Infrared target full life cycle end-to-end detection efficiency evaluation method

Abstract

The invention belongs to the technical field of infrared signals, and discloses an infrared target full-life-cycle end-to-end detection efficiency evaluation method, which comprises the following steps of: firstly, building detection scenes such as a target, a detection platform, a load and the sun, including elements such as a target full-process motion trail, a satellite orbit and observation geometry; performing conversion from a target coordinate system to a load image coordinate in a detection scene to realize sub-pixel-level target geometric simulation; constructing a finite element three-dimensional model of the target, calculating an instantaneous equilibrium temperature field of the target, and calculating full life cycle radiation characteristics of the target according to data such as the temperature field and the projection area; obtaining an infrared target and background gray scale through an infrared load radiation response model, and forming a detection image sequence; and finally, evaluating the dynamic detection efficiency according to the target gray value of the image end and the signal-to-noise ratio of the noise. The method can comprehensively and accurately describe the dynamic process of the detection efficiency of the infrared load on the dark and weak target along with time and scene changes, and is suitable for the fields of spaceflight and aviation.

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; S2. Dynamic radiation characteristics analysis: Secondly, use UG finite element analysis software to calculate the temperature field. By comprehensively considering the heat conduction, radiation, sun, earth and other factors of the target under different environmental conditions, the radiation characteristics of the target throughout its life cycle can be accurately calculated; S3. Infrared detection image simulation: Based on the geometric characteristics and motion trajectory of the target, the projection area of ​​the target at different time points is further calculated. Considering that there is relative motion between the target and the detector, and the target may be in a micro-motion state, it is necessary to determine whether each face element is within the field of view of the detector by calculating the angle between the normal vector of the target face element and the line of sight of the detector. For all face elements in the field of view of the detector, 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, simulated images are used to calculate the signal-to-noise ratio over the entire life cycle of the target.

[0007] First, establish the observation geometric coordinate system of the detection scene such as the detection target and platform, and use the earth-fixed coordinate system to build it. Its origin is at the center of mass of the earth (including the mass of the atmosphere and ocean), the coordinate system xoy plane is the equatorial plane of the earth, the z axis points to the North Pole CIO, 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 and the earth's gravitational field coefficient are all defined in this coordinate system.

[0008] Assume that the points in this coordinate system are represented as:

[0009] The coordinate system of the target during 3D modeling is the target body coordinate system. The coordinates of each point in the target during 3D modeling are referenced to this coordinate system. The points in this coordinate system are expressed as:

[0010] The satellite camera coordinate system takes the camera optical center as its origin, the z-axis as the optical axis direction (the direction from the 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.

[0011] The point in the satellite camera coordinate system is expressed as:

[0012] The points in the image coordinate system are represented as:

[0013] The target is at any point 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 ground-fixed coordinate system is , then the coordinates of the target in the ground-fixed coordinate system are

[0014] Assume that the coordinates of the satellite in the earth-fixed coordinate system are , the satellite pointing point 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 azimuth for

[0015] Assume that the satellite's rotation angle is , then the coordinates of the target in the satellite camera coordinate system are

[0016]

[0017] The coordinates of the target in the image coordinate system are:

[0018] Known Earth radius , let the target position vector be , the solar unit radiation vector is , let the normal vector of the target surface element be , the angle between the target and the sun is:

[0019] The sun is equivalent to a 5900K black body, and its irradiance is:

[0020] In the formula, is the radius of the sun, is the average distance between the Earth and the Sun, 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:

[0021] is the reflectivity of the surface element to solar radiation, is the projected area of ​​this facet, B is the set of facets in the detection field of view, It is expressed 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, is the rotation matrix of the target at time t, is the normal vector of target surface element i, LOS is the normal vector of the detector detection direction, It is the cosine value of the angle between the target surface element normal vector and the detector detection normal vector.

[0022] The amount of infrared radiation received by the target surface element for

[0023] is the reflectivity of the surface element to the earth’s radiation, is the infrared irradiance of the earth:

[0024] h is the flight altitude of the target.

[0025] The intensity of the earth radiation reflected by the target surface is:

[0026] Among them, the absorption rate of the surface element to the earth's radiation is expressed as ; Radiation angle coefficient of the target reflecting the earth's radiation It is determined by the angle between the surface element normal vector and the vector from the center of the earth to the center of the surface element.

[0027] Due to factors such as clouds and surface reflection, part of the solar radiation will be reflected. This part of radiation is called earth reflected solar radiation, which is mainly concentrated in the visible light to short-wave infrared band. The earth reflected solar radiation angular coefficient represents the projection ratio of the reflected radiation energy on the target surface element. The calculation formula is as follows:

[0028] 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. , indicating that the target is flying in the sunlight area. , indicating that the target is flying in the shadow area.

[0029] The intensity of solar radiation reflected by the earth from the target surface is:

[0030] in is the earth's reflectivity.

[0031] The target's own heat radiation is:

[0032] in, is the infrared emissivity of the surface element, The target surface The temperature, is at time t, in the band , target surface element radiance.

[0033] The total radiation intensity of the target is:

[0034] The scale of the target in the space-based detection scene is generally smaller than the detection resolution, so it can be considered as a point target detection scene. Due to the diffraction effect of the optical system, the point target exhibits a Gaussian distribution, in which the central pixel obtains most of the target energy. The percentage of the target energy received by the central pixel to the total radiation energy of the target is the energy concentration (EE). The number of electrons received by the detector pixel can be expressed as:

[0035] In the formula, Planck's constant , is the central wavelength, The speed of light , is the pixel integration time, is the detector quantum efficiency, Atmospheric transmittance at the target height, is the transmittance of the optical system (including optical occlusion factor), is the target radiation intensity, is the detection distance, It is the effective aperture of the detection system.

[0036] The target pixel gray value can be further calculated by the target signal electron number

[0037]

[0038] is the maximum full-well charge of the single-pixel integrating capacitor, The number of bits for electronic quantization, generally ranging from 8 to 16 bits. is the grayscale mean of the background noise.

[0039] The background noise is obtained by simulating and calculating the DC bias noise, which mainly includes the optical instrument background noise, dark current noise and external stray light background noise, which together constitute the system detection full well charge dominated by photon noise, instrument background noise electron number It is the sum of the radiation from all components of the optical instrument and the Dewar window. As shown in the following formula

[0040] Indicates the temperature of each optical component The number of near-field radiation electrons generated at operating temperature, The instrument background illumination generated by the optical element, in nW / mm 2 .

[0041] Dark current noise electron count The noise comes from the dark current of the infrared detector and is positively correlated with the integration time. The calculation formula is:

[0042] in is the dark current density per unit area, in A / cm 2 , dx is the pixel size, q is the charge. The dark current of the detector is related to the bias voltage and the detector operating temperature. Reducing the bias voltage and lowering the operating temperature can reduce the dark current.

[0043] The space target detection scene is the general limb deep space, so the earth radiation background The effect on the full well can be ignored, so the background noise grayscale mean is

[0044] The total noise per pixel of the system can be expressed as the RMS form of the number of electrons:

[0045] The signal-to-noise ratio of point targets is less affected by non-uniform noise, so the influence of spatial noise can be ignored, that is, the noise can be simplified to temporal noise.

[0046] Noise electron count is the number of electrons accumulated on the integrating capacitor when the pixel output signal is equal to the output RMS noise at the end of the integration time, including photon noise ( is the root mean square of the pixel photon noise, including signal noise, background noise and dark current noise, unit is e- / pixel), readout noise ( is the root mean square of pixel readout noise, unit is e- / pixel) and other temporal noise and response non-uniformity ( is the root mean square of the pixel response non-uniform noise, unit e- / pixel), dark current non-uniformity ( is the root mean square of the non-uniform noise of the pixel dark current, unit is e- / pixel) and other spatial noise. represents the root mean square of the sum of all photon noise related factors, which can be decomposed into:

[0047] 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.

[0048] The signal-to-noise ratio calculation method based on the target and noise energy form is applicable to the target image. In the case of background influence in the image, the measurement image data processing based on the target and background mainly uses the target neighborhood image signal-to-noise ratio As an intuitive quantitative indicator to measure whether the target is detectable, the formula is as follows:

[0049] In the formula, is the grayscale of the target area; is the grayscale mean of the target neighborhood, which is the background grayscale; is the standard deviation of the grayscale of the target neighborhood, that is, the grayscale of the background clutter, which is calculated using the subdomain image statistics method. Select M blocks (3×3~7×7 neighborhoods) of subdomain images (n×n) around the target area:

[0050]

[0051]

[0052] Where k is the block number, i, j are the row and column numbers of the sub-domain image; For the The grayscale of the subdomain image, is the mean of the kth subdomain.

[0053] Compared with the prior art, the present invention has the following advantages: 1) Full link elements: Fully consider the full link influencing parameters in the imaging process from the target end to the image end, including the target's radiation changes with the external environment, load response and noise terms, to ensure accurate analysis in complex space environments.

[0054] 2) Good data support: By comprehensively considering the changes in the target end under the detection scene, and through the influence of low-temperature optics, detector response and noise at the imaging end, it provides high-confidence infrared image sequence results, directly providing a data basis for subsequent target detection and tracking algorithms.

[0055] 3) Strong platform adaptability: This method can adapt to the simulation requirements of different types of space targets and can be applied to the performance analysis of different space-based and air-based detection platforms. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flowchart of the method of the present invention.

[0057] Figure 2 This is the detection scene setting of the method embodiment of the present invention.

[0058] Figure 3 It is the target segment radiance image of the method embodiment of the present invention.

[0059] Figure 4 It is the load parameter setting of the method embodiment of the present invention.

[0060] Figure 5 It is an infrared simulation image of the method embodiment of the present invention.

[0061] Figure 6 It is a dynamic signal-to-noise ratio curve of the method embodiment of the present invention. DETAILED DESCRIPTION

[0062] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments: like Figure 1 The flowchart of the method of the present invention is shown. The target type, background type, and detection platform parameters are initialized, and the target image coordinates are obtained according to the observation geometric coordinate system transformation; the target background radiation characteristics are calculated according to the solar and earth radiation to obtain the radiance map; multiple frames of infrared images are obtained according to the load response model; and finally, the image signal-to-noise ratio is calculated to evaluate the detection performance.

[0063] Step 1: Select the target, band and background, establish the target geometry and motion model based on the input target parameters, background parameters and satellite parameters, and display the spatial position of the detection scene. Figure 2 As shown, the detection scene setting of this embodiment is shown.

[0064] First, establish the observation geometric coordinate system of the detection scene such as the detection target and platform, and use the earth-fixed coordinate system to build it. Its origin is at the center of mass of the earth (including the mass of the atmosphere and ocean), the coordinate system xoy plane is the equatorial plane of the earth, the z axis points to the North Pole CIO, 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 and the earth's gravitational field coefficient are all defined in this coordinate system.

[0065] Assume that the points in this coordinate system are represented as:

[0066] The coordinate system of the target during 3D modeling is the target body coordinate system. The coordinates of each point in the target during 3D modeling are referenced to this coordinate system. The points in this coordinate system are expressed as:

[0067] The satellite camera coordinate system takes the camera optical center as its origin, the z-axis as the optical axis direction (the direction from the 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.

[0068] The point in the satellite camera coordinate system is expressed as:

[0069] The points in the image coordinate system are represented as:

[0070] The target is at any point 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 ground-fixed coordinate system is , then the coordinates of the target in the ground-fixed coordinate system are

[0071] Assume that the coordinates of the satellite in the earth-fixed coordinate system are , the satellite pointing point 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 azimuth for

[0072] Assume that the satellite's rotation angle is , then the coordinates of the target in the satellite camera coordinate system are

[0073] The coordinates of the target in the image coordinate system are

[0074] Step 2: Establish target and background radiation characteristic models, display multiple radiation characteristic information of the target, and superimpose and display the radiation brightness of the target and background.

[0075] Known Earth radius , let the target position vector be , the solar unit radiation vector is , let the normal vector of the target surface element be , the angle between the target and the sun is:

[0076] The sun is equivalent to a 5900K black body, and its irradiance is:

[0077] 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:

[0078] is the reflectivity of the surface element to solar radiation, is the projected area of ​​this facet, B is the set of facets in the detection field of view, It is expressed 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.

[0079] The amount of infrared radiation received by the target surface element for

[0080] is the reflectivity of the surface element to the earth’s radiation, is the infrared irradiance of the earth:

[0081] The intensity of the earth radiation reflected by the target surface is:

[0082] Due to factors such as clouds and surface reflection, part of the solar radiation will be reflected. This part of radiation is called earth reflected solar radiation, which is mainly concentrated in the visible light to short-wave infrared band. The earth reflected solar radiation angular coefficient represents the projection ratio of the reflected radiation energy on the target surface element. The calculation formula is as follows:

[0083] In the formula, It is the angle between the line connecting the center of the earth and the center of mass of the target surface element and the sun. When cos𝜙>0, it indicates that the target is flying in the sunlit area, and when cos𝜙≤0, it indicates that the target is flying in the shadow area.

[0084] The intensity of solar radiation reflected by the earth from the target surface is:

[0085] is the earth's reflectivity.

[0086] The target's own heat radiation is:

[0087] The total radiation intensity of the target is:

[0088] like Figure 3 Shown is the radiance image of the target segment of this embodiment.

[0089] Step 3: Establish an optical system model based on the input optical system parameters, establish a detector model based on the input detector parameters, establish an information acquisition system model based on the input information acquisition system parameters, and acquire multiple frames of simulation images. Figure 4 The load parameter setting of this embodiment is shown.

[0090] The scale of the target in the space-based detection scene is generally smaller than the detection resolution, so it can be considered as a point target detection scene. Due to the diffraction effect of the optical system, the point target exhibits a Gaussian distribution, in which the central pixel obtains most of the target energy. The percentage of the target energy received by the central pixel to the total radiation energy of the target is the energy concentration (EE). The number of electrons received by the detector pixel can be expressed as:

[0091] In the formula, is Planck's constant ( ), is the central wavelength, is the speed of light ( ), is the pixel integration time, is the detector quantum efficiency, Atmospheric transmittance at the target height, is the transmittance of the optical system (including optical occlusion factor), is the target radiation intensity, is the detection distance, It is the effective aperture of the detection system.

[0092] The target pixel gray value can be further calculated by the target signal electron number

[0093]

[0094] is the maximum full-well charge of the single-pixel integrating capacitor, The number of bits for electronic quantization, generally ranging from 8 to 16 bits. is the grayscale mean of the background noise.

[0095] The background noise is obtained by simulating and calculating the DC bias noise, which mainly includes the optical instrument background noise, dark current noise and external stray light background noise, which together constitute the photon noise-dominated system detection full well charge. The instrument background noise electron number Ninstr is the sum of the radiation of the optical instrument components and the Dewar window. As shown in the following formula

[0096] Indicates the temperature of each optical component The number of near-field radiation electrons generated at operating temperature, It is the instrument background illumination generated by the optical components, in nW / mm2.

[0097] Dark current noise electron count The noise comes from the dark current of the infrared detector and is positively correlated with the integration time. The calculation formula is:

[0098] in is the dark current density per unit area, in A / cm 2 , dx is the pixel size, q is the charge. The dark current of the detector is related to the bias voltage and the detector operating temperature. Reducing the bias voltage and lowering the operating temperature can reduce the dark current.

[0099] The space target detection scene is the general limb deep space, so the earth radiation background The effect on the full well can be ignored, so the background noise grayscale mean is

[0100] The total noise per pixel of the system can be expressed as the RMS form of the number of electrons:

[0101] The signal-to-noise ratio of point targets is less affected by non-uniform noise, so the influence of spatial noise can be ignored, that is, the noise can be simplified to temporal noise.

[0102] Noise electron count The number of electrons accumulated on the integrating capacitor when the pixel output signal is equal to the output RMS noise at the end of the integration time, including photon noise ( is the root mean square of the pixel photon noise, including signal noise, background noise and dark current noise, unit is e- / pixel), readout noise ( is the root mean square of pixel readout noise, unit is e- / pixel) and other temporal noise and response non-uniformity ( is the root mean square of the pixel response non-uniform noise, unit e- / pixel), dark current non-uniformity ( It is the root mean square of the non-uniform noise of the pixel dark current, unit is e- / pixel) and other spatial noise. represents the root mean square of the sum of all photon noise related factors, which can be decomposed into:

[0103] Total noise according to Quantization can be converted to the corresponding grayscale value. It is superimposed on the N×N infrared image in the form of Gaussian white noise. Figure 5 Shown is an infrared simulation image of this embodiment.

[0104] Step 4: Finally, the target coordinate position can be used to calculate the image signal-to-noise ratio and draw a dynamic curve for performance evaluation.

[0105]

[0106] The signal-to-noise ratio calculation method based on the target and noise energy form is applicable to the target image. In the case of background influence in the image, the measurement image data processing based on the target and background mainly uses the target neighborhood image signal-to-noise ratio As an intuitive quantitative indicator to measure whether the target is detectable, the formula is as follows:

[0107] In the formula, is the grayscale of the target area; is the grayscale mean of the target neighborhood, which is the background grayscale; is the standard deviation of the grayscale of the target neighborhood, that is, the grayscale of the background clutter, which is calculated using the subdomain image statistics method. Select M blocks (3×3~7×7 neighborhoods) of subdomain images (n×n) around the target area:

[0108]

[0109]

[0110] Where k is the block number, i, j are the row and column numbers of the sub-domain image; For the The grayscale of the subdomain image, is the mean value of the kth subdomain. Finally, a dynamic signal-to-noise ratio curve is drawn, such as Figure 6 The dynamic signal-to-noise ratio curve of this embodiment is shown.

[0111] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the technical solution of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records 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 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, describe the target's motion trajectory, position changes and observed geometric relationships during its life cycle; transform the target coordinate position, and transform the target body coordinates into 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 S1, the orbital parameters and detection payload of the observation satellite are configured, and the geometric relationship between the target's overall motion characteristics and the sun and the earth is combined to describe the target's motion trajectory, position changes, and observation geometric relationship during 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 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.

4. The method for evaluating the detection effectiveness of infrared targets throughout their entire life cycle according to claim 1, 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