An interference control method and system based on area source infrared decoy image

Abstract

The present application relates to the field of infrared jamming, and relates to a kind of interference control method and system based on area source infrared decoy image, the present application constructs the infrared image model of decoy foil, uses dynamic programming method to obtain the infrared radiation characteristic of area source infrared decoy, obtains the miss distance of missile based on infrared radiation characteristic simulation calculation and obtains successful interference rate, according to successful interference rate evaluation interference efficiency, finally through the interference efficiency adjustment area source infrared decoy launch system control method.The present application accurately obtains infrared radiation characteristic by dynamic programming method, combines simulation calculation interference success rate, optimizes interference control method, so as to significantly improve the miss rate of missile and infrared jamming capability.

Description

Technical Field

[0001] This invention relates to the field of interference control, and specifically to an interference control method and system based on area source infrared decoy images. Background Technology

[0002] Surface-source infrared (SRI) decoys are a defensive measure primarily used to interfere with the tracking systems of infrared-guided missiles. They work by releasing a large amount of infrared radiation energy, creating one or more high-intensity infrared radiation sources, thereby deceiving infrared-guided missiles and causing them to deviate from their real targets, thus protecting the target object. Specifically, SRIs are typically made of highly efficient infrared-radiating materials. When released near a target or along its flight path, they can simulate the infrared characteristics (such as temperature and radiation intensity) of the target object. By releasing these decoys at appropriate times and frequencies, a virtual "infrared target" can be created in the air, guiding infrared-guided missiles away from their actual targets, thereby protecting the target. Although SRIs are highly effective at interfering with infrared-guided missiles, several technical challenges remain in practical applications: First, the radiation characteristics of infrared decoys are affected by various factors, leading to unstable and unpredictable radiation effects; second, the actual combat environment is complex and variable, and the effectiveness of decoys varies significantly under different environmental conditions, making it difficult to guarantee effective missile interference in all situations.

[0003] Existing technologies primarily attempt to address the aforementioned technical problems through the following methods, but all of these methods have certain drawbacks: some technologies enhance infrared radiation characteristics by improving decoy materials and structures, but these improvements are often costly and have limited effectiveness, making them difficult to cope with complex and ever-changing combat environments; others attempt to find the optimal parameter combination by adjusting decoy launch parameters through extensive experiments. However, this method lacks systematicity and scientific basis, and the experiments are costly and inefficient; furthermore, some technologies utilize simulation tools to evaluate decoy effectiveness under single conditions, but cannot comprehensively simulate various variables in actual combat environments, resulting in inaccurate and incomplete evaluation results. Summary of the Invention

[0004] To address the limited jamming effect of surface-source infrared decoys, this invention proposes a jamming control method based on surface-source infrared decoy images in a first aspect. The method includes: constructing an infrared image model of the decoy foil; obtaining the infrared radiation characteristics of the surface-source infrared decoy based on the infrared image model, including: setting the number of foil pieces for the surface-source infrared decoy; treating how to construct the infrared image model as a subproblem of dynamic programming; recursively calling and storing the solutions to the subproblem until the number of foil pieces reaches a preset value, terminating the call and outputting the final solution; performing data conversion and image processing on the final solution to obtain an infrared diffusion image of the surface-source infrared decoy; obtaining the infrared radiation characteristics of the surface-source infrared decoy based on the infrared diffusion image using a neural network and image segmentation; performing simulation based on the infrared radiation characteristics, calculating the missile miss distance to obtain the jamming success rate, thereby evaluating the jamming effectiveness of the surface-source infrared decoy; and adjusting the launch parameters of the surface-source infrared decoy based on the jamming effectiveness.

[0005] This invention constructs an infrared image model of a decoy chaff, employs dynamic programming to obtain the infrared radiation characteristics of the area-source infrared decoy, calculates the jamming success rate and evaluates the jamming effectiveness based on simulation results, and finally formulates a corresponding jamming control strategy. Compared with existing technologies, this invention can accurately simulate the infrared decoy radiation characteristics under different environmental conditions, and optimizes the decoy's launch parameters through systematic simulation and evaluation, thereby significantly improving jamming effectiveness. Existing technologies mostly rely on experience and experimentation, lacking scientific basis, resulting in unstable jamming effects and difficulty in guaranteeing effective missile jamming under various conditions.

[0006] Furthermore, constructing an infrared image model of the decoy foil also includes: the infrared image model contains surface temperature field data of the decoy foil, and the temperature formula for when the metal layer of the foil fully reacts is as follows:

[0007] in For reaction enthalpy; Knudsen diffusion coefficient; The dimensionless Siller modulus; It is the hyperbolic tangent function; This indicates that the foil thickness is The temperature is The reaction concentration at that time; The heat exchange coefficient for foil convection; Ambient temperature; It is the Stefan-Boltzmann constant; The emissivity of the foil.

[0008] Furthermore, recursively calling and storing the solutions to the subproblems also includes: increasing the size of the recursive call process in the dynamic programming method by one decoy foil each time; and using a hash table to store intermediate results of the solutions to the subproblems in memory.

[0009] By progressively increasing the size of the decoy foil through recursive calls in the dynamic programming method and using a hash table to store intermediate results, computational and storage efficiency are significantly improved. Compared with traditional linear algorithms, this invention can greatly reduce computation time and storage space while maintaining accuracy, thus improving the real-time performance and scalability of the system.

[0010] Furthermore, the process of converting data and performing image processing on the final solution to obtain and store the infrared diffusion image of the surface-source infrared decoy also includes: interpolating the temperature field data, calculating the infrared radiation intensity using the gray body radiation law, allocating the infrared radiation intensity to the corresponding image pixels to generate a preliminary infrared radiation image; and performing image preprocessing and color mapping on the preliminary infrared radiation image to obtain the infrared diffusion image of the surface-source infrared decoy.

[0011] Furthermore, image preprocessing and color mapping of the preliminary infrared radiation image also include: applying Gaussian filtering, convolution operation smoothing, and Laplacian edge enhancement algorithm to the preliminary infrared radiation image; and mapping the preliminary infrared radiation image according to the infrared radiation intensity to achieve pseudo-color processing.

[0012] Gaussian filtering, convolution smoothing, and Laplacian edge enhancement algorithms are applied to the initial infrared radiation image, significantly improving image quality. Pseudo-color processing allows for a more intuitive display of regions with different radiation intensities, facilitating identification and analysis. Compared to unprocessed images, the infrared diffusion images generated by this invention offer significant advantages in visual effects and information delivery.

[0013] Furthermore, obtaining the infrared radiation characteristics of the surface-source infrared decoy through neural networks and image segmentation also includes: extracting high-dimensional features of the infrared diffusion image using a convolutional neural network algorithm; performing image segmentation using an improved K-means clustering algorithm to distinguish different temperatures; and obtaining the radiation characteristics of each segmented region, including temperature distribution, radiation intensity distribution, and region area.

[0014] By extracting high-dimensional features from infrared diffusion images using a convolutional neural network algorithm and performing image segmentation using an improved K-means clustering algorithm, different temperatures can be accurately distinguished. The radiation characteristics of each segmented region, including temperature distribution, radiation intensity distribution, and region area, are obtained, providing accurate and rich data support for subsequent simulations and evaluations. Compared to traditional image analysis methods, this invention significantly improves both accuracy and efficiency.

[0015] Furthermore, the simulation based on the infrared radiation characteristics also includes: using MATLAB simulation software and the Monte Carlo algorithm to simulate a large number of random samples based on the infrared radiation characteristics.

[0016] Furthermore, calculating the missile miss distance to obtain the jamming success rate and thus evaluate the jamming effectiveness of the surface-source infrared decoy also includes: using a linear regression model to calculate the missile miss distance, wherein the training data of the linear regression model is the infrared radiation characteristics of the surface-source infrared decoy, and the target variable is the missile miss distance; setting a missile miss distance threshold, calculating the proportion of all missile miss distances predicted by the regression model that are greater than the preset threshold, and using this as the jamming success rate; statistically calculating the average value of the missile miss distance and the average value of the jamming success rate in all simulation data, and weighted summing the two average values ​​to obtain the jamming effectiveness of the surface-source infrared decoy.

[0017] By using a linear regression model to calculate the missile miss distance and setting a miss distance threshold to calculate the jamming success rate, the jamming effectiveness of area-source infrared decoys can be accurately evaluated. Compared with existing empirical evaluation methods, this invention provides a scientific and quantitative evaluation method that can more accurately reflect the actual jamming effect of the decoy.

[0018] Furthermore, adjusting the emission parameters of the surface-source infrared decoy based on the interference effectiveness also includes: continuously adjusting the emission time, emission interval, and emission quantity of the surface-source infrared decoy in simulation tests, and recording the corresponding interference effectiveness after each adjustment; dynamically adjusting the emission parameters based on the recorded interference effectiveness to ensure that the interference effectiveness of the surface-source infrared decoy remains optimal.

[0019] In a second aspect, the present invention provides an interference control system based on area-source infrared decoy images, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the interference control method based on area-source infrared decoy images of the present invention is implemented.

[0020] The technical effects of this invention are as follows: This invention uses dynamic programming to accurately obtain infrared radiation characteristics, combines simulation calculations to determine the jamming success rate, and optimizes the jamming control method, thereby significantly improving the missile's miss rate and infrared jamming capability. Attached Figure Description

[0021] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1 This is a schematic flowchart illustrating an embodiment of the interference control method based on area source infrared decoy images in this invention; Figure 2 This is a schematic illustration of a grayscale image of the surface of a bait foil in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the logic block diagram of the infrared diffusion image acquisition process in an embodiment of the present invention; Figure 4 This is a schematic illustration of the infrared diffusion of a surface-source infrared decoy in the atmosphere over time, according to an embodiment of the present invention. Figure 5 This is a schematic block diagram illustrating the structure of an interference control system based on area-source infrared decoy images according to an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0024] Example of an interference control method based on area source infrared decoy images: like Figure 1 As shown, an interference control method based on area source infrared decoy images according to the present invention includes: S1. Construct an infrared image model of the decoy foil.

[0025] S1.1 Temperature field data calculation.

[0026] A bait foil is a circular foil with a porous active metal coating on its surface, such as... Figure 2 As shown, it has the characteristic of spontaneous combustion upon contact with oxygen. After the foil spontaneously combusts, it can form a strong infrared radiation source. For the foil cloud that spreads in space, it will form an infrared radiation cloud, affecting the infrared image of the target aircraft in the seeker's field of view and interfering with the target identification of infrared imaging missiles.

[0027] The porous structure of the decoy foil ensures that oxygen can not only react chemically with the active metal on the foil surface, but also diffuse into the interior of the active metal. The diffusion of oxygen in the porous structure follows Fick's Law, meaning that the volume flux of oxygen diffusion is directly proportional to the concentration gradient of oxygen diffusion, as shown in the following expression:

[0028] in, This represents the diffusion flux of oxygen. Oxygen concentration; This represents the oxygen diffusion length. Let be the oxygen diffusion coefficient. For active metals, the main chemical reaction during spontaneous combustion is the oxidation of iron. Other chemical reactions may also occur, but these reactions generate significantly less heat than the iron oxidation reaction, and are not on the same order of magnitude, so they can be ignored. Therefore, the temperature distribution on the surface of the decoy foil can be determined by calculating the heat released by the iron oxidation reaction.

[0029] The energy change inside the decoy foil is equal to the heat generated by the chemical reaction. The algebraic sum of the energy exchanged through heat conduction inside the foil and the heat exchanged with the ambient air is expressed as:

[0030] in L represents the foil area; L represents the foil thickness. Indicates the density of the foil; This indicates the specific heat capacity of the foil; Indicates the foil temperature; Indicates time; For reaction enthalpy; Indicates temperature The rate constant of the oxidation reaction under the given conditions; Indicates the specific surface area of ​​the foil's metal coating; Indicates oxygen concentration; Indicates the thermal conductivity of the foil; This represents the heat transfer coefficient of foil convection; It is the Stefan-Boltzmann constant; The emissivity of the foil; This indicates the vertical height from the surface of the foil substrate.

[0031] The heat exchange coefficient of foil convection The calculation expression is:

[0032] in Represents the Reynolds number; Represent Prandtl numbers; Indicates the thermal conductivity of the foil; This represents the characteristic length. In practice, the energy exchanged through heat conduction within the foil can be ignored, thus simplifying the algebraic and equilibrium equations to:

[0033] in Indicates the density of the foil; Indicates the effective surface area of ​​the foil; This represents the rate constant for the foil oxidation reaction; This is expressed as the vertical distance from the surface of the foil substrate. The oxygen concentration at the location; the meanings of the other symbols are explained above.

[0034] The temperature of the decoy foil surface in steady state is obtained by integrating and solving the simplified equation above, as shown below:

[0035] in Knudsen diffusion coefficient; The dimensionless Siller modulus; It is the hyperbolic tangent function; This indicates that the foil thickness is The temperature is The reaction concentration at that time; This represents the ambient temperature; for the meanings of the other symbols, please refer to the explanations above.

[0036] Substitute the parameters of the above equations into the numerical values ​​and input them into the COMSOL Multiphysic calculation software to solve for the temperature field data.

[0037] S1.2, Store data.

[0038] The calculated temperature field data can be stored on a solid-state drive (SSD) in HDF5 format. HDF5 is a high-efficiency storage format suitable for storing large-scale data, supporting various data types and providing fast data access. The specific storage steps include creating an HDF5 file and defining the dataset; then writing the temperature field data into the dataset, using appropriate data compression techniques to save storage space (LZF compression is used in this invention, but other compression techniques can be selected based on specific needs); finally, metadata, including data generation time and parameters obtained from the calculation model, is added to the HDF5 file for subsequent use.

[0039] S2. Obtain the infrared radiation characteristics of surface-source infrared decoys based on the idea of ​​dynamic programming.

[0040] S2.1 Recursively calling dynamic programming subproblems.

[0041] like Figure 3 As shown in the dashed box S2.1, the number of foils for the area-source infrared decoy is first determined. Assume there are a total of N foils. Then, how to construct the infrared image model is treated as a subproblem of dynamic programming. For each foil, its temperature field data is calculated and stored independently. Next, in the dynamic programming method, the number of foils is increased by one in each recursive call. The initial state is set to 0 foils. The number of foils is increased recursively. After each increase, it is checked whether the set number of foils (N) has been reached. If not, the increase continues, and a hash table is used to store intermediate results in memory to avoid redundant calculations. If the set number of foils (N) has been reached, the final solution is output.

[0042] By progressively increasing the size of the decoy foil through recursive calls in the dynamic programming method and using a hash table to store intermediate results, computational and storage efficiency are significantly improved. Compared with traditional linear algorithms, this invention can greatly reduce computation time and storage space while maintaining accuracy, thus improving the real-time performance and scalability of the system.

[0043] S2.2, Obtain infrared diffusion image.

[0044] like Figure 3 As shown in the dashed box S2.2, the final solution undergoes data conversion and image processing to obtain and store the infrared diffusion image of the surface-source infrared decoy. The temperature field data of the final solution is converted into the peak radiant intensity of the foil surface. for:

[0045] in It is the Stefan-Boltzmann constant; For the emissivity of the foil, Indicates the foil temperature. Given the ambient temperature. Assume the foil radiation follows the gray-body radiation law, and the emissivity is... Then the foil in the band The radiation intensity inside is:

[0046] in Indicates the area of ​​the foil; and Let be the radiation constant, and respectively have: ; The meanings of the remaining symbols are explained above. So, what is the meaning of a single foil in the band? The internal radiance is:

[0047] in Indicates a single foil in the band The radiance within; Indicates a single foil in the band Radiation intensity within; This represents the area of ​​the foil. Based on the above conversion formula between temperature field and radiance, interpolation is used to convert the temperature field data into a continuous infrared radiation intensity distribution, generating a preliminary infrared radiation image. Image processing is then performed on the preliminary infrared radiation to obtain an infrared diffusion image, including: First, Gaussian filtering and convolution operations are used to smooth the initial infrared radiation image and eliminate noise. Then, a Laplacian edge enhancement algorithm is employed to enhance image details. Next, the initial infrared radiation image is pseudo-color mapped according to the infrared radiation intensity for visual display. The pseudo-color mapping scheme can use the RGB color space, with the color mapping table customized according to the radiation intensity range. Finally, the infrared diffusion image is stored in TIFF format on a solid-state drive. The TIFF format supports high dynamic range and multi-layer image storage, making it suitable for storing infrared image data.

[0048] Gaussian filtering, convolution smoothing, and Laplacian edge enhancement algorithms are applied to the initial infrared radiation image, significantly improving image quality. Pseudo-color processing allows for a more intuitive display of regions with different radiation intensities, facilitating identification and analysis. Compared to unprocessed images, the infrared diffusion images generated by this invention offer significant advantages in visual effects and information delivery.

[0049] S2.3 Analyze the infrared diffusion image.

[0050] High-dimensional features of infrared diffusion images can be extracted using convolutional neural network (CNN) algorithms. CNN models can capture complex patterns and features of images and extract representative feature vectors. Further, K-means clustering can be used for image segmentation to distinguish different temperature regions. The K-means clustering algorithm can improve segmentation performance through initialization optimization and iterative convergence condition improvements. Finally, the radiation characteristics of each segmented region are obtained, including temperature distribution, radiation intensity distribution, and region area, and these characteristic parameters are used as input data for subsequent simulations.

[0051] By extracting high-dimensional features from infrared diffusion images using a convolutional neural network algorithm and performing image segmentation using K-means clustering, different temperatures can be accurately distinguished. The radiation characteristics of each segmented region, including temperature distribution, radiation intensity distribution, and region area, are obtained, providing accurate and rich data support for subsequent simulations and evaluations. Compared to traditional image analysis methods, this invention significantly improves both accuracy and efficiency.

[0052] S3. Simulate and calculate the missile miss distance to obtain the jamming success rate; evaluate the jamming effectiveness of surface source infrared decoys.

[0053] S3.1 Simulation process.

[0054] When active jamming is performed by releasing surface-source infrared decoys, the missile's trajectory is affected, resulting in a deviation from the ideal trajectory. This deviation is defined as the jamming error of the surface-source infrared decoy. Under jamming conditions, the jamming error, systematic error, and random error work together to produce the guidance error between the missile and the target aircraft. Systematic error is related to the missile's own structure and performance, and will not be discussed in detail in this invention. Random error is related to various external random factors. The use of surface-source infrared decoys will also have a certain impact on the distribution and expectation of the random error of the missile trajectory, but this impact is small and can be ignored. The jamming error reflects the jamming performance of the surface-source infrared decoy and is a direct reflection of the jamming effect. Therefore, the jamming error is used as the main reference for evaluating the jamming effectiveness of the surface-source infrared decoy. In the simulation calculation, it is assumed that the missile's working environment is ideal, that is, that the missile has no systematic error. The miss distance obtained from the simulation is the algebraic sum of the jamming error and the random error.

[0055] Based on infrared radiation characteristics, data in the storage space can be obtained by calling a window, and then the radiation characteristics of infrared decoys under different environmental conditions can be simulated using a large number of random samples using MATLAB simulation software and the Monte Carlo algorithm. The Monte Carlo algorithm estimates the behavior and performance of the system through a large number of random samples. The specific steps are as follows: First, set the simulation environment parameters, including temperature, humidity, wind speed, etc.; then, generate random samples based on the radiation characteristics of the infrared decoy to simulate the radiation under different conditions; next, run a large number of simulation tests and record the missile miss distance and related data for each test; finally, generate a large amount of simulation data to evaluate the performance of the infrared decoy in various scenarios.

[0056] S3.2 Calculate the missile miss distance: A linear regression model can be used to calculate the missile miss distance. The training data for the regression model consists of the infrared radiation characteristics of an area-source infrared decoy, and the target variable is the missile miss distance. First, training data, including infrared radiation characteristics and actual missile miss distances, is collected. Then, the linear regression model is trained using the dataset, and the model parameters are adjusted to minimize the prediction error. Finally, cross-validation is used to evaluate the model's performance, ensuring that the model has good predictive ability.

[0057] A missile miss distance threshold is set, and the proportion of all missile miss distances predicted by the regression model that exceed the preset threshold is calculated, which is used as the jamming success rate. The following example illustrates this: In one embodiment, the missile miss distance threshold is set to 30m. That is, if the distance between the actual missile impact point and the target missile impact point is greater than 30m, the jamming is considered successful; conversely, if the distance is less than 30m, the jamming is considered unsuccessful. All predicted jamming results are statistically analyzed, and the proportion of successful jamming out of the total number of jamming attempts is calculated. For example, in this embodiment, the success rate is 80%, so the jamming success rate for this embodiment is 0.8.

[0058] S3.3, Evaluate interference effectiveness: The average missile miss distance and the average jamming success rate were calculated across all simulation data. The average miss distance represents the missile's average deviation distance in the simulation, and the jamming success rate represents the proportion of missiles successfully jammed by the jamming system in the simulation. The two averages were then weighted and summed to comprehensively evaluate the jamming effectiveness of the infrared decoy. Jamming Effectiveness The calculation formula is as follows:

[0059] in This represents the average value of the missile's miss distance. This represents the average success rate of interference. and These are weighting coefficients, which can be set according to specific application requirements. Empirical values ​​are provided in this invention. ; The implementer can make adjustments according to the actual situation.

[0060] By using a linear regression model to calculate the missile miss distance and setting a miss distance threshold to calculate the jamming success rate, the jamming effectiveness of area-source infrared decoys can be accurately evaluated. Compared with existing empirical evaluation methods, this invention provides a scientific and quantitative evaluation method that can more accurately reflect the actual jamming effect of the decoy.

[0061] S4. Adjust the control method of the surface source infrared decoy launching system according to the interference effectiveness of the surface source infrared decoy.

[0062] In the simulation test, the launch time, launch interval, and number of area-source infrared decoys were continuously adjusted, and the interference effectiveness after each adjustment was recorded. Through simulation testing, the launch parameters corresponding to the maximum interference effectiveness were obtained.

[0063] Based on the emission parameters corresponding to the maximum interference effectiveness, the surface-source infrared decoy emission system is adjusted to ensure that the decoy can achieve the best interference effect in actual use. For example... Figure 4As shown, this is an infrared diffusion image of a surface-source infrared decoy in the atmosphere over time in one embodiment. At t=0.1s, since the surface-source infrared decoy has just been launched, the diffusion of the decoy foil in the atmosphere is insufficient, and the self-igniting metal has not completely burned. The surface radiation brightness is not very high, appearing as a faint bright spot in the image. At t=0.5s, the diffusion shape of the surface-source infrared decoy has basically formed. Since the combustion temperature of the decoy foil has not yet reached the peak combustion temperature, it can be seen from the simulation image that the overall radiation of the foil cloud is... The brightness is not very high, with higher brightness in the central area and lower brightness in the surrounding edge areas. At t=1s, the burning temperature of the decoy foil has reached its peak temperature, and the radiation brightness of the foil cloud reaches its maximum value with a uniform distribution. At t=2s, the foil cloud maintains a stable infrared radiation brightness, but the radiation brightness of the foil cloud is slightly lower than at 1s. At t=2.5s, the overall infrared radiation brightness of the foil cloud is weak. At t=3s, the radiation brightness of the decoy foil is low, and the foil cloud is completely integrated into the background.

[0064] In the simulation experiment of this embodiment, Furthermore, the jamming effectiveness of the area-source infrared decoy reaches its maximum when four decoys are launched simultaneously. Therefore, when facing an incoming missile attack in this embodiment, the area-source infrared decoy system should be adjusted to maintain the above-mentioned launch parameters to ensure the highest jamming success rate.

[0065] This invention constructs an infrared image model of a decoy chaff, employs dynamic programming to obtain the infrared radiation characteristics of the area-source infrared decoy, calculates the jamming success rate and evaluates the jamming effectiveness based on simulation results, and finally formulates a corresponding jamming control strategy. Compared with existing technologies, this invention can accurately simulate the infrared decoy radiation characteristics under different environmental conditions, and optimizes the decoy's launch parameters through systematic simulation and evaluation, thereby significantly improving jamming effectiveness. Existing technologies mostly rely on experience and experimentation, lacking scientific basis, resulting in unstable jamming effects and difficulty in guaranteeing effective missile jamming under various conditions.

[0066] Example of an interference control system based on area-source infrared decoy images: On the other hand, the present invention also provides an interference control system based on area-source infrared decoy images. For example... Figure 5 As shown, the interference control system based on area source infrared decoy images includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an interference control method based on area source infrared decoy images according to the first aspect of the present invention.

[0067] The interference control system based on area source infrared decoy images also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0068] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0069] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise explicitly specified.

[0070] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. An interference control method based on area-source infrared decoy images, characterized in that, The method includes: An infrared image model of the decoy foil is constructed; the infrared image model includes surface temperature field data of the decoy foil, and the temperature formula for when the metal layer of the foil fully reacts is as follows: ； in Enthalpy of reaction; Knuthian diffusion coefficient; The dimensionless Siller modulus; It is the hyperbolic tangent function; This indicates that the foil thickness is The temperature is The reaction concentration at that time; The heat exchange coefficient for foil convection; Ambient temperature; It is the Stefan-Boltzmann constant; The emissivity of the foil; The infrared radiation characteristics of the surface-source infrared decoy are obtained based on the infrared image model, including: Set the number of foil pieces for the area-source infrared decoy; treat how to construct the infrared image model as a subproblem using dynamic programming; recursively call and store the solutions to the subproblems until the number of foil pieces reaches a preset value, then terminate the call and output the final solution; The final solution is subjected to data conversion and image processing to obtain an infrared diffusion image of the surface-source infrared decoy. Specifically, the temperature field data in the final solution is interpolated, the infrared radiation intensity is calculated using the gray body radiation law, and the infrared radiation intensity is allocated to the corresponding image pixels to generate a preliminary infrared radiation image. The preliminary infrared radiation image is then preprocessed and color mapped to obtain an infrared diffusion image of the surface-source infrared decoy. The infrared radiation characteristics of the surface-source infrared decoy are obtained based on the infrared diffusion image through neural network and image segmentation. Simulations are performed based on the aforementioned infrared radiation characteristics. The interference success rate is obtained by calculating the missile miss distance, thereby evaluating the interference effectiveness of the surface-source infrared decoy. The launch parameters of the surface-source infrared decoy are then adjusted according to the interference effectiveness.

2. The interference control method based on area-source infrared decoy images according to claim 1, characterized in that, Recursively calling and storing the solutions to the subproblems includes: In the dynamic programming method, the recursive call process increases the size of one decoy foil piece at a time; The solutions to the subproblems are stored in memory using a hash table.

3. The interference control method based on area-source infrared decoy images according to claim 1, characterized in that, Image preprocessing and color mapping are performed on the preliminary infrared radiation image, including: The preliminary infrared radiation image is processed using Gaussian filtering, convolution operation smoothing, and Laplacian edge enhancement algorithm; The initial infrared radiation image is mapped according to the infrared radiation intensity to achieve pseudo-color processing.

4. The interference control method based on area-source infrared decoy images according to claim 1, characterized in that, The infrared radiation characteristics of surface-source infrared decoys are obtained through neural networks and image segmentation, including: The high-dimensional features of the infrared diffuse image are extracted using a convolutional neural network algorithm. Image segmentation was performed using an improved K-means clustering algorithm to distinguish different temperatures; The radiation characteristics of each segmented region are obtained, including temperature distribution, radiation intensity distribution, and region area.

5. The interference control method based on area-source infrared decoy images according to claim 1, characterized in that, Simulations based on the aforementioned infrared radiation characteristics include: Based on the aforementioned infrared radiation characteristics, a large number of random samples were simulated using MATLAB simulation software and the Monte Carlo algorithm.

6. The interference control method based on area-source infrared decoy images according to claim 1, characterized in that, The jamming effectiveness of surface-source infrared decoys is evaluated by calculating the missile miss distance and obtaining the jamming success rate, including: The missile miss distance is calculated using a linear regression model, where the training data for the linear regression model is the infrared radiation characteristics of a surface-source infrared decoy, and the target variable is the missile miss distance. Set a missile miss threshold, calculate the proportion of all missile misses predicted by the regression model that are greater than the preset threshold, and use this as the jamming success rate. The average missile miss rate and the average jamming success rate in all simulation data are statistically analyzed. The jamming effectiveness of the area source infrared decoy is obtained by weighted summation of the two average values.

7. The interference control method based on area-source infrared decoy images according to claim 1, characterized in that, Adjusting the emission parameters of the surface-source infrared decoy according to the interference effectiveness includes: In the simulation test, the launch time, launch interval and launch quantity of the surface source infrared decoy were continuously adjusted, and the corresponding interference effectiveness after each adjustment was recorded. The emission parameters are dynamically adjusted based on the recorded jamming effectiveness to ensure that the jamming effectiveness of the surface-source infrared decoy remains optimal.

8. An interference control system based on area-source infrared decoy images, characterized in that, It includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, it implements the interference control method based on area source infrared decoy images as described in any one of claims 1 to 7.

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