section of each prior rough model;
a prior model bin parameter obtaining module 210, configured to obtain a bin deflection angle of each prior coarse model, partition a bin of each prior coarse model according to the bin deflection angle, and count a bin fraction and a cosine value of a bin deflection angle mean value in each interval respectively;
the weight factor solving module 220 is configured to construct a linear equation set according to the complex radar scattering cross section of each prior coarse model, the face element proportion in each interval and the cosine value of the mean value of the face element deflection angles, and solve according to the linear equation set to obtain a weight factor of each interval;
and the to-be-estimated rough target RCS obtaining module 230 is configured to perform inversion according to the weight factor, a preset scaling factor and the surface element parameter of the to-be-estimated rough target to obtain a radar scattering cross section of the to-be-estimated rough target.
For specific definition of the apparatus for measuring RCS scaling of rough targets based on binning, reference may be made to the above definition of the method for measuring RCS scaling of rough targets based on binning, which is not described herein again. The modules in the device for measuring the RCS scaling of the rough target based on the bin distribution 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. 9. The computer device comprises a processor, a memory, a network interface, a display screen and an input device which are connected through 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 coarse target RCS 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. 8 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 one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor that when executing the computer program performs the steps of:
selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval, and respectively obtaining a complex radar scattering cross section of each prior rough model;
acquiring a surface element deflection angle of each prior rough model, partitioning a surface element of each prior rough model according to the surface element deflection angle, and respectively counting the surface element occupation ratio in each interval and the cosine value of the surface element deflection angle mean value;
constructing a linear equation set according to the complex radar scattering cross section of each prior rough model, the surface element proportion in each interval and the cosine value of the surface element deflection angle mean value, and solving according to the linear equation set to obtain the weight factor of each interval;
and carrying out inversion according to the weight factor, a preset scaling factor and the surface element parameters of the rough target to be estimated to obtain the radar scattering cross section of the rough target to be estimated.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval, and respectively obtaining a complex radar scattering cross section of each prior rough model;
acquiring a surface element deflection angle of each prior rough model, partitioning a surface element of each prior rough model according to the surface element deflection angle, and respectively counting the surface element occupation ratio in each interval and the cosine value of the surface element deflection angle mean value;
constructing a linear equation set according to the complex radar scattering cross section of each prior rough model, the surface element proportion in each interval and the cosine value of the surface element deflection angle mean value, and solving according to the linear equation set to obtain the weight factor of each interval;
and carrying out inversion according to the weight factor, a preset scaling factor and the surface element parameters of the rough target to be estimated to obtain the radar scattering cross section of the rough target to be estimated.
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 may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may 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 (Rambus) direct RAM (RDRAM), direct memory 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. The RCS scaling measurement method for the rough target based on the bin distribution is characterized by comprising the following steps of:
selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval, and respectively obtaining a complex radar scattering cross section of each prior rough model;
acquiring a surface element deflection angle of each prior rough model, partitioning a surface element of each prior rough model according to the surface element deflection angle, and respectively counting the surface element occupation ratio in each interval and the cosine value of the surface element deflection angle mean value;
constructing a linear equation set according to the complex radar scattering cross section of each prior rough model, the surface element proportion in each interval and the cosine value of the surface element deflection angle mean value, and solving according to the linear equation set to obtain the weight factor of each interval;
and carrying out inversion according to the weight factor, a preset scaling factor and the surface element parameters of the rough target to be estimated to obtain the radar scattering cross section of the rough target to be estimated.
2. The method of RCS scaling measurement on rough targets according to claim 1, wherein the complex radar cross section of the prior rough model is obtained by means of simulation calculation or RCS measurement.
3. The method for measuring RCS scaling of a rough target according to claim 2, wherein a bin bias angle of each prior rough model is an included angle between a rough surface bin normal vector and a smooth surface bin normal vector of each prior rough model.
4. The method according to claim 3, wherein partitioning bins of each of the a priori coarse models according to the bin bias angles comprises:
and carrying out logarithmic transformation on the surface element deflection angles, and dividing the surface element deflection angles into preset intervals at equal intervals according to intervals covered by transformation results.
5. The RCS scaling measurement method for the rough target according to claim 4, wherein the cosine value of the mean deflection angle of the surface element in each interval is calculated by using the following formula:
in the above formulae, subscriptspNumbering prior coarse models, subscripts, to distinguish different coarsenessqIs the number of the deflection angle interval, indicates different deflection angle intervals,
is shown aspThe prior coarse model is distributed atqThe number of surface elements in the surface element interval,
indicating bin bias angle within interval
Is measured.
6. The method according to claim 5, wherein a linear equation set is constructed according to the complex radar scattering cross section of each prior coarse model, the bin fraction in each interval and the cosine of the mean value of the bin drift angles as follows:
in the above-mentioned formula, the first and second,
is shown aspA priori rough model is distributed inqThe area element ratio in the area element interval,
representing the cosine of the mean of the bin bias angles in each bin,
representing each of said a priori coarse models at an observation angle of
The scattering cross-section of the complex radar in time,
representing the weight factor of each interval that needs to be solved.
7. The RCS scaling measurement method for the rough target according to claim 6, wherein the obtaining of the radar scattering cross section of the rough target to be estimated by performing inversion according to the weighting factor, a preset scaling factor and the bin parameter of the rough target to be estimated comprises:
substituting the weight factor, a preset scaling factor and surface element parameters of the rough target to be estimated into the following formula, and solving to obtain a complex radar scattering cross section of the rough target to be estimated;
then obtaining a radar scattering cross section of the rough target to be estimated according to the complex radar scattering cross section;
in the above-mentioned formula, the first and second,
representing the surface element of different interval surface elements of the rough target to be estimatedThe ratio of the water to the oil,
representing the cosine value of the surface element deflection angle mean value of the rough object to be estimated,
which represents a weight factor, is given by the weight factor,srepresents a scaling factor; and the bin occupation ratios of bins in different intervals of the rough target to be estimated and the cosine values of the bin deflection angle mean values are bin parameters of the rough target to be estimated.
8. The method according to any one of claims 1 to 7, wherein the number of prior coarse models is selected to be consistent with the number of the bins of each prior coarse model.
9. The RCS scaling measurement method for rough objects according to claim 8, characterized in that the roughness parameter of the rough object to be estimated meets the following requirements:
the correlation length of the rough target to be estimated is fixed, and the root mean square height is changed, wherein the root mean square height
Has a value range of
Correlation length
In that
Selecting a certain fixed value within the range;
or the correlation length of the rough target to be estimated changes along with the variation of the root mean square height
Has a value range of
Correlation length of
。
10. A bin distribution based RCS scaling measurement apparatus for rough objects, the apparatus comprising:
the system comprises a prior model RCS obtaining module, a rough model calculating module and a rough model calculating module, wherein the prior model RCS obtaining module is used for selecting a plurality of prior rough models with different surface roughness according to a rough target to be estimated in a preset roughness interval and respectively obtaining a complex radar scattering cross section of each prior rough model;
a prior model surface element parameter acquisition module, configured to acquire a surface element deflection angle of each prior coarse model, partition the surface element of each prior coarse model according to the surface element deflection angle, and count a surface element proportion and a cosine value of a surface element deflection angle mean value in each interval respectively;
the weight factor solving module is used for constructing a linear equation set according to the complex radar scattering cross section of each prior rough model, the surface element occupation ratio in each interval and the cosine value of the surface element deflection angle mean value, and solving according to the linear equation set to obtain the weight factor of each interval;
and the RCS acquisition module is used for carrying out inversion according to the weight factor, a preset scaling factor and surface element parameters of the rough target to be estimated so as to obtain the radar scattering cross section of the rough target to be estimated.
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