CN117147977A - Test quality estimation method for unmanned aerial vehicle external field composite scattering measurement - Google Patents
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Abstract
The invention discloses a test quality estimation method for unmanned aerial vehicle external field composite scattering measurement, which utilizes an external field sliding rail type test system to measure accurate scattering results of limited angles of a region to be measured; combining electromagnetic scattering mechanism and deep learning, and utilizing accurate scattering results to complete geometric model reconstruction of unknown rough surfaces, and carrying out refined geometric modeling of tank targets; calculating a composite scattering result of the refined tank target and the rough surface by using a bouncing ray method based on a composite model of the refined tank target and the rough surface, and taking the result as a standard answer; and comparing the calculated scattering result with a result given by the unmanned aerial vehicle-mounted composite scattering measurement system, and giving out unmanned aerial vehicle-mounted scattering measurement accuracy analysis, thereby giving out angular domain measurement reliability ranges of different areas under the same meteorological conditions. According to the invention, the accuracy analysis of the external field scattering measurement of the unmanned airborne radar in different angular areas is provided according to external field accurate experimental equipment and a convolutional neural network and a computational electromagnetism accurate algorithm.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle external field composite scattering measurement, and particularly relates to a test quality estimation method for unmanned aerial vehicle external field composite scattering measurement.
Background
Accurate external field composite scatterometry by making an accurate measurement of the object above a roughened surface is critical for object detection. In the aspect of external field scatterometry of targets, radars also show a plurality of different mounting modes, and particularly in the aspect of scatterometry of land targets, the radars are realized in an airborne or missile-borne mode. However, considering the measurement cost in the actual field test process of the target, the more practical field scattering measurement of the land target is performed by adopting the unmanned aerial vehicle mounted radar, the more important and application in scientific research and industry are obtained.
The external field test environment is different from the internal field test environment, and the external field test environment has no key factors such as controllable conditions, controllable environments and the like. If weather such as wind, rain, fog, haze and the like occurs in an area to be tested on an external test site, and inherent errors such as a radio frequency device, a mechanical system and the like are added, the test result of the unmanned aerial vehicle scattering measurement system is inaccurate. For unmanned aerial vehicle scattering measurement platform, because its size is less, power is weaker, receives the influence of wind very easily and causes radar irradiation area to produce the deviation to cause final test result inaccuracy, this is the biggest factor of influence unmanned aerial vehicle airborne radar outfield scattering test result.
Therefore, in order to more effectively utilize electromagnetic scattering data of the whole upper half space measured by the unmanned aerial vehicle, the collected scattering data should be accurately detected before use so as to reject or correct the scattering data with larger error. However, so far, few research theory and method about accuracy detection of the scatterometry result of the unmanned airborne radar cause that the unmanned airborne radar with low cost cannot be applied to the scatterometry in a large scale, so based on the above, the accuracy analysis of the outfield unmanned airborne composite scatterometry needs to be further optimized and improved.
Disclosure of Invention
The invention aims to provide a test quality estimation method for unmanned aerial vehicle external field composite scattering measurement, which solves the problem that the accuracy analysis of the existing external field unmanned aerial vehicle external field composite scattering measurement needs to be further optimized and improved.
The technical scheme adopted by the invention is as follows:
a test quality estimation method for unmanned aerial vehicle external field composite scattering measurement,
measuring an accurate scattering result of a limited angle of a region to be measured by using an external field sliding rail type test system;
combining electromagnetic scattering mechanism and deep learning, and utilizing accurate scattering results to complete geometric model reconstruction of unknown rough surfaces, and carrying out refined geometric modeling of tank targets;
calculating a composite scattering result of the refined tank target and the rough surface by using a bouncing ray method based on a composite model of the refined tank target and the rough surface, and taking the result as a standard answer;
and comparing the calculated scattering result with a result given by the unmanned aerial vehicle-mounted composite scattering measurement system, and giving out unmanned aerial vehicle-mounted scattering measurement accuracy analysis, thereby giving out angular domain measurement reliability ranges of different areas under the same meteorological conditions.
The invention is also characterized in that:
the method comprises the following steps:
step 1, selecting an experimental test site;
step 2, preparing a testing instrument: setting a vector network analyzer, respectively connecting a radio frequency line, a loss network, an antenna and the like in sequence, and connecting the radio frequency line, the loss network and the antenna to a master control system on a computer in a fiber mode;
step 3, constructing an outfield sliding rail type accurate measurement system: the method comprises the steps of constructing an outfield sliding rail track and a self-moving object-carrying measuring platform, and connecting the outfield sliding rail track and a general control system;
step 4, setting measurement parameters of a slide rail type accurate measurement system in an external field, wherein the set parameters are specifically as follows: the azimuth scanning range is 0-360 degrees, the sampling interval is 1 degree, the pitch angle scanning range is 10-35 degrees, the sampling interval is 1 degree, the sampling frequency is set to be any frequency band of 1GHz-40GHz, the polarization modes are set to be two polarization modes of VV and HH, and the system is started to measure after the setting is completed;
step 5, starting accurate scattering measurement of the limited angle area: echo data in a set angular domain is acquired through a sliding rail type accurate scattering measurement system, the data comprise the amplitude and the phase of a scattering field, the acquired data are processed through a matlab programming language, and a distribution diagram of the two-dimensional amplitude and the phase of a field to be measured is drawn;
step 6, according to an accurate background scattering measurement result, a geometric model of a region to be measured is inverted through a convolution neural network fused with an electromagnetic mechanism, and an accurate geometric model of a tank target and an actual rough surface is constructed;
step 7, collecting an environmental sample of the area to be measured, and obtaining the dielectric properties of the area to be measured through a waveguide method, namely obtaining dielectric constant epsilon and magnetic permeability mu;
step 8, calculating to obtain accurate electromagnetic scattering results of the upper half space of the area to be detected and the target by a bouncing ray method according to the accurate geometric model and the medium attribute of the tank target and the actual rough surface;
step 9, preparing an unmanned aerial vehicle test system: the method comprises the steps of sequentially connecting an optical fiber, a photoelectric conversion module, a vector network analyzer, horn antenna equipment and a master control system, and finally mounting an antenna on an unmanned aerial vehicle test platform;
step 10, setting measurement parameters of an unmanned aerial vehicle on-board measurement system, setting a scanning azimuth angle, a pitch angle, a frequency and a polarization mode according to experimental conditions, and then starting a test;
step 11, data processing and comparison: based on the composite scattering result calculated by the electromagnetic algorithm in the step 8, an error distribution diagram of the unmanned aerial vehicle testing system under different angular domains is given;
and step 12, analyzing the error distribution diagram obtained in the step 11, and giving out a credibility test angle range of the unmanned aerial vehicle airborne test system under the measurement environment so as to realize reliability evaluation of unmanned aerial vehicle outfield measurement experiments.
In the step (1) of the process,
the root mean square height is used for describing the distribution condition of the rough surface in the vertical direction, namely the difference condition of height fluctuation, and is defined as:
the correlation length describes the roughness of the roughness surface in the horizontal direction, and in order to describe the correlation between sampling points on the roughness surface, a correlation function is introduced, which is defined as:
G(R)＝E[f(x)f(x+R)] (2)；
after the values of the correlation length and the root mean square height are determined, modeling is carried out on the random rough surface according to the Monte Carlo method, wherein the modeling comprises the following steps:
wherein L represents the size of the constructed rough surface, M represents the number of sampling points, delta h The root mean square height is expressed, l is the associated length of the roughened surface, x n ,y n The nth sample point on the roughened surface is shown.
In step 2 and step 9:
the model of the vector network analyzer is Agilent N9951A; two driving programs Keysight IO library suite and Keysight Command Expert are installed to realize the control of the vector network analyzer in a visa programming mode; the test mode is selected as S21 by the vector network analyzer.
In the step 3, the outer field sliding rail type guide rail is an arc guide rail, and the outer field sliding rail type guide rail can form a circular track through modularized splicing;
the self-moving object-carrying measuring platform comprises an object-carrying platform, an antenna bracket and a driving motor; the equipment for the external field test is placed on the carrying platform and comprises a vector network analyzer, a power amplifier, a low-noise amplifier, a mobile power supply, an antenna bracket and a general control computer.
The step 6 is specifically as follows:
grid quality analysis and debugging are carried out by adopting the grid eidolon function in the light-weight Rhinoceros, and meanwhile, the operation and editing on the grid level are supported; repairing the grid eidolon analysis results by debugging one by one, filling and repairing the damaged surface, deleting the non-manifold edge, re-connecting the curved surface, deleting the free edge, and finally achieving the goal of electromagnetic simulation calculation;
building an accurate geometric model of an actual rough surface: based on the external field sliding rail type accurate scattering measurement system, the depth convolution neural network fused with the electromagnetic scattering mechanism is adopted to invert the geometric model of the rough surface, and the total scattering field of the rough surface can be expressed as:
wherein,total scattered field of representation +.>The mth triangular patch is +.>The scattered field in the direction, N, represents the total number of triangular patches, for which the scattered field can be expressed as:
wherein,represents the external normal vector of the mth triangular patch, R represents the distance between the mth triangular patch and the far field observation point, k is the wave number, and 1/Z 0 Is the free space wave impedance, vector +.>And->Is the vector of the incident electric field and the magnetic field on the mth triangular patch, and the following relationship is followed between the incident electric field and the magnetic field:
assume that the coordinates of three vertexes are respectively According to the geometric relationship, the external normal vector of the triangular patch +.>Can be further expressed as:
when the mesh of the model is sufficiently dense, it is assumed that the amplitude and phase of the incident wave on one facet do not change, and therefore the mth triangular patch has its scattered field further expressed as:
where ζ (m) represents the area of the triangular patch under investigation, the expression of which can be written as:
when the incident and scattering unit wave vectors are fixed, the scattered field of one triangular patch is only a function of three vertex coordinates of the triangular patch, and the derivation shows that the scattering of the rough surface is only a function of all geometric sampling point coordinates;
the mathematical relationship between the unknowns can be represented using a nonlinear polynomial equation as follows:
wherein the function isAnd->The method is a polynomial equation with the order of m, the functions abs () and ang () respectively represent the amplitude and the phase of a scattered field under the extracted observation angle, the convolutional neural network is used for solving a formula (10) based on the scattered field of the rough surface, three vertex coordinates of the triangular surface patch are solved, point cloud data of the rough surface are obtained, and a geometric model of the actual rough surface is further constructed.
In step 8, the bouncing ray method specifically comprises the following steps:
step 8.1, generating a ray tube; when the ray tube is generated, the following principle is required to be followed, namely, an equiphase surface similar to the target in size is generated along the opposite direction of the incident electric wave according to the geometric shape of the target, and the equiphase surface is split according to the wavelength of the incident wave;
step 8.2, ray tracing; before the ray tracing is carried out on the target, discretizing the target, and selecting a geometric target with complex grid discretization by using a triangular patch;
step 8.3, field intensity calculation; after ray tracing is completed, the induced current of the patches in the bright area can be integrated according to a physical optical method to calculate the scattered field intensity at the far field observation position, and the scattered fields generated by all triangular patches generating the induced current on the whole target at the observation position are overlapped to further calculate the scattered field of the target to be detected.
The step 10 is specifically as follows:
step 10.1, according to experimental conditions, setting airborne measurement parameters of the unmanned aerial vehicle through a master control system, setting azimuth angles to be 0-360 degrees, angle intervals to be 1 degree, pitch angles to be 0-90 degrees, sampling intervals to be 1 degree, setting frequency to be any frequency band which can be set to be 1-40GHz for acquisition, and setting polarization modes to be VV and Hhl polarization modes;
and 10.2, placing an actual tank target to be tested in an experimental test field, and measuring by using the unmanned aerial vehicle-mounted test parameters set in the step 10.1 to obtain a tank target and background composite scattering measurement result of the upper half space.
The step 11 specifically comprises the following steps:
the error is described using the following formula:
wherein Error is r Errors of a represented unmanned plane measurement system and a calculation result, |and|| 2 Representing the norm of a two-dimensional matrix, RCS representing the exact calculation of the bouncing ray method,a comparison of the measurement results on an unmanned aircraft is shown; according to the formula (11), two scattering data are calculated, and a matlab tool is utilized to draw a matrix, so that an error distribution diagram of the unmanned aerial vehicle airborne test system under the scene is obtained.
And the simple parking lot payment system based on the visible light technology confirms the decoded two-dimensional code information and payment amount information, verifies the payment validity and completes the payment transaction process.
The method has the beneficial effects that according to the method for estimating the test quality of the unmanned aerial vehicle outfield composite scatterometry, the accuracy analysis of the outfield scatterometry of the unmanned aerial vehicle radar in different angular areas is provided according to outfield accurate experimental equipment and a convolutional neural network and a computational electromagnetism accurate algorithm; the method fills the blank of the accuracy analysis of the airborne radar scattering measurement of the outfield unmanned aerial vehicle to a certain extent, lays a theoretical foundation for the subsequent development of large-scale scattering measurement of the unmanned aerial vehicle, and promotes the development of electromagnetic measurement; the method solves the problem of accuracy analysis of the out-field unmanned aerial vehicle-mounted composite scattering measurement to a certain extent, and has good application significance.
Drawings
FIG. 1 is a schematic diagram of a portable vector network analyzer in a test quality estimation method of unmanned aerial vehicle outfield composite scatterometry;
FIG. 2 is a schematic diagram of a measurement of a outfield slide rail in the test quality estimation method of the outfield composite scatterometry of the unmanned aerial vehicle of the present invention;
FIG. 3 is a schematic diagram of the accurate measurement of the external field slide rail in the test quality estimation method of the external field composite scatterometry of the unmanned aerial vehicle of the invention;
FIG. 4 is a flow chart of the rough surface geometric model construction in the test quality estimation method of the unmanned aerial vehicle outfield composite scatterometry;
FIG. 5 is a schematic diagram of the dielectric parameters measured by the waveguide method in the test quality estimation method of the unmanned aerial vehicle outfield composite scatterometry;
FIG. 6 is a schematic diagram of a drone testing system of the present invention;
FIG. 7 is a schematic diagram of a test system of an unmanned aerial vehicle in the test quality estimation method of unmanned aerial vehicle outfield composite scatterometry;
fig. 8 is an error distribution diagram of a test system of the unmanned aerial vehicle at 20GHz in the test quality estimation method of unmanned aerial vehicle outfield composite scatterometry.
Detailed Description
The test quality estimation method for the unmanned aerial vehicle outfield composite scattering measurement is described in detail below with reference to the accompanying drawings and the specific embodiments.
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. The quality evaluation of the unmanned aerial vehicle composite scatterometry system in the bare soil environment is taken as an example.
The invention provides a test quality estimation method for unmanned aerial vehicle outfield composite scattering measurement,
(1) Experimental test site selection and simulation of geometric properties.
Geometric model simulation of land type asperities. The simulation of the geometric model of the field to be tested is carried out to provide data support for the inversion of the geometric model of the following sixth step. The most important parameters in the rough surface modeling process are the height fluctuation root mean square and the associated length.
The root mean square of the height relief, also known as root mean square height, is used herein to describe mainly the vertical distribution of the roughened surface, i.e. the difference in the height relief, the symbol delta h To represent the root mean square height of the roughened surface, defined as:
the correlation length is another important physical quantity describing the asperity statistical parameter and is generally indicated by the symbol l. The root mean square height is different, the related length describes the roughness condition of the rough surface in the horizontal direction, and in order to describe the related correlation condition between sampling points on the rough surface, the invention introduces a related function which is defined as:
G(R)＝E[f(x)f(x+R)] (1)；
after the values of the correlation length and the root mean square height are determined, the random rough surface can be modeled according to the Monte Carlo method, and the method comprises the following steps:
wherein L represents the size of the rough surface of the construction, M represents the number of sampling points, delta h The root mean square height is expressed, l is the associated length of the roughened surface, x n ,y n The nth sample point on the roughened surface is shown.
Example 1;
according to the actual measurement environment requirement, the root mean square is set to be 0.03λ.ltoreq.l.ltoreq.0.30λ, the stepping amplitude is 0.3λ, the root mean square height is a fixed value of 0.3λ, the wavelength is λ=0.3m, a large number of different rough surface model databases are built, 30000 groups of models are built together, the subsequent neural network learning is facilitated, the rough surface model is inverted by using a scattering field, and the parameters can simulate rough surfaces of bare soil types in most cases.
Example 2;
(2) Preparing a test instrument;
as shown in fig. 3, (2.1) a vector network analyzer is first set, and the vector network analyzer used in the present invention is a portable handheld vector network, and the model is agilent N9951A, so as to facilitate the outfield test. Firstly, a computer master control system can control the vector network in an optical fiber communication mode, and the vector network analyzer is controlled in a visa programming mode by installing two driving programs Keysight IO library suite and Keysight Command Expert. The test mode is selected as "S21" by the vector network analyzer.
And (2.2) selecting a radio frequency wire with proper length, and simultaneously reducing the loss generated by overlong radio frequency wire as far as possible according to the connection length requirement of the vector to the antenna, so that the length of the radio frequency wire used in the experiment is 1.6 meters. Two horn antennas are selected to obtain single-station radar scattering data, the horn antennas are arranged on an antenna bracket, the antenna bracket is made of materials with good bearing performance, a flange plate is arranged to control the angle direction of an antenna cantilever, and the testing conditions of different pitching angles of the antenna are met.
Example 3;
and (3) constructing the external field sliding rail type accurate measurement system as shown in fig. 1 and 2.
(3.1) the outfield sliding rail type guide rail is an arc guide rail, a circular rail can be formed through modularized splicing, and all small arc guide rails are connected by adopting a buckle mode so as to ensure the continuity between the sliding rails. The radius after splicing is about 5 meters. After the guide rail is built, the azimuth angle which can be measured is 0-360 degrees, and the measured interval angle is 1 degree at minimum.
And (3.2) the self-moving object-carrying measuring platform comprises an object-carrying platform, an antenna bracket and a driving motor. All equipment for external field test is placed on the object carrying platform, and comprises a vector network analyzer, a power amplifier, a low-noise amplifier, a mobile power supply, an antenna bracket and a general control computer. The measurement parameters are set by the general control computer, the azimuth angle is 0-360 degrees in a one-step one-stop mode, and the stepping angle interval is 1 degree; and measuring scattering echo with pitch angle of 10-35 degrees and step angle interval of 1 degree. Thus, a maximum of 361×25 scatter echo acquisitions of the measurement angle can be achieved.
Example 4;
(4) And setting measurement parameters of the sliding rail type accurate measurement system.
After a test site is selected and a slide rail type accurate measurement system is built, measurement parameters are set on a general control computer. The parameters set are as follows: the azimuth scanning range is 0-360 degrees, the sampling interval is 1 degree, the pitch angle scanning range is 10-35 degrees, the sampling interval is 1 degree, the sampling frequency is set to be a single frequency band of 1GHz, the wavelength is lambda=0.3m, the polarization mode is set to be VV polarization, and the system is started to measure after the setting is completed.
(5) An accurate scattered echo measurement is started.
Echo data in a set angular domain is acquired through a slide rail type accurate scattering measurement system, the data comprise the amplitude and the phase of a scattering field, the acquired data are processed through matlab programming language, and a distribution diagram of the two-dimensional amplitude and the phase of a field to be measured is drawn.
(6) And constructing an accurate geometric model of the tank target and the actual rough surface.
(6.1) the invention carries out fine modification on the T72 tank target model of the open source so as to achieve the purpose of electromagnetic simulation calculation. Generally, the geometric model of the tank target mainly takes a Mesh model, but after the Mesh grid is formed, a large-scale editing grid is generally not supported, and the grid is edited directly from the grid with less time and labor consumption due to the structure and data characteristics of the Mesh grid. Typically, a small-scale inspection debug is performed on the grid. The problems of surface intersection, non-manifold edges and free edges generally occur in grids, and auxiliary repair is carried out by carrying out grid analysis by means of a grid analysis tool or hypermesh software. Manually repairing the grid eidolon analysis results by debugging one by one, filling and repairing the damaged surface, deleting the non-manifold edge, re-connecting the curved surfaces, and deleting the free edge. Finally, the target for electromagnetic simulation calculation is achieved.
As shown in fig. 4, (6.2) the actual matte geometry model construction. In order to more conveniently realize the construction of the geometric information of the rough surface of the unknown terrain, the invention adopts a depth convolution neural network fused with an electromagnetic scattering mechanism to invert the geometric model of the rough surface based on the external field sliding rail type accurate scattering measurement system. Based on the basic theory of physical optics and geometrical optics, the total scattered field for a rough surface can be expressed as:
in the method, in the process of the invention,total scattered field of representation +.>The mth triangular patch is +.>The scattered field in the direction, N, isTotal number of triangular patches. The fringe field for the mth triangular patch can be expressed as:
in the method, in the process of the invention,represents the external normal vector of the mth triangular patch, R represents the distance between the mth triangular patch and the far field observation point, k is the wave number, and 1/Z 0 Is free space wave impedance
Vector quantityAnd->Is the vector of the incident electric field and the magnetic field on the mth triangular patch, and the following relationship is followed between the incident electric field and the magnetic field:
obviously, the external normal vector of the dough sheetAnd there is an association between the three vertices of the triangular patch. We assume that the coordinates of the three vertices are +.> According to the geometric relationship, the external normal vector of the triangular patch +.>Can be further expressed as:
when the mesh of the model is sufficiently dense, it is assumed that the amplitude and phase of the incident wave on one facet do not change, and therefore the m-th triangular patch whose fringe field can be further expressed as:
where ζ (m) represents the area of the triangular patch under investigation. The expression can be written as:
from the above derivation, it can be seen that the fringe field of a triangular patch is only a function of the coordinates of the three vertices of the triangular patch when the incident and scattering unit wave vectors are fixed. The above derivation shows that the scattering of the roughened surface is only a function of the coordinates of all geometric sampling points.
Convolutional neural networks are important tools to describe complex nonlinear relationships between inputs and outputs, often used for nonlinear tasks such as parametric inversion. It can be easily found from the above formula that when the test azimuth/zenith angle and polarization mode of the transmitting/receiving antenna are fixed, the relationship between the unknown variable (three-dimensional cartesian coordinate values of the vertex of each face) and the known variable (single-site fringe field of the roughened surface) can be expressed by a polynomial. In other words, the mathematical relationship between the unknowns and the unknowns may be represented using a nonlinear polynomial equation as follows:
in the formula, the functionAnd->Is a polynomial equation of order m, and the functions abs () and ang () represent the amplitude and phase, respectively, of the scattered field at the extraction observation angle. The main task of the convolutional neural network is to solve the vertex coordinates of the surface patch based on the scattered field of the rough surface and obtain the point cloud data of the rough surface, so as to construct a geometric model of the rough surface.
Example 5;
the convolution neural network model is a densely connected convolution neural network (DenseNet), the selected core structure is DenseNet-121, the azimuth angle measured by the external field sliding rail type measuring system in the step (5) is firstly 0-360 degrees, the pitch angle is 10-35 degrees, the sampling interval is 1 degrees, namely 361 x 25 sampling points, in order to realize inversion of the convolution neural network more conveniently, the 361 x 25 sampling points are rearranged to form a two-dimensional matrix of 95 x 2, the elements in the matrix represent the amplitude and the phase of a scattering electric field respectively, and the number of the elements of the matrix is completely enough for inverting the point cloud model of a rough surface. And (3) inverting and outputting point cloud data of unknown rough surface topography through a convolutional neural network, and performing model reconstruction on the point cloud data to obtain a refined geometric model of the rough surface.
(7) And collecting dielectric parameters of the environmental sample of the region to be measured.
As shown in fig. 5, the dielectric parameters in the region to be measured are acquired here using a waveguide method. The concrete method is that a tweezers tool is used for collecting a soil sample in a region to be tested, the soil sample is placed in a waveguide, and the sample is packaged by using an adhesive tape (the dielectric constant of the adhesive tape is consistent with that of air and can be regarded as air). And then, connecting the waveguide with a vector network analyzer, measuring S parameters of the sample to be measured, and further calculating the dielectric constant and the magnetic permeability of the environmental sample to be measured.
(8) And calculating the accurate electromagnetic scattering result of the upper half space of the region to be detected and the target.
According to the fine geometric model of the tank target and the rough surface constructed in the step (6), the dielectric parameters of the rough surface obtained by the measurement in the step (7) are combined, and meanwhile, the material of the tank target is defined as an ideal conductor. The modeling work before the electromagnetic scattering calculation was completed up to this point. And then, performing single-station scattering calculation of the upper half space on the built composite model of the tank and the rough surface by using a bouncing ray method. The calculation parameters set here are: the incident azimuth angle is 0-360 degrees, the angle interval is 1 degree, the pitch angle is 0-90 degrees, the sampling interval is 1 degree, the frequencies are set to four frequency bands of 20GHz, 22GHz, 24GHz and 26GHz, and the polarization mode is set to VV polarization. And calculating the composite scattering of the target and the rough surface background by using a bouncing ray method, and obtaining the far-field RCS of the target and the rough surface background by using a calculation formula of a radar scattering cross section.
The bouncing ray method can be generally realized by the following three steps:
(8.1) tube generation. In the generation of the radiation tube, it is necessary to generate an equiphase surface having a size similar to that of the target along the opposite direction of the incident radio wave according to the geometry of the target, and to divide the equiphase surface according to the wavelength of the incident wave, and the division size is generally 1/10 wavelength.
(8.2) ray tracing. The discretization processing is needed before the ray tracing is carried out on the target, in general, the triangular patches are adopted for dividing the geometric target with complex grid discretization, because the coupling effect is low between the triangular patches in the ray tracing process, the influence on the total scattered field of the target is very small, in addition, the geometric shape of the triangular patches is simple, and the ray tracing process is more convenient and quicker.
(8.3) field strength calculation. After ray tracing is completed, the induced current of the surface patch of the bright area can be integrated according to a physical optical method to calculate the scattering field intensity at the far field observation position, and the scattering fields generated by all triangular surface patches generating the induced current on the whole target at the observation position are overlapped to calculate the scattering field of the target to be detected.
(9) The unmanned aerial vehicle test system is prepared.
As shown in fig. 6, (9.1) there is serious loss of the radio frequency line when the radio frequency line is adopted to transmit electromagnetic signals in the conventional way, when the length of the selected radio frequency line is 8m, the loss is as high as 8.5dB, the loss intensity is far higher than the requirement of the unmanned aerial vehicle test system on power, and in order to solve the problem, the unmanned aerial vehicle test system adopts an optical fiber and photoelectric conversion module to realize the signal source reliability transmission of the unmanned aerial vehicle radar. The signal transmission system comprises four photoelectric conversion modules and two optical fibers with the length of 35m, and the energy loss of the optical fibers with the length tested is only 8dB, so that the requirements of the outfield test are met. After the signal transmission medium is selected, the optical fiber, the photoelectric conversion module, the vector network analyzer, the horn antenna equipment and the master control system are sequentially connected, and finally the antenna is mounted on the unmanned aerial vehicle testing platform.
(9.2) setting a vector network analyzer through a master control system, wherein the vector network analyzer used by the invention is a portable handheld vector network, and the model of the vector network analyzer is Agilent N9951A so as to facilitate the outfield test. Firstly, a computer master control system can control the vector network in an optical fiber communication mode, and the vector network analyzer is controlled in a visa programming mode by installing two driving programs Keysight IO library suite and Keysight Command Expert. The test mode is selected as "S21" by the vector network analyzer.
As shown in fig. 7, (10) setting measurement parameters of the unmanned aerial vehicle-mounted measurement system;
and (10.1) setting unmanned aerial vehicle airborne measurement parameters through a general control system according to experimental conditions, wherein the azimuth angle is set to be 0-360 degrees, the angular interval is 1 degree, the pitch angle is set to be 0-90 degrees, the sampling interval is 1 degree, the frequency is set to be 20GHz, 22GHz, 24GHz and 26GHz for scattering data acquisition, and the polarization mode is set to be VV polarization because the unmanned aerial vehicle airborne measurement system acquires the scattering field distribution which is the whole upper half space of the region to be measured in the experiment. The parameter settings are consistent with electromagnetic calculation settings, so that measurement quality evaluation of the unmanned aerial vehicle-mounted scattering measurement system is facilitated.
And (10.2) placing an actual tank target to be tested in an experimental test field, and measuring by using the unmanned aerial vehicle-mounted test parameters set in the step (10.1) to obtain a tank target and background composite scattering measurement result of the upper half space.
(11) Processing and comparing data.
And (3) taking the composite scattering result calculated by using the bouncing ray method in the step (8) as an accurate value, and giving error distribution diagrams of the unmanned aerial vehicle testing system under different angular domains. The error is described herein using the following formula:
in the formula, error r Errors in the measurement system and calculation results of the represented drone, sign | I.I. | 2 Representing the norm of a two-dimensional matrix, RCS representing the exact calculation of the bouncing ray method,a comparison of the measurement results on an unmanned vehicle is shown. According to the above formula, the two scattering data are operated, and the matrix is drawn by using a matlab tool, so that an error distribution diagram of the unmanned aerial vehicle airborne test system under the scene is obtained.
As shown in fig. 8, (12) the error distribution map drawn in (11) is analyzed. FIG. 8 shows the error distribution diagram of the unmanned aerial vehicle test system at 20GHz, from which it can be seen that the azimuth angle is about 80-120 at 90 ° pitch angle; the 30 ° pitch angle and the 140 ° -240 ° azimuth angle are subject to large errors, which means that the measurement results of these angles are less reliable under the meteorological conditions.
According to the testing quality estimation method for the unmanned aerial vehicle outfield composite scattering measurement, outfield scattering measurement accuracy analysis of unmanned aerial vehicle radars in different angular areas is given according to outfield accurate experimental equipment and a convolution neural network and a computational electromagnetism accurate algorithm; the accuracy of the airborne composite scattering measurement of the outfield unmanned aerial vehicle is improved; the applicability of the method is improved.
Claims (9)
1. The test quality estimation method for the unmanned aerial vehicle external field composite scattering measurement is characterized in that,
measuring an accurate scattering result of a limited angle of a region to be measured by using an external field sliding rail type test system;
combining electromagnetic scattering mechanism and deep learning, and utilizing accurate scattering results to complete geometric model reconstruction of unknown rough surfaces, and carrying out refined geometric modeling of tank targets;
calculating a composite scattering result of the refined tank target and the rough surface by using a bouncing ray method based on a composite model of the refined tank target and the rough surface, and taking the result as a standard answer;
and comparing the calculated scattering result with a result given by the unmanned aerial vehicle-mounted composite scattering measurement system, and giving out unmanned aerial vehicle-mounted scattering measurement accuracy analysis, thereby giving out angular domain measurement reliability ranges of different areas under the same meteorological conditions.
2. The method for estimating the test quality of the unmanned aerial vehicle outfield composite scatterometry according to claim 1, wherein the method is specifically performed according to the following steps:
step 1, selecting an experimental test site;
step 2, preparing a testing instrument: setting a vector network analyzer, respectively connecting a radio frequency line, a loss network, an antenna and the like in sequence, and connecting the radio frequency line, the loss network and the antenna to a master control system on a computer in a fiber mode;
step 3, constructing an outfield sliding rail type accurate measurement system: the method comprises the steps of constructing an outfield sliding rail track and a self-moving object-carrying measuring platform, and connecting the outfield sliding rail track and a general control system;
step 4, setting measurement parameters of a slide rail type accurate measurement system in an external field, wherein the set parameters are specifically as follows: the azimuth scanning range is 0-360 degrees, the sampling interval is 1 degree, the pitch angle scanning range is 10-35 degrees, the sampling interval is 1 degree, the sampling frequency is set to be any frequency band of 1GHz-40GHz, the polarization modes are set to be two polarization modes of VV and HH, and the system is started to measure after the setting is completed;
step 5, starting accurate scattering measurement of the limited angle area: echo data in a set angular domain is acquired through a sliding rail type accurate scattering measurement system, the data comprise the amplitude and the phase of a scattering field, the acquired data are processed through a matlab programming language, and a distribution diagram of the two-dimensional amplitude and the phase of a field to be measured is drawn;
step 6, according to an accurate background scattering measurement result, a geometric model of a region to be measured is inverted through a convolution neural network fused with an electromagnetic mechanism, and an accurate geometric model of a tank target and an actual rough surface is constructed;
step 7, collecting an environmental sample of the area to be measured, and obtaining the dielectric properties of the area to be measured through a waveguide method, namely obtaining dielectric constant epsilon and magnetic permeability mu;
step 8, calculating to obtain accurate electromagnetic scattering results of the upper half space of the area to be detected and the target by a bouncing ray method according to the accurate geometric model and the medium attribute of the tank target and the actual rough surface;
step 9, preparing an unmanned aerial vehicle test system: the method comprises the steps of sequentially connecting an optical fiber, a photoelectric conversion module, a vector network analyzer, horn antenna equipment and a master control system, and finally mounting an antenna on an unmanned aerial vehicle test platform;
step 10, setting measurement parameters of an unmanned aerial vehicle on-board measurement system, setting a scanning azimuth angle, a pitch angle, a frequency and a polarization mode according to experimental conditions, and then starting a test;
step 11, data processing and comparison: based on the composite scattering result calculated by the electromagnetic algorithm in the step 8, an error distribution diagram of the unmanned aerial vehicle testing system under different angular domains is given;
and step 12, analyzing the error distribution diagram obtained in the step 11, and giving out a credibility test angle range of the unmanned aerial vehicle airborne test system under the measurement environment so as to realize reliability evaluation of unmanned aerial vehicle outfield measurement experiments.
3. The method for estimating the test quality of the unmanned aerial vehicle outfield composite scatterometry according to claim 1, wherein in step 1,
the root mean square height is used for describing the distribution condition of the rough surface in the vertical direction, namely the difference condition of height fluctuation, and is defined as:
the correlation length describes the roughness of the roughness surface in the horizontal direction, and in order to describe the correlation between sampling points on the roughness surface, a correlation function is introduced, which is defined as:
G(R)＝E[f(x)f(x+R)] (2)；
after the values of the correlation length and the root mean square height are determined, modeling is carried out on the random rough surface according to the Monte Carlo method, wherein the modeling comprises the following steps:
wherein L represents the size of the constructed rough surface, M represents the number of sampling points, delta h The root mean square height is expressed, l is the associated length of the roughened surface, x n ,y n The nth sample point on the roughened surface is shown.
4. The method for estimating the test quality of the unmanned aerial vehicle outfield composite scatterometry according to claim 1, wherein in step 2 and step 9:
the model of the vector network analyzer is Agilent N9951A; two driving programs Keysight IO library suite and Keysight Command Expert are installed to realize the control of the vector network analyzer in a visa programming mode; the test mode is selected as S21 by the vector network analyzer.
5. The method for estimating the test quality of the unmanned aerial vehicle outfield composite scatterometry according to claim 1, wherein in the step 3, the outfield slide rail type guide rail is an arc guide rail, and the outfield slide rail type guide rail can form a circular track through modularized splicing;
the self-moving object-carrying measuring platform comprises an object-carrying platform, an antenna bracket and a driving motor; the equipment for the external field test is placed on the carrying platform and comprises a vector network analyzer, a power amplifier, a low-noise amplifier, a mobile power supply, an antenna bracket and a general control computer.
6. The method for estimating the test quality of the unmanned aerial vehicle outfield composite scatterometry according to claim 1, wherein the step 6 is specifically:
grid quality analysis and debugging are carried out by adopting the grid eidolon function in the light-weight Rhinoceros, and meanwhile, the operation and editing on the grid level are supported; repairing the grid eidolon analysis results by debugging one by one, filling and repairing the damaged surface, deleting the non-manifold edge, re-connecting the curved surface, deleting the free edge, and finally achieving the goal of electromagnetic simulation calculation;
building an accurate geometric model of an actual rough surface: based on the external field sliding rail type accurate scattering measurement system, the depth convolution neural network fused with the electromagnetic scattering mechanism is adopted to invert the geometric model of the rough surface, and the total scattering field of the rough surface can be expressed as:
wherein,total scattered field of representation +.>The mth triangular patch is +.>The scattered field in the direction, N, represents the total number of triangular patches, for which the scattered field can be expressed as:
wherein,represents the external normal vector of the mth triangular patch, R represents the distance between the mth triangular patch and the far field observation point, k is the wave number, and 1/Z 0 Is the free space wave impedance, vector +.>And->Is the vector of the incident electric field and the magnetic field on the mth triangular patch, and the following relationship is followed between the incident electric field and the magnetic field:
assume that the coordinates of three vertexes are respectively According to the geometric relationship, the external normal vector of the triangular patch +.>Can be further expressed as:
when the mesh of the model is sufficiently dense, it is assumed that the amplitude and phase of the incident wave on one facet do not change, and therefore the mth triangular patch has its scattered field further expressed as:
where ζ (m) represents the area of the triangular patch under investigation, the expression of which can be written as:
when the incident and scattering unit wave vectors are fixed, the scattered field of one triangular patch is only a function of three vertex coordinates of the triangular patch, and the derivation shows that the scattering of the rough surface is only a function of all geometric sampling point coordinates;
the mathematical relationship between the unknowns can be represented using a nonlinear polynomial equation as follows:
wherein the function isAnd->The method is a polynomial equation with the order of m, the functions abs () and ang () respectively represent the amplitude and the phase of a scattered field under the extracted observation angle, the convolutional neural network is used for solving a formula (10) based on the scattered field of the rough surface, three vertex coordinates of the triangular surface patch are solved, point cloud data of the rough surface are obtained, and a geometric model of the actual rough surface is further constructed.
7. The method for estimating the test quality of the unmanned aerial vehicle outfield composite scatterometry according to claim 1, wherein in step 8, the bouncing ray method specifically comprises:
step 8.1, generating a ray tube; when the ray tube is generated, the following principle is required to be followed, namely, an equiphase surface similar to the target in size is generated along the opposite direction of the incident electric wave according to the geometric shape of the target, and the equiphase surface is split according to the wavelength of the incident wave;
step 8.2, ray tracing; before the ray tracing is carried out on the target, discretizing the target, and selecting a geometric target with complex grid discretization by using a triangular patch;
step 8.3, field intensity calculation; after ray tracing is completed, the induced current of the patches in the bright area can be integrated according to a physical optical method to calculate the scattered field intensity at the far field observation position, and the scattered fields generated by all triangular patches generating the induced current on the whole target at the observation position are overlapped to further calculate the scattered field of the target to be detected.
8. The method for estimating the test quality of the unmanned aerial vehicle outfield composite scatterometry according to claim 1, wherein the step 10 is specifically:
step 10.1, according to experimental conditions, setting airborne measurement parameters of the unmanned aerial vehicle through a master control system, setting azimuth angles to be 0-360 degrees, angle intervals to be 1 degree, pitch angles to be 0-90 degrees, sampling intervals to be 1 degree, setting frequency to be any frequency band which can be set to be 1-40GHz for acquisition, and setting polarization modes to be VV and Hhl polarization modes;
and 10.2, placing an actual tank target to be tested in an experimental test field, and measuring by using the unmanned aerial vehicle-mounted test parameters set in the step 10.1 to obtain a tank target and background composite scattering measurement result of the upper half space.
9. The method for estimating the test quality of the unmanned aerial vehicle outfield composite scatterometry according to claim 1, wherein step 11 is specifically:
the error is described using the following formula:
wherein Error is r Errors of a represented unmanned plane measurement system and a calculation result, |and|| 2 Representing the norm of a two-dimensional matrix, RCS representing the exact calculation of the bouncing ray method,a comparison of the measurement results on an unmanned aircraft is shown; according to the formula (11), two scattering data are calculated, and a matlab tool is utilized to draw a matrix, so that an error distribution diagram of the unmanned aerial vehicle airborne test system under the scene is obtained.
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