A method and device for modeling and analyzing detection performance of an airborne infrared system disturbed by clouds

Abstract

This invention provides a method and apparatus for modeling and analyzing the detection performance of an airborne infrared system with cloud interference. The method includes: dividing the cloud landscape into three different cloud scenarios based on the spatial geometric relationship between the cloud layer and the target and detector, and constructing an input parameter set; determining the equivalent target brightness and equivalent background brightness based on the input parameter set, and constructing a unified signal-to-noise ratio (SNR) model by combining the target radiance, background radiance, and atmospheric correction factor under clear-sky conditions, and solving the model; determining the SNR change rate and the detection distance change rate based on the solution results to construct a cloud interference model; comparing the SNR change rate and the detection distance change rate with measured data from field tests, and correcting the input parameter set in reverse based on the comparison error until the comparison error meets a preset threshold, thus completing the calibration of the cloud interference model. This improves the diversity, applicability, and practicality of the model, makes the analysis results traceable, and supports the evaluation of field test environments.

Description

Technical Field

[0001] This invention relates to the field of airborne infrared detection technology, and in particular to a method and apparatus for modeling and analyzing the detection performance of airborne infrared systems that are affected by cloud interference. Background Technology

[0002] As passive detection equipment for airborne platforms, airborne infrared systems' detection performance indicators (mainly including radiation intensity, signal-to-noise ratio, and effective range) directly affect the probability of detecting aerial targets and the warning time. Besides depending on detector sensitivity, optical system parameters, and target radiation characteristics, the detection performance of infrared systems is also significantly affected by atmospheric propagation conditions and meteorological factors. Among many meteorological elements, clouds, as one of the media most significantly affecting infrared energy transmission, are a key factor causing fluctuations in detection performance.

[0003] Currently, existing technologies for analyzing the impact of clouds on infrared detection performance have conducted in-depth research on infrared thermal imaging systems under different cloud and rain conditions and algorithms for detecting weak targets against a cloud background. However, existing technologies mostly focus on the attenuation effect of clouds in the path, resulting in analysis results that cannot match the diversity of field test environments; relying solely on empirical judgments of performance attenuation leads to large errors and cannot support field test environment assessments; verification links are broken, and closed-loop calibration is not formed, making the models less practical; there is no unified cloud impact analysis procedure or evaluation framework, and different studies use significantly different input parameters, calculation methods, and result criteria, resulting in incomparable and untraceable evaluation results. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for modeling and analyzing the detection performance of airborne infrared systems that are susceptible to cloud interference, thereby solving the problems of existing technologies where the analysis results cannot match the diversity of field test environments, cannot support field test environment evaluation, have insufficient model practicality, and lack traceability of evaluation results.

[0005] To address the aforementioned technical problems, the embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference, comprising: Based on the spatial geometric relationship between the cloud layer and the target and detector, cloud interference is divided into multiple preset cloud layer scenarios, and a corresponding set of input parameters is constructed for each preset cloud layer scenario. The multiple preset cloud layer scenarios include path cloud layer scenario, background cloud layer scenario and lower cloud layer scenario. The set of input parameters is a set of parameters used to describe the physical and optical characteristics of the cloud type. Based on the input parameter set, determine the equivalent target brightness and equivalent background brightness corresponding to each preset cloud layer scene; Equivalent target brightness, equivalent background brightness, target radiance under clear sky conditions, background radiance, and atmospheric correction factor are used to construct a unified signal-to-noise ratio (SNR) model. The unified SNR model is then solved. Based on the solution results, the corresponding SNR change rate and detection range change rate are determined to construct a cloud interference model. The signal-to-noise ratio change rate and the detection distance change rate are compared with the measured data from the field test. The input parameter set is then corrected in reverse based on the comparison error until the comparison error meets the preset threshold. This completes the calibration of the cloud interference model. The calibrated cloud interference model is used to predict the infrared detection performance of the airborne infrared system under different cloud types and spatial locations.

[0006] A second aspect of the present invention provides a device for modeling and analyzing the detection performance of an airborne infrared system that is susceptible to cloud interference, comprising: The partitioning and construction module is used to divide cloud interference into multiple preset cloud scenarios based on the spatial geometric relationship between the cloud layer and the target and detector, and to construct a corresponding input parameter set for each preset cloud scenario. The multiple preset cloud scenarios include path cloud scenario, background cloud scenario and lower cloud scenario. The input parameter set is a parameter set used to describe the physical and optical characteristics of the cloud type. The determination module is used to determine the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scene based on the input parameter set; The module is used to construct and solve equivalent target brightness, equivalent background brightness, target radiance under clear sky conditions, background radiance and atmospheric correction factor, construct a unified signal-to-noise ratio model, and solve the unified signal-to-noise ratio model. Based on the solution results, the corresponding signal-to-noise ratio change rate and detection range change rate are determined to construct the cloud interference model. The correction and calibration module is used to compare the rate of change of signal-to-noise ratio and the rate of change of detection distance with the measured data from field tests, and to correct the input parameter set in reverse according to the error of the comparison until the error of the comparison meets the preset threshold, thus completing the calibration of the cloud interference model. The calibrated cloud interference model is used to predict the infrared detection performance of the airborne infrared system under different cloud types and spatial locations.

[0007] Compared to existing technologies, the cloud interference airborne infrared system detection performance modeling and analysis method and apparatus provided by this invention divides cloud interference into multiple preset cloud scenarios based on the spatial geometric relationship between the cloud layer and the target and detector, and constructs a corresponding input parameter set for each preset cloud scenario. These preset cloud scenarios include path cloud scenarios, background cloud scenarios, and lower cloud scenarios. The input parameter set is a parameter set used to describe the physical and optical characteristics of the cloud shape. Based on the input parameter set, the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scenario are determined. A unified signal-to-noise ratio (SNR) model was constructed by considering the target radiance, background radiance, and atmospheric correction factor under clear-sky conditions. The unified SNR model was then solved, and the corresponding SNR change rate and detection range change rate were determined based on the solution results to construct a cloud interference model. The SNR change rate and detection range change rate were compared with measured data from field experiments, and the input parameter set was corrected in reverse based on the comparison error until the comparison error met a preset threshold. This completed the calibration of the cloud interference model. The calibrated cloud interference model was used to predict the infrared detection performance of airborne infrared systems under different cloud types and spatial locations. This approach divides cloud impact into three spatial scenarios: path clouds, background clouds, and lower clouds. Differential modeling based on the physical and optical characteristics of cloud types creates a multi-scenario, multi-cloud-type system analysis structure. This comprehensively covers various cloud distribution patterns in field experiments, significantly enhancing the model's diversity and applicability. A dual-index system of signal-to-noise ratio change rate and detection distance change rate enables unified, comparable, and calculable evaluation of detection performance under different cloud types, scenarios, and day / night conditions. This shifts infrared system performance prediction from qualitative experience to quantitative verification, improving the model's practicality. A bidirectional inversion calibration mechanism combining radiative transfer simulation and field experiment data ensures traceability of the analysis results. A standardized analysis process, combining cloud interference models with simulation verification and calibration, directly supports field experiment environment assessment, enhancing the scientific rigor of the system's field experiment adaptability verification. Attached Figure Description

[0008] 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, with the same or corresponding reference numerals denoteing the same or corresponding parts, wherein: Figure 1 The flowchart of the modeling and analysis method for the detection performance of an airborne infrared system with cloud interference is illustrated schematically. Figure 1 ; Figure 2 The flowchart of the modeling and analysis method for the detection performance of an airborne infrared system with cloud interference is illustrated schematically. Figure 2 ; Figure 3The diagram schematically illustrates the structure of a device for modeling and analyzing the detection performance of an airborne infrared system that is affected by cloud interference. Detailed Implementation

[0009] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0010] It should be noted that, unless otherwise stated, the technical or scientific terms used in this invention should have the ordinary meaning as understood by one of ordinary skill in the art.

[0011] The methods described in the embodiments of the present invention will be explained in detail below.

[0012] Figure 1 The flowchart of the method for modeling and analyzing the detection performance of airborne infrared systems against cloud interference in an embodiment of the present invention is illustrated schematically. Figure 1 , Figure 2 The flowchart of the modeling and analysis method for the detection performance of an airborne infrared system with cloud interference is illustrated schematically. Figure 2 See Figure 1 and Figure 2 As shown, the modeling and analysis method for the detection performance of the airborne infrared system interfering with cloud cover can include: S101. Based on the spatial geometric relationship between the cloud layer and the target and detector, the cloud interference is divided into multiple preset cloud scenarios, and a corresponding set of input parameters is constructed for each preset cloud scenario.

[0013] The system includes multiple preset cloud scenarios, such as path cloud scenarios, background cloud scenarios, and lower cloud scenarios. The input parameter set describes the physical and optical characteristics of the cloud type. The input parameter set includes the cloud height, cloud thickness, optical thickness, cloud top temperature, equivalent particle size, cloud emissivity, and cloud reflectivity corresponding to the cloud type.

[0014] Specifically, based on the spatial geometric relationship between the cloud layer and the target and detector, cloud interference is divided into multiple preset cloud scenarios, including: Step A1: When the cloud layer is located between the target and the detector, classify the cloud interference into a path cloud layer scenario.

[0015] Step A2: When the cloud layer is behind the target, classify the cloud interference as background cloud scene.

[0016] Step A3: When the cloud layer is below the detector platform, classify the cloud interference as the lower cloud layer scene.

[0017] Specifically, based on the spatial relative relationship between the target, detector, and clouds, the impact of clouds can be divided into three typical scenarios: 1) Path cloud scenario: The cloud layer is located between the target and the detector. Infrared radiation undergoes absorption and scattering in the transmission path, making it the most direct source of attenuation. 2) Background cloud scenario: The cloud layer is located behind the target. The cloud layer's own radiation superimposed on the sky background changes the radiation contrast between the target and the background. 3) Below cloud scenario: The cloud layer is located below the platform. Its top surface reflects solar radiation, forming stray light and a bright background, causing additional interference to the detector's field of view.

[0018] In actual flight missions, the path cloud scene, background cloud scene, and lower cloud scene can exist individually or simultaneously. This invention constructs a set of input parameters for each scene after scene determination. ,in For cloud height, For cloud thickness, For optical thickness, For equivalent particle size, The temperature at the top of the cloud. For cloud emissivity, This represents cloud reflectivity. Day / night conditions are used as input environmental parameters to determine whether cloud radiation intensity and reflectivity are effective.

[0019] Simultaneously, cloud type categories are determined based on cloud height, cloud thickness, and equivalent grain size. Table 1 shows relevant information for cloud type categories, including cirrus, stratus, and cumulus. Wherein, r... e The effective radius.

[0020] Table 1. Relevant Information on Cloud Types

[0021] S102. Based on the input parameter set, determine the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scene.

[0022] The equivalent target brightness corresponding to each preset cloud scene includes the first equivalent target brightness of the path cloud scene, the second equivalent target brightness of the background cloud scene, and the third equivalent target brightness of the lower cloud scene. The equivalent background brightness corresponding to each preset cloud scene includes the first equivalent background brightness of the path cloud scene, the second equivalent background brightness of the background cloud scene, and the third equivalent background brightness of the lower cloud scene.

[0023] For different spatial scenarios, the interference mechanism on infrared radiation transmission is analyzed, the main influencing parameters are extracted, and the equivalent target brightness and equivalent background brightness of each scenario are defined to achieve unified modeling.

[0024] Specifically, based on the input parameter set, the equivalent target brightness and equivalent background brightness corresponding to each preset cloud layer scene are determined, including: Step B1: When the cloud interference is a path cloud scene, calculate the spectral transmittance of the cloud layer according to the Mie scattering theory, and determine the corresponding first equivalent target brightness and first equivalent background brightness based on the spectral transmittance, band, target radiance and background radiance.

[0025] The main interference mechanism in path cloud scenarios is absorption-scattering attenuation.

[0026] Specifically, the expressions for the first equivalent target brightness and the first equivalent background brightness are as follows: ; in, The first equivalent target brightness, This is the first equivalent background brightness. For target radiance, Background radiance, and This indicates that the target signal is weakened by the cloud cover, while the background brightness remains basically unchanged. For wavelength spectral transmittance, For wavelength Optical thickness, Bandwidth transmission factor It is a wavelength of The average spectral transmittance of the detector across the entire operating wavelength range.

[0027] Step B2: When the background cloud scene is affected by cloud interference, determine the corresponding second equivalent target brightness and second equivalent background brightness based on the cloud emissivity and blackbody radiation brightness.

[0028] Specifically, in a background cloud scene, the clouds are located behind the target, and their own infrared radiation replaces the original sky background, which can be approximated by a gray body. The expressions for the second equivalent target brightness and the second equivalent background brightness are as follows: ; in, The second equivalent target brightness, The second equivalent background brightness, The radiance of the clouds. For cloud emissivity, Blackbody radiation brightness At temperature Under these conditions, an ideal blackbody at a wavelength The radiance at that location.

[0029] Step B3: When the cloud interference is a scene with clouds below, determine the corresponding third equivalent target brightness and third equivalent background brightness based on the average radiance of the reflected solar radiation and the cloud reflectivity.

[0030] Specifically, in the cloud scene below, the platform is located above the clouds. The top of the clouds reflects solar radiation during the day, slightly increasing the background brightness; at night, the reflection is negligible. The expressions for the third equivalent target brightness and the third equivalent background brightness are as follows: ; in, The third equivalent target brightness, The third equivalent background brightness, For background brightness increment, For cloud reflectivity, The average radiance of reflected solar radiation. Under daytime conditions, take the constant value as in the general case. Take 0 under nighttime conditions.

[0031] This invention integrates the physical characteristics of clouds, such as optical thickness, particle size distribution, temperature, emissivity, and reflectivity, into a radiative transfer modeling framework. It obtains the equivalent transmittance factor, background radiation term, and reflection enhancement term through Mie scattering and gray body radiation approximations, thereby achieving a unified characterization of cloud layers for signals, background, and scattered light.

[0032] S103, equivalent target brightness, equivalent background brightness, target radiance under clear sky conditions, background radiance, and atmospheric correction factor are used to construct a unified signal-to-noise ratio (SNR) model. The unified SNR model is then solved. Based on the solution results, the corresponding SNR change rate and detection range change rate are determined to construct a cloud interference model.

[0033] The results obtained include the detection range under cloud interference and the baseline detection range under clear sky conditions.

[0034] Specifically, given the constants of the detection system Atmospheric correction factor Below, the expressions for the unified signal-to-noise ratio model under the three types of cloud scenarios and clear sky conditions are all: ; in, In order to detect distance Signal-to-noise ratio and detection range at the location This refers to either the detection range under cloud interference or the baseline detection range under clear sky conditions. To detect system constants, In order to detect distance Atmospheric correction factor at the location Determined by general meteorological conditions such as visibility, humidity, and temperature. The equivalent target brightness and the target radiance under clear sky conditions are either the equivalent target brightness or the target radiance under clear sky conditions. The equivalent target brightness is either the equivalent background brightness or the background radiance under clear sky conditions. The equivalent target brightness includes the first equivalent target brightness of the path cloud scene, the second equivalent target brightness of the background cloud scene, and the third equivalent target brightness of the lower cloud scene. The equivalent background brightness includes the first equivalent background brightness of the path cloud scene, the second equivalent background brightness of the background cloud scene, and the third equivalent background brightness of the lower cloud scene.

[0035] The expression for the rate of change of signal-to-noise ratio is: ; in, To be at the reference distance The rate of change of signal-to-noise ratio at the specified location, with subscript 0 indicating clear sky conditions, and subscript... Interference from clouds, To achieve the desired result under cloud cover at the reference distance The signal-to-noise ratio calculated at that point. To determine the distance under clear sky conditions at the reference distance The signal-to-noise ratio is calculated at [location].

[0036] The expression for the rate of change of detection distance obtained by the threshold method is: ; in, To detect the rate of change of distance, The detection range under cloud interference. This is the baseline detection distance under clear sky conditions. , , The detection threshold set for the detection system. Under clear sky conditions, at the baseline detection distance Signal-to-noise ratio at the location. Due to cloud interference, at the detection range Signal-to-noise ratio at the location.

[0037] The process for solving the performance indicators is as follows: First, substitute the target radiance under clear sky conditions. Background radiance ,untie Benchmark detection range under clear sky conditions Secondly, considering the scene under cloud interference, the equivalent target brightness and equivalent background brightness are substituted. The equivalent target brightness includes the first equivalent target brightness. Second equivalent target brightness and the third equivalent target brightness The equivalent background brightness includes the first equivalent background brightness. Second equivalent background brightness and third equivalent background brightness ,untie have to Finally, substituting the formulas for the rate of change of signal-to-noise ratio and the rate of change of detection distance, we can solve for the result. and .

[0038] S104. Compare the signal-to-noise ratio change rate and the detection distance change rate with the measured data from the field test, and correct the input parameter set in reverse according to the comparison error until the comparison error meets the preset threshold, thus completing the calibration of the cloud interference model.

[0039] The calibrated cloud interference model is used to predict the infrared detection performance of the airborne infrared system under different cloud types and spatial locations. The preset threshold can be set to... .

[0040] Specifically, radiative transfer simulation is first performed using an atmospheric model based on Moderate Resolution Atmospheric Transmission (MODTRAN) and the cloud interference model constructed in this invention, with cloud type and optical thickness as input. Equivalent particle size Cloud top temperature Cloud emissivity Cloud reflectivity Calculate the bandwidth transmittance factor in three scenarios. Cloud radiance Background brightness increment The signal-to-noise ratio and detection range are obtained.

[0041] Subsequently, field tests and calibrations were conducted. Data measured using an airborne infrared system under varying cloud cover and day / night conditions were compared with results from a cloud interference model. The cloud top temperature was then corrected using least squares or Bayesian methods. Cloud emissivity Cloud reflectivity Parameters such as these.

[0042] Finally, error assessment and model convergence are performed. If the simulation and measured errors are... Then iteratively update the parameters. If the simulation error is less than or equal to the measured error... If the model has converged, a cloud disturbance model that can be verified in a closed loop has been formed.

[0043] This invention proposes a modeling and analysis method for the detection performance of an airborne infrared system with cloud interference. By classifying and analyzing the cloud interference mechanisms at different spatial locations, a corresponding quantitative model of radiative transfer is established. Combined with simulation calculations and actual test calibration, the quantitative calculation and verification of the attenuation of detection performance (radiation intensity, signal-to-noise ratio, and effective range) are realized.

[0044] This invention addresses the lack of system analysis, quantitative models, and verification frameworks for existing airborne infrared systems under cloud interference conditions. It proposes a modeling and analysis method for infrared detection performance applicable to multiple cloud types, spatial scenarios, and day / night conditions. This invention proposes a unified modeling approach for three preset cloud scenarios, covering various geometric configurations encountered in real-world field tests, significantly improving the completeness of the evaluation scenarios and the consistency with field tests. Furthermore, this invention proposes a closed-loop simulation-to-field test and parameter inversion process. Existing research often remains at the simulation or algorithm verification level. This invention defines a parameter inversion and calibration process based on flight test data, controlling errors within engineering thresholds (e.g., ±5%), and obtaining dataset results on the impact of clouds on infrared detection performance, supporting rapid pre-mission evaluation and online correction during missions. Traditional models often treat clouds as equivalent homogeneous layers, using only average transmittance. However, this invention is the first to incorporate the physical properties of three types of clouds—cirrus, stratus, and cumulus—optical thickness, grain size, temperature, emissivity, and reflectivity—into a unified radiative transfer equation. This equation describes the absorption, scattering, self-radiation, and albedo of clouds, respectively. As a result, the model can not only reflect the differences in characteristics of different cloud types, but also has clearly measurable parameters, making it easy to directly interface with measured meteorological data.

[0045] Based on the above Figure 1As can be seen from the implementation method, the embodiments of the present invention divide cloud interference into multiple preset cloud scenarios based on the spatial geometric relationship between the cloud layer and the target and detector, and construct corresponding input parameter sets for each preset cloud scenario. The multiple preset cloud scenarios include path cloud scenarios, background cloud scenarios, and lower cloud scenarios. The input parameter sets are parameter sets used to describe the physical and optical characteristics of the cloud type. Based on the input parameter sets, the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scenario are determined. A unified signal-to-noise ratio (SNR) model is constructed using the equivalent target brightness, equivalent background brightness, target radiance under clear sky conditions, background radiance, and atmospheric correction factor. The unified SNR model is solved, and based on the solution results, the corresponding SNR change rate and detection distance change rate are determined to construct the cloud interference model. The SNR change rate and detection distance change rate are compared with the measured data from field experiments, and the input parameter set is corrected in reverse according to the comparison error until the comparison error meets the preset threshold, thus completing the calibration of the cloud interference model. The calibrated cloud interference model is used to predict the infrared detection performance of the airborne infrared system under different cloud types and spatial locations. This approach divides cloud impact into three spatial scenarios: path clouds, background clouds, and lower clouds. Differential modeling based on the physical and optical characteristics of cloud types creates a multi-scenario, multi-cloud-type system analysis structure. This comprehensively covers various cloud distribution patterns in field experiments, significantly enhancing the model's diversity and applicability. A dual-index system of signal-to-noise ratio change rate and detection distance change rate enables unified, comparable, and calculable evaluation of detection performance under different cloud types, scenarios, and day / night conditions. This shifts infrared system performance prediction from qualitative experience to quantitative verification, improving the model's practicality. A bidirectional inversion calibration mechanism combining radiative transfer simulation and field experiment data ensures traceability of the analysis results. A standardized analysis process, combining cloud interference models with simulation verification and calibration, directly supports field experiment environment assessment, enhancing the scientific rigor of the system's field experiment adaptability verification.

[0046] Based on the same inventive concept, as an implementation of the above-mentioned method for modeling and analyzing the detection performance of an airborne infrared system with cloud interference, this embodiment of the invention also provides a device for modeling and analyzing the detection performance of an airborne infrared system with cloud interference. Figure 3 This is a structural diagram of the cloud interference airborne infrared system detection performance modeling and analysis device in an embodiment of the present invention. See also... Figure 3 As shown, the cloud interference airborne infrared system detection performance modeling and analysis device may include: The partitioning and construction module 301 is used to divide cloud interference into multiple preset cloud scenarios based on the spatial geometric relationship between the cloud layer and the target and the detector, and to construct a corresponding input parameter set for each preset cloud scenario. The multiple preset cloud scenarios include path cloud scenario, background cloud scenario and lower cloud scenario. The input parameter set is a parameter set used to describe the physical and optical characteristics of the cloud type. The determination module 302 is used to determine the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scene based on the input parameter set; The construction and solution module 303 is used to construct a unified signal-to-noise ratio model based on the equivalent target brightness, equivalent background brightness, target radiance under clear sky conditions, background radiance, and atmospheric correction factor, and to solve the unified signal-to-noise ratio model. Based on the solution results, the corresponding signal-to-noise ratio change rate and detection range change rate are determined to construct the cloud interference model. The correction and calibration module 304 is used to compare the signal-to-noise ratio change rate and the detection distance change rate with the measured data from the field test, and to correct the input parameter set in reverse according to the comparison error until the comparison error meets the preset threshold, thereby completing the calibration of the cloud interference model. The calibrated cloud interference model is used to predict the infrared detection performance of the airborne infrared system under different cloud types and spatial locations.

[0047] In the segmentation and construction module 301, cloud interference is divided into multiple preset cloud scenarios based on the spatial geometric relationship between the cloud layer and the target and the detector. These scenarios include: when the cloud layer is located between the target and the detector, the cloud interference is classified as a path cloud scenario; when the cloud layer is located behind the target, the cloud interference is classified as a background cloud scenario; and when the cloud layer is located below the detector's platform, the cloud interference is classified as a lower cloud scenario.

[0048] The determination module 302 is specifically used to calculate the spectral transmittance of the cloud layer according to Mie scattering theory when the cloud interference is a path cloud layer scene, and determine the corresponding first equivalent target brightness and first equivalent background brightness based on the spectral transmittance, band, target radiance, and background radiance; when the cloud interference is a background cloud layer scene, determine the corresponding second equivalent target brightness and second equivalent background brightness based on the cloud emissivity and blackbody radiance; when the cloud interference is a lower cloud layer scene, determine the corresponding third equivalent target brightness and third equivalent background brightness based on the average radiance of reflected solar radiation and cloud reflectivity; the equivalent target brightness corresponding to each preset cloud layer scene includes the first equivalent target brightness of the path cloud layer scene, the second equivalent target brightness of the background cloud layer scene, and the third equivalent target brightness of the lower cloud layer scene, and the equivalent background brightness corresponding to each preset cloud layer scene includes the first equivalent background brightness of the path cloud layer scene, the second equivalent background brightness of the background cloud layer scene, and the third equivalent background brightness of the lower cloud layer scene.

[0049] In module 302, the expressions for the first equivalent target brightness and the first equivalent background brightness are as follows: ; in, The first equivalent target brightness, For target radiance, The first equivalent background brightness, Background radiance, For wavelength spectral transmittance, For wavelength Optical thickness, Bandwidth transmission factor It is a wavelength of The average spectral transmittance of the detector across the entire operating wavelength range; The expressions for the second equivalent target brightness and the second equivalent background brightness are as follows: ; in, The second equivalent target brightness, The second equivalent background brightness, The radiance of the clouds. For cloud emissivity, Blackbody radiation brightness At temperature Under these conditions, an ideal blackbody at a wavelength Radiance at that location; The expressions for the third equivalent target brightness and the third equivalent background brightness are as follows: ; in, The third equivalent target brightness, The third equivalent background brightness, For background brightness increment, For cloud reflectivity, The average radiance of reflected solar radiation. Take a constant value under daytime conditions. Take 0 under nighttime conditions.

[0050] In the construction and solution module 303, the expression for the unified signal-to-noise ratio model is: ; in, In order to detect distance Signal-to-noise ratio and detection range at the location This refers to either the detection range under cloud interference or the baseline detection range under clear sky conditions. To detect system constants, In order to detect distance Atmospheric correction factor at the location The equivalent target brightness and the target radiance under clear sky conditions are either the equivalent target brightness or the target radiance under clear sky conditions. The equivalent target brightness is either the equivalent background brightness or the background radiance under clear sky conditions. The equivalent target brightness includes the first equivalent target brightness of the path cloud scene, the second equivalent target brightness of the background cloud scene, and the third equivalent target brightness of the lower cloud scene. The equivalent background brightness includes the first equivalent background brightness of the path cloud scene, the second equivalent background brightness of the background cloud scene, and the third equivalent background brightness of the lower cloud scene. The solution results include the detection range under cloud interference and the reference detection range under clear sky conditions.

[0051] In the construction and solution module 303, the expression for the rate of change of signal-to-noise ratio is: ; in, To be at the reference distance The rate of change of signal-to-noise ratio at the specified location, with subscript 0 indicating clear sky conditions, and subscript... Interference from clouds, To achieve the desired result under cloud cover at the reference distance The signal-to-noise ratio calculated at that point. To determine the distance under clear sky conditions at the reference distance The signal-to-noise ratio is calculated at [location].

[0052] In the construction and solution module 303, the expression for the rate of change of the detection distance is: ; in, To detect the rate of change of distance, The detection range under cloud interference. This is the baseline detection distance under clear sky conditions. , , The detection threshold set for the detection system. Under clear sky conditions, at the baseline detection distance Signal-to-noise ratio at the location. Due to cloud interference, at the detection range Signal-to-noise ratio at the location.

[0053] The device may also include a cloud type determination module, used to determine the cloud type of the cloud based on the cloud height, cloud thickness and equivalent particle size before determining the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scene according to the input parameter set. The cloud type includes cirrus, stratus and cumulus.

[0054] This invention aims to establish a systematic and standardized analytical framework capable of quantitatively describing and calculating the transmission attenuation and background interference mechanisms of infrared radiation signals under different spatial locations of clouds (including three typical scenarios: in the target-detector path, behind the target, and below the detector). It constructs classification models for three typical spatial arrangements of clouds: in the path, in the background, and below the detector, enabling unified analysis and comparison of the variation patterns of infrared detection performance under different cloud distributions, thus improving the applicability of the analysis results to complex combat environments. This invention can provide quantitative basis and standardized analytical tools for performance prediction, field test design, and environmental adaptability verification of airborne infrared systems under cloudy and complex weather conditions, fundamentally improving the scientific rigor and reliability of equipment performance evaluation.

[0055] It should be noted that the above description of the embodiment of the cloud interference airborne infrared system detection performance modeling and analysis device is similar to the description of the above embodiment of the cloud interference airborne infrared system detection performance modeling and analysis method, and has similar beneficial effects. For any technical details not disclosed in the embodiments of the cloud interference airborne infrared system detection performance modeling and analysis device of this invention, please refer to the description of the embodiment of the cloud interference airborne infrared system detection performance modeling and analysis method of this invention for understanding.

[0056] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference, characterized in that, include: Based on the spatial geometric relationship between the cloud layer and the target and detector, cloud interference is divided into multiple preset cloud layer scenarios, and a corresponding set of input parameters is constructed for each preset cloud layer scenario. The multiple preset cloud layer scenarios include path cloud layer scenario, background cloud layer scenario and lower cloud layer scenario. The set of input parameters is a set of parameters used to describe the physical and optical characteristics of the cloud type. Based on the input parameter set, determine the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scene; The equivalent target brightness, the equivalent background brightness, the target radiance under clear sky conditions, the background radiance, and the atmospheric correction factor are used to construct a unified signal-to-noise ratio model. The unified signal-to-noise ratio model is then solved. Based on the solution results, the corresponding signal-to-noise ratio change rate and detection distance change rate are determined to construct a cloud interference model. The signal-to-noise ratio change rate and the detection distance change rate are compared with the measured data from the field test, and the input parameter set is corrected in reverse according to the comparison error until the comparison error meets the preset threshold. The calibration of the cloud interference model is then completed. The calibrated cloud interference model is used to predict the infrared detection performance of the airborne infrared system under different cloud types and spatial locations.

2. The method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference according to claim 1, characterized in that, Based on the spatial geometric relationship between the cloud layer and the target and detector, cloud interference is divided into multiple preset cloud scenarios, including: When the cloud layer is located between the target and the detector, the cloud interference is classified as the path cloud scene; When the cloud layer is located behind the target, the cloud layer interference is classified as the background cloud layer scene; When the cloud layer is located below the platform of the detector, the cloud interference is classified as the lower cloud layer scene.

3. The method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference according to claim 1, characterized in that, The input parameter set includes cloud height, cloud thickness, optical thickness, cloud top temperature, equivalent particle size, cloud emissivity, and cloud reflectivity corresponding to the cloud type.

4. The method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference according to claim 3, characterized in that, The equivalent target brightness corresponding to each preset cloud scene includes the first equivalent target brightness of the path cloud scene, the second equivalent target brightness of the background cloud scene, and the third equivalent target brightness of the lower cloud scene. The equivalent background brightness corresponding to each preset cloud scene includes the first equivalent background brightness of the path cloud scene, the second equivalent background brightness of the background cloud scene, and the third equivalent background brightness of the lower cloud scene. The step of determining the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scene based on the input parameter set includes: When the cloud interference is the path cloud scene, the spectral transmittance of the cloud is calculated according to the Mie scattering theory, and the corresponding first equivalent target brightness and first equivalent background brightness are determined according to the spectral transmittance, band, target radiance and background radiance. When the cloud interference is the background cloud scene, the corresponding second equivalent target brightness and second equivalent background brightness are determined based on the cloud emissivity and blackbody radiation brightness. When the cloud interference is the cloud scene below, the corresponding third equivalent target brightness and the third equivalent background brightness are determined based on the average radiance of the reflected solar radiation and the reflectivity of the cloud.

5. The method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference according to claim 4, characterized in that, The expressions for the first equivalent target brightness and the first equivalent background brightness are as follows: ； in, The first equivalent target brightness. The target radiance. The first equivalent background brightness, The background radiance, For wavelength Spectral transmittance, For wavelength Optical thickness, Bandwidth transmission factor It is a wavelength of The average spectral transmittance of the detector across the entire operating wavelength range; The expressions for the second equivalent target brightness and the second equivalent background brightness are as follows: ； in, This refers to the second equivalent target brightness. This is the second equivalent background brightness. The radiance of the clouds. The emissivity of the cloud layer. The blackbody radiance is the blackbody radiance. At temperature Under these conditions, an ideal blackbody at a wavelength Radiance at that location; The expressions for the third equivalent target brightness and the third equivalent background brightness are respectively: ； in, The third equivalent target brightness, The third equivalent background brightness, For background brightness increment, The cloud reflectivity, The average radiance of the reflected solar radiation, the Taking a constant under daytime conditions, the... Take 0 under nighttime conditions.

6. The method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference according to claim 1, characterized in that, The solution results include the detection range under cloud interference and the baseline detection range under clear sky conditions. The expression for the unified signal-to-noise ratio model is: ； in, In order to detect distance The signal-to-noise ratio at the location, the detection distance This refers to either the detection range under cloud interference or the baseline detection range under clear sky conditions. To detect system constants, To the detection range The atmospheric correction factor mentioned above, The equivalent target radiance and the target radiance under clear sky conditions are either either the equivalent target radiance or either the target radiance under clear sky conditions. The equivalent target brightness is either the equivalent background brightness or the background radiance under clear sky conditions. The equivalent target brightness includes the first equivalent target brightness of the path cloud scene, the second equivalent target brightness of the background cloud scene, and the third equivalent target brightness of the lower cloud scene. The equivalent background brightness includes the first equivalent background brightness of the path cloud scene, the second equivalent background brightness of the background cloud scene, and the third equivalent background brightness of the lower cloud scene.

7. The method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference according to claim 1, characterized in that, The expression for the rate of change of the signal-to-noise ratio is: ； in, To be at the reference distance The signal-to-noise ratio change rate at the specified location, with subscript 0 indicating the clear sky condition, subscript... For the cloud interference, To be at the reference distance under the cloud interference. The signal-to-noise ratio calculated at that point. Under the clear sky conditions, at the reference distance The signal-to-noise ratio is calculated at [location].

8. The method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference according to claim 6, characterized in that, The expression for the rate of change of the detection distance is: ； in, The rate of change of the detection distance. The detection range under cloud interference is [the specified range]. This is the baseline detection distance under clear sky conditions. , , The detection threshold set for the detection system. Under the aforementioned clear sky conditions, at the reference detection distance Signal-to-noise ratio at the location Under the interference of the cloud layer, at the detection distance Signal-to-noise ratio at the location.

9. The method for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference according to claim 3, characterized in that, Before determining the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scene based on the input parameter set, the method further includes: The cloud type is determined based on the cloud height, cloud thickness, and equivalent particle size. The cloud type includes cirrus, stratus, and cumulus.

10. A device for modeling and analyzing the detection performance of an airborne infrared system dealing with cloud interference, characterized in that, include: The partitioning and construction module is used to divide cloud interference into multiple preset cloud scenarios based on the spatial geometric relationship between the cloud layer and the target and the detector, and to construct a corresponding input parameter set for each preset cloud scenario. The multiple preset cloud scenarios include path cloud scenarios, background cloud scenarios and lower cloud scenarios. The input parameter set is a parameter set used to describe the physical and optical characteristics of the cloud type. The determination module is used to determine the equivalent target brightness and equivalent background brightness corresponding to each preset cloud scene based on the input parameter set; The module for constructing and solving is used to construct a unified signal-to-noise ratio (SNR) model based on the equivalent target brightness, the equivalent background brightness, the target radiance under clear sky conditions, the background radiance, and the atmospheric correction factor, and to solve the unified SNR model. Based on the solution results, the corresponding SNR change rate and detection distance change rate are determined to construct a cloud interference model. The correction and calibration module is used to compare the signal-to-noise ratio change rate and the detection distance change rate with the field test data, and to reversely correct the input parameter set according to the comparison error until the comparison error meets the preset threshold, thereby completing the calibration of the cloud interference model. The calibrated cloud interference model is used to predict the infrared detection performance of the airborne infrared system under different cloud types and spatial locations.

Images