CN114492240A - Infrared pneumatic optical imaging simulation method, storage medium and computer equipment - Google Patents

Infrared pneumatic optical imaging simulation method, storage medium and computer equipment Download PDF

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CN114492240A
CN114492240A CN202210085084.8A CN202210085084A CN114492240A CN 114492240 A CN114492240 A CN 114492240A CN 202210085084 A CN202210085084 A CN 202210085084A CN 114492240 A CN114492240 A CN 114492240A
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饶鹏
张淑媛
陈忻
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Shanghai Institute of Technical Physics of CAS
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Abstract

The invention belongs to the technical field of optical imaging, and discloses an infrared pneumatic optical imaging simulation method, a storage medium and computer equipment, wherein wave front change values under different flight states are obtained through flow field dynamics and optical numerical simulation, a mapping relation between the wave front change values and flight state parameters is established, and a continuously-changing degradation model is constructed; constructing an infrared scene simulation model, and outputting a sequence infrared image of a detection stage and a corresponding imaging flight state of the sequence infrared image; and inputting the imaging flight state into a continuously-changing Gaussian superposition model, and performing convolution operation on the obtained continuously-changing point spread function and the corresponding infrared image to realize real-time infrared pneumatic optical effect degradation imaging. The invention establishes a pneumatic continuous degradation model coupled with a flight state, establishes an infrared detection visual simulation model, and can truly simulate an infrared detection imaging process in a high-speed motion state; the model of the invention is simpler, less costly and less time consuming.

Description

Infrared pneumatic optical imaging simulation method, storage medium and computer equipment
Technical Field
The invention belongs to the technical field of optical imaging, and particularly relates to an infrared pneumatic optical imaging simulation method, a storage medium and computer equipment.
Background
Currently, airborne infrared optical systems play an important role in remote sensing. With the development of aerospace technology, image distortion caused by the aerodynamic optical effect has become a serious problem in an airborne infrared optical imaging system. The large density gradient of turbulent compressible flow around the optical window is a direct cause of aerodynamic optical distortion. The flow field outside the optical window may include complex flow structures such as free shear layers, expansion waves, turbulent boundary layers, shock waves. The density gradient profile is unstable and constantly changing. According to the Gladstone-Dale relationship, the refractive index of air is directly proportional to its density. Due to the variation of the refractive index, the light beam passing through the aerodynamic flow exhibits severe wavefront distortion, resulting in blurring, light deflection and jitter. These airborne optical effects adversely affect the imaging quality of the airborne optical system. Therefore, in order to improve the quality and accuracy of the onboard infrared remote sensing task, it is necessary to study imaging changes caused by high-speed flight conditions.
At present, a common method for estimating the imaging influence caused by the pneumatic optical transmission effect is to study based on numerical calculation of a flow field and optical transmission, and the method uses Computational Fluid Dynamics (CFD) software, so that the calculation amount is large, and only analysis can be performed on a limited flight state. Meanwhile, due to the fact that the cost is high, special cameras and optical elements are needed for infrared imaging, wind tunnel tests and flight tests are limited in use, and a large amount of continuous infrared pneumatic degradation image data cannot be obtained. However, although the degradation phenomenon caused by the aerodynamic optical effect such as image blur, jitter, line of sight error, saturation and the like can be simulated by the existing simulation model established based on phenomenology, the typical parameters of the model still need to be set independently according to empirical values, so that the model is only suitable for describing the image quality degradation law and cannot be simulated in real time according to the flight state. Therefore, in order to effectively study the pneumatic optics and the real-time correction thereof at low cost, a simulation method for simulating the infrared pneumatic optical effect capable of simulating the real-time scene simulation needs to be designed.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the existing method for estimating the imaging influence caused by the aerodynamic optical transmission effect has large calculation amount, can only analyze limited flight state, has higher cost and limited use of wind tunnel test and flight test, and cannot obtain a large amount of continuous infrared aerodynamic degradation image data.
(2) The existing simulation model is only suitable for describing an image quality degradation law and cannot carry out real-time simulation according to the flight state.
The difficulty in solving the above problems and defects is:
(1) the flight experiment platform is complex in design, long in development period and high in cost, and a low-cost and high-response-speed flight experiment platform is still lacking in China at present; wind tunnel experiments cannot simulate real flight environments and are only suitable for simulating the influence of single environmental factors;
(2) it is necessary to consider how to combine the results of a few numerical simulations, to construct a model that can be continuously varied,
so as to meet the changing trend of the pneumatic mechanism under different influence factors.
The significance of solving the problems and the defects is as follows:
(1) due to cost limitation and effect limitation, the method for constructing the dynamic infrared scene imaging simulation is a simple method for verifying and correcting the algorithm;
(2) fuzzy models under different parameters can be used as prior information to guide more accurate and more efficient correction algorithm research;
(3) the degradation simulation model of different flight states can be simulated, and a large amount of data can be provided for training an advanced deep learning algorithm.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an infrared pneumatic optical imaging simulation method, a storage medium and computer equipment.
The invention is realized in this way, and an infrared pneumatic optical imaging simulation method comprises the following steps:
step one, obtaining wavefront change values sigma under different flight states through flow field dynamics and optical numerical simulation; obtaining parameters capable of reflecting the intensity change of the pneumatic optical effect through accurate numerical simulation, and guiding the construction of a degradation model;
establishing a mapping relation between the wavefront change value sigma and the flight state parameters, and constructing a continuously-changing degradation model; constructing a continuously-changing mapping relation, obtaining a degradation model continuously changing along with different flight state parameters, and simultaneously ensuring that excessive calculation cost cannot be consumed in a numerical simulation step;
constructing an infrared scene simulation model, and outputting a sequence infrared image of a detection stage and a corresponding imaging flight state of the sequence infrared image; based on modeling of an infrared scene, an infrared imaging degradation simulation process of a full link can be constructed;
and step four, inputting the imaging flight state into a continuously-changing Gaussian superposition model to obtain a group of continuously-changing point spread functions, and performing convolution operation on the continuously-changing point spread functions and the corresponding infrared image to realize real-time infrared pneumatic optical effect degradation imaging. And the paired infrared images and flight parameters are used as the input of the step four, and the infrared detection images with the detection visual angle and the flight parameters changing simultaneously can be obtained through the operation of the step four.
Further, the flow field dynamics numerical simulation method in the first step adopts: reynolds average Navier-Stokes equation, direct numerical simulation DNS, and large vortex simulation LES.
Further, in the first step, optical numerical simulation adopts geometrical optics, physical optics and Fourier optics methods to analyze and calculate the distortion of the light after passing through the flow field.
Further, the method for constructing the mapping relationship in the second step specifically includes:
(1) preparing a data set;
(2) regression analysis was performed. Fitting by using a training set, wherein the fitting method can be polynomial fitting, Gaussian fitting, least square-based linear regression and nonlinear regression;
(3) performing regression diagnosis, finding and eliminating abnormal points by checking a residual error map, and performing regression analysis again;
(4) and carrying out model inspection, and calculating the evaluation index value of each regression model.
Further, the step (1) of preparing the data set comprises:
obtaining wave front change parameters sigma under N groups of flight state parameters through flow field simulation and optical simulation, randomly sequencing the flight state parameters and the corresponding wave front change parameters sigma, and dividing the flight state parameters into a training set and a test set according to proportion;
drawing a scatter diagram of the sigma value and each flight state parameter, carrying out correlation coefficient analysis and significance test to obtain the correlation coefficient and confidence coefficient of the dependent variable sigma and each flight state parameter independent variable, judging the correlation relationship between the variables, and eliminating abnormal values.
Further, in the regression analysis in the step (2), a training set is used for fitting, and the fitting method is polynomial fitting, Gaussian fitting, linear regression based on least square or nonlinear regression.
Further, the step (4) performs model checking, and in calculating the evaluation index value of each regression model, when the evaluation index value of the regression model exceeds the index threshold value, and the prediction accuracy of the test set is combined, the corresponding regression model is used as a mapping formula of sigma.
Further, the evaluation index values of the regression model in the step (4) comprise a goodness of fit R-square, a regression coefficient significance test index, a regression equation significance test index or a correlation coefficient significance test index.
Further, the degradation model and the parameter setting thereof in the second step are specifically as follows:
Figure BDA0003487355670000041
Figure BDA0003487355670000042
Figure BDA0003487355670000043
M＝μ·Ma/H
wherein, ω isiIs a weight parameter, σ is an intensity parameter, xmAnd ymFor the offset control parameter, M is the number of turbulence units.
Further, the method for constructing the infrared scene simulation model in step three includes:
firstly, three-dimensional modeling and surface temperature field setting are carried out, three-dimensional geometric models of a target and a background are established by utilizing three-dimensional modeling software, and the surface temperature of each part of a scene is set according to an empirical value;
then determining an imaging observation area and a flight state corresponding to each imaging frame according to the received flight state parameters, and the field of view parameters and the imaging parameters of the sensor;
the amount of infrared radiation is then calculated. Taking the whole scene as a gray body, calculating the infrared radiation intensity by using the Planck's law, and obtaining an infrared radiation characteristic image under observation;
and finally, calculating a voltage value of the infrared radiation intensity after photoelectric conversion, and quantizing the output voltage into a gray value.
It is a further object of the invention to provide a computer arrangement comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of the infrared pneumatic optical imaging simulation method.
It is a further object of the invention to provide a computer readable storage medium, storing a computer program which, when executed by a processor, causes the processor to perform the steps of the infrared aero-optical imaging simulation method.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention establishes a pneumatic continuous degradation model coupled with a flight state, establishes an infrared detection visual simulation model, and can truly simulate an infrared detection imaging process in a high-speed motion state; compared with a numerical simulation method and a flight test method, the method disclosed by the invention has the advantages of simpler model, low cost and less time consumption.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a simulation method of infrared pneumatic optical imaging according to an embodiment of the present invention.
Fig. 2 is a flowchart of a simulation method of infrared pneumatic optical imaging according to an embodiment of the present invention.
Fig. 3 is a wave front simulation graph σ of different flight states provided by the embodiment of the invention. Wherein, each flight state is respectively: (1) speed 2Ma, height 10 km; (2) speed 3Ma, height 5 km; (3) speed 4Ma, altitude 2 km.
Fig. 4 is a simulation diagram of a point spread function according to the variation of the flying height provided by the embodiment of the invention.
Fig. 5 is a simulation diagram of a point spread function varying with flight speed according to an embodiment of the present invention.
Fig. 6 is a sequence of infrared aero-optical images provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides an infrared pneumatic optical imaging simulation method, a storage medium and a computer device, and the present invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for simulating infrared pneumatic optical imaging according to the embodiment of the present invention includes:
s101, obtaining wavefront change values sigma in different flight states through flow field dynamics and optical numerical simulation;
s102, establishing a mapping relation between a wavefront change value sigma and flight state parameters, and constructing a continuously-changed degradation model;
s103, constructing an infrared scene simulation model, and outputting a sequence infrared image of a detection stage and a corresponding imaging flight state of the sequence infrared image;
and S104, inputting the imaging flight state into a continuously-changing Gaussian superposition model to obtain a group of continuously-changing point spread functions, and performing convolution operation on the continuously-changing point spread functions and the corresponding infrared image to realize real-time infrared pneumatic optical effect degradation imaging.
The invention is further described with reference to specific examples.
The infrared pneumatic optical imaging simulation method provided by the embodiment of the invention specifically comprises the following steps:
firstly, simulating near-field non-uniform flow fields under different conditions by using a Computational Fluid Dynamics (CFD) technology, acquiring density distribution on an infrared light path, and then acquiring a refractive index field according to a linear relation between density and refractive index. Based on the calculation result of the CFD, the transmission path of light is tracked, and the wave surface distortion σ of the exit pupil of the optical system is calculated by a physical optical method.
Randomly sequencing the wavefront change parameters sigma under the N groups of flight state parameters and the flight state parameters, and dividing the parameters into a training set and a test set according to proportion. Drawing a scatter diagram of the sigma value and each flight state parameter, carrying out correlation coefficient analysis and significance test to obtain correlation coefficients and confidence degrees of the dependent variable sigma and each flight state parameter independent variable, judging correlation relations among the variables, and eliminating abnormal values; next, regression analysis was performed. Fitting by using a training set, wherein the fitting method can be polynomial fitting, Gaussian fitting, least square-based linear regression and nonlinear regression; then carrying out regression diagnosis, finding out and eliminating abnormal points by checking a residual error map, and then carrying out regression analysis again; and finally, carrying out model inspection by using the test set, calculating the goodness of fit of each regression model, and taking the condition that the prediction precision of the test set is higher when the goodness of fit exceeds 0.9, wherein the corresponding regression model is used as a sigma mapping formula. If the model that we want can not be obtained, the process shifts to the regression analysis again. Then introducing the obtained mapping formula into a degradation model to obtain a PSF which can continuously change along with flight state parameters:
Figure BDA0003487355670000071
and secondly, constructing a real-time infrared detection scene simulation model, and firstly carrying out three-dimensional modeling and surface temperature field setting on the scene. And establishing a three-dimensional geometric model of the target and the background by utilizing three-dimensional modeling software. The surface temperature of each part of the scene is set based on empirical values. And determining the imaging observation area and the corresponding flight state of each imaging frame according to the received flight state parameters, the field of view parameters of the sensor and the imaging parameters. And (3) regarding the target and the background in the observation area as gray bodies, calculating the infrared radiation amount by utilizing the Planck's law, and obtaining an infrared radiation characteristic image:
Figure BDA0003487355670000072
and calculating the output voltage after photoelectric attenuation, wherein L represents the radiant quantity, Opttran represents the optical transmittance of the optical system, D represents the aperture of the optical system, and f represents the focal length of the optical system.
Figure BDA0003487355670000073
Then, the imaging gray value is obtained through quantitative calculation. Wherein [ G ] ismin,Gmax]Is the gray scale quantization range, VLAnd VHRespectively a minimum output voltage and a maximum output voltage.
Figure BDA0003487355670000074
And finally, the infrared scene simulation model outputs an infrared sequence image and a flight state parameter list of the detection stage. And inputting the flight state parameter list into a Gaussian superposition degradation model to obtain a group of continuously-changed point diffusion functions, and performing convolution operation on the group of point diffusion functions and the infrared image corresponding to the imaging frame to realize real-time infrared pneumatic optical effect degradation imaging simulation.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. An infrared pneumatic optical imaging simulation method is characterized by comprising the following steps:
step one, obtaining wavefront change values sigma under different flight states through flow field dynamics and optical numerical simulation;
establishing a mapping relation between the wavefront change value sigma and the flight state parameters, and constructing a continuously-changing degradation model;
constructing an infrared scene simulation model, and outputting a sequence infrared image of a detection stage and a corresponding imaging flight state of the sequence infrared image;
and step four, inputting the imaging flight state into a continuously-changing Gaussian superposition model to obtain a group of continuously-changing point spread functions, and performing convolution operation on the continuously-changing point spread functions and the corresponding infrared image to realize real-time infrared pneumatic optical effect degradation imaging.
2. The method for simulating infrared pneumatic optical imaging according to claim 1, wherein the method for simulating flow field dynamics in the first step adopts: reynolds average Navier-Stokes equation, direct numerical simulation DNS, and large vortex simulation LES.
3. The infrared pneumatic optical imaging simulation method according to claim 1, wherein in the first step, the optical numerical simulation adopts geometrical optics, physical optics and Fourier optics to analyze and calculate the distortion of the light after passing through the flow field.
4. The infrared pneumatic optical imaging simulation method according to claim 1, wherein the method for constructing the mapping relationship in the second step is specifically as follows:
(1) preparing a data set;
(2) carrying out regression analysis; fitting by using a training set, wherein the fitting method can be polynomial fitting, Gaussian fitting, least square-based linear regression and nonlinear regression;
(3) performing regression diagnosis, finding and eliminating abnormal points by checking a residual error map, and performing regression analysis again;
(4) and carrying out model inspection, and calculating the evaluation index value of each regression model.
5. The infrared aero-optical imaging simulation method of claim 4 wherein the step (1) of preparing a data set comprises:
obtaining wave front change parameters sigma under N groups of flight state parameters through flow field simulation and optical simulation, randomly sequencing the flight state parameters and the corresponding wave front change parameters sigma, and dividing the flight state parameters into a training set and a test set according to proportion;
drawing a scatter diagram of the sigma value and each flight state parameter, carrying out correlation coefficient analysis and significance test to obtain the correlation coefficient and confidence coefficient of the dependent variable sigma and each flight state parameter independent variable, judging the correlation relationship between the variables, and eliminating abnormal values.
6. The infrared pneumatic optical imaging simulation method of claim 4, wherein in the step (2) of performing regression analysis, the fitting is performed by using a training set, and the fitting method is polynomial fitting, Gaussian fitting, least square-based linear regression or nonlinear regression;
performing model inspection, and calculating the evaluation index value of each regression model, wherein when the evaluation index value of the regression model exceeds an index threshold value and is combined with the prediction precision of the test set, the corresponding regression model is used as a sigma mapping formula;
and (4) the evaluation index values of the regression model in the step (4) comprise a goodness of fit R-square, a regression coefficient significance test index, a regression equation significance test index or a correlation coefficient significance test index.
7. The infrared pneumatic optical imaging simulation method according to claim 1, wherein the degradation model and the parameter settings thereof in the second step are specifically as follows:
Figure FDA0003487355660000021
Figure FDA0003487355660000022
Figure FDA0003487355660000023
M＝μ·Ma/H
wherein, ω isiIs a weight parameter, σ is an intensity parameter, xmAnd ymFor the offset control parameter, M is the number of turbulence units.
8. The infrared pneumatic optical imaging simulation method according to claim 1, wherein the method for constructing the infrared scene simulation model in step three comprises:
firstly, three-dimensional modeling and surface temperature field setting are carried out, three-dimensional geometric models of a target and a background are established by utilizing three-dimensional modeling software, and the surface temperature of each part of a scene is set according to an empirical value;
then determining an imaging observation area and a flight state corresponding to each imaging frame according to the received flight state parameters, and the field of view parameters and the imaging parameters of the sensor;
calculating the infrared radiation amount, taking the whole scene as a gray body, calculating the infrared radiation intensity by utilizing the Planck's law, and obtaining an infrared radiation characteristic image under observation;
and finally, calculating a voltage value of the infrared radiation intensity after photoelectric conversion, and quantizing the output voltage into a gray value.
9. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of the infrared aero-optical imaging simulation method according to any one of claims 1 to 8.
10. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to carry out the steps of the infrared aero-optical imaging simulation method of any one of claims 1 to 8.
CN202210085084.8A 2022-01-25 2022-01-25 Infrared pneumatic optical imaging simulation method, storage medium and computer equipment Pending CN114492240A (en)

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