An end-to-end detection efficiency evaluation method for infrared targets throughout their entire life cycle

Abstract

The present invention belongs to the technical field of infrared signal, and discloses an end-to-end detection efficiency evaluation method for infrared targets throughout the life cycle, which includes the following steps: First, build detection scenarios such as targets, detection platforms and payloads, and the sun, including elements such as the entire movement trajectory of the target, satellite orbit, and observation geometry, perform the coordinate conversion from the target coordinate system to the payload image coordinate in the detection scenario, and realize the sub-pixel level target geometry simulation; construct a three-dimensional finite element model of the target, calculate the instantaneous equilibrium temperature field of the target, and calculate the radiation characteristics of the target throughout the life cycle according to data such as the temperature field and projection area; obtain the gray levels of the infrared target and the background through the infrared payload radiation response model to form a detection image sequence; finally, evaluate the dynamic detection efficiency according to the signal-to-noise ratio of the target gray level and noise in the image; the present invention can comprehensively and accurately describe the dynamic process of the detection efficiency of the infrared payload for dim targets changing with time and scenarios, and is applicable to the aerospace and aviation fields.

Description

Technical Field

[0001] The present invention relates to the field of infrared signal technology, and specifically to a dynamic evaluation method for infrared detection effectiveness based on the end-to-end life cycle of a space target. The method can be used to generate infrared payload image sequences, aiming to accurately simulate and calculate the radiation changes of the target in the space environment throughout the entire process, and to deeply understand and analyze the dynamic detection effectiveness of the payload image obtained under the external sky light and ground shadow environment. Background Art

[0002] With the continuous advancement of aerospace technology, there is a growing demand for analyzing the impact of space target coupling environment on payload detection capabilities. In particular, in the analysis of infrared radiation characteristics, the impact of target motion trajectory changes, attitude changes, and radiation from the surrounding sun, earth, and other environments on infrared payload detection images requires accurate dynamic simulation. Existing infrared radiation simulation methods mainly focus on static or simplified scenes, lacking a comprehensive consideration of infrared radiation changes of space targets throughout their life cycle.

[0003] In the existing technology, many simulation methods take the target radiation intensity as a fixed value, without considering its changes with the scene and its own posture. The detection performance of infrared payloads mostly remains at the numerical calculation level, and the dynamic sequence of infrared images under the detection perspective cannot be obtained, which makes it difficult to support the subsequent verification of target detection, tracking and recognition algorithms.

[0004] In order to meet the demand for quantitative dynamic high-precision analysis of the detection capability of space targets throughout their life cycle, a simulation method that can comprehensively, dynamically and accurately simulate the target end to the infrared image detection end is urgently needed. Therefore, how to provide a simulation and evaluation method that can dynamically simulate the changes in the complex radiation environment during the entire life cycle of the space target end, as well as the high-confidence infrared image end of the infrared payload in the detection scenario, has become a difficult problem that needs to be solved in the current technical field. Summary of the invention

[0005] The present invention provides an end-to-end detection effectiveness evaluation method for an infrared target throughout its life cycle, which aims to accurately quantify and analyze the dynamic detection signal-to-noise ratio and other effectiveness of the target throughout its life cycle, and has high application value.

[0006] S1. Detection scenario construction: First, build an accurate geometric model of the target through 3D modeling technology, configure the observation satellite orbit parameters and detection payload, and combine the target's full motion characteristics with the geometric relationship between the sun and the earth to fully describe the target's motion trajectory, position changes and observation geometric relationship during its life cycle;

[0007] S2. Dynamic Radiation Characteristic Analysis: Secondly, use UG finite element analysis software to calculate the temperature field. By comprehensively considering heat conduction, radiation, and factors such as the sun and the earth under different environmental conditions of the target, accurately calculate the radiation characteristics of the target throughout its life cycle;

[0008] S3. Infrared Detection Image Simulation: Based on the geometric characteristics and motion trajectory of the target, further calculate the projected area of the target at different time points. Considering the relative motion between the target and the detector, and the target may be in a micro-motion state, it is necessary to calculate the angle between the normal vector of the target surface element and the line of sight of the detector to determine whether each surface element is within the field of view of the detector. For all surface elements within the field of view of the detector, add up their projected areas to obtain the total projected area of the target surface. Further, through the infrared payload radiation response model, calculate the infrared target and background grayscales respectively to form a sequence of detection images;

[0009] S4. Dynamic Efficiency Evaluation: Finally, according to the coordinate position, grayscale value of each frame of the target and the standard deviation of the neighborhood space noise, use the simulated images to calculate the signal-to-noise ratio during the entire life cycle of the target.

[0010] First, establish an observation geometric coordinate system for detection scenarios such as detection targets and platforms. It is built using the geocentric coordinate system, with its origin at the center of the earth's mass (including the mass of the atmosphere, ocean, etc.). The xoy plane of the coordinate system is the earth's equatorial plane, the z-axis points to the CIO at the north pole, and the x-axis points to the intersection of the Greenwich meridian and the equatorial plane. This coordinate system is fixed on the earth, and the observation platform, as well as the earth's gravitational field coefficient, etc., are defined in this coordinate system.

[0011] Assume that the points in this coordinate system are represented as:

[0012]

[0013] The coordinate system during the 3D modeling of the target is the target body coordinate system. The coordinates of each point during the 3D modeling of the target are referenced to this coordinate system. The points in this coordinate system are represented as:

[0014]

[0015] The satellite camera coordinate system has the camera optical center as the origin, the z-axis as the optical axis direction (the direction from the pointing point to the optical center), and the x-axis and y-axis directions are determined by the detector direction. The image coordinate system is a two-dimensional coordinate system, with its origin at the center of the image plane, and the x-axis and y-axis directions are the same as those of the satellite camera coordinate system.

[0016] The points in the satellite camera coordinate system are represented as:

[0017]

[0018] The points in the image coordinate system are represented as:

[0019]

[0020] An arbitrary point of the target in the body coordinate system , the yaw angle of the target relative to the earth-fixed coordinate system is , the pitch angle is , the roll angle is , the origin of the target body coordinate system in the earth-fixed coordinate system is , then the coordinates of the target in the earth-fixed coordinate system are

[0021]

[0022] Assume that the coordinates of the satellite in the earth-fixed coordinate system are , the point of the satellite pointing in the earth-fixed coordinate system is , thus the polar angle of the satellite relative to the pointing point in the earth-fixed coordinate system is and the azimuth angle are

[0023]

[0024] Assume that the rotation angle of the satellite is , then the coordinates of the target in the satellite camera coordinate system are

[0025]

[0026]

[0027] The coordinates of the target in the image coordinate system are:

[0028]

[0029] Given the radius of the earth , let the position vector of the target be , the solar unit radiation vector be , let the normal vector of the surface element of the target be , the angle between the target and the sun is:

[0030]

[0031] The sun can be equivalent to a black body at 5900K, and its irradiance is:

[0032]

[0033] In the formula, is the radius of the sun, is the average distance between the sun and the earth, is the wavelength, is the first radiation constant, is the second radiation constant. Then for the target surface element , the solar radiation intensity reflected by the target surface at time 𝑡 is:

[0034]

[0035] is the reflectivity of the surface element to solar radiation, is the projected area of this surface element, B is the set of surface elements in the detection field of view, represents the total projected area obtained by adding the projected areas of the target at the current moment, is the radiance of the surface element, is the rotation matrix of the target at time t, is the normal vector of the target surface element i, LOS is the normal vector of the detector detection direction, is the cosine value of the angle between the normal vector of the target surface element and the normal vector of the detector detection direction.

[0036] The amount of Earth infrared radiation received by the surface element of the target surface is

[0037]

[0038] is the reflectivity of the surface element to Earth radiation, is the infrared irradiance of the Earth:

[0039]

[0040] h is the flight altitude of the target.

[0041] The intensity of Earth radiation reflected by the target surface is:

[0042]

[0043] Among them, the absorptivity of the surface element to Earth radiation is expressed as ; the radiation view factor of the target reflecting Earth radiation is determined by the angle between the normal vector of the surface element and the vector from the center of the Earth to the center of the surface element.

[0044] Due to the influence of factors such as cloud cover and surface reflection, part of the solar radiation will be reflected. This part of the radiation is called Earth-reflected solar radiation, which is mainly concentrated in the visible to short-wave infrared band. The Earth-reflected solar radiation view factor represents the proportion of the reflected radiation energy projected onto the surface element of the target surface. Its calculation formula is as follows:

[0045]

[0046] In the formula, It is the angle between the line connecting the center of the Earth and the centroid of the target surface element and the Sun. When , it indicates that the target flies in the solar illumination area. When , it indicates that the target flies in the shadow area.

[0047] The intensity of the Earth-reflected solar radiation reflected by the surface element of the target surface is:

[0048]

[0049] Where is the Earth albedo.

[0050] The self-thermal radiation of the target is:

[0051]

[0052] Among them, is the infrared emissivity of the surface element, is the target surface element temperature, At time t, in the wavelength band , the target surface element radiance.

[0053] The total radiation intensity of the target is:

[0054]

[0055] In the space-based detection scenario, the scale of the target is generally smaller than the detection resolution, so it can be considered as a point target detection scenario. Due to the diffraction effect of the optical system, the point target shows a Gaussian distribution. Most of the target energy is obtained by the central pixel. The percentage of the energy received by the central pixel in the total radiation energy of the target is the energy concentration (EE). The number of electrons received by the detector pixel for the target energy can be expressed as:

[0056]

[0057] In the formula, is the Planck constant , is the central wavelength, is the speed of light , is the pixel integration time, is the detector quantum efficiency, is the atmospheric transmittance at the altitude where the target is located, is the optical system transmittance (including the optical occlusion factor), is the target radiation intensity, is the detection distance, is the effective aperture of the detection system.

[0058] The gray value of the target pixel can be further calculated through the number of electrons of the target signal.

[0059]

[0060] is the maximum full well charge of the single pixel integration capacitor. is the electronic quantization bit number, generally in the range of 8 to 16 bits. is the average gray value of the background noise.

[0061] The background noise is obtained by simulating and calculating the DC bias noise of each component, mainly including the background noise of the optical instrument, the dark current noise and the external stray light background noise, etc. Together, they constitute the system detection full well charge dominated by photon noise, and the number of electrons of the instrument background noise is the sum of the radiation of each component of the optical instrument and the dewar window. As shown in the following formula

[0062]

[0063] represents the temperature at which each optical element is located. The number of electrons of the near-field radiation generated at the working temperature. is the instrument background illuminance generated by the optical element, and the unit is nW / mm 2 .

[0064] The number of electrons of the dark current noise The noise comes from the dark current of the infrared detector and is positively correlated with the integration time. The calculation formula is:

[0065]

[0066] where is the dark current density per unit area, and the unit is A / cm 2 , dx is the pixel size, and q is the charge quantity. The dark current of the detector is related to the bias voltage and the working temperature of the detector. Reducing the bias voltage and lowering the working temperature can reduce the dark current.

[0067] The spatial target detection scenario is generally the limb deep space. Therefore, the Earth radiation background is small and can be ignored for the influence on the full well. Therefore, the average gray value of the background noise is

[0068]

[0069] The total noise of a single pixel of the system can be expressed in the form of the root mean square of the number of electrons

[0070]

[0071] The signal-to-noise ratio of a point target is less affected by non-uniformity noise. Therefore, the influence of spatial noise can be ignored, that is, the noise can be simplified to temporal noise.

[0072]

[0073] Number of noise electrons It is the number of electrons accumulated on the integration capacitor when the signal output by the pixel at the end of the integration time is equal to the root mean square noise of the output, including photon noise ( is the root mean square of the pixel photon noise, including signal noise, background noise and dark current noise, with the unit e- / pixel), readout noise ( is the root mean square of the pixel readout noise, with the unit e- / pixel), etc., temporal noise and response non-uniformity ( is the root mean square of the pixel response non-uniformity noise, with the unit e- / pixel), dark current non-uniformity ( is the root mean square of the pixel dark current non-uniformity noise, with the unit e- / pixel), etc., spatial noise. Represents the root mean square generated by the sum of all photon noise-related components. According to different radiation sources, it can be decomposed into:

[0074]

[0075] Total noise According to quantization, it can be converted to the corresponding gray value. It is superimposed on the N×N infrared image in the form of Gaussian white noise.

[0076] The signal-to-noise ratio calculation method based on the target and noise energy forms is applicable to the target image. In the case where the image is affected by the background, the measurement image data processing based on the target and background mainly uses the signal-to-noise ratio of the target neighborhood image As an intuitive quantitative index to measure whether the target can be detected, its formula is as follows:

[0077]

[0078] In the formula, is the gray value of the target area; is the average gray value of the target neighborhood, that is, the background gray value; is the standard deviation of the gray value of the target neighborhood, that is, the gray value of the background clutter, which is calculated by the sub-region image statistics method. Select M blocks (3×3~7×7 neighborhood can be taken) of sub-region images (n×n) around the target area:

[0079]

[0080]

[0081]

[0082] In the formula, k is the block serial number, and i, j are the row and column numbers of the sub-domain image; is the gray scale of the sub-domain image, and is the average value of the k-th sub-domain.

[0083] Compared with the prior art, the present invention has the following advantages:

[0084] 1) Complete link elements: All link influence parameters in the imaging process from the target end to the image end are fully considered, including the radiation change of the target with the external environment, the load response and the noise term, which can ensure accurate analysis in a complex space environment.

[0085] 2) Good data support: By comprehensively considering the changes at the target end in the detection scenario and the influences of cryogenic optics, detector response and noise at the imaging end, high-confidence infrared image sequence results are provided, directly providing a data basis for subsequent target detection and tracking algorithms.

[0086] 3) Strong platform adaptability: This method can adapt to the simulation requirements of different types of space targets and can be applied to the effectiveness analysis of different detection platforms such as space-based and air-based platforms. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 is a flow block diagram of the method of the present invention.

[0088] Figure 2 is the detection scenario setting of the embodiment of the method of the present invention.

[0089] Figure 3 is the radiance image of the target segment in the embodiment of the method of the present invention.

[0090] Figure 4 is the load parameter setting in the embodiment of the method of the present invention.

[0091] Figure 5 is the infrared simulation image in the embodiment of the method of the present invention.

[0092] Figure 6 is the dynamic signal-to-noise ratio curve in the embodiment of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0093] The present invention will be further described in detail below in conjunction with the drawings and embodiments:

[0094] As Figure 1The flowchart of the method of the present invention is shown as follows. Initialize the target type, background type, and detection platform parameters, and obtain the target image coordinates according to the transformation of the observation geometric coordinate system; calculate the target background radiation characteristics according to the solar and earth radiation, and obtain the radiance map; obtain multiple frames of infrared images according to the payload response model; finally, calculate the image signal-to-noise ratio to evaluate the detection efficiency.

[0095] Step 1: Select the target, band, and background, establish the target geometry and motion model according to the input target parameters, background parameters, and satellite parameters, and display the spatial position of the detection scene. As Figure 2 shown, it is the detection scene setting of this embodiment.

[0096] First, establish the observation geometric coordinate system of the detection scene such as the detection target and platform, and build it using the earth-fixed coordinate system. Its origin is at the earth's centroid (including the mass of the atmosphere, ocean, etc.). The xoy plane of the coordinate system is the earth's equatorial plane, the z-axis points to the CIO at the North Pole, and the x-axis points to the intersection of the Greenwich meridian and the equatorial plane. This coordinate system is fixed on the earth, and the observation platform, the earth's gravitational field coefficient, etc. are all defined in this coordinate system.

[0097] Assume that the points in this coordinate system are represented as:

[0098]

[0099] The coordinate system during the 3D modeling of the target is the target body coordinate system. The coordinates of each point during the 3D modeling of the target are all referenced to this coordinate system. The points in this coordinate system are represented as:

[0100]

[0101] The satellite camera coordinate system has the camera optical center as the origin, the z-axis as the optical axis direction (the direction from the pointing point to the optical center), and the x-axis and y-axis directions are determined by the detector direction. The image coordinate system is a two-dimensional coordinate system, with its origin at the center of the image plane, and the x-axis and y-axis directions are the same as those of the satellite camera coordinate system.

[0102] The points in the satellite camera coordinate system are represented as:

[0103]

[0104] The points in the image coordinate system are represented as:

[0105]

[0106] Any point of the target in the body coordinate system , the yaw angle of the target relative to the earth-fixed coordinate system is , the pitch angle is , the roll angle is , and the origin of the target body coordinate system in the earth-fixed coordinate system is , the coordinates of the target in the Earth-fixed coordinate system are

[0107]

[0108] Assume that the coordinates of the satellite in the Earth-fixed coordinate system are , and the point of the satellite pointing in the Earth-fixed coordinate system is , from which the polar angle of the satellite relative to the pointing point in the Earth-fixed coordinate system is obtained and the azimuth angle are

[0109]

[0110] Assume that the rotation angle of the satellite is , then the coordinates of the target in the satellite camera coordinate system are

[0111]

[0112] The coordinates of the target in the image coordinate system are

[0113]

[0114] Step 2: Establish a radiation characteristic model of the target and the background, display multiple radiation characteristic information of the target, and superimpose and display the radiance of the target and the background.

[0115] Given the radius of the Earth , let the position vector of the target be , the unit radiation vector of the sun be , let the normal vector of the surface element of the target be , the angle between the target and the sun is:

[0116]

[0117] The sun can be equivalent to a black body at 5900K, and its irradiance is:

[0118]

[0119] In the formula, is the radius of the sun, is the average distance between the sun and the Earth. Then for the target surface element , the solar radiation intensity reflected by the target surface at time 𝑡 is:

[0120]

[0121] is the reflectivity of the surface element to solar radiation, is the projected area of this surface element, and B is the set of surface elements in the detection field of view, Denoted as the total projected area obtained by adding the projected areas of the targets at the current moment, is the radiance of the surface element.

[0122] The amount of terrestrial infrared radiation received by the surface element of the target surface is

[0123]

[0124] is the reflectivity of the surface element to terrestrial radiation, is the infrared irradiance of the earth:

[0125]

[0126] The intensity of the terrestrial radiation reflected by the target surface is:

[0127]

[0128] Due to the influence of factors such as cloud layer and surface reflection, part of the solar radiation will be reflected. This part of the radiation is called the earth-reflected solar radiation, which is mainly concentrated in the visible to short-wave infrared bands. The view factor of the earth-reflected solar radiation represents the projection ratio of the reflected radiation energy on the surface element of the target. Its calculation formula is as follows:

[0129]

[0130] In the formula, is the angle between the line connecting the center of the earth and the centroid of the target surface element and the sun. When cos 𝜙 > 0, it indicates that the target is flying in the sunlit area. When cos 𝜙 ≤ 0, it indicates that the target is flying in the shadow area.

[0131] The intensity of the earth-reflected solar radiation reflected by the surface element of the target surface is:

[0132]

[0133] is the earth reflectivity.

[0134] The self-thermal radiation of the target is:

[0135]

[0136] The total radiation intensity of the target is:

[0137]

[0138] Such as Figure 3 shown is the radiance image of the target segment in this embodiment.

[0139] Step 3: Establish an optical system model based on the input optical system parameters, establish a detector model based on the input detector parameters, obtain system parameters according to the input information, establish an information acquisition system model, and obtain multiple frames of simulation images. As Figure 4 The payload parameter settings of this embodiment are shown as follows.

[0140] The scale of the target in the space-based detection scenario is generally smaller than the detection resolution, so it can be considered as a point target detection scenario. Due to the diffraction effect of the optical system, the point target shows a Gaussian distribution, where the central pixel obtains most of the target energy. The percentage of the target energy received by the central pixel in the total radiation energy of the target is the energy concentration (EE). The number of electrons received by the detector pixel for the target energy can be expressed as:

[0141]

[0142] In the formula, is the Planck constant ( ), is the central wavelength, is the speed of light ( ), is the pixel integration time, is the detector quantum efficiency, is the atmospheric transmittance at the altitude where the target is located, is the optical system transmittance (including the optical occlusion factor), is the target radiation intensity, is the detection distance, is the effective aperture of the detection system.

[0143] The gray value of the target pixel can be further calculated through the target signal electron number

[0144]

[0145] is the maximum full well charge of the single pixel integration capacitor, is the electronic quantization bit number, generally in the range of 8 - 16 bits, is the average gray value of the background noise.

[0146] The background noise is obtained by simulating and calculating each DC bias noise, mainly including the background noise of the optical instrument, dark current noise, and external stray light background noise, etc. Together, they form the system detection full well charge dominated by photon noise. The number of electrons Ninstr of the instrument background noise is the sum of the radiation of each component of the optical instrument and the Dewar window. As shown in the following formula

[0147]

[0148] Indicates the temperature at which each optical element is located The number of near-field radiation electrons generated at the operating temperature, is the instrument background illuminance generated by the optical element, with the unit of nW / mm2.

[0149] The number of dark current noise electrons The noise comes from the dark current of the infrared detector and is positively correlated with the integration time. The calculation formula is

[0150]

[0151] where is the dark current density per unit area, with the unit of A / cm 2 , dx is the pixel size, and q is the charge quantity. The dark current of the detector is related to the bias voltage and the operating temperature of the detector. Reducing the bias voltage and lowering the operating temperature can reduce the dark current.

[0152] The detection scenario of the space target is general limb deep space. Therefore, the earth radiation background is small and can be ignored for the influence on the full well. Therefore, the background noise gray level mean value is

[0153]

[0154] The total noise of a single pixel of the system can be expressed in the form of the root mean square of electrons

[0155]

[0156] The signal-to-noise ratio of the point target is less affected by the non-uniformity noise. Therefore, the influence of the spatial noise can be ignored, that is, the noise can be simplified to the time noise

[0157]

[0158] The number of noise electrons is the number of electrons accumulated on the integration capacitor when the signal output by the pixel at the end of the integration time is equal to the root mean square noise of the output, including photon noise( is the root mean square of the pixel photon noise, including signal noise, background noise and dark current noise, with the unit of e- / pixel), readout noise( is the root mean square of the pixel readout noise, with the unit of e- / pixel) and other time noises and response non-uniformity( is the root mean square of the pixel response non-uniformity noise, with the unit of e- / pixel), dark current non-uniformity( is the root mean square of the pixel dark current non-uniformity noise, with the unit of e- / pixel) and other spatial noises. Represents the root mean square generated by the sum of all photon noise-related, and can be disassembled according to different radiation sources as:

[0159]

[0160] Total noise According to Quantization can be converted to the corresponding gray value. It is superimposed on the N×N infrared image in the form of Gaussian white noise. As Figure 5 Shown is the infrared simulation image of this embodiment.

[0161] Step 4: Finally, using the target coordinate position, the signal-to-noise ratio of the image can be statistically calculated and a dynamic curve can be plotted for performance evaluation.

[0162]

[0163] The signal-to-noise ratio calculation method based on the target and noise energy forms is applicable to the target image. In the case where the image is affected by the background, the measurement image data processing based on the target and the background mainly uses the signal-to-noise ratio of the target neighborhood image As an intuitive quantitative index to measure whether the target can be detected, its formula is as follows:

[0164]

[0165] In the formula, is the gray value of the target area; is the average gray value of the target neighborhood, that is, the background gray value; is the standard deviation of the gray value of the target neighborhood, that is, the gray value of the background clutter, which is calculated by the sub-region image statistics method. Select M blocks (3×3~7×7 neighborhood can be taken) of sub-region images (n×n) around the target area:

[0166]

[0167]

[0168]

[0169] In the formula, k is the block number, and i, j are the row and column numbers of the sub-region image; is the gray value of the k-th sub-region image, is the average value of the k-th sub-region. Finally, a dynamic signal-to-noise ratio curve is plotted. As Figure 6 Shown is the dynamic signal-to-noise ratio curve of this embodiment.

[0170] The above are only embodiments of the present invention, and common general technical solutions or characteristics in the solution are not described in detail herein. It should be noted that for those skilled in the art, without departing from the technical solution of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application shall be subject to the content of its claims, and the specific implementation manners and the like recorded in the specification can be used to interpret the content of the claims.

Claims

1. A method for evaluating the detection effectiveness of infrared targets throughout their entire life cycle, characterized in that The method supports dynamic simulation image calculation of signal-to-noise ratio, and includes the following steps: S1. Detection scene construction: Use 3D modeling technology to build an accurate geometric model of the target. By configuring the observation satellite orbit parameters and detection payload, combined with the target's full motion characteristics and the geometric relationship between the sun and the earth, the target's motion trajectory, position changes and observation geometry during its life cycle are described. The target coordinate position is transformed, and the target body coordinates are converted to the payload image two-dimensional coordinate system; S2. Dynamic radiation characteristics analysis of the target: Use finite element analysis software to calculate the temperature field, consider the relationship between the thermal radiation of the target under different environmental conditions and the factors of the sun and the earth, and accurately calculate the radiation characteristics of the target throughout its life cycle; S3, infrared detection image simulation: based on the geometric characteristics and motion trajectory of the target, the projection area of ​​the target in the time dimension is further calculated. For all the facets in the detector field of view, their projection areas are added together to obtain the overall projection area of ​​the target surface. The infrared load radiation response model is further used to calculate the infrared target and background grayscales to form a detection image sequence. S4. Dynamic performance evaluation: Finally, based on the target's coordinate position, grayscale value, and neighborhood spatial noise standard deviation in each frame, the signal-to-noise ratio of the target throughout its life cycle is calculated using simulated images.

2. The method for evaluating the detection effectiveness of infrared targets throughout their entire life cycle according to claim 1, characterized in that: In step S2, a target finite element three-dimensional model is constructed, the target temperature field is calculated, and the relationship between the target's thermal radiation under different environments and factors such as the sun and the earth is considered to obtain the radiation characteristics of the target throughout its life cycle.

3. The method for evaluating the detection effectiveness of infrared targets throughout their entire life cycle according to claim 1 is characterized in that: In step S3, it is necessary to consider the relative motion between the target and the detector and the situation that the target is in a micro-motion state. Therefore, it is necessary to calculate the angle between the normal vector of the target face element and the line of sight of the detector, so as to determine whether each face element is within the field of view of the detector, and calculate the projection area of ​​the target in the field of view of the detector; further, through the infrared load radiation response model, the infrared target and background grayscale are calculated respectively to form a detection image sequence.

Images