Infrared interference efficiency evaluation method and system based on multi-scale Gaussian-wavelet energy suppression ratio

Abstract

The invention discloses an infrared interference efficiency evaluation method and system based on a multi-scale Gaussian-wavelet energy suppression ratio, and mainly solves the problem of lack of description of actual space-time characteristics in the prior art. According to the scheme, the method comprises the steps that an infrared simulation scene containing an aircraft and a decoy missile is constructed, the infrared radiation intensity of the scene is calculated, and a simulation infrared image sequence is generated through a sensor imaging model; performing multi-scale Gaussian smoothing and multilayer wavelet decomposition based on the sequence, extracting high-frequency sub-band and low-frequency sub-band features under each scale, and performing feature fusion; calculating the Gaussian-wavelet transform suppression ratio of the aircraft and the decoy projectile under multiple scales based on the fusion features, and generating a suppression ratio coefficient matrix; performing continuous centroid tracking on the aircraft and the decoy projectile in the time domain to accumulate the motion trail and spatial-temporal characteristic data of energy distribution of the aircraft and the decoy projectile; and integrating the spatial-temporal characteristic data and the suppression ratio coefficient matrix to evaluate the infrared interference efficiency, and deciding an optimal tracking object. The method can accurately reflect the deep spatial-temporal characteristic difference of the aircraft and the decoy projectile, improves the accuracy of infrared interference efficiency evaluation, and can be used for system combat, anti-guided air defense and intelligence analysis.

Description

Technical Field

[0001] This invention belongs to the field of defense technology, and specifically relates to an infrared jamming effectiveness evaluation method and system, which can be applied to systems warfare, anti-missile and air defense, intelligence analysis and other fields. Background Technology

[0002] Infrared imaging guidance technology plays a crucial role in modern air defense and missile defense systems. It passively seeks targets by detecting their own infrared radiation, offering advantages such as good concealment and strong resistance to electronic jamming. To counter the threat of interceptor missiles, aircraft commonly employ the deployment of infrared decoy flares. By simulating the radiation characteristics of real targets, these flares interfere with the identification and tracking process of interceptor missile seekers, thereby increasing survivability. Therefore, accurate and rapid dynamic assessment of the real-time jamming effectiveness after decoy flare deployment is a core element in determining the effectiveness of current countermeasures and supporting robust target decision-making by interceptor missiles.

[0003] However, accurately assessing the effectiveness of decoy infrared jamming in complex real-world aerial combat scenarios presents significant technical challenges. First, high-performance decoy flares can highly simulate the radiation intensity and spectral profile of real aircraft in specific wavebands, resulting in extremely similar infrared image characteristics at a single moment or resolution. Traditional threshold-based or simple template-matching discrimination methods are prone to failure. Second, the entire combat process is characterized by rapid and dynamic changes. The relative motion, attitude changes, and transient changes in the background environment between the target, decoy, and interceptor cause the target's apparent characteristics and spatial relationships to evolve continuously and rapidly. Furthermore, in real-world operating environments, the infrared imaging seeker of the interceptor introduces various physical degradation effects into its imaging chain, including optical scattering, sensor noise, and nonlinear response. These effects, coupled with the real-world jamming scenario, further increase the difficulty of extracting stable and reliable evaluation indicators from the observation data.

[0004] Currently, typical jamming effectiveness assessment methods mostly focus on post-event analysis or static simulations based on idealized models, making it difficult to provide real-time, quantitative assessment results during dynamic confrontation. For the quantitative analysis of decoy suppression capabilities, existing technologies primarily use the Decoy-Target Gray-Scale Ratio (DTGR) algorithm to calculate the suppression ratio. This algorithm uses the infrared scene gray-scale image output by the sensor as the data source, treating both the target and the decoy as point radiation sources. It quantitatively characterizes the decoy's suppression capability against the target in the current scene by calculating its adversarial gray-scale suppression coefficient. This method is entirely based on the gray-scale data of the output image, possessing advantages such as not relying on specific prior models, strong scene adaptability, and good robustness to gray-scale abrupt changes, providing a direct quantitative approach for jamming effectiveness assessment.

[0005] However, when applied to simulations of high-speed, dynamic, and complex real-world adversarial scenarios, this type of suppression ratio algorithm based on single-frame images and single-scale grayscale statistics still has limitations. It primarily relies on the instantaneous grayscale comparison between the target and the decoy, failing to fully explore and utilize the differences in energy distribution across multiple scale spatial frequency bands, and lacking a coherent analysis of the consistency of the spatiotemporal trajectories of the target and decoy over time. Furthermore, it does not consider the local contrast of the target and decoy in the image, background interference, etc. When the infrared characteristics of the target and decoy are highly similar, or when severely affected by the degradation effects of complex sensors, the suppression ratio based on single-scale grayscale data may not stably and sensitively reflect the actual changes in the interference situation, and the robustness and accuracy of the evaluation results need to be improved.

[0006] Patent application CN202511020679.5 discloses a method and system for simulating and determining the sensing capability of infrared weapons based on dynamic attenuation of radiative transmission. This scheme calculates the radiative transmission attenuation process of target aircraft plumes and infrared decoy flares in complex atmospheric environments in real time. It integrates multi-dimensional dynamic attenuation factors such as distance, time, and atmospheric transmittance to construct a dynamic infrared sensing model, enabling refined simulation and determination of the interceptor's sensing and tracking capabilities under decoy interference. While this method overcomes the limitations of traditional static threshold models in adapting to different environments to some extent, improving the fit between simulation results and real-world dynamic scenarios, and providing a framework for evaluating the sensing capabilities of interceptors in infrared countermeasures, the core of this scheme still focuses on simulating signal attenuation from the physical level of radiative transmission and comparing the final sensing intensity with a fixed threshold. There is room for further refinement and supplementation when facing actual infrared imaging-guided countermeasure scenarios. Specifically, because its evaluation logic mainly relies on scalar comparison of radiation intensity, this method fails to fully explore and utilize the deep spatiotemporal differences between the target and the decoy in the infrared imaging sequence, such as their energy distribution characteristics in the multi-scale spatial frequency domain and the consistency pattern of their motion trajectory over time. This results in low discrimination ability and poor robustness of the evaluation model in extreme scenarios where the radiation characteristics of the target and the decoy are highly similar or affected by the degradation effect of complex sensors. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of the prior art by proposing an infrared interference effectiveness evaluation method and system based on multi-scale Gaussian-wavelet energy suppression ratio. This method aims to improve the discriminative power and robustness of the evaluation model and enhance the accuracy of the evaluation device by effectively utilizing spatiotemporal characteristics.

[0008] The technical idea of ​​this invention is to develop a new evaluation method that can more comprehensively and stably reflect the real interference effectiveness in dynamic confrontation by further introducing the analysis and fusion of multi-dimensional information at the imaging feature level, based on the existing radiation physics model, and solve the problem of the lack of description of actual spatiotemporal characteristics in the existing technology.

[0009] Based on the above ideas, the technical solution of the present invention includes the following:

[0010] 1. A method for evaluating the effectiveness of infrared interference based on multi-scale Gaussian-wavelet energy suppression ratio, characterized in that it includes:

[0011] (1) Construct an infrared simulation scene containing aircraft and decoys in the Blender engine, calculate its infrared radiation intensity, and generate a simulated infrared image sequence consistent with real observations through the sensor imaging model.

[0012] (2) Perform multi-scale Gaussian smoothing and multi-layer wavelet decomposition on the simulated infrared image sequence, extract the high-frequency sub-band and low-frequency sub-band features at each scale, and perform feature fusion;

[0013] (3) Based on the fused features, calculate the Gaussian-wavelet transform suppression ratio of the aircraft and the decoy missile at multiple scales, and generate the suppression ratio coefficient matrix;

[0014] (4) Continuously track the center of mass of the aircraft and decoy missile in the time domain to accumulate spatiotemporal characteristic data of their motion trajectory and energy distribution; combine the spatiotemporal characteristic data with the suppression ratio coefficient matrix to evaluate the infrared jamming effectiveness and decide the optimal tracking target.

[0015] Furthermore, in step (2), the multi-scale Gaussian smoothing and multi-layer wavelet decomposition of the simulated infrared image sequence is performed using the Haar wavelet basis to decompose the image sequence into a wavelet pyramid; each layer of decomposition is performed at the position Low-frequency subband coefficient From the previous low-frequency sub-band It is obtained through low-pass filtering and downsampling.

[0016] Furthermore, in step (2), high-frequency and low-frequency sub-band features at each scale are extracted and feature fusion is performed, which includes:

[0017] (2a) in High-frequency sub-band features are extracted at the coordinate values ​​respectively. and low-frequency subband characteristics ;

[0018] (2b) Characteristics of high-frequency subbands and low-frequency subband characteristics Perform linear fusion to obtain Feature fusion function at coordinate values ;

[0019] (2c) Feature fusion function Discretize and sum to obtain the fused eigenvalues. .

[0020] Furthermore, in step (3), the suppression ratio of the aircraft and the decoy missile under multiple scales is calculated, and a suppression ratio coefficient matrix is ​​generated. This process includes:

[0021] (3a) Based on the infrared radiation characteristics of the aircraft after feature fusion infrared radiation characteristics of decoy flares Calculate the suppression ratio coefficient at multiple scales ;

[0022] (3b) The suppression ratio coefficient under the multi-scale Arranged in columns, forming a suppression ratio coefficient matrix. .

[0023] 2. An infrared interference effectiveness evaluation system based on multi-scale Gaussian-wavelet energy suppression ratio, characterized in that it comprises:

[0024] The scene modeling module is used to create simulation scenarios for evaluating infrared interference effectiveness in the simulation engine.

[0025] The infrared radiation calculation module is used to calculate the infrared radiation values ​​of aircraft targets and decoy flares, and generate simulated infrared image sequences.

[0026] The wavelet decomposition module is used to perform multi-scale Gaussian smoothing and multi-layer wavelet decomposition on simulated infrared image sequences.

[0027] The feature fusion module is used to extract high-frequency and low-frequency subband features at various scales and perform feature fusion.

[0028] The suppression ratio calculation module is used to calculate the multi-scale suppression ratio coefficients based on the feature fusion data and generate the suppression ratio coefficient matrix.

[0029] The centroid tracking module is used to perform centroid tracking and accumulate suppression ratio coefficient data;

[0030] The interference effectiveness evaluation module is used to determine the tracking target based on the multi-scale suppression ratio coefficient.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] Firstly, because the present invention uses wavelet decomposition and integrates the features of high-frequency subband and low-frequency subband, it can more accurately reflect the differences in deep spatiotemporal features of aircraft and decoy flares, more accurately assess the ability to distinguish local details of decoy flares, and improve the accuracy of infrared jamming effectiveness assessment.

[0033] Secondly, this invention has lower computational complexity and better system robustness because it obtains the multi-scale suppression ratio index through discretization fusion. Attached Figure Description

[0034] Figure 1 This is a flowchart of the infrared interference effectiveness evaluation method based on multi-scale Gaussian-wavelet energy suppression ratio of the present invention.

[0035] Figure 2 This is a radiation intrinsic distribution map in the method of the present invention;

[0036] Figure 3 This is a radiation dispersion distribution diagram in the method of the present invention;

[0037] Figure 4 This is a schematic diagram of centroid tracking in the method of the present invention;

[0038] Figure 5 This is a block diagram of the infrared interference effectiveness evaluation system based on multi-scale Gaussian-wavelet energy suppression ratio of the present invention;

[0039] Figure 6 This is a schematic diagram of an interceptor missile tracking an aircraft target used in the infrared jamming effectiveness assessment of this invention. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0041] It should be noted that the step numbers in the specification and claims of this invention are only for the purpose of clearly describing the embodiments of this invention and facilitating understanding, and their order is not limited.

[0042] Example 1: Infrared jamming effectiveness evaluation method based on multi-scale Gaussian-wavelet energy suppression ratio

[0043] Reference Figure 1 The implementation steps of this example include the following:

[0044] Step 1: Construct an infrared interference performance evaluation simulation scenario based on the simulation engine.

[0045] Add air combat elements such as aircraft, decoy flares, infrared radiation characteristic targets, and interceptor missile sensor imaging systems to the Blender engine to form an infrared anti-jamming effectiveness evaluation simulation scenario. Among them, aircraft and decoy flares are infrared radiation characteristic targets that include infrared texture information such as surface temperature field, three-dimensional model, and spectral emissivity.

[0046] Step 2: Calculate the intrinsic infrared radiative exitance and generate an intrinsic image sequence.

[0047] 2.1) In the simulation scenario constructed in step 1, the discretized series of the intrinsic infrared emissivity of the aircraft and the decoy flares are calculated based on the three-dimensional coordinates of the aircraft and the decoy flares. :

[0048] ,

[0049] in, Representing discrete variables, Represents discrete wavelengths. Represents absolute temperature. Represents the speed of light. Denotes Planck's constant. Represents the Boltzmann constant;

[0050] 2.2) Through The calculation formula, with a fixed absolute temperature The discretized series of the intrinsic infrared emissivity of the aircraft was calculated. Fixed absolute temperature The discretized series of the intrinsic infrared emissivity of the decoy flare was calculated. ;

[0051] 2.3) Based on the infrared radiative exitance discretization series calculated in step 2.1), and... band To band Summing yields the intrinsic infrared emissivity. :

[0052] ;

[0053] 2.4) Through The calculation formula, in discretized wavelength exist Within the spectral range, at absolute temperature The intrinsic infrared emissivity of the aircraft was obtained at that time. At absolute temperature The intrinsic infrared emissivity of the decoy flare is obtained at that time. ;

[0054] 2.5) The infrared emissivity of the aircraft calculated in step 2.4) The decoy flare's infrared radiance is placed at the pixel corresponding to the aircraft in the intrinsic image. The pixels corresponding to the decoy flares placed in the intrinsic image generate the intrinsic image sequence, such as... Figure 2 As shown.

[0055] Step 3: Add diffusion effects to the intrinsic image sequence to generate a simulated image sequence.

[0056] The diffusion effect refers to the phenomenon where, during the process of receiving infrared radiation, the originally concentrated radiation energy of the sensor diffuses on the image plane due to the sensor effect, forming a diffuse spot distribution.

[0057] 3.1) Calculate the infrared radiation intensity after dispersion by the real sensor model based on the three-dimensional coordinates of the aircraft and the three-dimensional coordinates of the decoy. :

[0058] ,

[0059] in, Indicates intrinsic infrared emissivity. Indicates to the center radial distance of the point This represents the beam radius, i.e., the area of ​​the dispersion spot. width;

[0060] 3.2) The intrinsic infrared radiance of the aircraft Intrinsic infrared emissivity of decoy flares Substitute respectively The calculation formula yields the aircraft dispersion distribution. and the dispersion distribution of decoy grenades ;

[0061] ,

[0062] ,

[0063] 3.3) The intrinsic infrared radiance of the aircraft in the intrinsic image sequence from step 2. Replace with the aircraft dispersion distribution calculated in step 3.2). The intrinsic infrared emissivity of the decoy flares in the intrinsic image sequence Replace with the aircraft dispersion distribution calculated in step 3.2). A simulated image sequence was constructed, and the diffusion distribution effect in this simulated image sequence is shown in the figure below. Figure 3 As shown.

[0064] Step 4: Perform multi-level wavelet decomposition on the simulated image sequence.

[0065] 4.1) Low-frequency wavelet decomposition of the simulated image sequence was performed using the Haar wavelet basis to obtain the low-frequency subband coefficients. :

[0066] ,

[0067] in, This represents the low-pass filter coefficients corresponding to the Haar wavelet. This indicates the filter coefficient index in the horizontal direction. This indicates the filter coefficient index in the vertical direction. Indicates a progressive basis;

[0068] 4.2) Using the low-frequency wavelet decomposition algorithm in step 4.1), the low-frequency sub-band features of the simulated image sequence are obtained after low-frequency wavelet decomposition. ;

[0069] ,

[0070] in, Indicates will Upsample back to the original size;

[0071] 4.3) The simulated image sequence is decomposed into high-frequency wavelet coefficients using the Haar wavelet basis to obtain the horizontal high-frequency sub-band coefficients. Vertical high-frequency subband coefficient Diagonal high-frequency subband coefficient :

[0072] ,

[0073] , ,

[0074] in, These are the filter coefficients;

[0075] 4.4) Apply the horizontal high-frequency subband coefficients calculated above. Vertical high-frequency subband coefficient Diagonal high-frequency subband coefficient The energy is upsampled to the original size and then superimposed to obtain the position. High-frequency subband characteristics at the location :

[0076] .

[0077] Step 5: Perform feature fusion on the simulated image sequence.

[0078] 5.1) Characteristics of low-frequency subbands High-frequency subband characteristics Perform linear fusion to obtain Feature fusion function at coordinate values :

[0079] ,

[0080] in, Weighting coefficients representing high-frequency features Weighting coefficients representing low-frequency features;

[0081] This example Take, but is not limited to, 0.7. Take, but is not limited to, 0.3.

[0082] 5.2) Regarding the above feature fusion function Discretize and sum to obtain the fused eigenvalues. :

[0083] ;

[0084] 5.3) Through the above-mentioned fusion feature values The calculation formula is used to calculate the fusion feature value of the simulated image sequence of the aircraft, and obtain the multi-scale fusion feature value of the aircraft. :

[0085] ,

[0086] in, Represents a sequence of aircraft images;

[0087] By performing fusion feature value calculation on the simulated image sequence of the decoy flares, the multi-scale fusion feature values ​​of the decoy flares are obtained. :

[0088] ,

[0089] in, This represents a sequence of aircraft images.

[0090] Step 6: Generate the suppression ratio coefficient matrix.

[0091] 6.1) Based on the infrared radiation characteristics of the aircraft after feature fusion infrared radiation characteristics of decoy flares Calculate the suppression ratio coefficient at multiple scales :

[0092] ;

[0093] 6.2) according to Arranged in rows and columns 1, we obtain the suppression ratio coefficient matrix. ;

[0094] Step 7: Perform centroid tracking.

[0095] 7.1) Based on the grayscale distribution in the simulated image sequence of the aircraft and decoy flares generated in step 3. and Calculate the centroid coordinates :

[0096] ,

[0097] ,

[0098] in, Represents pixels, Represents pixels of coordinate, Represents pixels of coordinate;

[0099] 7.2) Set the interceptor missile's viewing angle to the centroid coordinates calculated in step 7.1). Centroid tracking is performed, and the result of the centroid tracking is shown in the figure below. Figure 4 As shown.

[0100] Step 8: Evaluate the infrared jamming effectiveness.

[0101] 8.1) The suppression ratio coefficient matrix obtained in step 7 The average value is used to obtain the multi-scale suppression ratio coefficient. :

[0102] ;

[0103] 8.2) Based on the multi-scale suppression ratio coefficient Conduct jamming effectiveness assessments and determine the target of the interceptor missile:

[0104] If the suppression ratio coefficient A value greater than 1 indicates that the decoy missile has a strong jamming effect, and the interceptor missile is determined to be tracking the decoy missile.

[0105] If the suppression ratio coefficient If the value is less than 1, it indicates that the decoy missile's jamming effect is weak, and the interceptor missile's target is determined to be an aircraft.

[0106] Example 2: Infrared interference effectiveness evaluation system based on multi-scale Gaussian-wavelet energy suppression ratio

[0107] Reference Figure 5 This example includes: Scene Modeling Module 1, Infrared Radiation Calculation Module 2, Wavelet Decomposition Module 3, Feature Fusion Module 4, Suppression Ratio Calculation Module 5, Centroid Tracking Module 6, and Interference Effectiveness Evaluation Module 7. Infrared Radiation Calculation Module 2 includes: Emitter Preprocessing Submodule 21, Intrinsic Image Synthesis Submodule 22, and Diffusion Postprocessing Submodule 23.

[0108] The working principle of the entire system is as follows:

[0109] The scenario modeling module 1 is used to add air combat elements such as aircraft, decoy missiles and other infrared radiation characteristic targets and interceptor missile sensor imaging systems to form an infrared anti-jamming effectiveness evaluation simulation scenario.

[0110] The infrared radiation calculation module 2 is used to obtain a simulated infrared image sequence of the aircraft and decoy missiles in a simulated scene using the three-dimensional coordinate data of the aircraft and decoy missiles. The exitance preprocessing submodule 21 is used to calculate the intrinsic radiative exitance of the aircraft and decoy missiles in the simulated scene constructed by the scene modeling module 1 and pass it to the intrinsic image synthesis submodule 22. The intrinsic image synthesis submodule 22 is used to construct an infrared intrinsic image sequence from the intrinsic radiative exitance of the aircraft and decoy missiles and pass it to the dispersion postprocessing submodule 23. The dispersion postprocessing submodule 23 is used to add a dispersion effect to the intrinsic image sequence generated by the intrinsic image synthesis submodule 22 to generate a simulated infrared image sequence of the aircraft and decoy missiles and pass it to the wavelet decomposition module 3 and the centroid tracking module 6.

[0111] The wavelet decomposition module 3 is used to perform multi-scale decomposition on the simulated infrared image sequence generated by the infrared radiation calculation module 2 to obtain the high-frequency sub-band features and low-frequency sub-band features of the aircraft and decoy missiles, and then transmit them to the feature fusion module 4.

[0112] The feature fusion module 4 is used to fuse the high-frequency subband features and low-frequency subband features of the aircraft and decoy missiles to obtain the fused feature values ​​of the aircraft and decoy missiles, and then transmit them to the suppression ratio calculation module 5.

[0113] The suppression ratio calculation module 5 is used to calculate the multi-scale suppression ratio coefficient using the fusion feature value generated by the feature fusion module 4, and then transmits it to the interference effectiveness evaluation module 7.

[0114] The centroid tracking module 6 is used to calculate the centroid position of the simulated image sequence of the aircraft and decoy generated by the infrared radiation calculation module 2, perform centroid tracking, and transmit the centroid position coordinates to the interference effectiveness evaluation module 7.

[0115] The jamming effectiveness evaluation module 7 is used to evaluate the jamming effectiveness of the decoy missile using the multi-scale suppression ratio coefficient generated by the suppression ratio calculation module 5 and the centroid position coordinates generated by the centroid tracking module 6, and to determine the target to be tracked by the interceptor missile.

[0116] It should be noted that the above functional modules can be implemented, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, they can be implemented, in whole or in part, as a program instruction product. A program instruction product includes one or a set of program instructions. When the program instructions are loaded and executed on a computer, the described process or function is generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The program instructions can be stored in a computer-readable and writable storage medium, or transferred from one computer's readable and writable storage medium to another.

[0117] In this embodiment, the direct coupling or communication connection between the modules can be achieved through indirect coupling or communication connection via interfaces, devices, or modules. The functional modules and sub-modules in this embodiment can dynamically reside within a single processing unit, or each module can exist physically independently, or two or more modules can dynamically reside within a single processing unit. When these dynamic components are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable and writable storage medium. This storage medium can be a memory, disk, or optical disc, etc.

[0118] The effects of this invention can be further illustrated by the following simulation results:

[0119] 1. Simulation conditions:

[0120] Set the absolute temperature of the aircraft absolute temperature of decoy grenades Lower bound of the interceptor missile sensor response band Upper Realm The sensor effect model is a diffusion effect model, and the simulation time is... .

[0121] 2. Simulation content:

[0122] Using this invention, a typical air combat scenario including an aircraft, decoy flares, and interceptor missiles is constructed under the above simulation conditions. The infrared jamming effectiveness of the decoy flares is evaluated, and the target of the interceptor missile is determined based on the effectiveness evaluation. The results are as follows: Figure 5 .

[0123] Depend on Figure 6 It is evident that the interceptor missile's target is an aircraft, indicating that under the current simulation scenario and parameter settings, the decoy missile's infrared jamming effectiveness is low. Its radiation intensity, spatiotemporal separation characteristics, and motion features failed to effectively deceive the interceptor missile's sensors, which could still lock onto the aircraft target through multi-scale feature fusion and center-of-mass tracking. Simulation results demonstrate that this simulation system can effectively evaluate the jamming effectiveness of decoy missiles in typical air combat scenarios.

Claims

1. An infrared jamming effectiveness evaluation method based on multi-scale Gaussian-wavelet energy suppression ratio, characterized in that, include: (1) Construct an infrared simulation scene containing aircraft and decoys in the Blender engine, calculate its infrared radiation intensity, and generate a simulated infrared image sequence consistent with real observations through the sensor imaging model. (2) Perform multi-scale Gaussian smoothing and multi-layer wavelet decomposition on the simulated infrared image sequence, extract the high-frequency sub-band and low-frequency sub-band features at each scale, and perform feature fusion; (3) Based on the fused features, calculate the Gaussian-wavelet transform suppression ratio of the aircraft and the decoy missile at multiple scales, and generate the suppression ratio coefficient matrix; (4) Continuously track the center of mass of the aircraft and decoy missiles in the time domain to accumulate spatiotemporal characteristic data of their motion trajectory and energy distribution; By combining spatiotemporal feature data with the suppression ratio coefficient matrix, the effectiveness of infrared jamming is evaluated, and the optimal tracking target is determined.

2. The method according to claim 1, characterized in that, The calculation of its infrared radiation intensity in (1) is based on the calculation of the target's infrared radiant exitance using the blackbody radiation law. and for specific bands to Discretization is performed to obtain the discrete series of infrared radiation intensity in this band. The calculation formula is as follows: ， in, Denotes Planck's constant. Represents the speed of light. Represents Boltzmann's constant. Represents absolute temperature. This represents the wavelength increment.

3. The method according to claim 1, characterized in that, In (2), the multi-scale Gaussian smoothing and multi-layer wavelet decomposition of the simulated infrared image sequence is performed using the Haar wavelet basis to perform a three-layer wavelet pyramid decomposition on the image sequence; each layer of decomposition is performed at the position Low-frequency subband coefficient From the previous low-frequency sub-band It is obtained through low-pass filtering and downsampling, and its calculation formula is: ， in, This represents the low-pass filter coefficients corresponding to the Haar wavelet. This indicates the filter coefficient index in the horizontal direction. This indicates the filter coefficient index in the vertical direction.

4. The method according to claim 1, characterized in that, In step (2), high-frequency and low-frequency sub-band features at each scale are extracted and feature fusion is performed, which includes: (2a) Characteristics of high-frequency subbands and low-frequency subband characteristics Perform linear fusion to obtain Feature fusion function at coordinate values : ， in, Weighting coefficients representing high-frequency features Weighting coefficients representing low-frequency features; (2b) Feature fusion function Discretize and sum to obtain the fused eigenvalues. : ， in, Indicates the width of the image. Indicates the height of the image.

5. The method according to claim 1, characterized in that, The calculation of the Gaussian-wavelet transform suppression ratio between the aircraft and the decoy missile at multiple scales in (3) and the generation of the suppression ratio coefficient matrix are implemented as follows: (3a) Based on the infrared radiation characteristic values ​​of the aircraft after feature fusion infrared radiation characteristics of decoy flares Calculate the suppression ratio coefficient at multiple scales : ； (3b) The suppression ratio coefficient under the multi-scale Arranged in columns, forming a suppression ratio coefficient matrix. , This is the simulation time.

6. The method according to claim 1, characterized in that, The continuous centroid tracking of the aircraft and decoy missile in the time domain in (4) to accumulate spatiotemporal characteristic data of their trajectory and energy distribution includes the following: (4a) Calculate the centroid coordinates based on the grayscale distribution in the simulated image. : ， ， in, Represents pixels, Represents pixels grayscale value, Represents pixels of coordinate, Represents pixels of coordinate; (4b) Set the interceptor missile's viewing angle to the coordinates of the centroid. Perform centroid tracking.

7. The method according to claim 1, characterized in that, The evaluation of infrared jamming effectiveness and the determination of the optimal tracking target in (4) by integrating spatiotemporal feature data and the suppression ratio coefficient matrix are based on the calculated suppression ratio coefficients at multiple scales. If the suppression ratio coefficient is greater than 1, it indicates that the decoy flare has a strong jamming effect, and the target to be tracked is determined to be the decoy flare. If the suppression ratio coefficient is less than 1, it indicates that the decoy flare's interference effect is weak, and the target to be tracked is determined to be an aircraft.

8. An infrared interference effectiveness evaluation system based on multi-scale Gaussian-wavelet energy suppression ratio, characterized in that, include: The scene modeling module is used to create simulation scenarios for evaluating infrared interference effectiveness in the simulation engine. The infrared radiation calculation module is used to calculate the infrared radiation values ​​of aircraft targets and decoy flares, and generate simulated infrared image sequences. The wavelet decomposition module is used to perform multi-scale Gaussian smoothing and multi-layer wavelet decomposition on simulated infrared image sequences. The feature fusion module is used to extract high-frequency and low-frequency subband features at various scales and perform feature fusion. The suppression ratio calculation module is used to calculate the multi-scale suppression ratio coefficients based on the feature fusion data and generate the suppression ratio coefficient matrix. The centroid tracking module is used to perform centroid tracking and accumulate suppression ratio coefficient data; The interference effectiveness evaluation module is used to determine the tracking target based on the multi-scale suppression ratio coefficient.

9. The system according to claim 1, characterized in that, The infrared radiation calculation module includes: The radiance preprocessing submodule is used to calculate the discretized radiance. The intrinsic image synthesis submodule is used to synthesize an initial intrinsic image using the radiation exitance of the aircraft and the decoy missile. The diffusion post-processing submodule is used to add diffusion spots to the intrinsic image and output a simulated image sequence.

Images