Modeling method, device and medium for radiation characteristics of infrared jammers

Abstract

The disclosed embodiments disclose a method, device, and medium for modeling the radiation characteristics of an infrared jammer. The method may include: performing polynomial curve fitting on the airflow influence factor in the initial radiation mathematical model of the infrared jammer based on the measured radiation intensity data at different flight speeds to obtain a fitting polynomial for the airflow influence factor; obtaining a Mach number calculation formula at different altitudes based on the speed of sound and the flight speed of the infrared jammer; obtaining a functional expression of the airflow influence factor based on the fitting polynomial for the airflow influence factor and the Mach number calculation formula; and constructing a radiation simulation model of the infrared jammer that varies with flight speed and altitude by combining the initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influence factor. This technical solution improves the accuracy of the radiation simulation model of the infrared jammer and reduces the construction cost of the radiation simulation model of the infrared jammer.

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 typically release infrared jammers during flight. The rapid combustion of the infrared charge in the jammer produces an infrared source with similar infrared radiation characteristics to the target, such as an aircraft, but with energy 2-80 times that of the target. After release, this infrared source gradually separates from the target, causing the infrared seeker's tracking field of view in infrared air defense equipment to gradually shift toward the jammer and away from the target, resulting in tracking interference or target loss, making it impossible for the infrared air defense equipment to accurately engage 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 release 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 embodiments of the present disclosure provide a method, device, and medium for modeling the radiation characteristics of an infrared jamming flare. The theoretical model is corrected by a small amount of measured data to construct a radiation simulation model of the infrared jamming flare, thereby improving the accuracy of the radiation simulation model of the infrared jamming flare and reducing the construction cost of the radiation simulation model of the infrared jamming flare.

[0006] The technical solution of the present disclosure is achieved as follows:

[0007] In a first aspect, an embodiment of the present disclosure provides a method for modeling the radiation characteristics of an infrared countermeasure bomb, including:

[0008] For the airflow influence factor in the initial radiation mathematical model of the infrared jammer, a polynomial curve fitting is performed based on the measured radiation intensity data of the infrared jammer at different flight speeds to obtain a fitting polynomial for the airflow influence factor that varies with flight speed;

[0009] 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;

[0010] Obtaining 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;

[0011] The initial radiation mathematical model of the infrared jamming flare and the functional expression of the airflow influencing factor are combined to construct a radiation simulation model of the infrared jamming flare that changes with flight speed and altitude.

[0012] 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,

[0013] 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 for the airflow influence factor that varies with the flight speed;

[0014] The first obtaining part is configured to obtain a calculation formula for the Mach number at different altitudes based on the speed of sound that changes with the altitude of the infrared jamming flare and the flight speed of the infrared jamming flare;

[0015] 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;

[0016] 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 flight speed and altitude.

[0017] In a third aspect, an embodiment of the present disclosure provides a computing device, comprising: a memory and a processor; wherein:

[0018] The memory is used to store a computer program that can be run on the processor;

[0019] The processor is used to execute the modeling method of the infrared jamming bomb radiation characteristics described in the first aspect when running the computer program.

[0020] 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 infrared jamming bomb radiation characteristics described in the first aspect.

[0021] The embodiments of the present disclosure provide a method, device, and medium for modeling the radiation characteristics of an infrared jamming flare. The method corrects the airflow influence factor in the initial radiation mathematical model of the infrared jamming flare using a small amount of measured data, thereby constructing a radiation simulation model of the infrared jamming flare that changes with flight speed and altitude. This improves the accuracy of the radiation simulation model of the infrared jamming flare and reduces the construction cost of the radiation simulation model of the infrared jamming flare. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a method for modeling the radiation characteristics of an infrared countermeasure bomb provided in an embodiment of the present disclosure;

[0023] Figure 2 A graph showing the airflow impact factor of the infrared jammer provided by an embodiment of the present disclosure as a function of airflow velocity;

[0024] Figure 3 A graph showing the speed of sound changing with altitude (excluding the stratosphere) provided in an embodiment of the present disclosure.

[0025] Figure 4 A graph showing the speed of sound changing with altitude (considering the stratosphere) provided in an embodiment of the present disclosure;

[0026] Figure 5 A reduced-surface combustion model provided for an embodiment of the present disclosure;

[0027] Figure 6 An overall flow chart of a method for modeling the radiation characteristics of infrared countermeasure bombs provided in an embodiment of the present disclosure;

[0028] Figure 7 A schematic diagram of a device for modeling the radiation characteristics of an infrared jamming flare provided in an embodiment of the present disclosure;

[0029] Figure 8 A schematic diagram of the hardware structure of a computing device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] The technical solutions in the present disclosure will be described clearly and completely below with reference to the accompanying drawings in the present disclosure.

[0031] 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 purpose, it is necessary to use a large amount of experimental data of infrared jamming bombs to design an anti-interference algorithm. Since it takes a lot of resources to implement an infrared jamming bomb delivery experiment, 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 embodiment of the present disclosure hopes to provide a technical solution for modeling the radiation characteristics of infrared jamming bombs, based on the initial radiation mathematical model of the infrared jamming bomb, that is, the theoretical model, the airflow influence factor in the theoretical model is corrected by 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 jamming bomb provided by an embodiment of the present disclosure, the method comprising:

[0032] S101: performing polynomial curve fitting on the airflow influence factor in the initial radiation mathematical model of the infrared jammer according to the measured radiation intensity data of the infrared jammer at different flight speeds to obtain a fitting polynomial for the airflow influence factor that varies with flight speed;

[0033] S102: Obtaining a Mach number calculation formula at different altitudes based on the speed of sound that varies with the altitude of the infrared jammer and the flight speed of the infrared jammer;

[0034] S103: 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;

[0035] S104: Combining the initial radiation mathematical model of the infrared jamming flare and the functional expression of the airflow influencing factor, a radiation simulation model of the infrared jamming flare that changes with flight speed and altitude is constructed.

[0036] 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.

[0037] against Figure 1In some possible implementations of the technical solution shown, the airflow influence factor in the initial radiation mathematical model of the infrared jamming flare is subjected to polynomial curve fitting based on the measured radiation intensity data of the infrared jamming flare at different flight speeds to obtain a fitting polynomial for the airflow influence factor that varies with flight speed, including:

[0038] The corresponding measured values ​​of airflow influence factors are calculated based on the measured radiation intensity data of infrared jammers at different flight speeds;

[0039] 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 changes with the flight speed.

[0040] For the above implementation, in some examples, the corresponding measured value of the airflow impact 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:

[0041] (1)

[0042] in, is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interference munition agent per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; is the wavelength Radiation efficiency within the band:

[0043] (2)

[0044] in, is the first radiation constant, and its value is ; The second radiation constant 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.

[0045] In the context of aviation and aerodynamics, the flight speed of the infrared jamming flare relative to the air is the airspeed. The infrared jamming flare that moves at a high speed relative to the air means that the infrared jamming flare moves in the air at a higher airspeed. The airflow velocity is related to the flight speed of the infrared jamming flare, because the flight speed of the infrared jamming flare will affect the flow of air around it, thereby generating a relative airflow. The airflow generated when the infrared jamming flare flies at 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 jamming flare. The flight speed of the infrared jamming flare in the air and the effect of the air flow generated by the flight speed on the combustion process of the jamming flare can be measured through the airflow influencing 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, from Figure 2 As can be seen in the figure, when burning under dynamic conditions, the radiation intensity of the infrared countermeasure drops sharply as the airflow speed increases, dropping to one-tenth of the static radiation intensity at sea level. Therefore, for high-speed infrared countermeasures, it is necessary to consider the impact of high-speed airflow on the radiation characteristics of the infrared grain during combustion in order to modify the initial radiation mathematical model of the infrared countermeasure.

[0046] For the above example, the corresponding measured value of the airflow influence factor is calculated based on the measured radiation intensity data of the infrared jamming bomb at different flight speeds. Specifically, the measured radiation intensity data of the infrared jamming bomb at different flight speeds is collected through high-speed photography, infrared detectors or other measuring equipment. The collected measured radiation intensity data is cleaned, that is, outliers and noise are eliminated to ensure that the data reflects real physical phenomena. The measured radiation intensity data is 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 showing the change in radiation intensity with flight speed can also be drawn to visually observe the change in the radiation intensity of the infrared jamming bomb with flight speed, so as to analyze the impact of the infrared jamming bomb on the radiation performance of the infrared jamming bomb 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 speed to ensure that the dependent variable (the airflow influence factor measured value) is arranged in the order of the independent variable (flight speed).

[0047] For the above implementation, in some examples, performing polynomial curve fitting on the measured value of the airflow impact factor according to the flight speed to obtain a fitting polynomial of the airflow impact factor that varies with the flight speed includes:

[0048] Based on 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:

[0049] (3)

[0050] in, is the airflow influencing factor; is the Mach number;

[0051] The airflow influencing factor is subjected to polynomial curve fitting to obtain polynomial coefficients, and a fitting polynomial of the airflow influencing factor that varies with flight speed is obtained according to the polynomial coefficients:

[0052] (4)

[0053] 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 fitting polynomial.

[0054] 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 subjected to curve fitting to construct a functional expression of the airflow influence factor and the Mach number. Specifically, in the research on 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 is 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 Function. The curve fitting is to seek a smooth curve to represent the measurement data with noise, and to seek the relationship or change trend between two function variables from the data points to 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 fitting 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 mathematical models of binomial and exponential functions 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.

[0055] 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.

[0056] 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 polynomial of the airflow influence factor that varies with flight speed is obtained based on the polynomial coefficients. Specifically, the curve fitting process uses mathematical software, such as the curve fitting tool in MATLAB, to perform polynomial fitting or other types of curve fitting on the airflow influence factor calculated based on the measured radiation intensity data using the polyfit function, 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:

[0057] Input Data , , use the polyfit function in MATLAB to fit, and set the highest order of the fitting polynomial to : ;

[0058] 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 fitting polynomial.

[0059] Use the polyval function to calculate the fitted value:

[0060] Run MATLAB to calculate the array The value of , which is the polynomial coefficient. Therefore, the polynomial obtained by fitting n times is the fitting polynomial of the airflow influencing factor that changes with flight speed:

[0061]

[0062] 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 fitting polynomial.

[0063] In summary, when infrared countermeasures fly through the air at different speeds, they encounter different dynamic conditions, including friction with the air, pressure changes, and aerodynamic heating. These factors affect the combustion efficiency and pattern of the infrared charge, and thus the radiation intensity produced by the infrared countermeasures. The airflow influencing factor fitting polynomial captures and quantifies the impact of the infrared countermeasures' flight speed on their radiation characteristics. In infrared countermeasures and defense systems, such curve fitting simulations are crucial for predicting the performance of infrared countermeasures and designing effective countermeasures. By analyzing the radiation characteristics of infrared countermeasures at different flight speeds, the anti-interference performance of infrared air defense equipment can be better evaluated and optimized.

[0064] against Figure 1 In some possible implementations of the technical solution shown, the formula for calculating the Mach number at different altitudes based on the speed of sound that varies with the altitude of the infrared jammer and the flight speed of the infrared jammer includes:

[0065] Calculate the altitude based on the relationship between sound speed and temperature The speed of sound at is:

[0066] (5)

[0067] 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;

[0068] According to the flight speed and altitude of the infrared jammer The Mach number calculation formula of the infrared jamming bomb is obtained by calculating the speed of sound at:

[0069] (6)

[0070] 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 .

[0071] 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 varies with temperature, as the temperature increases, the speed of sound increases. Temperature is also related to the altitude of the infrared jammer. For example, in the troposphere, the temperature drops by approximately 0.65 degrees Celsius for every 100-meter increase in altitude. In the stratosphere, the temperature increases with altitude. The mesosphere and thermosphere are well above 50 km and are not considered. The relationship between the altitude and temperature of the infrared jammer is shown in Table 1:

[0072]

[0073] Table 1

[0074] For the above implementation, in some examples, the flight speed and altitude of the infrared jammer are The Mach number calculation formula for the infrared jammer is calculated based on the speed of sound at the target. Accordingly, the Mach number is also temperature-dependent: the higher the temperature, the lower the Mach number at the same flight speed. Specifically, as shown in Table 1, the impact of the infrared jammer's flight altitude on the Mach number shows that within the troposphere, as altitude increases, air density decreases, leading to a drop in temperature and a decrease in the speed of sound. Therefore, at the same flight speed, the infrared jammer's Mach number increases, affecting the aircraft's aerodynamic characteristics. Similarly, within the stratosphere, temperature increases with altitude, resulting in a decrease in the Mach number of the infrared jammer at high altitude at the same flight speed.

[0075] against Figure 1 In some possible implementations of the technical solution shown, the formula for calculating the Mach number at different altitudes based on the speed of sound that varies with the altitude of the infrared jammer and the flight speed of the infrared jammer also includes:

[0076] Based on ground reference Celsius and altitude Get altitude degrees Celsius :

[0077] (7)

[0078] According to the conversion relationship between Kelvin and Celsius , get the altitude The Mach number of the infrared jammer at is:

[0079] (8)

[0080] 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 .

[0081] Since the stratosphere is located at an altitude of 11km-50km, the temperature at the bottom of the stratosphere is approximately -55 degrees Celsius. Due to the effect of ozone, the temperature at the top of the stratosphere is approximately between -3 degrees Celsius and 24 degrees Celsius. However, unlike the troposphere, the temperature of the stratosphere does not change linearly. At the bottom of the stratosphere, the temperature initially remains unchanged or rises slightly as the altitude increases. In addition, considering that ozone reaches its maximum value at an altitude of 20km-25km, the atmospheric temperature below 20km remains at a lower level. Therefore, the atmospheric temperature at an altitude of 11km-15km is calculated as -55 degrees Celsius. Accordingly, the Mach number of the infrared jammer at an altitude of 11-15km is:

[0082] (9)

[0083] 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.

[0084] Based on the above implementation and example, the curve of sound speed changing with altitude, without considering the stratosphere, the infrared jammer at the same airspeed, see Figure 3 , which specifically shows a curve of sound speed changing with altitude, where the horizontal axis is altitude and the vertical axis is sound speed. 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 altitude provided by the embodiment of the present disclosure, it can be seen 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 sound speed variation characteristics of the stratosphere, which is of great significance for accurately calculating high-altitude infrared data.

[0085] against Figure 1 In some possible implementations of the technical solution shown, the function expression of the airflow influence factor of the infrared jamming bomb obtained based on the airflow influence factor fitting polynomial and the Mach number calculation formula includes:

[0086] Substituting 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 is:

[0087] (10)

[0088] 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.

[0089] Specifically, regarding the above implementation, since the airflow influencing factor of infrared jammers varies not only with flight speed but also with altitude, the relationship between the Mach number and the airflow influencing factor is that the Mach number directly affects the compressibility and flow characteristics of the airflow, as well as various physical processes related to the fluid. Specifically, the Mach number is closely related to multiple aspects such as fluid compressibility, shock waves, boundary layers, aerodynamic heating, flow losses, aerodynamic stability, and aerodynamic design. For example, aerodynamic stability, the stability of an 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.

[0090] 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 varies with flight speed and altitude, including:

[0091] Get the expression of the initial radiation mathematical model of the infrared jammer:

[0092]

[0093] 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;

[0094] (11)

[0095] in, is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interference munition agent per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; is the wavelength Radiation efficiency within the band:

[0096]

[0097] in, is the first radiation constant, and its value is ; The second radiation constant 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.

[0098] For the above implementation method, specifically, the function expression of the airflow influence 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.

[0099] Because actual processes cannot guarantee absolute uniformity in the density and composition of infrared charge pellets, combustion exhibits a certain degree of randomness, which in turn leads to a certain degree of randomness in radiation intensity. In some examples, the method for modeling the radiation characteristics of infrared countermeasure flares also includes establishing an autoregressive moving average (ARMA) model to simulate the randomness of radiation intensity and determining model coefficients through parameter estimation. The ARMA model is a statistical model widely used in time series data analysis. It combines autoregressive (AR) and moving average (MA) methods to simulate and predict the dependency structure in data series. The AR component assumes that the current value can be linearly predicted from several previous historical values. The order of the AR model determines the number of historical values ​​used to predict the current value. The MA component focuses on the dependency structure of the model error term and assumes that the current value can be represented by the model's white noise (i.e., a linear combination of random errors without autocorrelation) and its historical values. Using the ARMA model, the randomness of the infrared countermeasure flare's radiation intensity can be simulated, thereby improving the model's prediction accuracy. Specifically, the randomness of the radiation intensity of the infrared jamming bomb is simulated by the ARMA model, which includes 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. It is also used to avoid overfitting and is considered to be a 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.

[0100] Through the above steps, the process of establishing the ARMA model is described with specific examples. In some examples, it is implemented through MATLAB code. Specifically, the autocorrelation coefficient and partial autocorrelation coefficient properties are calculated by 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 get the coefficient estimate of 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:

[0101] (12)

[0102] in, for The value of the random variable at time t; is the value of the random variable in period 1; is the value of the random variable in period 2; for Random interference at any moment; is the random disturbance in the first period; is the random disturbance in the second period; 、 、 、 are all unitless constant coefficients.

[0103] 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 the time.

[0104] In summary, the ARMA model can more accurately simulate the radiation characteristics of infrared jammers under different flight conditions, providing more accurate data support for the design and evaluation of infrared countermeasure systems.

[0105] against Figure 1 In some possible implementations of the technical solution shown, the method further includes:

[0106] The initial radiation mathematical model of infrared jamming bomb including airflow influence factor is constructed based on the surface reduction combustion method.

[0107] For the above implementation, specifically, the infrared jamming bomb burns an infrared agent to generate the required radiation intensity of the infrared jamming bomb, wherein the infrared agent is the chemical component that constitutes the infrared jamming bomb, and its composition determines the radiation characteristics of the infrared jamming bomb generated during combustion, such as radiation intensity, spectral distribution and combustion characteristics. The infrared agent usually exists in the physical form of a solid powder column. In some examples, the powder column of the infrared jamming bomb, that is, the infrared powder column, can be a solid body of a rectangular parallelepiped, a cylinder or other shapes. The radiation energy of the infrared jamming bomb is generated by igniting the infrared powder column. The reduced surface combustion method is usually used in rocket science, solid fuel engines and the design of infrared jamming bombs. It is a model used to describe the changes in the geometric shape of solid propellants or powder columns during the combustion process. The combustion efficiency, the thrust generated and the radiation characteristics can be predicted by the model. The reduced surface combustion method is used to simulate the reduced surface combustion model of the infrared powder column during the combustion process, 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 combustion per unit time is It is certain that, is the surface area of ​​the rectangular charge. The depth of infrared charge combustion per unit time is the linear burning velocity. In this disclosure, the specific implementation of the initial radiation mathematical model of infrared jammers based on the reduced surface combustion method, which includes airflow influencing factors, is as follows:

[0108] Assume that the total radiation energy radiated outward when the infrared jammer burns is , then the radiation intensity of the infrared jammer is Total radiant energy Divide by get:

[0109] (13)

[0110] 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 combustion. Indicates the total energy generated by the infrared jammer during the entire combustion process. represents the static radiation coefficient. According to the law of conservation of energy, the relationship between the total radiation energy and the total energy of the infrared jamming bomb is:

[0111] (14)

[0112] The total energy generated by the infrared jammer during the combustion process , it can be expressed as:

[0113] (15)

[0114] 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.

[0115] Substituting equations (14) and (15) into equation (13), the radiation intensity of the infrared jamming flare is:

[0116] (16)

[0117] 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.

[0118] For a specific wavelength The radiation intensity of the infrared jamming bomb when burning within the band , according to Planck's formula and Stefan-Boltzmann's law, the wavelength is obtained The radiation intensity of the infrared jammer within the band is:

[0119] (17)

[0120] Considering the influence of airflow factors on the combustion process of infrared grains, a mathematical model of the initial radiation of infrared jamming bombs is constructed.

[0121] When infrared jammers are stationary or moving at low speed, they mainly transfer energy to the outside through radiation. However, when infrared jammers are moving at high speed relative to the air, the energy generated by the pyrotechnic agent burning under the action of strong airflow is greatly lost, resulting in a decrease in the total radiation energy of the infrared jammer when burning. In order to reflect the influence 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 mathematical model of the initial radiation of the infrared jammer is expressed as:

[0122]

[0123] in, is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interference munition agent per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; is the wavelength Radiation efficiency within the band:

[0124]

[0125] in, is the first radiation constant, and its value is ; The second radiation constant is 0.01438769 ; The temperature of the infrared jammer when burning; is the full spectrum emissivity; is the spectral emissivity within the selected simulation band; is the Stefan-Boltzmann constant.

[0126] 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.

[0127] In summary, the initial radiation mathematical model of the infrared jammer is obtained through the above steps. 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.

[0128] Combine Figure 1 The technical solution shown is described in detail with specific embodiments. Figure 1 The technical solution shown in Figure 6, which shows an overall flow chart of the method for modeling the radiation characteristics of infrared jammers provided by an embodiment of the present disclosure. Specifically, an initial radiation mathematical model of the infrared jammer, including airflow influencing factors, is constructed based on the reduced-area combustion method. This initial radiation mathematical model is based on the geometric dimensions of the infrared charge, the infrared charge density, the linear combustion velocity, the total energy generated by combustion, the static radiation coefficient, and the Stefan-Boltzmann law. Measured radiation intensity data of the infrared jammer at different flight speeds is collected using high-speed photography, infrared detectors, or other measuring equipment. The collected measured radiation intensity data is cleaned to eliminate outliers and noise to ensure that the data reflects actual physical phenomena. The measured radiation intensity data is then sorted by flight speed to analyze the relationship between the measured radiation intensity data and flight speed. Based on the measured radiation intensity data of infrared jammers at different flight speeds, the corresponding peak radiation intensity during dynamic combustion is obtained. 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 airflow influence factor values ​​are sorted according to the corresponding flight speed, and a polynomial curve fitting is performed on the measured airflow influence factor values. For example, polynomial curve fitting is performed using the polyfit and polyval functions in MATLAB to obtain polynomial coefficients. Based on the polynomial coefficients, a polynomial fitting polynomial describing the variation of the airflow influence factor with the flight speed of the infrared jammer is obtained. Since the airflow influence factor of the infrared jammer varies not only with flight speed but also with altitude, a formula for calculating the Mach number of the infrared jammer at different altitudes is obtained based on the flight speed in a 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 functional expression of the airflow influence factor. Combined with the initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influence factor, a radiation simulation model of the infrared jammer that changes with flight speed and altitude is constructed. 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.

[0129] 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,

[0130] The fitting section 701 is configured to perform polynomial curve fitting on the airflow influence factor in the initial radiation mathematical model of the infrared jamming flare based on the measured radiation intensity data of the infrared jamming flare at different flight speeds to obtain a fitting polynomial for the airflow influence factor that varies with flight speed;

[0131] The first obtaining part 702 is configured to obtain a Mach number calculation formula at different altitudes based on the speed of sound that changes with the altitude of the infrared jamming flare and the flight speed of the infrared jamming flare;

[0132] The second obtaining part 703 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;

[0133] The construction part 704 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 flight speed and altitude.

[0134] In some examples, the fitting portion 701 is configured to:

[0135] The corresponding measured values ​​of airflow influence factors are calculated based on the measured radiation intensity data of infrared jammers at different flight speeds;

[0136] 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 changes with the flight speed.

[0137] In some examples, the fitting portion 701 is configured to:

[0138] Based on 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:

[0139] (3)

[0140] in, is the airflow influencing factor; is the Mach number;

[0141] The airflow influencing factor is subjected to polynomial curve fitting to obtain polynomial coefficients, and a fitting polynomial of the airflow influencing factor that varies with flight speed is obtained according to the polynomial coefficients:

[0142] (4)

[0143] 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 fitting polynomial.

[0144] In some examples, the first obtaining portion 702 is configured to:

[0145] Calculate the altitude based on the relationship between sound speed and temperature The speed of sound at is:

[0146] (5)

[0147] 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;

[0148] According to the flight speed and altitude of the infrared jammer The Mach number calculation formula of the infrared jamming bomb is obtained by calculating the speed of sound at:

[0149] (6)

[0150] 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 .

[0151] In some examples, the first obtaining portion 702 is further configured to:

[0152] Based on ground reference Celsius and altitude Get altitude degrees Celsius :

[0153] (7)

[0154] According to the conversion relationship between Kelvin and Celsius , get the altitude The Mach number of the infrared jammer at is:

[0155] (8)

[0156] 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 .

[0157] In some examples, the second obtaining portion 703 is configured to:

[0158] Substituting 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 is:

[0159] (10)

[0160] 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.

[0161] In some examples, the building portion 704 is configured to:

[0162] Get the expression of the initial radiation mathematical model of the infrared jammer:

[0163] (1)

[0164] 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;

[0165] (11)

[0166] in, is the static radiation coefficient; is the mass change rate of the infrared jammer; The heat generated by the combustion of the interference munition agent per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; is the wavelength Radiation efficiency within the band:

[0167]

[0168] in, is the first radiation constant, and its value is ; The second radiation constant 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.

[0169] 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 can be at least one of a smart phone, a smart watch, a desktop computer, a laptop computer, a virtual reality terminal, an augmented reality terminal, a wireless terminal and a portable laptop computer. The computing device has a communication function and can access a wired network or a wireless network. The computing device can generally refer to one of a plurality of terminals, and those skilled in the art will know that the number of the above terminals can be more or less. In some examples, the computing device can receive data for modeling the radiation characteristics of infrared jamming flares 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.

[0170] 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 .

[0171] Optionally, the processor 810 utilizes various interfaces and circuits to connect various components within the computing device. It executes instructions, programs, code sets, or instruction sets stored in the memory 820, as well as accesses data stored in the memory 820, to perform various functions of the computing device and process data. Optionally, the processor 810 can be implemented in at least one hardware form factor selected from the group consisting of a digital signal processing (DSP), an FPGA, and a programmable logic array (PLA). The processor 810 can integrate one or a combination of a central processing unit (CPU), a GPU, a neural network processing unit (NPU), and a baseband chip. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the touchscreen display; the NPU implements artificial intelligence (AI) functions; and the baseband chip handles wireless communications. It is understood that the baseband chip can also be implemented as a separate chip, rather than integrated into the processor 810.

[0172] The memory 820 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 820 includes non-transitory computer-readable storage medium. The memory 820 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 820 may include a program storage area and a data storage area. 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.), and instructions for implementing each of the above method embodiments. The data storage area may store data created based on the use of the computing device.

[0173] In addition, those skilled in the art will understand that the structures of the computing devices shown in the above figures do not limit the computing devices. The computing devices may include more or fewer components than shown, or may combine certain components or arrange the components differently. For example, the computing devices may also include a display screen, a camera assembly, a microphone, a speaker, a radio frequency circuit, an input unit, sensors (such as an accelerometer, an angular velocity sensor, a fiber optic 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.

[0174] 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 infrared jamming bomb radiation characteristics as described in the above embodiments.

[0175] An embodiment of the present disclosure also provides 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 each of the above embodiments.

[0176] Those skilled in the art will appreciate 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. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0177] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A modeling method for the radiation characteristics of infrared jammers, 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 based on the measured radiation intensity data of the infrared jammer at different flight speeds to obtain a fitting polynomial for the airflow influence factor that varies with 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 jamming bomb according to the airflow influence factor fitting polynomial and the Mach number calculation formula; 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 flight speed and altitude is constructed; Wherein, the function expression of the airflow influence factor of the infrared jamming bomb obtained according to the airflow influence factor fitting polynomial and the Mach number calculation formula includes: Substituting 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 is: 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.

2. The method according to claim 1, characterized in that The polynomial curve fitting is performed based on the measured radiation intensity data of the infrared jamming bomb at different flight speeds to obtain a fitting polynomial for the airflow influence factor that varies with the flight speed, including: The corresponding measured values ​​of airflow influence factors are calculated based on the measured radiation intensity data of infrared jammers 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 changes 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: Based on 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 influencing factor is subjected to polynomial curve fitting to obtain polynomial coefficients, and a fitting polynomial of the airflow influencing factor that varies with 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 fitting polynomial.

4. The method according to claim 1, wherein The Mach number calculation formula at different altitudes is obtained based on the speed of sound that changes with the altitude of the infrared jamming bomb and the flight speed of the infrared jamming bomb, 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 jamming bomb 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 obtained based on the speed of sound that changes with the altitude of the infrared jamming bomb and the flight speed of the infrared jamming bomb also includes: Based on ground reference Celsius and altitude Get altitude degrees 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 method of constructing a radiation simulation model of the infrared jammer that changes with flight speed and altitude by combining the initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influencing factor includes: Get the expression of the initial radiation mathematical model of the infrared jammer: 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 interference munition per unit mass; For a specific wavelength The radiation intensity of the infrared jammer when burning within the band; is the wavelength Radiation efficiency within the band: in, is the first radiation constant, and its value is ; The second radiation constant 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.

7. A modeling device for the radiation characteristics of infrared jamming bombs, characterized in that: The device comprises: 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 for the airflow influence factor that varies with the flight speed; The first obtaining part is configured to obtain a calculation formula for the Mach number at different altitudes based on the speed of sound that changes with the altitude of the infrared jamming flare and the flight speed of the infrared jamming flare; 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 construct a radiation simulation model of the infrared jammer that varies with flight speed and altitude by combining the initial radiation mathematical model of the infrared jammer and the functional expression of the airflow influencing factor; Wherein, the function expression of the airflow influence factor of the infrared jamming bomb obtained according to the airflow influence factor fitting polynomial and the Mach number calculation formula includes: Substituting 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 is: 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.

8. A computing device, characterized in that The computing device includes: 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 flare radiation characteristics according to any one of claims 1 to 6 when running the computer program.

9. 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 the processor to implement the modeling method of the infrared countermeasures radiation characteristics according to any one of claims 1 to 6.

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