CN113238195B - GPU-based false scene interference signal simulation parallel implementation method - Google Patents
GPU-based false scene interference signal simulation parallel implementation method Download PDFInfo
- Publication number
- CN113238195B CN113238195B CN202110357392.7A CN202110357392A CN113238195B CN 113238195 B CN113238195 B CN 113238195B CN 202110357392 A CN202110357392 A CN 202110357392A CN 113238195 B CN113238195 B CN 113238195B
- Authority
- CN
- China
- Prior art keywords
- interference signal
- gpu
- radar
- scattering point
- thread
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000004088 simulation Methods 0.000 title claims abstract description 35
- 230000006870 function Effects 0.000 claims abstract description 38
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 14
- 238000003786 synthesis reaction Methods 0.000 claims abstract description 14
- 238000012545 processing Methods 0.000 claims abstract description 13
- 239000011159 matrix material Substances 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 39
- 238000013499 data model Methods 0.000 claims description 6
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 description 6
- 238000013461 design Methods 0.000 description 5
- 230000001934 delay Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- HPTJABJPZMULFH-UHFFFAOYSA-N 12-[(Cyclohexylcarbamoyl)amino]dodecanoic acid Chemical compound OC(=O)CCCCCCCCCCCNC(=O)NC1CCCCC1 HPTJABJPZMULFH-UHFFFAOYSA-N 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 2
- 239000000306 component Substances 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000002592 echocardiography Methods 0.000 description 2
- 230000007274 generation of a signal involved in cell-cell signaling Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000001308 synthesis method Methods 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/38—Jamming means, e.g. producing false echoes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a GPU-based simulation parallel implementation method for a false scene interference signal, which comprises the following steps: obtaining radar interception pulse signal data; storing the false scene interference signal parameter value and radar interception pulse signal data into a constant memory of the GPU; based on the GPU thread of the first kernel function, obtaining additional delay and compensation phase required by the current scattering point interference signal of each thread block of the first kernel function, and storing the additional delay and compensation phase into a constant memory of the GPU in a matrix array form; obtaining current scattering point interference signals in each thread block of the second kernel function based on the GPU thread of the second kernel function; and based on the superposition synthesis model, carrying out superposition synthesis processing on the current scattering point interference signal to obtain an integral false scene interference signal. The method can complete modulation generation of the false scene interference signal under the conditions of complex and huge radar data and no hardware support, and has good portability and expandability.
Description
Technical Field
The invention belongs to the technical field of electronic countermeasure, and particularly relates to a simulation parallel implementation method for a false scene interference signal based on a Graphic Processing Unit (GPU).
Background
The synthetic aperture radar (Synthetic Aperture Radar, SAR) is an important military target in the electronic countermeasure field, has the characteristics of all-day, all-weather, long-distance and high-resolution imaging and the like, forms a great threat to the strategic targets, military facilities and military information security, and provides a serious challenge to a radar electronic countermeasure system. False scene interference signals of the synthetic aperture radar are widely applied as an extremely effective deception striking means, but the calculation amount for carrying out simulation on each scattering point target in the scene is huge, the operation processing speed is low, and the signal generating process is extremely time-consuming. Meanwhile, with the increasing complexity of modern battlefield electromagnetic environments, the traditional serial processing generation mode of false scene interference signals based on digital devices such as a large-scale integrated circuit, a radio frequency memory and the like has the operational speed and efficiency which are not enough to support the real-time performance of interference and adjustment of battle players according to battlefield environments.
The GPU is called a graphics processing unit (Graphics processing unit, GPU) as a core component of a graphics card, has a large amount of parallel operation resources, and can provide floating point computing performance tens times to hundreds times that of a CPU. Therefore, the GPU is applied to many engineering fields, and the efficiency of engineering calculation can be greatly improved.
For this reason, zhu Shoubao et al in its published paper "research on a method for rapidly generating SAR false image interference signals" ("Chongqing university school (Nature science edition) 2012,24 (03): 314-318"), proposed a rapid algorithm for generating false scene target signals aiming at the conventional false scene interference signal generation process, fully utilized the important conclusion that synthetic aperture radar echoes used at the same distance have the same time delay amount and echoes in the same azimuth have the same phase term, and improved the conventional algorithm. The signal simulation synthesis speed is increased, and the requirement of real-time performance of generating the false scene signal is further met.
However, the above method has limited computational power and cannot be applied to complex and huge radar data.
Disclosure of Invention
In order to meet the requirement of high real-time performance of SAR false scene interference signal simulation, the invention provides a GPU-based false scene interference signal simulation parallel implementation method by utilizing the GPU parallel computing capability, and the simulation computation of the interference signal can be completed at a high speed. The technical problems to be solved by the invention are realized by the following technical scheme:
a GPU-based simulation parallel implementation method for a false scene interference signal comprises the following steps:
obtaining radar interception pulse signal data according to the radar interception pulse signal data model;
storing the false scene interference signal parameter value and the radar interception pulse signal data into a constant memory of the GPU;
based on a GPU thread of a first kernel function, obtaining additional delay and compensation phase required by a current scattering point interference signal of each thread block of the first kernel function according to an additional delay calculation model and a compensation phase calculation model through parallel calculation, and storing the additional delay and the compensation phase into a constant memory of the GPU in a matrix array mode;
based on a GPU thread of the second kernel function, obtaining the current scattering point interference signal in each thread block of the second kernel function according to additional delay and compensation phase required by the current scattering point interference signal stored in a constant memory of the GPU;
and based on the superposition synthesis model, carrying out superposition synthesis processing on the current scattering point interference signals of all the thread blocks of the second kernel function to obtain an integral false scene interference signal.
In one embodiment of the invention, the radar interception pulse signal data model is:
wherein s (tau) is radar interception pulse signal data,t is distance to fast time, T p For pulse width, f 0 For carrier frequency, K is the frequency modulation slope.
In one embodiment of the present invention, storing the false scene interference signal parameter value and the radar intercept pulse signal data into a constant memory of a GPU includes:
setting a false scene interference signal parameter value in a CPU;
storing the false scene interference signal parameter value and the radar interception pulse signal data in a CPU memory;
and copying the parameter values of the false scene interference signals and the radar interception pulse signal data stored in the CPU into a constant memory of the GPU.
In one embodiment of the present invention, the false scene interference signal parameter value at least includes a backscattering coefficient intensity per scattering point, a false scene azimuth number, and a false scene distance number of the interference signal.
In one embodiment of the present invention, the computing resources allocated by the first kernel function are composed of n×m parallel thread blocks, each thread block is 1-dimensional, each thread block is composed of only 1 thread, where N is a false scene azimuth point number, and M is a false scene distance azimuth point number.
In one embodiment of the invention, the additional delay computation model is:
wherein ,Δτi Additional delay required for interference signal of current scattering point, R ic For the simulation inclined distance from the interference signal of the current scattering point to the radar, R 0c For the central skew of the jammer to the radar, c is the speed of light.
In one embodiment of the present invention, the compensation phase calculation model is:
wherein ,compensating phase, f, required for the interference signal of the current scattering point R Is Doppler slope, t is azimuth slow time, R i (t) is the simulated skew distance from the interference signal of the current scattering point to the radar after the time t is flown, R 0 And (t) is the central inclined distance of the jammer after the time t from the jammer to the radar, and lambda is the wavelength of a radar signal.
In one embodiment of the present invention, the computing resources allocated by the second kernel function are composed of N×M parallel thread blocks, each of which is 2-dimensional, each of which isAnd the thread is formed, wherein N is the false scene azimuth point number, M is the false scene distance point number, and L is radar interception pulse signal data.
In one embodiment of the present invention, the current scattering point interference signal in each thread block of the first kernel is obtained according to the additional delay and compensation phase parallel calculation required by the current scattering point interference signal stored in the constant memory of the GPU, including:
carrying out convolution calculation on the additional delay required by the interference signal of the current scattering point and the radar interception pulse signal data to obtain a convolution calculation result;
multiplying the compensation phase required by the interference signal of the current scattering point with the convolution calculation result to obtain a multiplication calculation result;
and sequencing and splicing the multiplied calculation results of each thread in the thread block to obtain the current scattering point interference signal.
In one embodiment of the present invention, the superposition synthesis model is:
wherein ,fALL (τ, t) is the overall false scene interference signal, N is the number of false scene azimuth points, M is the number of false scene distance directions points, σ ij Is the backscattering coefficient value of the scattering point, s (tau, t) is radar interception pulse signal data, and delta tau ij The additional delay required for the current scattering point interference signal,the delta is the impulse function, which is the compensation phase required for the current scattering point disturbance signal.
The invention has the beneficial effects that:
in the simulation parallel implementation method of the false scene interference signal, firstly, intercepted radar signal pulse data is obtained from a reconnaissance jammer and is used as an original signal to be modulated; then, the modulation parameters required for generating each scattering point in the scene to be synthesized, including additional delay and compensation phases, are calculated in the kernel function of the GPU. And finally, calling the kernel function to perform signal modulation generation in the GPU, and adding and synthesizing all the result signals to be transmitted back to the CPU. Different from the method for generating the interference signal based on the Digital Radio Frequency Memory (DRFM) technology, the method adopts a purely software processing flow, has no dependence on hardware, has better portability and shorter development period.
On the other hand, the GPU is introduced to serve as an acceleration component for signal synthesis, the CPU is matched with the GPU to complete program operation, the GPU is responsible for parallelizable large-scale data calculation, and the operation speed is fully improved. And the technical mode programming designed in the method is simple, and the engineering implementation difficulty is small.
In conclusion, the method can complete modulation generation of the false scene interference signal under the conditions of complex and huge radar data and no hardware support, and has good portability and expandability.
The simulation parallel implementation method for the SAR false scene interference signals is realized by utilizing the massive parallel computing capability of the GPU, and the generation speed and efficiency of the interference signals are improved.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a flow diagram of a method for implementing simulation parallelism of a virtual scene interference signal based on a GPU according to an embodiment of the present invention;
FIG. 2 is a flowchart of another false scene interference signal algorithm according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of CUDA heterogeneous programming according to an embodiment of the present invention;
FIG. 4 is a first kernel thread design model provided by an embodiment of the present invention;
FIG. 5 is a second kernel thread design model provided by an embodiment of the present invention;
fig. 6 is a video SAR echo simulation runtime diagram provided in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The application scenario of this embodiment is: the method aims at the condition that an enemy target is detected by transmitting signals in the air to detect and interfere the enemy, namely, firstly, the radar signals intercepting the enemy are modulated, and then, the signals are transmitted out after being forwarded, so that the detection performance of the enemy radar target is reduced.
Referring to fig. 1, fig. 2 and fig. 3, fig. 1 is a schematic flow chart of a GPU-based simulation parallel implementation method for a false scene interference signal according to an embodiment of the present invention, fig. 2 is a schematic flow chart of another algorithm for a false scene interference signal according to an embodiment of the present invention, and fig. 3 is a schematic flow chart of CUDA heterogeneous programming according to an embodiment of the present invention. The embodiment provides a GPU-based simulation parallel implementation method for a false scene interference signal, which comprises the following steps of 1 to 5, wherein:
and step 1, obtaining radar interception pulse signal data according to a radar interception pulse signal data model.
Specifically, the CPU (Central Processing Unit ) reads radar interception pulse signal data to be modulated and radar information obtained by other reconnaissance, wherein the radar information comprises radar azimuth, radar speed, radar center skew and the like, the radar interception pulse signal data is used as radar interception pulse signal data of a modulation basis, and a radar interception pulse signal data model is expressed as follows:
wherein s (tau) is radar interception pulse signal data,t is distance to fast time, T p For pulse width, f 0 For carrier frequency, K is the frequency modulation slope.
And 2, storing the parameter values of the interference signals of the false scene and the radar interception pulse signal data into a constant memory of the GPU, wherein the false scene is an interference scene.
In this embodiment, the parameter value of the interference signal of the false scene at least includes the backscattering coefficient intensity of each scattering point of the interference signal, the azimuth point number of the false scene and the distance point number of the false scene.
In a specific embodiment, step 2 may specifically include steps 2.1 to 2.3, wherein:
step 2.1, setting a false scene interference signal parameter value in a CPU;
step 2.2, storing the false scene interference signal parameter value and radar interception pulse signal data in a CPU memory;
and 2.3, copying the false scene interference signal parameter value and radar interception pulse signal data stored in the CPU into a constant memory of the GPU.
That is, in this embodiment, first, a false scene interference signal parameter value is set in the CPU, and the false scene interference signal parameter value and the read radar interception pulse signal data are stored in the CPU memory; and further, a memory space is opened on the GPU, and the parameter values of the false scene interference signals and radar interception pulse signal data are copied from the CPU memory to a constant memory of the GPU.
And 3, a GPU thread based on the first kernel function performs parallel calculation according to an additional delay calculation model and a compensation phase calculation model to obtain additional delay and compensation phases required by the current scattering point interference signals of each thread block of the first kernel function, and stores the additional delay and the compensation phases into a constant memory of the GPU in a matrix array form, wherein the current scattering point interference signals are false scattering points to be modulated.
In this embodiment, first, a first kernel function and its GPU thread are set, and the GPU calculates, in parallel, modulation parameter values required for generating the false scene interference. The GPU thread design of the first kernel function is shown in fig. 4, and the computing resource allocated by the first kernel function is composed of n×m parallel thread blocks, where N is the number of false scene azimuth points, M is the number of false scene distance azimuth points, each thread block is 1 dimension, and each thread block is composed of only 1 thread, and is used for parallel computing of modulation parameter values required by interference generation.
In a specific embodiment, step 3 may specifically include steps 3.1 to 3.3, wherein:
and 3.1, calculating the additional delay required by the interference signal of the current scattering point of each thread block of the first kernel function according to the additional delay calculation model.
Specifically, the additional delay required for modulating the current scattering point interference signal is calculated in the GPU according to constant information, wherein the additional delay calculation model is as follows:
wherein ,Δτi Additional delay required for interference signal of current scattering point, R ic For the simulation inclined distance from the interference signal of the current scattering point to the radar, R 0c For the central skew of the jammer to the radar, c is the speed of light.
And 3.2, calculating the compensation phase required by the current scattering point interference signal of each thread block of the first kernel function according to the compensation phase calculation model.
Specifically, a compensation phase required for modulating the current scattering point interference signal is calculated in the GPU according to constant information, wherein the compensation phase calculation model is as follows:
wherein ,compensating phase, f, required for the interference signal of the current scattering point R Is Doppler slope, t is azimuth slow time, R i (t) is the simulated skew distance from the interference signal of the current scattering point to the radar after the time t is flown, R 0 And (t) is the central inclined distance of the jammer after the time t from the jammer to the radar, and lambda is the wavelength of a radar signal.
And 3.3, storing the calculated additional delay and compensation phase into a constant memory of the GPU in a matrix array in a mode corresponding to the relevant NxM scattering points.
That is, after all the additional delays and compensating phases are calculated by the n×m thread blocks, the matrix array storing the additional delays and compensating phases is also n×m, and the additional delays and compensating phases stored in the matrix array correspond to the positions of the thread blocks, so that indexing is facilitated during the calculation of the second kernel function.
And 4, a GPU thread based on the second kernel function obtains the current scattering point interference signal in each thread block of the second kernel function according to additional delay and compensation phase parallel calculation required by the current scattering point interference signal stored in a constant memory of the GPU.
In this embodiment, the second kernel function and the GPU thread thereof are set, and the GPU modulates the interference signal of each scattering point. The GPU thread design of the second kernel function is shown in fig. 5, and the computation resources allocated by the second kernel function are composed of n×m parallel thread blocks, where N is the false scene azimuth point number, M is the false scene distance azimuth point number, each thread block is 2-dimensional, the thread composition of each thread block is related to the radar interception pulse signal data length L involved in modulation, and in this embodiment, the thread composition of each thread block isFor parallel metersThe modulation result of each sampled data point of the signal is calculated.
In a specific embodiment, step 4 may specifically include steps 4.1 to 4.3, wherein:
and 4.1, carrying out convolution calculation on the additional delay required by the interference signal of the current scattering point and the radar interception pulse signal data to obtain a convolution calculation result.
Step 4.2, multiplying the compensation phase required by the interference signal of the current scattering point and the convolution calculation result to obtain a multiplication calculation result;
and 4.3, sequencing and splicing the multiplication calculation results of each thread in the thread block to obtain the current scattering point interference signal.
In this embodiment, the order of the points of the multiplication result calculated in step 4.2 should be consistent with the order of the radar interception pulse signal data, for example, the radar interception pulse signal data to be modulated is 12345678, and then the order of the stored data is consistent after each point calculation is completed, 1 the 1 position of the 1 and 2 the 2 positions of the 1, i.e. the multiplication result of each thread in the thread block is sequenced and spliced into the current scattering point interference signal according to the order of the radar interception pulse signal data.
And 5, based on a superposition synthesis model, carrying out superposition synthesis processing on the current scattering point interference signals of all the thread blocks of the second kernel function to obtain an overall false scene interference signal, and transmitting the overall false scene interference signal back to the CPU, wherein the superposition synthesis model is as follows:
wherein ,fALL (τ, t) is the overall false scene interference signal, N is the number of false scene azimuth points, M is the number of false scene distance directions points, σ ij Is the backscattering coefficient value of the scattering point, s (tau, t) is radar interception pulse signal data, and delta tau ij The additional delay required for the current scattering point interference signal,the delta is the impulse function, which is the compensation phase required for the current scattering point disturbance signal.
The invention provides a GPU-based false scene interference signal simulation parallel implementation method, which combines a false scene interference signal generation algorithm and a GPU parallel processing technology to realize efficient false scene interference signal simulation. The invention uses a CPU module for data management and logic control, and specifically comprises the steps of reading radar interception pulse signal data, setting false scene interference parameters, existing memory allocation, CPU-to-GPU data transmission, GPU thread allocation and kernel function call. The method uses the GPU to perform signal simulation algorithm processing, and specifically comprises scattering point modulation parameter calculation and false scene interference signal modulation generation.
In summary, it can be seen that, unlike the Digital Radio Frequency Memory (DRFM) technology-based interference signal synthesis method, the scheme adopts a purely software processing flow, has no dependence on hardware, has better portability and shorter development period. On the other hand, the GPU is introduced to serve as an acceleration component for signal synthesis, the CPU is matched with the GPU to complete program operation, the GPU is responsible for parallelizable large-scale data calculation, and the operation speed is fully improved. And the technical mode programming designed in the method is simple, and the engineering implementation difficulty is small.
In conclusion, the method can complete modulation generation of the false scene interference signal under the conditions of complex and huge radar data and no hardware support, and has good portability and expandability.
The effects of the present invention will be further described with reference to simulation experiments.
Fig. 6 is a runtime diagram of a virtual scene interference signal simulation provided by the embodiment of the invention, and from the perspective of signal simulation runtime, the virtual scene interference signal simulation based on the GPU has an obvious speed improvement compared with the virtual scene interference signal simulation based on the CPU, so that the simulation runtime is greatly shortened, and the feasibility and effectiveness of the parallel implementation scheme design are verified.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by program instructions and associated hardware, and the foregoing program may be stored in a computer readable storage medium, which when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: various media capable of storing program codes, such as ROM, RAM, magnetic disk or optical disk; the latter hardware is mainly the investigation receiver, which is the precondition for implementing the method, and the received signal is stored in the storage medium for subsequent processing.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic point described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristic data points described may be combined in any suitable manner in any one or more embodiments or examples. Further, one skilled in the art can engage and combine the different embodiments or examples described in this specification.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (10)
1. The method for realizing simulation parallelism of the false scene interference signals based on the GPU is characterized by comprising the following steps of:
obtaining radar interception pulse signal data according to the radar interception pulse signal data model;
storing the false scene interference signal parameter value and the radar interception pulse signal data into a constant memory of the GPU;
based on a GPU thread of a first kernel function, obtaining additional delay and compensation phase required by a current scattering point interference signal of each thread block of the first kernel function according to an additional delay calculation model and a compensation phase calculation model through parallel calculation, and storing the additional delay and the compensation phase into a constant memory of the GPU in a matrix array mode;
based on a GPU thread of the second kernel function, obtaining the current scattering point interference signal in each thread block of the second kernel function according to additional delay and compensation phase required by the current scattering point interference signal stored in a constant memory of the GPU;
and based on the superposition synthesis model, carrying out superposition synthesis processing on the current scattering point interference signals of all the thread blocks of the second kernel function to obtain an integral false scene interference signal.
2. The GPU-based false scene interference signal simulation parallel implementation method of claim 1, wherein the radar interception pulse signal data model is:
wherein s (tau) is radar interception pulse signal data,t is distance to fast time, T p For pulse width, f 0 For carrier frequency, K is the frequency modulation slope.
3. The GPU-based virtual scene interference signal emulation parallel implementation method of claim 1, wherein storing the virtual scene interference signal parameter values and the radar intercept pulse signal data into a constant memory of a GPU comprises:
setting a false scene interference signal parameter value in a CPU;
storing the false scene interference signal parameter value and the radar interception pulse signal data in a CPU memory;
and copying the parameter values of the false scene interference signals and the radar interception pulse signal data stored in the CPU into a constant memory of the GPU.
4. The GPU-based virtual scene interference signal simulation parallel implementation method of claim 2 or 3, wherein the virtual scene interference signal parameter value at least comprises a backscattering coefficient intensity per scattering point, a virtual scene azimuth point number, and a virtual scene distance point number of the interference signal.
5. The GPU-based virtual scene interference signal emulation parallel implementation method of claim 1, wherein the computing resources allocated by the first kernel function are composed of N x M parallel thread blocks, each thread block is 1-dimensional, each thread block is composed of only 1 thread, wherein N is a virtual scene azimuth point number, and M is a virtual scene distance azimuth point number.
6. The GPU-based virtual scene interference signal simulation parallel implementation method of claim 1 or 5, wherein the additional latency calculation model is:
wherein ,Δτi Additional delay required for interference signal of current scattering point, R ic For the simulation inclined distance from the interference signal of the current scattering point to the radar, R 0c For the central skew of the jammer to the radar, c is the speed of light.
7. The GPU-based virtual scene interference signal simulation parallel implementation method of claim 6, wherein the compensation phase computation model is:
wherein ,compensating phase, f, required for the interference signal of the current scattering point R Is Doppler slope, t is azimuth slow time, R i (t) is the simulated skew distance from the interference signal of the current scattering point to the radar after the time t is flown, R 0 And (t) is the central inclined distance of the jammer after the time t from the jammer to the radar, and lambda is the wavelength of a radar signal.
8. The GPU-based virtual scene interference signal simulation parallel implementation method of claim 1, wherein the computation resources allocated by the second kernel function are composed of N x M parallel thread blocks, each of which is 2-dimensional, each of which is composed ofAnd the thread is formed, wherein N is the false scene azimuth point number, M is the false scene distance point number, and L is radar interception pulse signal data.
9. The GPU-based virtual scene interference signal simulation parallel implementation method of claim 1, wherein the obtaining the current scattering point interference signal in each thread block of the first kernel according to the additional delay and the compensation phase parallel computation required by the current scattering point interference signal stored in the constant memory of the GPU comprises:
carrying out convolution calculation on the additional delay required by the interference signal of the current scattering point and the radar interception pulse signal data to obtain a convolution calculation result;
multiplying the compensation phase required by the interference signal of the current scattering point with the convolution calculation result to obtain a multiplication calculation result;
and sequencing and splicing the multiplied calculation results of each thread in the thread block to obtain the current scattering point interference signal.
10. The GPU-based virtual scene interference signal simulation parallel implementation method of claim 1, wherein the superposition synthesis model is:
wherein ,fALL (τ, t) is the overall false scene interference signal, N is the number of false scene azimuth points, M is the number of false scene distance directions points, σ ij Is the backscattering coefficient value of the scattering point, s (tau, t) is radar interception pulse signal data, and delta tau ij The additional delay required for the current scattering point interference signal,the delta is the impulse function, which is the compensation phase required for the current scattering point disturbance signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110357392.7A CN113238195B (en) | 2021-04-01 | 2021-04-01 | GPU-based false scene interference signal simulation parallel implementation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110357392.7A CN113238195B (en) | 2021-04-01 | 2021-04-01 | GPU-based false scene interference signal simulation parallel implementation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113238195A CN113238195A (en) | 2021-08-10 |
| CN113238195B true CN113238195B (en) | 2023-10-13 |
Family
ID=77130932
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110357392.7A Active CN113238195B (en) | 2021-04-01 | 2021-04-01 | GPU-based false scene interference signal simulation parallel implementation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113238195B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104849698A (en) * | 2015-05-21 | 2015-08-19 | 中国人民解放军海军工程大学 | Radar signal parallel processing method and system based on heterogeneous multinucleated system |
| RU2622904C1 (en) * | 2016-04-07 | 2017-06-21 | Закрытое акционерное общество "Научно-исследовательский институт современных телекоммуникационных технологий" | Method for distortion of radar image in space radar station with synthetic antenna aperture |
| CN111337888A (en) * | 2020-04-13 | 2020-06-26 | 北京航天长征飞行器研究所 | Dense decoy jamming method, computer device and computer readable storage medium |
-
2021
- 2021-04-01 CN CN202110357392.7A patent/CN113238195B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104849698A (en) * | 2015-05-21 | 2015-08-19 | 中国人民解放军海军工程大学 | Radar signal parallel processing method and system based on heterogeneous multinucleated system |
| RU2622904C1 (en) * | 2016-04-07 | 2017-06-21 | Закрытое акционерное общество "Научно-исследовательский институт современных телекоммуникационных технологий" | Method for distortion of radar image in space radar station with synthetic antenna aperture |
| CN111337888A (en) * | 2020-04-13 | 2020-06-26 | 北京航天长征飞行器研究所 | Dense decoy jamming method, computer device and computer readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于OpenCL的数字相控阵雷达干扰模拟;王开拓;白雪;郭晓宇;;计算机测量与控制(第09期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113238195A (en) | 2021-08-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US6075480A (en) | Down range returns simulator | |
| US11454703B2 (en) | Methods and systems for testing automotive radar using radar data cube emulator | |
| CN109683147B (en) | Method and device for generating chaotic pulse stream signal in real time and electronic equipment | |
| CN108205135A (en) | The radar video imaging method of quick rear orientation projection is merged based on no interpolation | |
| Gromek et al. | Passive SAR imaging using DVB‐T illumination for airborne applications | |
| CN111896952B (en) | Multi-target deception jamming method and device based on 1-bit quantization | |
| CN113050059A (en) | Group target focusing super-resolution direction of arrival estimation method by using co-prime array radar | |
| CN115128559A (en) | Airborne phased array radar real-time signal level echo simulation method and device | |
| CN113109797B (en) | Method and device for detecting target of frequency modulation continuous wave staring radar and computer equipment | |
| CN113238195B (en) | GPU-based false scene interference signal simulation parallel implementation method | |
| Zhao et al. | Computational design of optimal waveforms for MIMO radar via multi-dimensional iterative spectral approximation | |
| CN114047483A (en) | Radar waveform design method based on initial phase-PRI joint modulation | |
| CN113484829B (en) | Method for generating 1-bit multi-decoy deception jamming aiming at synthetic aperture radar | |
| Jiang et al. | Optimization method for multiple phases sectionalized modulation jamming against linear frequency modulation radar based on a genetic algorithm | |
| CN108427111A (en) | A kind of radar range finding method and device | |
| Zhang et al. | GPU-based parallel back projection algorithm for the translational variant BiSAR imaging | |
| CN108594229A (en) | The compensation method of Doppler effect two dimension, device and storage medium in satellite-borne SAR arteries and veins | |
| Douchin et al. | SE-Workbench-RF: Performant and high-fidelity raw data generation for various radar applications | |
| CN112698280B (en) | Double-base SAR real-time echo simulation method based on DSP and FPGA architecture | |
| Liu et al. | Study on Simulation Method of Radar Game Confrontation Process | |
| CN116520256B (en) | Airborne early warning radar interference recognition method and device based on deep learning | |
| Liu et al. | Research on anti-point source jamming method of airborne radar based on artificial intelligence | |
| Sheng et al. | SAR echo simulation from numerous scattering cells based on GPU | |
| Decker et al. | Physics-based, real-time MIMo radar simulation for autonomous driving | |
| Damnjanović et al. | On Hardware Implementations of Two-Dimensional Fast Fourier Transform for Radar Signal Processing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |