An infrared aerodynamic optical imaging simulation method, a storage medium and a computer device

Abstract

The application belongs to the technical field of optical imaging, and discloses an infrared aerodynamic optical imaging simulation method, a storage medium and a computer device. A wavefront change value under different flight states is obtained through numerical simulation of flow field dynamics and optics, a mapping relationship between the wavefront change value and a flight state parameter is established, and a continuously changing degradation model is constructed. An infrared scene simulation model is constructed, and a sequence infrared image in a detection stage and corresponding imaging flight states are output. The imaging flight states are input into the continuously changing Gaussian superposition model, convolution operation is performed on the obtained continuously changing point spread function and the corresponding infrared image, and real-time infrared aerodynamic optical effect degradation imaging is realized. The application establishes an aerodynamic continuously changing degradation model coupled with flight states, establishes an infrared detection visual simulation model, and can truly simulate an infrared detection imaging process under a high-speed motion state. The model of the application is simpler, has a small cost and consumes less time.

Description

Technical Field

[0001] This invention belongs to the field of optical imaging technology, and in particular relates to an infrared aero-optical imaging simulation method, storage medium, and computer equipment. Background Technology

[0002] Currently, airborne infrared optical systems play a crucial role in remote sensing. With the development of aerospace technology, image distortion caused by aero-optical effects has become a serious problem in airborne infrared optical imaging systems. The large density gradient of the turbulent compressible flow around the optical window is a direct cause of aero-optical distortion. The flow field outside the optical window may include complex flow structures such as free shear layers, expansion waves, turbulent boundary layers, and shock waves. The density gradient distribution is unstable and constantly changing. According to the Gladstone-Dale relation, the air refractive index is proportional to its density. Due to the change in the refractive index, the beam passing through the aerodynamic flow experiences severe wavefront distortion, leading to blurring, light deflection, and jitter. These aerospace optical effects adversely affect the imaging quality of airborne optical systems. Therefore, to improve the quality and accuracy of airborne infrared remote sensing missions, it is necessary to study the imaging changes caused by high-speed flight conditions.

[0003] Currently, the common method for estimating the imaging effects caused by aero-optical transmission is to conduct numerical calculations based on flow fields and optical transmission. These methods utilize computational fluid dynamics (CFD) software, resulting in high computational costs and limiting their analysis to finite flight conditions. Furthermore, due to high costs and the need for specialized cameras and optical components for infrared imaging, wind tunnel and flight tests are limited, making it impossible to obtain large amounts of continuous infrared aero-optical degradation image data. While current phenomenological simulation models can simulate degradation phenomena caused by aero-optical effects such as image blurring, jitter, line-of-sight errors, and saturation, the typical parameters of these models still need to be set individually based on empirical values. Therefore, these models are only suitable for describing the laws of image quality degradation and cannot perform real-time simulations based on flight conditions. Therefore, to effectively and cost-effectively study aero-optics and its real-time correction, it is necessary to design simulation methods for infrared aero-optical effects that can simulate real-time scenarios.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0005] (1) Existing methods for estimating the imaging effects caused by aero-optical transmission effects are computationally intensive, can only be analyzed for limited flight conditions, and are costly. Their use in wind tunnel and flight tests is limited, and they cannot obtain a large amount of continuous infrared aerodynamic degradation image data.

[0006] (2) Existing simulation models are only suitable for describing the law of image quality degradation and cannot be used for real-time simulation based on flight status.

[0007] The difficulty in solving the above problems and defects is as follows:

[0008] (1) Flight test platforms are complex to design, have long development cycles and high costs. Currently, China still lacks low-cost, high-response flight test platforms. Wind tunnel experiments cannot simulate real flight environments and are only suitable for simulating the effects of a single environmental factor.

[0009] (2) It is necessary to consider how to combine the results of a small number of numerical simulations to construct a continuously changing model.

[0010] This is to ensure that the changes in aerodynamic mechanisms can be consistent under different influencing factors.

[0011] The significance of solving the above problems and defects is as follows:

[0012] (1) Due to cost constraints and limited effectiveness, constructing dynamic infrared scene imaging simulation is a simple method to verify the correction algorithm;

[0013] (2) Fuzzy models under different parameters can serve as prior information to guide the research of more accurate and efficient correction algorithms;

[0014] (3) It can simulate degradation models under different flight conditions and provide a large amount of data for training advanced deep learning algorithms. Summary of the Invention

[0015] To address the problems existing in the prior art, this invention provides an infrared aero-optical imaging simulation method, a storage medium, and a computer device.

[0016] This invention is implemented as follows: an infrared aero-optical imaging simulation method includes:

[0017] Step 1: Obtain the wavefront variation value σ under different flight conditions through numerical simulation of flow dynamics and optics; through accurate numerical simulation, obtain parameters that can reflect the intensity changes of aero-optical effects, which can be used to guide the construction of the degradation model.

[0018] Step 2: Establish the mapping relationship between the wavefront variation value σ and the flight state parameters, and construct a continuously varying degradation model. Constructing a continuously varying mapping relationship can yield a degradation model that varies continuously with different flight state parameters, while ensuring that excessive computational costs are not consumed in the numerical simulation step.

[0019] Step 3: Construct an infrared scene simulation model and output the sequence of infrared images during the detection phase and their corresponding imaging flight states; based on the modeling of the infrared scene, a full-link infrared imaging degradation simulation process can be constructed.

[0020] Step four involves inputting the imaging flight state into a continuously varying Gaussian superposition model to obtain a set of continuously varying point spread functions. These functions are then convolved with the corresponding infrared images to achieve real-time infrared aero-optical degradation imaging. Pairs of infrared images and flight parameters are used as inputs to step four. This step allows for the generation of infrared detection images where the detection angle and flight parameters change simultaneously.

[0021] Furthermore, the flow field dynamics numerical simulation methods in step one are: Reynolds-averaged Navier-Stokes equations, direct numerical simulation (DNS), and large eddy simulation (LES).

[0022] Furthermore, in step one, the optical numerical simulation uses geometric optics, physical optics, and Fourier optics methods to analyze and calculate the distortion of light rays after passing through the flow field.

[0023] Furthermore, the method for constructing the mapping relationship in step two is as follows:

[0024] (1) Preparation of the dataset;

[0025] (2) Perform regression analysis. Fit the data using the training set. The fitting method can be multinomial fitting, Gaussian fitting, linear regression based on least squares, or nonlinear regression.

[0026] (3) Perform regression diagnosis, identify and remove outliers by examining the residual plot, and then perform regression analysis again;

[0027] (4) Perform model testing and calculate the evaluation index values ​​of each regression model.

[0028] Furthermore, the preparation of the dataset in step (1) includes:

[0029] Through flow field simulation and optical simulation, wavefront variation parameter σ under N sets of flight state parameters is obtained. The flight state parameters and the corresponding wavefront variation parameter σ are randomly sorted and divided into training set and test set according to the proportion.

[0030] Plot a scatter plot of σ values ​​and various flight state parameters, perform correlation coefficient analysis and significance tests, obtain the correlation coefficients and confidence levels between the dependent variable σ and the independent variables of each flight state parameter, determine the correlation between variables, and eliminate outliers.

[0031] Furthermore, in step (2) of the regression analysis, the training set is used for fitting. The fitting methods are polynomial fitting, Gaussian fitting, linear regression based on least squares, or nonlinear regression.

[0032] Furthermore, in step (4), model verification is performed, and the evaluation index values ​​of each regression model are calculated. When the evaluation index value of the regression model exceeds the index threshold, and the prediction accuracy of the test set is combined, the corresponding regression model is used as the mapping formula for σ.

[0033] Furthermore, the evaluation index values ​​of the regression model in step (4) include the goodness-of-fit R-square, the significance test index of the regression coefficient, the significance test index of the regression equation, or the significance test index of the correlation coefficient.

[0034] Furthermore, the degradation model and its parameter settings in step two are as follows:

[0035]

[0036]

[0037]

[0038] M = μ·Ma / H

[0039] Where, ω i Here, σ is the weight parameter, and x is the intensity parameter. m and y m M is the offset control parameter, where M is the number of turbulent units.

[0040] Furthermore, the method for constructing the infrared scene simulation model in step three includes:

[0041] The first step is 3D modeling and surface temperature field setting. 3D modeling software is used to create 3D geometric models of the target and background, and the surface temperature of each part of the scene is set according to empirical values.

[0042] Then, based on the received flight status parameters, as well as the sensor's field of view parameters and imaging parameters, the imaging observation area and the flight status corresponding to each imaging frame are determined.

[0043] Next, the infrared radiation is calculated. The entire scene is treated as a gray volume, and Planck's law is used to calculate the infrared radiation intensity, obtaining an infrared radiation characteristic image under observation.

[0044] Finally, the voltage value after photoelectric conversion of the infrared radiation intensity is calculated, and the output voltage is quantized into a grayscale value.

[0045] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the infrared aero-optical imaging simulation method.

[0046] Another object of the present invention is 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.

[0047] Combining all the above technical solutions, the advantages and positive effects of this invention are as follows: This invention establishes an aerodynamic continuous degradation model coupled with flight state and establishes an infrared detection visual simulation model, which can realistically simulate the infrared detection imaging process under high-speed motion state; compared with numerical simulation methods and flight test methods, the model of this invention is simpler, less costly and less time-consuming. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of the infrared aero-optical imaging simulation method provided in the embodiments of the present invention.

[0050] Figure 2 This is a flowchart illustrating the infrared aero-optical imaging simulation method provided in this embodiment of the invention.

[0051] Figure 3 This is a wavefront simulation diagram σ for different flight states provided in the embodiments of the present invention. The flight states are as follows: (1) speed 2Ma, altitude 10km; (2) speed 3Ma, altitude 5km; (3) speed 4Ma, altitude 2km.

[0052] Figure 4 This is a simulation diagram of the point spread function varying with flight altitude provided in an embodiment of the present invention.

[0053] Figure 5 This is a simulation diagram of the point spread function varying with flight speed provided in an embodiment of the present invention.

[0054] Figure 6 This is an infrared aero-optical image sequence provided in the embodiments of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0056] To address the problems existing in the prior art, the present invention provides an infrared aero-optical imaging simulation method, a storage medium, and a computer device. The present invention will be described in detail below with reference to the accompanying drawings.

[0057] like Figure 1 As shown, the infrared aero-optical imaging simulation method provided in this embodiment of the invention includes:

[0058] S101, the wavefront variation value σ under different flight conditions is obtained through numerical simulation of flow field dynamics and optics;

[0059] S102, Establish the mapping relationship between wavefront variation value σ and flight state parameters, and construct a continuously changing degradation model;

[0060] S103, construct an infrared scene simulation model and output the sequence of infrared images during the detection phase and their corresponding imaging flight states;

[0061] S104 inputs the imaging flight state into a continuously changing Gaussian superposition model to obtain a set of continuously changing point spread functions. By performing a convolution operation with the corresponding infrared image, real-time infrared aero-optical effect degradation imaging is achieved.

[0062] The present invention will be further described below with reference to specific embodiments.

[0063] The infrared aero-optical imaging simulation method provided in this embodiment of the invention specifically includes:

[0064] First, computational fluid dynamics (CFD) is used to simulate near-field non-uniform flow fields under different conditions to obtain the density distribution along the infrared light path. Then, the refractive index field is obtained based on the linear relationship between density and refractive index. Based on the CFD calculation results, the light transmission path is traced, and the wavefront distortion σ of the exit pupil of the optical system is calculated using physical optics methods.

[0065] The wavefront variation parameter σ under the N sets of flight state parameters is randomly sorted and proportionally divided into training and test sets. A scatter plot of σ values ​​against each flight state parameter is plotted, and correlation coefficient analysis and significance testing are performed to obtain the correlation coefficients and confidence levels between the dependent variable σ and the independent variables of each flight state parameter. This determines the correlation between variables and removes outliers. Next, regression analysis is performed. The training set is used for fitting, and the fitting method can be polynomial fitting, Gaussian fitting, least squares-based linear regression, or nonlinear regression. Then, regression diagnosis is performed by identifying and removing outliers by examining the residual plot, and regression analysis is repeated. Finally, the test set is used to test the model, calculating the goodness of fit of each regression model. When the goodness of fit exceeds 0.9, and the prediction accuracy of the test set is higher, the corresponding regression model is used as the mapping formula for σ. If the desired model cannot be obtained, regression analysis is repeated. The obtained mapping formula is then introduced into the degenerate model to obtain the PSF that continuously varies with the flight state parameters.

[0066]

[0067] Secondly, a real-time infrared detection scene simulation model is constructed, starting with 3D modeling of the scene and setting of the surface temperature field. 3D modeling software is used to create 3D geometric models of the target and background. The surface temperature of each part of the scene is set based on empirical values. Based on the received flight state parameters, as well as the sensor's field-of-view and imaging parameters, the imaging observation area and the corresponding flight state for each imaging frame are determined. The target and background within the observation area are treated as gray bodies, and Planck's law is used to calculate the infrared radiation amount, obtaining an infrared radiation characteristic image.

[0068]

[0069] Next, the output voltage after photoelectric attenuation is calculated, where L represents the radiation amount, Opttran is 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.

[0070]

[0071] Then, quantization calculations are performed to obtain the image grayscale value. Where [G] min G max ] represents the gray measurement range, V L and V H These are the minimum output voltage and the maximum output voltage, respectively.

[0072]

[0073] Finally, the infrared scene simulation model outputs a sequence of infrared images and a list of flight state parameters for the detection phase. The flight state parameter list is then input into a Gaussian superposition degradation model to obtain a set of continuously varying point spread functions. By convolving these point spread functions with the infrared images of the corresponding imaging frames, real-time infrared aero-optical effect degradation imaging simulation is achieved.

[0074] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry 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 the above-described hardware circuitry and software, such as firmware.

[0075] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An infrared aerodynamic-optical imaging simulation method, characterized in that, The infrared aero-optical imaging simulation method includes: Step 1: Obtain the wavefront variation value σ under different flight conditions through numerical simulation of flow field dynamics and optics; Step 2: Establish the mapping relationship between the wavefront variation value σ and the flight state parameters, and construct a continuously changing degradation model; Step 3: Construct an infrared scene simulation model and output the sequence of infrared images during the detection phase and their corresponding imaging flight states; Step 4: Input the imaging flight state into a continuously changing Gaussian superposition model to obtain a set of continuously changing point spread functions. By performing a convolution operation with the corresponding infrared image, real-time infrared aero-optical effect degradation imaging is achieved. The numerical simulation methods for flow field dynamics in step one are: Reynolds-averaged Navier-Stokes equations, direct numerical simulation (DNS), and large eddy simulation (LES). In step one, the optical numerical simulation uses geometric optics, physical optics, and Fourier optics methods to analyze and calculate the distortion of light rays after passing through the flow field. The method for constructing the mapping relationship in step two is as follows: (1) Preparation of the dataset; (2) Perform regression analysis; fit the training set. The fitting method can be polynomial fitting, Gaussian fitting, linear regression based on least squares, or nonlinear regression. (3) Perform regression diagnosis, identify and remove outliers by examining the residual plot, and then perform regression analysis again; (4) Conduct model testing and calculate the evaluation index values ​​for each regression model; The preparation of the dataset in step (1) includes: Through flow field simulation and optical simulation, wavefront variation parameter σ under N sets of flight state parameters is obtained. The flight state parameters and the corresponding wavefront variation parameter σ are randomly sorted and divided into training set and test set according to the proportion. Plot a scatter plot of σ values ​​and various flight state parameters, perform correlation coefficient analysis and significance tests, obtain the correlation coefficients and confidence levels between the dependent variable σ and the independent variables of each flight state parameter, determine the correlation between variables, and eliminate outliers.

2. The method of claim 1, wherein the infrared aerodynamic optical imaging simulation is performed by a computer system. In step (2), the regression analysis is performed using the training set. The fitting methods are polynomial fitting, Gaussian fitting, linear regression based on least squares, or nonlinear regression. In step (4), the model is tested and the evaluation index value of each regression model is calculated. When the evaluation index value of the regression model exceeds the index threshold, the corresponding regression model is used as the mapping formula of σ, in conjunction with the prediction accuracy of the test set. The evaluation index values ​​of the regression model in step (4) include the goodness-of-fit R-square, the significance test index of the regression coefficient, the significance test index of the regression equation, or the significance test index of the correlation coefficient.

3. The method of claim 1, wherein the infrared aerodynamic optical imaging simulation is performed by a computer system. The specific degradation model and its parameter settings in step two are as follows: ； M = μ·Ma / H Where ωi is the weighting parameter, σ is the intensity parameter, xm and ym are the offset control parameters, and M is the number of turbulent units.

4. The method of claim 1, wherein the infrared aerodynamic optical imaging simulation is performed by a computer system. The method for constructing the infrared scene simulation model in step three includes: The first step is 3D modeling and surface temperature field setting. 3D modeling software is used to create 3D geometric models of the target and background, and the surface temperature of each part of the scene is set according to empirical values. Then, based on the received flight status parameters, as well as the sensor's field of view parameters and imaging parameters, the imaging observation area and the flight status corresponding to each imaging frame are determined. Next, the infrared radiation is calculated. The entire scene is treated as a gray body, and Planck's law is used to calculate the infrared radiation intensity to obtain the infrared radiation characteristic image under observation. Finally, the voltage value after photoelectric conversion of the infrared radiation intensity is calculated, and the output voltage is quantized into a grayscale value.

5. A computer device, comprising: The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the infrared aero-optical imaging simulation method according to any one of claims 1 to 4.

6. 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 according to any one of claims 1 to 4.

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