Modeling method and device for radiation characteristics of infrared jamming bomb and medium

Abstract

The embodiment of the invention discloses a modeling method and device for radiation characteristics of an infrared jamming bomb and a medium. The method comprises the following steps: aiming at an airflow influence factor in an initial radiation mathematical model of the infrared jamming bomb, calculating the radiation characteristics of the infrared jamming bomb; performing polynomial curve fitting according to the actually measured radiation intensity data at different flight speeds to obtain an airflow influence factor fitting polynomial; obtaining Mach number calculation formulas at different altitudes according to the sound velocity and the flight speed of the infrared jamming bomb; obtaining a function expression of the airflow impact factor according to the airflow impact factor fitting polynomial and the Mach number calculation formula; and combining the initial radiation mathematical model of the infrared jamming bomb and the function expression of the airflow influence factor to construct and obtain a radiation simulation model of the infrared jamming bomb changing along with the flight speed and the altitude. According to the technical scheme, the accuracy of the radiation simulation model of the infrared jamming bomb is improved, and the construction cost of the radiation simulation model of the infrared jamming bomb is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of infrared jamming simulation technology, and in particular to a modeling method, device and medium for the radiation characteristics of an infrared jamming bomb. Background Art

[0002] In modern warfare, aircraft generally release infrared jammers during flight. The rapid combustion of infrared charge in the infrared jammer produces an infrared source with infrared radiation characteristics similar to that of the target, such as an aircraft, and with an energy of about 2-80 times that of the target. After release, the infrared source gradually separates from the target, causing the tracking field of the infrared seeker in the infrared air defense equipment to gradually deviate from the target, resulting in tracking interference or loss of the target, so that the infrared air defense equipment cannot accurately hit the target.

[0003] For infrared air defense equipment, how to avoid the interference of infrared jamming bombs and successfully hit the target is its main evaluation indicator. To this end, it is necessary to use a large amount of experimental data of infrared jamming bombs to design an anti-interference algorithm.

[0004] Since implementing an infrared jammer dropping experiment requires a lot of resources, and the measured data of most infrared jammers are difficult to obtain due to confidentiality requirements, in the process of designing anti-interference algorithms for infrared air defense equipment, it is necessary to combine high-precision digital simulation and semi-physical simulation to obtain experimental data of infrared jammers. Summary of the invention

[0005] The disclosed embodiments provide a method, device and medium for modeling the radiation characteristics of an infrared jammer. The theoretical model is corrected by a small amount of measured data to construct a radiation simulation model of the infrared jammer, thereby improving the accuracy of the radiation simulation model of the infrared jammer and reducing the construction cost of the radiation simulation model of the infrared jammer.

[0006] The technical solution of the present disclosure is achieved as follows: In a first aspect, an embodiment of the present disclosure provides a method for modeling radiation characteristics of an infrared jammer, including: For the airflow influence factor in the initial radiation mathematical model of the infrared jammer, a polynomial curve fitting is performed according to the measured radiation intensity data of the infrared jammer at different flight speeds to obtain a fitting polynomial of the airflow influence factor that changes with the flight speed; A calculation formula for the Mach number at different altitudes is obtained based on the speed of sound that changes with the altitude of the infrared jammer and the flight speed of the infrared jammer; Obtaining a functional expression of the airflow influence factor of the infrared jammer according to the airflow influence factor fitting polynomial and the Mach number calculation formula; The initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influencing factor are combined to construct a radiation simulation model of the infrared jammer that changes with the flight speed and altitude.

[0007] In a second aspect, an embodiment of the present disclosure provides a device for modeling the radiation characteristics of an infrared jamming bomb, the device comprising: a fitting part, a first obtaining part, a second obtaining part, and a construction part; wherein: The fitting part is configured to perform polynomial curve fitting on the airflow influence factor in the initial radiation mathematical model of the infrared jamming bomb according to the measured radiation intensity data of the infrared jamming bomb at different flight speeds to obtain a fitting polynomial of the airflow influence factor that changes with the flight speed; The first obtaining part is configured to obtain a Mach number calculation formula at different altitudes according to the speed of sound that changes with the altitude of the infrared jamming bomb and the flight speed of the infrared jamming bomb; The second obtaining part is configured to obtain a functional expression of the airflow influence factor of the infrared jamming bomb according to the airflow influence factor fitting polynomial and the Mach number calculation formula; The construction part is configured to combine the initial radiation mathematical model of the infrared jamming bomb and the functional expression of the airflow influencing factor to construct a radiation simulation model of the infrared jamming bomb that changes with the flight speed and altitude.

[0008] In a third aspect, an embodiment of the present disclosure provides a computing device, the computing device comprising: a memory and a processor; wherein: The memory is used to store a computer program that can be run on the processor; The processor is used to execute the modeling method of the infrared countermeasure bomb radiation characteristics described in the first aspect when running the computer program.

[0009] In a fourth aspect, an embodiment of the present disclosure provides a computer storage medium storing at least one instruction, wherein the at least one instruction is used to be executed by a processor to implement the modeling method of the radiation characteristics of the infrared jamming bomb described in the first aspect.

[0010] The disclosed embodiments provide a method, device and medium for modeling the radiation characteristics of an infrared jammer. The airflow influence factor in the initial radiation mathematical model of the infrared jammer is corrected by a small amount of measured data, and a radiation simulation model of the infrared jammer that changes with flight speed and altitude is constructed, thereby improving the accuracy of the radiation simulation model of the infrared jammer and reducing the construction cost of the radiation simulation model of the infrared jammer. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flow chart of a method for modeling the radiation characteristics of an infrared jammer provided in an embodiment of the present disclosure; Figure 2 A curve diagram showing the change of the airflow influence factor of the infrared jammer provided in the embodiment of the present disclosure with the airflow velocity; Figure 3 A graph showing the speed of sound changing with altitude provided in an embodiment of the present disclosure (without considering the stratosphere); Figure 4 A graph showing the speed of sound changing with altitude (considering the stratosphere) provided for an embodiment of the present disclosure; Figure 5 A reduced-surface combustion model provided for an embodiment of the present disclosure; Figure 6 An overall flow chart of a method for modeling the radiation characteristics of an infrared jammer provided in an embodiment of the present disclosure; Figure 7 A schematic diagram of a modeling device for the radiation characteristics of an infrared jammer provided in an embodiment of the present disclosure; Figure 8 A schematic diagram of the hardware structure of a computing device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] The technical solutions in the present disclosure will be described clearly and completely below in conjunction with the accompanying drawings in the present disclosure.

[0013] For infrared air defense equipment, how to avoid the interference of infrared jamming bombs and successfully hit the target are its main evaluation indicators. For this reason, it is necessary to use a large amount of experimental data of infrared jamming bombs to design anti-interference algorithms. Since implementing an infrared jamming bomb delivery experiment requires a lot of resources, and most of the measured data of infrared jamming bombs are difficult to obtain due to confidentiality requirements, therefore, in the process of designing anti-interference algorithms for infrared air defense equipment, it is necessary to combine high-precision digital simulation and semi-physical simulation to obtain the experimental data of infrared jamming bombs. Based on this, the embodiments of the present disclosure hope to provide a technical solution for modeling the radiation characteristics of infrared jamming bombs, based on the initial radiation mathematical model of infrared jamming bombs, that is, the theoretical model, the airflow influencing factors in the theoretical model are corrected through a small amount of measured data to construct a radiation simulation model of the infrared jamming bomb, thereby improving the accuracy of the radiation simulation model of the infrared jamming bomb and reducing the construction cost of the radiation simulation model of the infrared jamming bomb. See. Figure 1 , which shows a modeling method for the radiation characteristics of an infrared jammer provided by an embodiment of the present disclosure, the method comprising: S101: For the airflow influence factor in the initial radiation mathematical model of the infrared jammer, polynomial curve fitting is performed according to the measured radiation intensity data of the infrared jammer at different flight speeds to obtain a fitting polynomial of the airflow influence factor that varies with the flight speed; S102: Obtaining a Mach number calculation formula at different altitudes according to the speed of sound that changes with the altitude of the infrared jammer and the flight speed of the infrared jammer; S103: Obtaining a function expression of the airflow influence factor of the infrared jammer according to the airflow influence factor fitting polynomial and the Mach number calculation formula; S104: Combining the initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influencing factor, a radiation simulation model of the infrared jammer that changes with the flight speed and altitude is constructed.

[0014] According to the description of the above scheme, the embodiment of the present disclosure targets the airflow influence factor in the initial radiation mathematical model of the infrared jamming bomb, corrects the airflow influence factor through a small amount of measured data, and constructs a radiation simulation model of the infrared jamming bomb that changes with the flight speed and altitude, thereby improving the accuracy of the radiation simulation model of the infrared jamming bomb and reducing the construction cost of the radiation simulation model of the infrared jamming bomb.

[0015] against Figure 1 In some possible implementations of the technical solution shown, the airflow influence factor in the initial radiation mathematical model of the infrared jammer is subjected to polynomial curve fitting based on the measured radiation intensity data of the infrared jammer at different flight speeds to obtain a fitting polynomial of the airflow influence factor that varies with the flight speed, including: The corresponding measured value of the airflow influence factor is calculated based on the measured radiation intensity data of the infrared jammer at different flight speeds; The measured value of the airflow influence factor is subjected to polynomial curve fitting according to the flight speed to obtain a fitting polynomial of the airflow influence factor that varies with the flight speed.

[0016] For the above implementation, in some examples, the corresponding measured value of the airflow influence factor is calculated based on the measured radiation intensity data of the infrared jammer at different flight speeds. Specifically, in the present disclosure, the initial radiation mathematical model of the infrared jammer is expressed as: (1) in, is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interfering ammunition agent per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; The wavelength Radiation efficiency within the band: (2) in, is the first radiation constant, and its value is ; The second radiation constant, the value is 0.01438769 ; is the Kelvin temperature when the infrared jammer burns; is the full spectrum emissivity; is the spectral emissivity within the selected simulation band; is the Stefan-Boltzmann constant.

[0017] In the context of aviation and aerodynamics, the flight speed of the infrared jammer relative to the air is the airspeed. The infrared jammer that moves at a high speed relative to the air means that the infrared jammer moves in the air at a higher airspeed. The airflow velocity is related to the flight speed of the infrared jammer, because the flight speed of the infrared jammer will affect the flow of air around it, thereby generating a relative airflow. The airflow generated when the infrared jammer flies at a high speed will have a significant effect on the combustion characteristics and radiation intensity of the infrared charge. Therefore, it is important to consider the effect of airflow velocity on the radiation characteristics of the infrared jammer. The flight speed of the infrared jammer in the air and the effect of the air flow generated by the flight speed on the combustion process of the jammer can be measured through the airflow influence factor It can be understood that the airflow influence factor Varies with the flight speed of the infrared jammer. Figure 2 , which shows the curve of the airflow influence factor of the infrared jammer changing with the airflow speed, where the left and right vertical axes are both airflow influence factors. Figure 2 It can be seen that when burning under dynamic conditions, as the airflow speed increases, the radiation intensity of the infrared jammer drops sharply, dropping to one tenth of the static radiation intensity at sea level. Therefore, for high-speed infrared jammers, it is necessary to consider the impact of high-speed airflow on the radiation characteristics of infrared charge during combustion, so as to modify the initial radiation mathematical model of the infrared jammer.

[0018] For the above example, the measured radiation intensity data of the infrared jammer at different flight speeds are used to calculate the corresponding measured value of the airflow influence factor. Specifically, the measured radiation intensity data of the infrared jammer at different flight speeds are collected by high-speed photography, infrared detectors or other measuring equipment, and the collected measured radiation intensity data are cleaned, that is, outliers and noise are excluded to ensure that the data reflects real physical phenomena, and the measured radiation intensity data are sorted according to the flight speed to analyze the relationship between the measured radiation intensity data and the flight speed. In some examples, a graph of the radiation intensity changing with the flight speed can also be drawn to visually observe the change of the radiation intensity of the infrared jammer with the flight speed, so as to analyze the influence of the infrared jammer on the radiation performance of the infrared jammer when it is flying at high speed. According to the measured radiation intensity data of the infrared jamming bomb at different flight speeds, the corresponding radiation intensity peak value during dynamic combustion is obtained, and the radiation intensity peak value during dynamic combustion at different flight speeds is divided by the radiation intensity peak value during static combustion to obtain the corresponding airflow influence factor measured values ​​at different flight speeds. The airflow influence factor measured values ​​are sorted according to the corresponding flight speeds to ensure that the dependent variable (the airflow influence factor measured value) is arranged in the order of the independent variable (flight speed).

[0019] For the above implementation, in some examples, performing polynomial curve fitting on the measured value of the airflow influence factor according to the flight speed to obtain a fitting polynomial of the airflow influence factor that varies with the flight speed includes: According to the discrete data points of the airflow influence factor of the infrared jammer at different flight speeds, curve fitting is performed to construct the functional expression of the airflow influence factor and the Mach number: (3) in, is the airflow influencing factor; is the Mach number; The airflow influence factor is subjected to polynomial curve fitting to obtain polynomial coefficients, and a fitting polynomial of the airflow influence factor that changes with the flight speed is obtained according to the polynomial coefficients: (4) in, An array of flight speeds of infrared jammers; are the polynomial coefficients; is an array of airflow influence factors corresponding to different flight speeds; is the order of the fitted polynomial.

[0020] For the above examples, specifically, in some examples, the discrete data points of the airflow influence factor of the infrared jammer at different flight speeds are used for curve fitting to construct a functional expression of the airflow influence factor and the Mach number. Specifically, in the research of the infrared jammer, a set of data points has been obtained through experiments or other measurement methods, and the data points represent the airflow influence factor of the infrared jammer. The relationship between the change of flight speed and the airflow influence factor of the infrared jammer As the flight speed changes, the data is a series of points. When calculating, it is necessary to convert the data points into a function with a smaller error. Therefore, the curve fitting method is used to form The curve fitting is to seek a smooth curve to represent the noisy measurement data, seek the relationship or change trend between two function variables from the data points, and obtain the function expression of the curve fitting. In detail, in order to obtain a continuous function with small error from discrete data points , a curve fitting method can be used. The curve fitting usually involves statistical analysis and mathematical optimization techniques, such as the least squares method, to determine the model parameters so that the error between the model's predictions and actual observations at all data points is minimized to determine the best fit line or curve. In actual operation, it is used to construct a mathematical function or curve based on a set of data points, for example, through different binomial, exponential function mathematical models or by selecting the model that best represents the data. Through fitting, one or more parameters or polynomial coefficients can be obtained, which define the shape of the curve. The function is applied to the initial radiation mathematical model of the infrared jammer to predict the radiation performance of the infrared jammer at different flight speeds.

[0021] It should be noted that the Mach number is the ratio of the flight speed of the infrared jamming bomb to the speed of sound at the corresponding altitude. It is a dimensionless number used to describe the ratio of the speed of the infrared jamming bomb in the fluid to the speed of sound in the fluid. The relationship between the Mach number and the flight speed is: when the flight speed of an object is equal to the speed of sound in the surrounding medium, its Mach number is 1. For example, on the ground or at low altitude, the speed of 1 Mach is about 340 meters per second. The flight speed of the infrared jamming bomb is expressed in terms of the Mach number, that is, the flight speed of the infrared jamming bomb is a multiple of the speed of sound. Accordingly, the data point can be expressed as the airflow influence factor of the infrared jamming bomb Variation with Mach number.

[0022] For the above implementation, in some examples, the airflow influence factor is subjected to polynomial curve fitting to obtain polynomial coefficients, and a polynomial fitting of the airflow influence factor that changes with the flight speed is obtained according to the polynomial coefficients. Specifically, the curve fitting process uses mathematical software, such as the curve fitting tool in MATLAB, and uses the polyfit function to perform polynomial fitting or other types of curve fitting on the airflow influence factor obtained by calculating the measured radiation intensity data, and selects a suitable fitting model, such as a linear, quadratic, exponential or higher-order polynomial model to best match the data points. The specific fitting process is: Input Data , , use the polyfit function in MATLAB to fit, and set the highest order of the fitting polynomial to : ; in, An array of flight speeds of infrared jammers; is an array of airflow influence factors corresponding to different flight speeds; is the array of polynomial coefficients, is the order of the fitted polynomial.

[0023] Use the polyval function to calculate the fitted value:

[0024] Run MATLAB to calculate the array The value of , which is the polynomial coefficient, so the polynomial obtained by fitting n times is the fitting polynomial of the airflow influence factor that changes with the flight speed:

[0025] in, An array of flight speeds of infrared jammers; are the polynomial coefficients; is an array of airflow influence factors corresponding to different flight speeds; is the order of the fitted polynomial.

[0026] In summary, when infrared jammers fly at different speeds in the air, they encounter different dynamic conditions, including friction with the air, pressure changes, and aerodynamic heating. The above factors will affect the combustion efficiency and mode of the infrared charge, and then affect the radiation intensity generated by the infrared jammer. The airflow influencing factor fitting polynomial is to capture and quantify the influence of the flight speed of the infrared jammer on the radiation characteristics. In infrared countermeasures and defense systems, the curve fitting simulation is crucial for predicting the performance of infrared jammers and designing effective countermeasures. By analyzing the radiation characteristics of infrared jammers at different flight speeds, the anti-interference performance of infrared air defense equipment can be better evaluated and optimized.

[0027] against Figure 1 In some possible implementations of the technical solution shown, the Mach number calculation formula at different altitudes is obtained according to the speed of sound that changes with the altitude of the infrared jammer and the flight speed of the infrared jammer, including: Calculate the altitude based on the relationship between sound speed and temperature The speed of sound at is: (5) in, is the altitude of the infrared jammer; is the altitude The speed of sound at is the Kelvin temperature when the infrared jammer burns; According to the flight speed and altitude of the infrared jammer The Mach number calculation formula of the infrared jammer is obtained by calculating the speed of sound at: (6) in, is the altitude of the infrared jammer; is the flight speed of the infrared jammer; is the altitude The speed of sound at is the altitude The Mach number at .

[0028] For the above implementation, in some examples, the altitude is calculated based on the relationship between the speed of sound and the temperature. Specifically, since the speed of sound changes with temperature, the speed of sound increases with increasing temperature, and the temperature is related to the altitude of the infrared jammer. For example, in the troposphere, the temperature drops by about 0.65 degrees Celsius when the altitude increases by 100 meters. In the stratosphere, the temperature increases with increasing altitude. The mesosphere and thermosphere are much higher than 50km and are not considered. The relationship between the altitude and temperature of the infrared jammer is shown in Table 1:

[0029] Table 1 For the above implementation, in some examples, the flight speed and altitude of the infrared jammer are The Mach number calculation formula of the infrared jammer is obtained by calculating the speed of sound at the place. Correspondingly, the Mach number is also related to temperature. The higher the temperature, the smaller the Mach number at the same flight speed. The influence of the flight altitude of the infrared jammer on the Mach number, specifically, as shown in Table 1, in the troposphere, as the altitude increases, the air density decreases, resulting in a drop in temperature and a decrease in the speed of sound. Therefore, at the same flight speed, the Mach number of the infrared jammer increases, which will affect the aerodynamic characteristics of the aircraft. Correspondingly, in the stratosphere, the temperature increases with the increase in altitude. At the same flight speed, the Mach number of the infrared jammer in the high altitude decreases.

[0030] against Figure 1 In some possible implementations of the technical solution shown, the Mach number calculation formula at different altitudes is obtained according to the speed of sound that changes with the altitude of the infrared jammer and the flight speed of the infrared jammer, and further includes: Based on ground reference Celsius and altitude Get altitude Celsius : (7) According to the conversion relationship between Kelvin and Celsius , get the altitude The Mach number of the infrared jammer at is: (8) in, is the altitude of the infrared jammer; is the flight speed of the infrared jammer; is the ground reference temperature in degrees Celsius; is the altitude degrees Celsius at is the Kelvin temperature when the infrared jammer burns; is the altitude The Mach number at .

[0031] Since the stratosphere is located at an altitude of 11km-50km, the temperature at the bottom of the stratosphere is about -55 degrees Celsius; due to the effect of ozone, the temperature at the top of the stratosphere is about -3 degrees Celsius to 24 degrees Celsius; but unlike the troposphere, the temperature of the stratosphere does not change linearly. At the bottom of the stratosphere, as the altitude increases, the temperature initially remains unchanged or rises slightly. In addition, considering that ozone reaches its maximum value at an altitude of 20km-25km, the atmospheric temperature below 20km remains at a relatively low level, so the atmospheric temperature within the altitude range of 11km-15km is calculated using -55 degrees Celsius. Accordingly, the Mach number of the infrared jammer at an altitude of 11-15km is: (9) In summary, the aircraft's drag decreases at high altitudes, and a higher airspeed can be achieved with the same thrust performance, thus increasing the importance of calculating the altitude factor.

[0032] Based on the above implementation and examples, the curve of sound speed changing with altitude, when the stratosphere is not considered, the infrared jammer at the same airspeed, see Figure 3 , which specifically shows a curve of the speed of sound changing with altitude, where the horizontal axis is the altitude and the vertical axis is the speed of sound. It can be seen that in the troposphere, the speed of sound decreases with increasing altitude. When considering the stratosphere, the speed of sound of infrared jammers at the same airspeed changes with altitude, see Figure 4 , which shows a curve diagram of the sound speed changing with the altitude provided by the embodiment of the present disclosure, it can be known that due to Figure 4 Taking into account the characteristics of the stratosphere, a more accurate sound speed curve is provided. Figure 3 It can be seen that this application takes into account the changing characteristics of the sound speed in the stratosphere, which is of great significance for the accurate calculation of high-altitude infrared data.

[0033] against Figure 1 In some possible implementations of the technical solution shown, the function expression of the airflow influence factor of the infrared jammer is obtained according to the airflow influence factor fitting polynomial and the Mach number calculation formula, including: Substitute the Mach number calculation formula of the infrared jammer into the airflow influence factor fitting polynomial to obtain the function expression of the airflow influence factor: (10) in, is the altitude of the infrared jammer; is the flight speed of the infrared jammer; is the altitude Mach number at ; is the order of the fitting polynomial; are the polynomial coefficients.

[0034] For the above implementation, specifically, since the airflow influencing factor of the infrared jammer changes not only with the change of flight speed, but also with the change of altitude. The relationship between the Mach number and the airflow influencing factor is that the size of the Mach number directly affects the compressibility of the airflow, flow characteristics and various physical processes related to the fluid. Specifically, the Mach number is closely related to the compressibility of the fluid, shock waves, boundary layers, aerodynamic heating, flow losses, aerodynamic stability and aerodynamic design. For example, aerodynamic stability, the stability of the aircraft in high-speed flow is significantly affected by the Mach number, and the Mach number needs to be considered to ensure stability in supersonic and hypersonic flows.

[0035] against Figure 1 In some possible implementations of the technical solution shown, the initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influencing factor are combined to construct a radiation simulation model of the infrared jammer that changes with the flight speed and altitude, including: The expression of the initial radiation mathematical model of the infrared jammer is obtained:

[0036] Substituting the function expression of the airflow influence factor into the expression of the initial radiation mathematical model of the infrared jamming bomb to obtain the radiation simulation model of the infrared jamming bomb; (11) in, is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interfering ammunition agent per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; The wavelength Radiation efficiency within the band:

[0037] in, is the first radiation constant, and its value is ; The second radiation constant, the value is 0.01438769 ; is the Kelvin temperature when the infrared jammer burns; is the full spectrum emissivity; is the spectral emissivity within the selected simulation band; is the Stefan-Boltzmann constant.

[0038] For the above implementation method, specifically, the function expression of the airflow influencing factor is substituted into the expression of the initial radiation mathematical model of the infrared jamming bomb to obtain the radiation simulation model of the infrared jamming bomb, and the radiation simulation model of the infrared jamming is applied to the digital or semi-physical simulation test in the weapon field to reproduce the release scenario of the infrared jamming bomb, and then the anti-interference capability of the prevention and control equipment is verified and analyzed to provide support for the design optimization of the anti-interference performance of the infrared air defense equipment.

[0039] Since it is impossible to ensure that the density and composition of the infrared charge are absolutely uniform in the actual process, the combustion will have a certain randomness, which will lead to a certain randomness in the radiation intensity. In some examples, the modeling method of the radiation characteristics of the infrared jammer also includes: establishing an autoregressive moving average model (ARMA) to simulate the randomness of the radiation intensity and determining the model coefficients through parameter estimation. The ARMA model is a statistical model widely used in time series data analysis, which combines the two methods of autoregressive (AR) and moving average (MA) to simulate and predict the dependency structure in the data sequence. Among them, in the AR part, it is assumed that the current value can be linearly predicted by several previous historical values, and the order of the AR model determines the number of historical values ​​used to predict the current value. The MA part focuses on the dependency structure of the model error term, assuming that the current value can be represented by the white noise of the model, that is, the linear combination of random errors without autocorrelation and their historical values. Through the ARMA model, the randomness of the radiation intensity of the infrared jammer can be simulated to improve the prediction accuracy of the model. Specifically, the randomness of the radiation intensity of infrared jammers is simulated by the ARMA model, including the following steps: using the moment estimation method to estimate the parameters of the AR part. The model order is determined by quantitatively analyzing the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The AIC criterion is a statistical criterion for model selection, which takes into account the goodness of fit and complexity of the model to avoid overfitting. The BIC criterion, also known as the Schwarz Information Criterion (SIC), is a model selection criterion based on the Bayesian framework, which is also used to avoid overfitting and is considered to be the Bayesian version of AIC in some cases. Then, establish Model, where is the order of the AR part, is the order of the MA part. Finally, error analysis: After the ARMA model is established, the error data between the measured curve and the ARMA model are used to optimize the model parameters.

[0040] Through the above steps, the process of establishing the ARMA model is described with specific embodiments. In some examples, it is implemented through MATLAB code. Specifically, the autocorrelation coefficient and partial autocorrelation coefficient properties are calculated through time series data. The function graphs are all tailing forms to determine whether the ARMA model can be used for modeling. The model order can be determined by quantitatively analyzing the AIC criterion and the BIC criterion. Model. Moment estimation is used when estimating parameters. First, the AR part is estimated. For the ARMA model, the corresponding extension Equation, through the The equation is solved to obtain the coefficient estimate of the AR part. Sequence, replace the coefficients of the AR part with the calculated estimates to obtain the MA sequence, and then use the MA moment estimation method to generate the simulation model of the infrared jammer radiation intensity as follows: (12) in, for The value of the random variable at time; is the value of the random variable in period 1; is the value of the random variable in the second period; for Random interference at any time; is the random disturbance in the first period; is the random disturbance in the second period; , , , All are unitless constant coefficients.

[0041] It should be noted that in The value of the random variable at time yes , and random perturbations , The multivariate function of for The error term is the random interference at time.

[0042] In summary, the ARMA model can more accurately simulate the radiation characteristics of infrared jammers under different flight conditions, providing more precise data support for the design and evaluation of infrared countermeasure systems.

[0043] against Figure 1In some possible implementations of the technical solution shown, the method further includes: The initial radiation mathematical model of infrared jammer including airflow influence factor is constructed according to the reduced area combustion method.

[0044] For the above implementation, specifically, the infrared jammer burns an infrared agent to generate the required radiation intensity of the infrared jammer, wherein the infrared agent is a chemical component that constitutes the infrared jammer, and its composition determines the radiation characteristics of the infrared jammer generated during combustion, such as radiation intensity, spectral distribution, and combustion characteristics. The infrared agent usually exists in the physical form of a solid-state charge. In some examples, the charge of the infrared jammer, that is, the infrared charge, can be a solid body of a rectangular, cylindrical, or other shape. The infrared charge is ignited to generate the radiation energy of the infrared jammer. The reduced-surface combustion method is usually used in rocket science, solid fuel engines, and the design of infrared jammers. It is a model used to describe the changes in the geometric shape of solid propellants or charges during the combustion process. The model can be used to predict the combustion efficiency, the thrust generated, and the radiation characteristics. The reduced-surface combustion model of the infrared charge during the combustion process is simulated using the reduced-surface combustion method, such as Figure 5 As shown, assuming that the infrared charge is a rectangular charge, for a fixed type of infrared jammer, the size of the infrared charge is fixed, the density is uniform, and the depth of burning per unit time is It is certain that, is the surface area of ​​the rectangular charge. The depth of infrared charge burning per unit time is the linear burning speed. In the present disclosure, the specific implementation of the initial radiation mathematical model of the infrared jammer including the airflow influence factor according to the surface reduction combustion method is as follows: The total radiation energy radiated outward when the infrared jammer burns is set to , then the radiation intensity of the infrared jammer Total radiant energy Divide by get: (13) for According to the law of conservation of energy, the total radiation energy radiated outward when the infrared jammer burns is only a part of the total energy generated by the combustion. It indicates the total energy generated by the infrared jammer during the entire combustion process. represents the static radiation coefficient, then according to the law of conservation of energy, the relationship between the total radiation energy and the total energy of the infrared jammer is: (14) For the total energy generated by the infrared jammer during the combustion process , it can be expressed as: (15) in, is the mass change rate of the infrared charge; It is the heat generated by the combustion of unit mass of infrared jamming ammunition.

[0045] Substituting equations (14) and (15) into equation (13), the radiation intensity of the infrared jammer is: (16) in, is the radiation intensity of the infrared jammer; is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interference ammunition per unit mass.

[0046] For a specific wavelength The radiation intensity of the infrared jammer within the band when it burns , according to Planck's formula and Stefan-Boltzmann's law, we get the wavelength The radiation intensity of the infrared jammer within the band is: (17) Considering the influence of airflow factors on the combustion process of infrared charge, the initial radiation mathematical model of infrared jammer is constructed. When a stationary or slow-moving infrared jammer burns, it mainly transfers energy to the outside through radiation. However, when an infrared jammer moves at a high speed relative to the air, a large amount of energy is lost when the pyrotechnic agent burns under the action of strong airflow, resulting in a reduction in the total radiation energy of the infrared jammer when it burns. In order to reflect the effect of airflow speed on the radiation intensity of the jammer, the airflow influence factor is introduced. , which is used to express the ratio of the peak radiation intensity of the infrared jammer during dynamic combustion to the peak radiation intensity during static combustion. When the influence of the infrared jammer is considered, the initial radiation mathematical model of the infrared jammer is expressed as:

[0047] in, is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interfering ammunition agent per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; The wavelength Radiation efficiency within the band:

[0048] in, is the first radiation constant, and its value is ; The second radiation constant, the value is 0.01438769 ; The temperature of the infrared jammer when it burns; is the full spectrum emissivity; is the spectral emissivity within the selected simulation band; is the Stefan-Boltzmann constant.

[0049] It should be noted that is the full spectrum emissivity, For the selected simulation band The spectral emissivity within is a part of the full spectral emissivity. For infrared jammer flames, the value of this parameter is close to 1.

[0050] In summary, the initial radiation mathematical model of the infrared jammer is obtained through the above steps, and the initial radiation mathematical model of the infrared jammer is used to simulate the entire combustion process of the infrared jammer, and the relationship between the radiation intensity and mass change rate of the infrared jammer at different time points, the heat generated during the combustion of the jamming ammunition, and the radiation efficiency is predicted.

[0051] Combination Figure 1 The technical solution shown is described in detail with specific embodiments. Figure 1 The technical solution shown in Figure 6, which shows the overall flow chart of the modeling method of the radiation characteristics of the infrared jammer provided by the embodiment of the present disclosure. Specifically, the initial radiation mathematical model of the infrared jammer including the airflow influencing factor is constructed according to the reduced surface combustion method, that is, the radiation mathematical model of the traditional jammer, and the initial radiation mathematical model is obtained based on the geometric size of the infrared charge, the density of the infrared charge, the linear combustion speed, the total energy generated by the combustion, the static radiation coefficient and the Stefan-Boltzmann law. The measured radiation intensity data of the infrared jammer at different flight speeds are collected by high-speed photography, infrared detectors or other measuring equipment, and the collected measured radiation intensity data are cleaned, that is, outliers and noise are excluded to ensure that the data reflects the real physical phenomenon, and the measured radiation intensity data are sorted according to the flight speed to analyze the relationship between the measured radiation intensity data and the flight speed. According to the measured radiation intensity data of the infrared jammer at different flight speeds, the corresponding peak radiation intensity during dynamic combustion is obtained, and the peak radiation intensity during dynamic combustion at different flight speeds is divided by the peak radiation intensity during static combustion to obtain the corresponding measured values ​​of the airflow influence factor at different flight speeds. The measured values ​​of the airflow influence factor are sorted according to the corresponding flight speeds, and the measured values ​​of the airflow influence factor are fitted with a polynomial curve. For example, polyfit and polyval functions are used in MATLAB to fit the polynomial curve to obtain polynomial coefficients, and the airflow influence factor fitting polynomial describing the change of the airflow influence factor with the flight speed of the infrared jammer is obtained according to the polynomial coefficients. Since the airflow influence factor of the infrared jammer changes not only with the change of the flight speed, but also with the change of the altitude. Therefore, the Mach number calculation formula of the infrared jammer at different altitudes is obtained according to the flight speed in the high-altitude environment and the speed of sound at the corresponding altitude. The Mach number calculation formula of the infrared jammer is substituted into the airflow influence factor fitting polynomial to obtain the function expression of the airflow influence factor. A radiation simulation model of the infrared jammer that changes with the flight speed and altitude is constructed by combining the initial radiation mathematical model of the infrared jammer and the function expression of the airflow influence factor. The radiation simulation model of the infrared jammer can simulate the radiation characteristics of the infrared jammer in a high-speed and high-altitude environment.

[0052] Based on the same inventive concept as the above technical solution, see Figure 7 , which shows a modeling device 700 for the radiation characteristics of an infrared jamming bomb provided by an embodiment of the present disclosure, the device 700 includes: a fitting part 701, a first obtaining part 702, a second obtaining part 703 and a construction part 704; wherein, The fitting part 701 is configured to perform polynomial curve fitting on the airflow influence factor in the initial radiation mathematical model of the infrared jamming bomb according to the measured radiation intensity data of the infrared jamming bomb at different flight speeds to obtain a fitting polynomial of the airflow influence factor that changes with the flight speed; The first obtaining part 702 is configured to obtain a Mach number calculation formula at different altitudes according to the speed of sound that changes with the altitude of the infrared jammer and the flight speed of the infrared jammer; The second obtaining part 703 is configured to obtain a functional expression of the airflow influence factor of the infrared jammer according to the airflow influence factor fitting polynomial and the Mach number calculation formula; The construction part 704 is configured to combine the initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influencing factor to construct a radiation simulation model of the infrared jammer that changes with the flight speed and altitude.

[0053] In some examples, the fitting portion 701 is configured as: The corresponding measured value of the airflow influence factor is calculated based on the measured radiation intensity data of the infrared jammer at different flight speeds; The measured value of the airflow influence factor is subjected to polynomial curve fitting according to the flight speed to obtain a fitting polynomial of the airflow influence factor that varies with the flight speed.

[0054] In some examples, the fitting portion 701 is configured as: According to the discrete data points of the airflow influence factor of the infrared jammer at different flight speeds, curve fitting is performed to construct the functional expression of the airflow influence factor and the Mach number: (3) in, is the airflow influencing factor; is the Mach number; The airflow influence factor is subjected to polynomial curve fitting to obtain polynomial coefficients, and a fitting polynomial of the airflow influence factor that changes with the flight speed is obtained according to the polynomial coefficients: (4) in, An array of flight speeds of infrared jammers; are the polynomial coefficients; is an array of airflow influence factors corresponding to different flight speeds; is the order of the fitted polynomial.

[0055] In some examples, the first obtaining portion 702 is configured to: Calculate the altitude based on the relationship between sound speed and temperature The speed of sound at is: (5) in, is the altitude of the infrared jammer; is the altitude The speed of sound at is the Kelvin temperature when the infrared jammer burns; According to the flight speed and altitude of the infrared jammer The Mach number calculation formula of the infrared jammer is obtained by calculating the speed of sound at: (6) in, is the altitude of the infrared jammer; is the flight speed of the infrared jammer; is the altitude The speed of sound at is the altitude The Mach number at .

[0056] In some examples, the first obtaining portion 702 is further configured to: Based on ground reference Celsius and altitude Get altitude Celsius : (7) According to the conversion relationship between Kelvin and Celsius , get the altitude The Mach number of the infrared jammer at is: (8) in, is the altitude of the infrared jammer; is the flight speed of the infrared jammer; is the ground reference temperature in degrees Celsius; is the altitude degrees Celsius at is the Kelvin temperature when the infrared jammer burns; is the altitude The Mach number at .

[0057] In some examples, the second obtaining portion 703 is configured to: Substitute the Mach number calculation formula of the infrared jammer into the airflow influence factor fitting polynomial to obtain the function expression of the airflow influence factor: (10) in, is the altitude of the infrared jammer; is the flight speed of the infrared jammer; is the altitude Mach number at ; is the order of the fitting polynomial; are the polynomial coefficients.

[0058] In some examples, the building portion 704 is configured to: The expression of the initial radiation mathematical model of the infrared jammer is obtained: (1) Substituting the function expression of the airflow influence factor into the expression of the initial radiation mathematical model of the infrared jamming bomb to obtain the radiation simulation model of the infrared jamming bomb; (11) in, is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interfering ammunition agent per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; The wavelength Radiation efficiency within the band:

[0059] in, is the first radiation constant, and its value is ; The second radiation constant, the value is 0.01438769 ; is the Kelvin temperature when the infrared jammer burns; is the full spectrum emissivity; is the spectral emissivity within the selected simulation band; is the Stefan-Boltzmann constant.

[0060] Please refer to Figure 8, which shows a schematic diagram of the hardware structure of a computing device provided by an exemplary embodiment of the present disclosure. In some examples, the computing device may be at least one of a smart phone, a smart watch, a desktop computer, a laptop, a virtual reality terminal, an augmented reality terminal, a wireless terminal, and a laptop. The computing device has a communication function and can access a wired network or a wireless network. The computing device may generally refer to one of a plurality of terminals, and those skilled in the art may know that the number of the above terminals may be more or less. In some examples, the computing device may receive data for modeling the radiation characteristics of an infrared jamming bomb based on the wired network or wireless network to which it is connected. It can be understood that the computing device undertakes the calculation and processing work of the technical solution of the present disclosure, and the embodiments of the present disclosure are not limited to this.

[0061] like Figure 8 As shown, the computing device in the present disclosure may include one or more of the following components: a processor 810 and a memory 820 .

[0062] Optionally, the processor 810 uses various interfaces and lines to connect various parts of the entire computing device, and executes various functions of the computing device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 820, and calling data stored in the memory 820. Optionally, the processor 810 can be implemented in at least one hardware form of digital signal processing (DSP), FPGA, and programmable logic array (PLA). The processor 810 can integrate one or a combination of a central processing unit (CPU), a GPU, a neural network processor (NPU), and a baseband chip. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the touch display; the NPU is used to implement artificial intelligence (AI) functions; and the baseband chip is used to process wireless communications. It can be understood that the above-mentioned baseband chip may not be integrated into the processor 810, but may be implemented by a single chip.

[0063] The memory 820 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 820 includes a non-transitory computer-readable storage medium. The memory 820 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 820 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above various method embodiments, etc.; the data storage area may store data created according to the use of the computing device, etc.

[0064] In addition, those skilled in the art can understand that the structure of the computing device shown in the above drawings does not constitute a limitation on the computing device, and the computing device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, the computing device also includes a display screen, a camera assembly, a microphone, a speaker, a radio frequency circuit, an input unit, a sensor (such as an acceleration sensor, an angular velocity sensor, an optical fiber sensor, etc.), an audio circuit, a WiFi module, a power supply, a Bluetooth module and other components, which will not be described in detail here.

[0065] An embodiment of the present disclosure also provides a computer-readable storage medium storing at least one instruction, wherein the at least one instruction is used to be executed by a processor to implement the modeling method of the radiation characteristics of the infrared countermeasure bomb as described in the above embodiments.

[0066] The embodiments of the present disclosure also provide a computer program product, which includes computer instructions stored in a computer-readable storage medium; a processor of a computing device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computing device executes to implement the modeling method of the infrared jamming bomb radiation characteristics described in the above-mentioned embodiments.

[0067] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present disclosure can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. Storage media can be any available media that can be accessed by general or special-purpose computers.

[0068] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A modeling method for the radiation characteristics of an infrared jammer, characterized in that: The method comprises: For the airflow influence factor in the initial radiation mathematical model of the infrared jammer, a polynomial curve fitting is performed according to the measured radiation intensity data of the infrared jammer at different flight speeds to obtain a fitting polynomial of the airflow influence factor that changes with the flight speed; A calculation formula for the Mach number at different altitudes is obtained based on the speed of sound that changes with the altitude of the infrared jammer and the flight speed of the infrared jammer; Obtaining a functional expression of the airflow influence factor of the infrared jammer according to the airflow influence factor fitting polynomial and the Mach number calculation formula; The initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influencing factor are combined to construct a radiation simulation model of the infrared jammer that changes with the flight speed and altitude.

2. The method according to claim 1, characterized in that The polynomial curve fitting is performed according to the measured radiation intensity data of the infrared jamming bomb at different flight speeds to obtain a fitting polynomial of the airflow influence factor that varies with the flight speed, including: The corresponding measured value of the airflow influence factor is calculated based on the measured radiation intensity data of the infrared jammer at different flight speeds; The measured value of the airflow influence factor is subjected to polynomial curve fitting according to the flight speed to obtain a fitting polynomial of the airflow influence factor that varies with the flight speed.

3. The method according to claim 2, characterized in that The step of performing polynomial curve fitting on the measured value of the airflow influence factor according to the flight speed to obtain a fitting polynomial of the airflow influence factor that varies with the flight speed includes: According to the discrete data points of the airflow influence factor of the infrared jammer at different flight speeds, curve fitting is performed to construct the functional expression of the airflow influence factor and the Mach number: in, is the airflow influencing factor; is the Mach number; The airflow influence factor is subjected to polynomial curve fitting to obtain polynomial coefficients, and a fitting polynomial of the airflow influence factor that changes with the flight speed is obtained according to the polynomial coefficients: in, An array of flight speeds of infrared jammers; are the polynomial coefficients; is an array of airflow influence factors corresponding to different flight speeds; is the order of the fitted polynomial.

4. The method according to claim 1, characterized in that: The Mach number calculation formula at different altitudes is obtained according to the sound speed that changes with the altitude of the infrared jammer and the flight speed of the infrared jammer, including: Calculate the altitude based on the relationship between sound speed and temperature The speed of sound at is: in, is the altitude of the infrared jammer; is the altitude The speed of sound at The Kelvin temperature when the infrared jammer is burning; According to the flight speed and altitude of the infrared jammer The Mach number calculation formula of the infrared jammer is obtained by calculating the speed of sound at: in, is the altitude of the infrared jammer; is the flight speed of the infrared jammer; is the altitude The speed of sound at is the altitude The Mach number at .

5. The method according to claim 1, characterized in that The Mach number calculation formula at different altitudes is obtained according to the sound speed that changes with the altitude of the infrared jammer and the flight speed of the infrared jammer, and also includes: Based on ground reference Celsius and altitude Get altitude Celsius : According to the conversion relationship between Kelvin and Celsius , get the altitude The Mach number of the infrared jammer at is: in, is the altitude of the infrared jammer; is the flight speed of the infrared jammer; is the ground reference temperature in degrees Celsius; is the altitude degrees Celsius at is the Kelvin temperature when the infrared jammer burns; is the altitude The Mach number at .

6. The method according to claim 1, characterized in that The function expression of the airflow influence factor of the infrared jammer obtained according to the airflow influence factor fitting polynomial and the Mach number calculation formula includes: Substitute the Mach number calculation formula of the infrared jammer into the airflow influence factor fitting polynomial to obtain the function expression of the airflow influence factor: in, is the altitude of the infrared jammer; is the flight speed of the infrared jammer; is the altitude Mach number at ; is the order of the fitting polynomial; are the polynomial coefficients.

7. The method according to claim 1, characterized in that The method of combining the initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influencing factor to construct a radiation simulation model of the infrared jammer that changes with the flight speed and altitude includes: The expression of the initial radiation mathematical model of the infrared jammer is obtained: Substituting the function expression of the airflow influence factor into the expression of the initial radiation mathematical model of the infrared jamming bomb to obtain the radiation simulation model of the infrared jamming bomb; in, is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interfering munition agent per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; The wavelength Radiation efficiency within the band: in, is the first radiation constant, and its value is ; The second radiation constant, the value is 0.01438769 ; is the Kelvin temperature when the infrared jammer burns; is the full spectrum emissivity; is the spectral emissivity within the selected simulation band; is the Stefan-Boltzmann constant.

8. A modeling device for the radiation characteristics of an infrared jammer, characterized in that: The device comprises: a fitting part, a first obtaining part, a second obtaining part and a building part; wherein, The fitting part is configured to perform polynomial curve fitting on the airflow influence factor in the initial radiation mathematical model of the infrared jamming bomb according to the measured radiation intensity data of the infrared jamming bomb at different flight speeds to obtain a fitting polynomial of the airflow influence factor that changes with the flight speed; The first obtaining part is configured to obtain a Mach number calculation formula at different altitudes according to the speed of sound that changes with the altitude of the infrared jamming bomb and the flight speed of the infrared jamming bomb; The second obtaining part is configured to obtain a functional expression of the airflow influence factor of the infrared jamming bomb according to the airflow influence factor fitting polynomial and the Mach number calculation formula; The construction part is configured to combine the initial radiation mathematical model of the infrared jamming bomb and the functional expression of the airflow influencing factor to construct a radiation simulation model of the infrared jamming bomb that changes with the flight speed and altitude.

9. A computing device, characterized in that The computing device comprises: a processor and a memory; wherein, The memory is used to store a computer program that can be run on the processor; The processor is used to execute the modeling method of the infrared countermeasure bomb radiation characteristics described in any one of claims 1 to 7 when running the computer program.

10. A computer storage medium, characterized in that: The storage medium stores at least one instruction, and the at least one instruction is used to be executed by a processor to implement the modeling method of the radiation characteristics of the infrared countermeasure bomb as described in any one of claims 1 to 7.

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