CN108711335A - A kind of distributed large scene radar imagery emulation mode of realization and its system - Google Patents
A kind of distributed large scene radar imagery emulation mode of realization and its system Download PDFInfo
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- CN108711335A CN108711335A CN201810516990.2A CN201810516990A CN108711335A CN 108711335 A CN108711335 A CN 108711335A CN 201810516990 A CN201810516990 A CN 201810516990A CN 108711335 A CN108711335 A CN 108711335A
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- G09B9/00—Simulators for teaching or training purposes
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Abstract
The invention discloses a kind of distributed large scene radar imagery emulation mode of realization and its systems, including step：S1 is imaged basic data processing：Including the classification of terrain and its features data and the processing of terrestrial digital elevation model；S2 scanning ranges are determining and divide：It is calculated according to the scanning range parameter of carrier aircraft Position and orientation parameters, the range of radar and resolution ratio and antenna, including distance divides scanning range as several regular facet units along distance to size range and orientation scanning range to orientation；S3 carries out calculating and radar image Fusion Features to the backscattering coefficient of facet model, obtains a wave beam imaging data；S4 generates emulating image and shows.The airborne radar that can be used for different model, landform and atural object to different zones carry out simulation imaging.
Description
Technical field
The present invention relates to radar imagery simulation technical fields, in particular to a kind of distributed large scene radar of realization
Imaging simulation method and its system.
Background technology
Radar imagery provides Informational support for the battle reconnaissance of operational aircraft, monitoring, navigation and precision strike, has whole day
Wait the characteristics of ground mapping is carried out to ground and sea.One of key content of army's flight training at present is exactly to study how to close
Reason makes pilot, automatic pilot and radar system reach best fit, is flying using the ground mapping function of airborne radar
In the case of member's operation is simple as possible, the accurate discrimination to target and effectively attack are realized.However, global function simulator and actual load
Training is since expensive, training court environment is limited, the restriction of factors is difficult regular, multiple batches of opens more than student's quantity etc.
Exhibition, and simulated training function of the global function simulator in terms of radar is also perfect not to the utmost.Therefore, mould is carried out by emulation radar
Quasi- training is to solve the problems, such as this desirable route.
The research that foreign countries emulate airborne radar imaging is more comprehensive, and deeply, software development technique is advanced, degree of commercialization
Height, application is good, but is blocked to China.And the domestic research to airborne radar imaging emulation is relatively late, most radars are imitative
True system is only illustrated from principle, does not provide complete simulation process, and difficulty is shown in commercially produced product, in integrality, is led to
Gap with property, compatibility etc. and foreign countries is also bigger.
Radar imagery simulation study at present can be divided into three classes：The first kind is emulated in strict accordance with radar imagery process from mesh
Space is marked to the echo forming process of signal space, and the imaging process from signal space to image space focuses on thunder
Up to Simulation of Echo Signal, then utilizes and obtain emulating image with the same or analogous imaging algorithm of real system；Second class is by thunder
Up to a signal transduction system is regarded as, uses backscattering coefficient figure as input, radar is obtained with ssystem transfer function phase convolution
Emulating image, these two kinds of methods simulation accuracy is high, and obtained analog image verisimilitude is strong, but due to being related to signal simulation, leads to
Chang Fangfa is complicated and calculation amount is very big, real-time is poor, generally requires to realize by the way that DSP, FPGA etc. are hardware-accelerated, this way
Although very close to actual load, for the application towards simulated training, technical research and use cost are all relatively high
's.Third class method obtains radar image based on characteristic simulation, by the geometrical model and radiation patterns of radar imagery.It is imitative
True method is simple, and real-time is high, but simulation accuracy is not so good as preceding two classes method.In addition, most of radar imagery Simulating software package
Containing multiple system modules, and the execution of each module and intermodule communication are executed using serial mode, and simulation velocity is slow, and efficiency is low,
It is difficult to meet the needs of radar real time scan imaging.
In conclusion there are following problems for existing radar imagery emulation：
(1) external to China's technology blockage, and domestic majority rests in theoretical research, difficulty is shown in commercially produced product；
(2) simulation imaging algorithm is complicated, and computationally intensive, real-time is poor, need to be by hardware-accelerated, but R&D costs are high；
(3) each system module of imaging simulation software is executed using serial mode, and speed is slow, and efficiency is low, cannot meet imaging
It is required that.
Invention content
For the above-mentioned prior art the problem of, it is imitative that the present invention provides a kind of distributed large scene radar imagery of realization
True method and its system.The airborne radar that can be used for different model, landform and atural object to different zones carry out simulation imaging.
For the radar image that pilot sees, only areas imaging, image resolution ratio, pixel grey scale and image refreshing side need to be determined
Four factors such as formula can realize the emulation purpose towards simulated training.
To achieve the above object, the present invention provides the following technical solutions：
A kind of distributed large scene radar imagery emulation mode of realization, including step：
S1 is imaged basic data processing：Including the classification of terrain and its features data and terrestrial digital elevation model (Digital
Elevation Model, vehicle economy M) processing；
S2 scanning ranges are determining and divide：According to carrier aircraft Position and orientation parameters, the range of radar and resolution ratio and day
The scanning range parameter of line is calculated, including distance is to size range and orientation scanning range, along distance to and orientation
It is several regular facet units to divide scanning range；
S3 carries out calculating and radar image Fusion Features to the backscattering coefficient of facet model, obtains a wave beam
Imaging data；
S4 generates emulating image and shows.
Further, the terrain and its features data classification, refers to being calculated according to image classification aerial photograph or satellite image
Method is classified using remote sensing image processing software；
The processing of the terrestrial digital elevation model, referring to will be according to the certain space sampling interval using fractal regressive method
It surveys obtained altitude data and is interpolated to resolution ratio higher ground tee section；
Terrain and its features grouped data and terrestrial digital elevation model data, are read into computer in the form of two-dimensional array
In memory, then utilize CUDA (Compute Unified Devices Architecture calculate Unified Device framework) right
Function should be copied to copy in the CUDA Texture memories of video memory equipment.
Further, the step of acquisition wave beam imaging data includes：
S31 is according to distance to Chi Cunfanwei [MinDist, MaxDist]With orientation Sao Miaofanwei [StartAngle,
EndAngle], in conjunction with range resolution XResolution and orientation sweep spacing YResolution by scanning imagery model
It encloses and is divided into DIMX*DIMY regular facet unit, wherein：
DIMX=(MaxDist-MinDist)/XResolution；
DIMY=(EndAngle-StartAngle)/YResolution；
Thread is configured for each facet unit：Specified two dimension thread block blocks sizes are (nBlockX, nBlockY), two dimension
Thread lattice grids sizes are (DIMX/nBlockX, DIMY/nBlockY), and two-dimentional thread block is respectively tieed up size and used respectively
BlockDim.x and blockDim.y indicates that in the block index of any online journey of thread uses threadIdx.x and threadIdx.y
It indicates, respectively tie up size in two-dimentional thread lattice is indicated with gridDim.x and gridDim.y respectively, and any thread block is in thread lattice
Index indicated with blockIdx.x and blockIdx.y, wherein：
BlockDim.x=nBlockX, blockDim.y=nBlockY；
GridDim.x=DIMX/nBlockX, gridDim.y=DIMY/nBlockY；
Index of each thread in thread lattice be：
Range gate indexes idxX=threadIdx.x+blockIdx.x*blockDim.x；
Orientation door indexes idxY=threadIdx.y+blockIdx.y*blockDim.y；
Therefore the index of each facet unit is also (idxX, idxY)；
Calculate each facet unit idxX, idxY) distance DistX and azimuth AzimY in areas imaging：
DistX=MinDist+idxX*XResolution；
AzimY=StartAngle+idxY*YResolution；
S32 reads the terrain and its features classification of facet unit (DistX, AzimY) corresponding position from CUDA Texture memories
With terrestrial digital elevation model data；
The radar wave incidence angle of each facet model of S33 combination carrier aircraft location determinations, each facet unit mould of parallel computation
The backscattering coefficient of type obtains wave beam RCS (radar cross section) value, is stored in CUDA global storages；
The wave beam RCS value that S34 obtains step S33 and radar system transmission function phase convolution, i.e. radar image are special
Sign fusion；
S35 obtains a wave beam imaging data, is stored in the CUDA global storages of video memory equipment.
Further, described image is shown including step：
S41 copies to obtain wave beam imaging data from the CUDA global storages of video memory equipment in computer
In depositing；
One wave beam imaging data is filled into image buffer using fan-shaped filling algorithm and shown by S42；
The image buffer is in main frame memory according to picture size (ImageWidth*
ImageHeight) physical memory (ImageWidth*ImageHeight*nPixel) distributed, wherein ImageWidth generations
Table image pixel width, ImageHeight representative image pixels talls, nPixel representative image port numbers.
Further, the backscattering coefficient calculating step of facet model includes：
S331：According to the principle of least square, the terrestrial digital elevation model in each corresponding region of facet unit is taken, is carried out
Interpolation fitting obtains the facet model tangent with Earth surface plane；
S332：Calculate the radar wave incidence angle of each facet model；
S333：Facet model backscattering coefficient is public according to the average backscattering coefficient empirical model of unit area
Formula is calculated：
In backscattering coefficient empirical model formula, β is the radar wave incidence angle of facet model, σhFor ground level
Fluctuating standard deviation, λ are radar wavelength, and A, B, C, D are the empiricals sorted out according to radar measurements, to differently species
Type value is different；
S334：The backscattering coefficient of each facet model is subjected to coherent superposition, obtains the RCS values of a wave beam.
Further, each facet model radar wave incidence angle is calculated using facet model, passes through each facet
The center vector sum normal vector of unit determines.
Further, facet unit size should be less than resolution ratio and be more than radar wavelength, and small as possible with preferably approximate earth's surface
It rises and falls.
In addition, the present invention also provides a kind of distributed large scene radar imagery analogue systems of realization, including：
It is imaged basic data processing module, for the processing to terrain and its features data classification and terrestrial digital elevation model；
Scanning range determination and division module, for according to carrier aircraft Position and orientation parameters, the range of radar and resolution ratio
And the scanning range parameter of antenna calculates, and determines scanning range, and scanning range is divided into number to orientation along distance
A rule facet unit；
Parallel computation module, the backscattering coefficient for facet model calculates and radar image Fusion Features；
Image-forming module, for generating emulating image and showing.
Further, the imaging basic data processing module includes following submodule：
Terrain and its features data classification submodule, for classifying to aerial photograph or satellite image；
Terrestrial digital elevation model handles submodule, and being used for will be according to the certain space sampling interval using fractal regressive method
It surveys obtained altitude data and is interpolated to resolution ratio higher ground tee section.
Further, the parallel computation module includes following submodule：
Facet model setting up submodule takes the land in each corresponding region of facet unit according to the principle of least square
Ground digital elevation model carries out interpolation fitting, obtains a facet model tangent with Earth surface plane；
Radar wave incidence angle computational submodule, for being calculated according to the center vector sum normal vector of each facet unit
The radar wave incidence angle of facet model；
Backscattering coefficient computational submodule is used for the backscattering coefficient of each facet model of parallel computation, to knot
Fruit coherent superposition obtains a wave beam RCS value, is stored in CUDA global storages；
Radar image Fusion Features submodule, for by a wave beam RCS value and radar system transmission function phase convolution, obtaining
To wave beam imaging data, it is stored in the CUDA global storages of video memory equipment.
Beneficial effects of the present invention are as follows：
(1) present invention by simple and effective facet model and anticipate completion terrain and its features grouped data and
Law of DEM Data calculates wave beam RCS values, and then generates emulating image, dexterously avoids signal-level simulation and needs to carry out
The process of analogue echoes, emulation mode is simpler than signal-level simulation, and calculation amount is small；
(2) the altitude data interpolation that the present invention will be surveyed using fractal interpolation technology according to the certain space sampling interval
Imaging resolution for resolution ratio higher ground tee section, radar can be improved 8~32 times；
(3) the present invention is based on the data of wave beam of CUDA concurrent programs algorithm pair to carry out backscattering coefficient calculating and thunder
Up to multi-features.When radar antenna fan sweeping speed is 30 °/s, CPU serial algorithms are compared, when the average calculating operation of this algorithm
Between be substantially reduced to a quarters of CPU serial algorithms, when fan sweeping speed is 60 °/s, CPU serial algorithm average calculating operation times
Corresponding to increase by twice, real-time has been unable to meet requirements, and the average calculating operation time of this algorithm almost without increase because scanning
Speed improves caused more calculation amounts and is solved by increasing more parallel threads.Therefore, the present invention uses
CUDA parallel algorithms carry out the real-time that imaging simulation well ensures radar scanning.
Description of the drawings
Fig. 1 is the flow diagram of imaging simulation method of the present invention；
Fig. 2 is the flow diagram that the present invention obtains wave beam imaging data；
Fig. 3 is the backscattering coefficient calculation process schematic diagram of facet model of the present invention；
Fig. 4 is the flow diagram that image of the present invention is shown；
Fig. 5 is the schematic diagram of Imaging Simulation System of the present invention；
Fig. 6 is that scanning range of the present invention is divided into the schematic diagram after several facet units.
Specific implementation mode
It is right with reference to the attached drawing of the present invention in order to make those skilled in the art more fully understand technical scheme of the present invention
Technical scheme of the present invention carries out clear, complete description, and based on the embodiment in the application, those of ordinary skill in the art exist
The other similar embodiments obtained under the premise of not making creative work, shall fall within the protection scope of the present application.
Embodiment one：
As shown in Figure 1, a kind of distributed large scene radar imagery emulation mode of realization, including step：
S1 is imaged basic data processing：Including the classification of terrain and its features data and the processing of terrestrial digital elevation model；
S2 scanning ranges are determining and divide：According to carrier aircraft Position and orientation parameters, the range of radar and resolution ratio and day
The scanning range parameter of line is calculated, including distance is to size range and orientation scanning range, along distance to and orientation
It is several regular facet units to divide scanning range；As shown in fig. 6, the facet unit after dividing is the medium and small checker board of figure.
S3 carries out calculating and radar image Fusion Features to the backscattering coefficient of facet model, obtains a wave beam
Imaging data；
S4 generates emulating image and shows.
The terrain and its features data classification refers to aerial photograph or satellite image according to image classification algorithms or utilization
Remote sensing image processing software is classified；
The terrestrial digital elevation model processing, refers to that will be surveyed according to the certain space sampling interval using fractal regressive method
The altitude data measured is interpolated to resolution ratio higher ground tee section；
Terrain and its features grouped data and terrestrial digital elevation model data, are read into computer in the form of two-dimensional array
In memory, then copy function is corresponded to using CUDA and copied in the CUDA Texture memories of video memory equipment.
As shown in Fig. 2, as preferred embodiment, the step of obtaining wave beam imaging data, includes：
S31 is according to distance to Chi Cunfanwei [MinDist, MaxDist]With orientation Sao Miaofanwei [StartAngle,
EndAngle], in conjunction with range resolution XResolution and orientation sweep spacing YResolution by scanning imagery model
It encloses and is divided into DIMX*DIMY regular facet unit, wherein：
DIMX=(MaxDist-MinDist)/XResolution；
DIMY=(EndAngle-StartAngle)/YResolution；
Thread is configured for each facet unit：Specified two dimension thread block blocks sizes are (nBlockX, nBlockY), two dimension
Thread lattice grids sizes are (DIMX/nBlockX, DIMY/nBlockY), and two-dimentional thread block is respectively tieed up size and used respectively
BlockDim.x and blockDim.y indicates that in the block index of any online journey of thread uses threadIdx.x and threadIdx.y
It indicates, respectively tie up size in two-dimentional thread lattice is indicated with gridDim.x and gridDim.y respectively, and any thread block is in thread lattice
Index indicated with blockIdx.x and blockIdx.y, wherein：
BlockDim.x=nBlockX, blockDim.y=nBlockY；
GridDim.x=DIMX/nBlockX, gridDim.y=DIMY/nBlockY；
Index of each thread in thread lattice be：
Range gate indexes idxX=threadIdx.x+blockIdx.x*blockDim.x；
Orientation door indexes idxY=threadIdx.y+blockIdx.y*blockDim.y；
Therefore the index of each facet unit is also (idxX, idxY)；
Calculate distance DistX and azimuth AzimY of each facet unit (idxX, idxY) in areas imaging：
DistX=MinDist+idxX*XResolution；
AzimY=StartAngle+idxY*YResolution；
S32 reads the terrain and its features classification of facet unit (DistX, AzimY) corresponding position from CUDA Texture memories
With terrestrial digital elevation model data；
The radar wave incidence angle of each facet model of S33 combination carrier aircraft location determinations, each facet unit mould of parallel computation
The backscattering coefficient of type obtains a wave beam RCS value, is stored in CUDA global storages；
The wave beam RCS value that S34 obtains step S34 and radar system transmission function phase convolution, i.e. radar image are special
Sign fusion；
S35 obtains a wave beam imaging data, is stored in the CUDA global storages of video memory equipment.
Facet unit size should be less than resolution ratio and be more than radar wavelength λ, and small as possible with preferably approximate surface relief.
As shown in figure 3, as preferred embodiment, the backscattering coefficient of facet model calculates step and includes：
S331：According to the principle of least square, the terrestrial digital elevation model in each corresponding region of facet unit is taken, is carried out
Interpolation fitting obtains the facet model tangent with Earth surface plane；
S332：The radar wave that facet model is calculated according to the center vector sum normal vector of each facet unit enters
Firing angle β；
S333：Facet model backscattering coefficient is public according to the average backscattering coefficient empirical model of unit area
Formula is calculated：
In backscattering coefficient empirical model formula, β is the radar wave incidence angle of facet model, σhFor ground level
Fluctuating standard deviation, λ are radar wavelength, and A, B, C, D are the empiricals sorted out according to radar measurements, to differently species
Type value is different；
S334：By the backscattering coefficient σ of each facet model0Coherent superposition is carried out, the RCS of a wave beam is obtained
Value.
As shown in figure 4, further, described image is shown including step：
S41 copies to obtain wave beam imaging data from the CUDA global storages of video memory equipment in computer
In depositing；
One wave beam imaging data is filled into image buffer using fan-shaped filling algorithm and shown by S42；
The image buffer is in main frame memory according to picture size (ImageWidth*
ImageHeight) physical memory (ImageWidth*ImageHeight*nPixel) distributed, wherein ImageWidth generations
Table image pixel width, ImageHeight representative image pixels talls, nPixel representative image port numbers.
As shown in figure 5, a kind of distributed large scene radar imagery analogue system of realization, including：
It is imaged basic data processing module, for handling the classification of terrain and its features data and terrestrial digital elevation model；
Scanning range determination and division module, for according to carrier aircraft Position and orientation parameters, the range of radar and resolution ratio
And the scanning range parameter of antenna calculates, and determines scanning range, and scanning range is divided into number to orientation along distance
A rule facet unit；
Parallel computation module, the backscattering coefficient for facet model calculates and radar image Fusion Features；
Image-forming module, for generating emulating image and showing.
Further, the imaging basic data processing module includes following submodule：
Terrain and its features data classification submodule, for classifying to aerial photograph or satellite image；
Terrestrial digital elevation model handles submodule, and being used for will be according to the certain space sampling interval using fractal regressive method
It surveys obtained altitude data and is interpolated to resolution ratio higher ground tee section.
Further, the parallel computation module includes following submodule：
Facet model setting up submodule takes the land in each corresponding region of facet unit according to the principle of least square
Ground digital elevation model carries out interpolation fitting, obtains a facet model tangent with Earth surface plane；
Radar wave incidence angle computational submodule, for being calculated according to the center vector sum normal vector of each facet unit
The radar wave incidence angle of facet model；
Backscattering coefficient computational submodule is used for the backscattering coefficient of each facet model of parallel computation, to knot
Fruit coherent superposition obtains a wave beam RCS value, is stored in CUDA global storages；
Radar image Fusion Features submodule, for by a wave beam RCS value and radar system transmission function phase convolution, obtaining
To wave beam imaging data, it is stored in the CUDA global storages of video memory equipment.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiment being appreciated that.
Claims (10)
1. a kind of distributed large scene radar imagery emulation mode of realization, which is characterized in that including step：
S1 is imaged basic data processing：Including the classification of terrain and its features data and the processing of terrestrial digital elevation model；
S2 scanning ranges are determining and divide：According to carrier aircraft Position and orientation parameters, the range of radar and resolution ratio and antenna
Scanning range parameter is calculated, including distance is divided along distance to orientation to size range and orientation scanning range
Scanning range is several regular facet units；
S3 carries out calculating and radar image Fusion Features to the backscattering coefficient of facet model, obtains a wave beam imaging
Data；
S4 generates emulating image and shows.
2. realizing distributed large scene radar imagery emulation mode according to claim 1, which is characterized in that the landform
Object data are classified, refer to aerial photograph or satellite image according to image classification algorithms or using remote sensing image processing software into
Row classification；
The processing of the terrestrial digital elevation model refers to that will be surveyed according to the certain space sampling interval using fractal regressive method
Obtained altitude data is interpolated to resolution ratio higher ground tee section；
Terrain and its features grouped data and terrestrial digital elevation model data, are read into calculator memory in the form of two-dimensional array
In, then copy function is corresponded to using CUDA copy in the CUDA Texture memories of video memory equipment.
3. realizing distributed large scene radar imagery emulation mode according to claim 2, which is characterized in that obtain wave beam at
As the step of data includes：
S31 is according to distance to Chi Cunfanwei [MinDist, MaxDist]With orientation Sao Miaofanwei [StartAngle,
EndAngle], in conjunction with range resolution XResolution and orientation sweep spacing YResolution by scanning imagery model
It encloses and is divided into DIMX*DIMY regular facet unit, wherein：
DIMX=(MaxDist-MinDist)/XResolution；
DIMY=(EndAngle-StartAngle)/YResolution；
Thread is configured for each facet unit：Specified two dimension thread block blocks sizes are (nBlockX, nBlockY), two-dimentional thread
Lattice grids sizes be (DIMX/nBlockX, DIMY/nBlockY), two-dimentional thread block respectively tie up size respectively use blockDim.x and
BlockDim.y indicates that any online journey of thread index in the block is indicated with threadIdx.x and threadIdx.y, two-dimensional line
Cheng Gezhong respectively ties up size and is indicated respectively with gridDim.x and gridDim.y, and index of any thread block in thread lattice is used
BlockIdx.x and blockIdx.y expressions, wherein：
BlockDim.x=nBlockX, blockDim.y=nBlockY；
GridDim.x=DIMX/nBlockX, gridDim.y=DIMY/nBlockY；
Index of each thread in thread lattice be：
Range gate indexes idxX=threadIdx.x+blockIdx.x*blockDim.x；
Orientation door indexes idxY=threadIdx.y+blockIdx.y*blockDim.y；
Therefore the index of each facet unit is also (idxX, idxY)；
Calculate distance DistX and azimuth AzimY of each facet unit (idxX, idxY) in areas imaging：
DistX=MinDist+idxX*XResolution；
AzimY=StartAngle+idxY*YResolution；
S32 reads the terrain and its features classification and land of facet unit (DistX, AzimY) corresponding position from CUDA Texture memories
Ground Law of DEM Data；
The radar wave incidence angle of each facet model of S33 combination carrier aircraft location determinations, each facet model of parallel computation
Backscattering coefficient obtains a wave beam RCS value, is stored in CUDA global storages；
The wave beam RCS value that S34 obtains step S33 is melted with radar system transmission function phase convolution, i.e. radar image feature
It closes；
S35 obtains a wave beam imaging data, is stored in the CUDA global storages of video memory equipment.
4. realizing distributed large scene radar imagery emulation mode according to claim 3, which is characterized in that described image is aobvious
Show including step：
Obtain wave beam imaging data is copied to calculator memory by S41 from the CUDA global storages of video memory equipment
In；
One wave beam imaging data is filled into image buffer using fan-shaped filling algorithm and shown by S42；
The image buffer is in main frame memory according to picture size (ImageWidth*
ImageHeight) physical memory (ImageWidth*ImageHeight*nPixel) distributed, wherein ImageWidth generations
Table image pixel width, ImageHeight representative image pixels talls, nPixel representative image port numbers.
5. realizing distributed large scene radar imagery emulation mode according to claim 3, which is characterized in that facet unit mould
The backscattering coefficient of type calculates step：
S331：According to the principle of least square, the terrestrial digital elevation model in each corresponding region of facet unit is taken, into row interpolation
Fitting, obtains the facet model tangent with Earth surface plane；
S332：Calculate the radar wave incidence angle of each facet model；
S333：Facet model backscattering coefficient according to unit area average backscattering coefficient empirical model formula into
Row is calculated：
In backscattering coefficient empirical model formula, β is the radar wave incidence angle of facet model, σhIt rises and falls for ground level
Standard deviation, λ are radar wavelength, and A, B, C, D are the empiricals sorted out according to radar measurements, are taken to different types of ground objects
Value is different；
S334：The backscattering coefficient of each facet model is subjected to coherent superposition, obtains the RCS values of a wave beam.
6. realizing distributed large scene radar imagery emulation mode according to claim 5, which is characterized in that each facet unit
Model radar wave incidence angle is calculated using facet model, passes through the center vector sum normal direction arrow of each facet unit
Amount determines.
7. realizing distributed large scene radar imagery emulation mode according to claim 1, which is characterized in that facet unit ruler
The very little resolution ratio that should be less than is more than radar wavelength, and small as possible with preferably approximate surface relief.
8. a kind of distributed large scene radar imagery analogue system of realization, which is characterized in that including：
It is imaged basic data processing module, for the processing to terrain and its features data classification and terrestrial digital elevation model；
Scanning range is determining and division module, for according to carrier aircraft Position and orientation parameters, the range of radar and resolution ratio and
The scanning range parameter of antenna calculates, and determines scanning range, and scanning range is divided into several rule to orientation along distance
Then facet unit；
Parallel computation module, the backscattering coefficient for facet model calculates and radar image Fusion Features；
Image-forming module, for generating emulating image and showing.
9. the distributed large scene radar imagery analogue system of realization according to claim 8, which is characterized in that the imaging
Basic data processing module includes following submodule：
Terrain and its features data classification submodule, for classifying to aerial photograph or satellite image；
Terrestrial digital elevation model handles submodule, for that will be surveyed according to the certain space sampling interval using fractal regressive method
Obtained altitude data is interpolated to resolution ratio higher ground tee section.
10. the distributed large scene radar imagery analogue system of realization according to claim 8, which is characterized in that it is described simultaneously
Row computing module includes following submodule：
Facet model setting up submodule takes the land number in each corresponding region of facet unit according to the principle of least square
Word elevation model carries out interpolation fitting, obtains a facet model tangent with Earth surface plane；
Radar wave incidence angle computational submodule, for calculating facet according to the center vector sum normal vector of each facet unit
The radar wave incidence angle of model of element；
Backscattering coefficient computational submodule is used for the backscattering coefficient of each facet model of parallel computation, to result phase
Dry superposition, obtains a wave beam RCS value, is stored in CUDA global storages；
Radar image Fusion Features submodule, for by a wave beam RCS value and radar system transmission function phase convolution, obtaining wave
Beam imaging data is stored in the CUDA global storages of video memory equipment.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN109558568B (en) * | 2018-11-21 | 2022-09-16 | 西安电子科技大学 | Target RCS calculation method based on CUDA |
| CN110426688A (en) * | 2019-07-02 | 2019-11-08 | 中国航空工业集团公司雷华电子技术研究所 | A kind of SAR analogue echoes method based on terrain backgrounds target |
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| CN114397624A (en) * | 2022-03-22 | 2022-04-26 | 北京蓝天航空科技股份有限公司 | Data configuration-based compatible radar self-checking picture generation method and device |
| CN114397624B (en) * | 2022-03-22 | 2022-07-15 | 北京蓝天航空科技股份有限公司 | Data configuration-based compatible radar self-inspection picture generation method and device |
| CN116663333A (en) * | 2023-07-28 | 2023-08-29 | 北京四象爱数科技有限公司 | Satellite-borne SAR imaging optimization method, device and medium based on simulation model |
| CN116663333B (en) * | 2023-07-28 | 2023-10-24 | 北京四象爱数科技有限公司 | Satellite-borne SAR imaging optimization method, device and medium based on simulation model |
| CN117077438A (en) * | 2023-10-12 | 2023-11-17 | 西安羚控电子科技有限公司 | Synthetic aperture radar simulation method and device based on image integration and extraction |
| CN117077438B (en) * | 2023-10-12 | 2024-01-26 | 西安羚控电子科技有限公司 | Synthetic aperture radar simulation method and device based on image integration and extraction |
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