Infrared imaging modeling method for long-distance non-uniform atmospheric near space small target

Abstract

The invention relates to an infrared imaging modeling method for a long-distance non-uniform atmospheric near space small target. The method comprises the following steps: establishing atmospheric scene data; determining the spatial position and orientation of the detection platform and the spatial position and orientation of the target, and determining a ground object observation part and a limb observation part; performing reverse ray tracing iteration from the surface of the detector pixel, and calculating atmospheric path radiation of the limb observation part and a background radiation part of the ground object observation part on a ray tracing path to obtain a background radiation result; starting from the fragment of the target, performing forward ray tracing iteration to calculate a ray tracing path and obtain the position of the target in the detector pixel, and calculating the radiation quantity after attenuation in the ray tracing path to obtain a target radiation result; and fusing the background radiation result and the target radiation result, superposing a detector effect, and converting the fused imaging simulation radiation signal into a gray value to obtain an imaging simulation result. According to the method, the imaging simulation of the near space scene target background is refined.

Description

Technical Field

[0001] The present invention belongs to the technical field of space exploration, and particularly relates to an infrared imaging modeling method for small targets in the near space of long-distance non-uniform atmosphere. Background Technique

[0002] With the continuous maturity of space launch technology, the investment of various countries in space development has been increasing day by day. Among them, space exploration is an indispensable important part, and the infrared space exploration system is an indispensable part, playing an important role in multiple fields such as ground remote sensing and space target detection, with advantages such as a wide observation range and outstanding observation ability. The near space area within the atmosphere has been the focus of sky development and utilization by various countries in recent years. Therefore, it is of great importance and foresight to study the imaging problem of near space targets by infrared detectors carried on space imaging platforms. Due to the high-cost characteristics of the space exploration platform system, it is impossible to conduct a large number of experiments in the real space environment to obtain imaging data. Therefore, it is of great practical significance to establish a computer model for imaging simulation of near space target backgrounds by a high-precision space exploration platform.

[0003] In order to establish an imaging simulation model of the near space target background, the existing technical methods are as follows:

[0004] In the article "Influence of Atmospheric Refraction on Radiation Transmission Characteristics in the Visible Light Band" by Hu Shuai, based on the Monte Carlo method, a vector radiative transfer model considering atmospheric refraction was presented, realizing the simulation of the random motion process of photons at the homogeneous gas layer and the coupled surface, and calculating parameters such as the Stokes vector, degree of polarization, and radiation flux of direct light and diffuse light. In the article "Research on Imaging Characteristics of Hypersonic Targets for Space-Based Infrared Early Warning" by Li Peize, based on fluid simulation software, an infrared radiation characteristic model of near-space targets moving at high speed was established using a turbulence model, and a space-based infrared early warning software platform was built on the basis of an open-source rendering engine to generate multi-spectral dimensional image sequences of targets under complex backgrounds. In the article "Research on Modeling and Simulation Method for the Influence of Aero-Optical Effects on the Image Quality of High-Speed Aircraft" by Wang Hui, a calculation model for the aero-optical transmission effect of the optical dome of the target was established by ray tracing, and the aero-optical transmission effect and aero-radiation effect of the optical dome were simulated and calculated. In the article "Starlight Atmospheric Refraction Correction Model Based on NCEP Database" by Yang Yufeng, aiming at the problem of low accuracy of the American Standard Atmosphere and the International Reference Atmosphere reference models, an atmospheric parameter spatio-temporal variation model was established using high-resolution atmospheric parameter data from the National Centers for Environmental Prediction of the United States combined with the Fourier interpolation algorithm. According to the atmospheric refractive index at different altitudes and different longitudes and latitudes, the propagation path of starlight in the atmosphere was calculated, and a starlight atmospheric refraction model was established. He Yuanfeng established an imaging simulation system and method for a satellite in the patent with the application number 202410228014.2, established a physical model based on the micro-surface theory for the reflection modeling of the target, and realized an integrated simulation platform for multi-module scheduling and integration using a three-dimensional rendering dynamic rendering engine.

[0005] However, in the current technical solutions, firstly, classical ray analysis models are basically used to achieve imaging simulation, that is, the influence of the Earth's atmospheric medium on the light transmission path in the case of limb sounding by medium and low Earth orbit satellites is not considered, resulting in the lack of simulation accuracy of limb background radiation or target radiation values; secondly, detailed modeling is mostly carried out for only one part of the radiation characteristics of near-space targets, the transmission path of imaging light, and the simulation system effects, and simplified models are used for the other parts, without unified consideration of the three in space. The modeling of near-space targets often lacks or simplifies the full-process simulation from target radiation to imaging by the space detection platform. And the targets are mostly analyzed under steady-state conditions, without considering the simulation of transient conditions during the operation of the targets, resulting in a decrease in authenticity. For background modeling, an equivalent background model is often adopted, lacking the diversity of background data from a global perspective, different observation positions, different environmental impacts, and different spectral bands, resulting in low background simulation accuracy. Summary of the Invention

[0006] To solve the above problems existing in the prior art, the present invention provides an infrared imaging modeling method for small targets in the near-space of long-distance non-uniform atmosphere. The technical problems to be solved by the present invention are realized through the following technical solutions:

[0007] An embodiment of the present invention provides an infrared imaging modeling method for small targets in the near-space of long-distance non-uniform atmosphere, including the steps of:

[0008] Establishing atmospheric scene data;

[0009] Determining the spatial position and orientation of the space detection platform, as well as the spatial position and orientation of the target, and determining the ground object observation part and the limb observation part according to the spatial position and orientation of the space detection platform;

[0010] Performing reverse ray tracing iteration starting from the detector pixel surface, and calculating the atmospheric path radiation of the limb observation part and the ground object radiation of the ground object observation part on the ray tracing path to obtain the background radiation result;

[0011] Performing forward ray tracing iteration calculation of the ray tracing path starting from the target fragment, obtaining the position of the target on the detector pixel, and calculating the attenuated radiation amount in the ray tracing path to obtain the target radiation result;

[0012] Fusing the background radiation result and the target radiation result, superimposing the detector effect, and converting the fused imaging simulation radiation signal into a gray value to obtain the imaging simulation result.

[0013] In an embodiment of the present invention, establishing the atmospheric scene data includes:

[0014] For the atmospheric scene below the mesosphere, converting the atmospheric parameter data in the target data source for the atmospheric isobaric surface and altitude, and for the atmospheric scene above the mesosphere, performing the conversion of the atmospheric isobaric surface and altitude according to the standard atmospheric model to obtain the atmospheric height distribution data;

[0015] Calculating the temperature, relative humidity, atmospheric density, and atmospheric refractive index at different altitudes according to the atmospheric height distribution data.

[0016] In an embodiment of the present invention, the conversion formula for the atmospheric isobaric surface and altitude is:

[0017] P = -7.19045×10 4 ×e -0.2538h +1.7210×10 5 ×e -0.1682h

[0018] where P is the actual atmospheric pressure and h is the altitude.

[0019] In an embodiment of the present invention, calculating the temperature, relative humidity, atmospheric density, and atmospheric refractive index at different altitudes based on the atmospheric altitude distribution data includes:

[0020] Interpolating the temperature and relative humidity in the atmospheric altitude distribution data using three-dimensional linear interpolation to obtain the temperature and relative humidity at different altitudes;

[0021] Calculating the atmospheric density at different altitudes using the temperature and atmospheric pressure at different altitudes:

[0022]

[0023] where ρ is the atmospheric density, P is the actual atmospheric pressure, P0 is the standard physical atmospheric pressure, T0 is the absolute zero, and T is the actual absolute temperature;

[0024] Calculating the atmospheric refractive index at different altitudes using the atmospheric density at different altitudes:

[0025]

[0026] n = 1 + ρ × Kgd

[0027] where D is a coefficient, usually taken as 0.00752, λ is the optical wavelength, Kgd is the Gladstone Dale constant, and n is the atmospheric refractive index.

[0028] In an embodiment of the present invention, determining the ground object observation part and the limb observation part based on the spatial position and orientation of the space exploration platform includes:

[0029] Determining the intersection point group of the reverse light of the detector image plane and the outer surface of the Earth's atmosphere according to the spatial position and orientation of the space exploration platform, taking the light ray that intersects the Earth's surface of the reverse light of the detector image plane as the ground object observation part, and removing the ground object observation part from the intersection point group to obtain the limb observation part.

[0030] In an embodiment of the present invention, calculating the atmospheric path radiation of the limb observation part and the ground object radiation of the ground object observation part on the light ray tracing path to obtain the background radiation result includes:

[0031] Using atmospheric radiation calculation software to recursively calculate the atmospheric path radiation of the limb observation part on the light ray tracing path;

[0032] Extracting ground object radiation data from the ground object radiation database through longitude and latitude, and calculating the attenuation of the ground object radiation data after atmospheric transmission using atmospheric radiation calculation software to obtain the ground object radiation of the ground object observation part;

[0033] Superimpose the atmospheric path radiation of the edge observation part and the ground object radiation of the ground object observation part to obtain the background radiation result.

[0034] In an embodiment of the present invention, the expressions of the background radiation result and the target radiation result are both:

[0035] P = I·A opt / l 2 = L·A s ·A opt / l 2

[0036] where P is the radiation power before the entrance pupil, A opt is the area of the entrance pupil of the optical system, l is the distance between the background or target and the entrance pupil, L is the radiation luminance of the background or target, A s is the surface area of the radiation source, and I is the radiation intensity before the entrance pupil.

[0037] In an embodiment of the present invention, both the reverse ray tracing iteration and the forward ray tracing iteration use the fourth-order Runge-Kutta algorithm, and their expressions are both:

[0038]

[0039] where, is the unit vector of the ray at the (i + 1)-th step, is the unit vector of the ray at the i-th step, h is the ray tracing step size, is the product of the tangential vector of the ray at the (i + 1)-th step and the refractive index distribution, is the product of the tangential vector of the ray at the i-th step and the refractive index distribution, F1, F2, F3, F4, L1, L2, L3, L4 are all coefficients to be calculated, n is the refractive index at the preset position, is the refractive index gradient at the preset position, and the refractive index and refractive index gradient of L1 are taken at the value of r i at the position, the refractive index and refractive index gradient of L2 are taken at at the position, the refractive index and refractive index gradient of L3 are taken at at the position, the refractive index and refractive index gradient of L4 are taken at r i3 = r i + hF3 at the position.

[0040] In an embodiment of the present invention, superimpose the detector effect, convert the fused imaging simulation radiation signal into a gray value, and obtain the imaging simulation result, including:

[0041] Obtain the radiation power value on the detector surface after the fused imaging simulation radiation signal passes through the optical system:

[0042] P spix = τ opt ×P opt = τ opt ×L×A pix ×A opt / f 2

[0043] where P spix is the radiation power value received by the pixel, τ opt is the radiation transmittance of the optical system, P opt is the radiation power before the optical system, L is the radiation luminance value of the corresponding radiation source on the pixel, A pix is the pixel area, A opt is the entrance pupil area of the optical system, and f is the focal length of the optical system;

[0044] Convert the radiation power value on the detector surface to the response voltage on a single pixel of the detector:

[0045]

[0046] where U pixel is the response voltage on the pixel, P in (λ) is the input radiation power on the detector pixel, R(λ) is the detector responsivity, λ is the spectral wavelength, and τ int is the detector imaging integration time;

[0047] Perform a Fourier transform on the response voltage from the spatial domain to obtain the response voltage in the frequency domain distribution, multiply the response voltage in the frequency domain distribution by the modulation transfer function to obtain the response voltage frequency domain distribution with the spatial transfer effect added, and then perform an inverse Fourier transform on the response voltage frequency domain distribution with the spatial transfer effect added to obtain the response voltage with the spatial transfer effect superimposed; where the modulation transfer function is:

[0048] MTF sys = MTF optic ·MTF dect

[0049] where MTF optic is the modulation transfer function of the optical system, and MTF dect is the modulation transfer function of the detector system;

[0050] Add noise to the response voltage with the spatial transfer effect superimposed to obtain the response voltage with noise added;

[0051] Convert the response voltage with noise added to a grayscale value to obtain the imaging simulation result:

[0052]

[0053] Among them, G is the gray value of the imaging image, and U max = n·qC, where n is the full well charge number of the detector unit, q is the absolute value of the electron charge, C is the integration capacitance of the detector unit, and U min is the noise equivalent voltage value of the detector, and U min = U noise , and G max is the upper limit of gray quantization.

[0054] In an embodiment of the present invention, the noise includes one or more of thermal noise, dark current noise, and shot noise represented by a Gaussian noise model.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] The infrared imaging modeling method of the present invention first determines the ground object observation part and the limb observation part of the atmosphere, performs reverse ray tracing iteration for the background part, calculates the background radiation result, performs forward ray tracing iteration for the target part, calculates the target radiation result, and finally fuses the background radiation result and the target radiation result, and performs imaging effect simulation of the detector in space to generate the final imaging simulation result. It not only considers the influence of the problem of light deflection on the quality of imaging simulation, but also more precisely calculates the influence received by the radiation light in the atmosphere and the radiation amount of the limb background, refines the imaging simulation of the target background in the near-space scene, and improves the fidelity of the modeling and simulation effect; and comprehensively uses the method of simulating all elements of the signal from the emission of target and background radiation to the imaging gray level, and can perform simulation analysis for different satellite orbits, different target position states, different times, different bands, different detector parameters, etc. globally, improving the authenticity of infrared imaging simulation. Description of the Drawings

[0057] Figure 1 is a schematic flow chart of an infrared imaging modeling method for a long-distance non-uniform atmosphere near-space small target provided by an embodiment of the present invention;

[0058] Figure 2 is a schematic diagram of a simulation scene provided by an embodiment of the present invention;

[0059] Figure 3 is a schematic diagram of the propagation path of light in the stratified medium of the earth's atmosphere provided by an embodiment of the present invention;

[0060] Figure 4 is a schematic diagram of a ray tracing algorithm for a stratified medium of the atmosphere provided by an embodiment of the present invention;

[0061] Figure 5 is a diagram of the object-image space relationship of the spatial point target imaging scene provided by an embodiment of the present invention. Detailed implementation manners

[0062] The following further describes the present invention in detail with reference to specific embodiments, but the implementation manners of the present invention are not limited thereto.

[0063] Embodiment 1

[0064] Please refer to Figure 1 , Figure 1 , which is a schematic flowchart of an infrared imaging modeling method for small targets in the near space of long - distance non - uniform atmosphere provided by an embodiment of the present invention. This infrared imaging modeling method combines background - target - long - distance atmospheric transmission path - detector effect, and mainly includes three parts: establishing scene data, calculating target radiation, and calculating background radiation. After the simulation starts, the system will start from the established scene data, perform forward and backward ray tracing algorithms according to the set scene to obtain the ray propagation path, calculate the radiation simulation data of the target and the background and fuse them, and finally obtain the simulation image. The specific steps are as follows:

[0065] S1. Establish atmospheric scene data.

[0066] In this embodiment, it is necessary to simulate the targets in the near space under the limb observation of medium - low - orbit satellites. Therefore, it is necessary to first establish atmospheric data. Please refer to Figure 2 , Figure 2 , which is a schematic diagram of the simulation scene provided by an embodiment of the present invention. Step S1 includes:

[0067] S11. For the atmospheric scene below the mesosphere, convert the atmospheric parameter data in the target data source for the atmospheric isobaric surface and altitude; among them, the atmospheric parameter data in the target data source includes the publicly available atmospheric parameter data (NCEP) of the National Centers for Environmental Prediction of the United States with high resolution, and this data is in the form of isobaric surface distribution; for the atmospheric scene above the mesosphere, perform the conversion of the atmospheric isobaric surface and altitude according to the standard atmospheric model. Thus, the atmospheric altitude distribution data with uneven intervals in height below and above the mesosphere is obtained.

[0068] The conversion formula of the atmospheric isobaric surface and altitude interpolation model is:

[0069] P = - 7.19045×10 4 ×e -0.2538h +1.7210×10 5 ×e -0.1682h

[0070] where P is the actual atmospheric pressure and h is the altitude.

[0071] S21. Calculate the temperature, relative humidity, atmospheric density, and atmospheric refractive index at different altitudes according to the atmospheric altitude distribution data. Specifically, it includes:

[0072] Interpolate the temperature and relative humidity in the atmospheric height distribution data using three-dimensional linear interpolation to obtain the temperature and relative humidity at different heights;

[0073] Calculate the atmospheric density at different heights using the temperature and atmospheric pressure at different heights:

[0074]

[0075] where ρ is the atmospheric density, P is the actual atmospheric pressure, P0 is the standard physical atmospheric pressure, T0 is the absolute zero, and T is the actual absolute temperature;

[0076] Calculate the atmospheric refractive index at different heights using the atmospheric density at different heights:

[0077]

[0078] n = 1 + ρ × Kgd

[0079] where D is a coefficient, usually taken as 0.00752, λ is the optical wavelength, Kgd is the Gladstone Dale constant, and n is the atmospheric refractive index.

[0080] Through the establishment of the above atmospheric scenario data, atmospheric data with a size of 720×360×200 is finally generated, that is, each data grid area is divided by 0.5 degrees of global longitude and latitude and 0.5 km of height.

[0081] The method of this embodiment can be applied to the near space on a global scale. Since the atmospheric data stratified and segmented according to longitude, latitude, and height is constructed, the simulation for different limb cut heights, different satellite observation points, different bands, and different times from a global perspective can be realized, and the measured atmospheric module data can also be quickly replaced to improve the regional accuracy or conduct relevant analysis.

[0082] S2. Determine the spatial position and orientation of the space exploration platform, as well as the spatial position and orientation of the target, and determine the ground object observation part and the limb observation part according to the spatial position and orientation of the space exploration platform.

[0083] Specifically, after the atmospheric data is generated, the three-dimensional scene can be set, mainly to determine the spatial position and orientation of the space exploration platform, as well as the spatial position and orientation of the target. For the space exploration platform, it is also necessary to set the camera distortion geometric imaging model carried by the satellite to obtain the initial input state of the ray tracing algorithm.

[0084] After setting the space exploration platform and the target, combining the spatial position and orientation of the space exploration platform, and using the principle that light intersects with the ellipsoid to form a point group, determine the point group of the intersection points of the reverse light rays on the detector image plane and the outer surface of the Earth's atmosphere. Take the light rays that intersect the Earth's surface from the reverse light rays on the detector image plane as the ground object observation part, and remove the ground object observation part from the point group to screen out the light rays in the limb observation mode, obtaining the limb observation part for implementing the ray tracing algorithm.

[0085] S3. Perform reverse ray tracing iteration starting from the detector pixel surface, and calculate the atmospheric path radiation of the limb observation part and the background radiation distribution of the ground object observation part on the ray tracing path to obtain the background radiation result.

[0086] In this embodiment, the background modeling part considers the radiation of the ground object background and the atmospheric limb background. Starting from the detector pixel, use the reverse ray tracing algorithm for simulation, simulate the refraction and transmission process of the light rays in different media in the atmosphere, and perform reverse recursive calculation to obtain the distribution of the radiation signal composed of the accurate limb atmospheric background radiation and the ground object background radiation after atmospheric attenuation on the image plane.

[0087] Specifically, first, use reverse ray tracing at the background, that is, starting from the detector pixel surface, the reverse light rays emitted from the detector undergo ray tracing iteration to obtain the ray tracing path.

[0088] Then, use the sky background radiation mode of the atmospheric radiation calculation software modtran to recursively calculate the atmospheric path radiation of the limb observation part on the ray tracing path; extract the ground object background radiation from the ground object radiation database according to the longitude and latitude, and use the atmospheric radiation calculation software modtran to calculate the attenuation of the ground object background radiation after atmospheric transmission to obtain the background radiation distribution of the ground object observation part. That is, use the atmospheric radiation calculation software modtran to recursively calculate the atmospheric path radiation of the limb observation part on the ray tracing path; obtain the ground object background radiation from the ground object background database according to the longitude and latitude of the intersection point of the light ray and the Earth, use the atmospheric radiation calculation software modtran to recursively calculate the light ray attenuation of the ground object observation part on the ray tracing path, and superimpose the ground object background radiation and the light ray attenuation to obtain the background radiation distribution of the ground object observation part.

[0089] Finally, superimpose the atmospheric path radiation of the limb observation part and the background radiation distribution of the ground object observation part to obtain the background radiation result.

[0090] S4. Perform forward ray tracing iteration starting from the fragments of the target to calculate the ray tracing path and obtain the position of the target on the detector pixel, and calculate the attenuated radiation amount in the ray tracing path to obtain the target radiation result.

[0091] In this embodiment, in the target modeling part, based on the analysis of the transient target radiation characteristics, forward ray tracing of the target radiation is performed to simulate the influence of factors such as the radiation transmittance during the atmospheric transmission of light rays.

[0092] Specifically, first, for the target, forward ray tracing is used. Based on the existing target radiation characteristics, the light rays start from the target's pixels and are traced until they reach the detector pixels, obtaining the ray tracing path. Then, according to the detector response parameters, etc., the position of the target at the pixel is obtained, and the generation of the high-speed target trailing effect under a long integration time is calculated; finally, the radiation attenuation of the light ray path is calculated by the atmospheric radiation calculation software modtran, and the attenuated radiation amount in the path, that is, the target radiation result, is obtained.

[0093] Specifically, the calculation expression for the radiation power propagating to the entrance pupil (before entering the optical system) in the object space from the radiation source:

[0094] P = I·A opt / l 2 = L·A s ·A opt / l 2

[0095] where P is the radiation power before the entrance pupil, A opt is the area of the entrance pupil of the optical system, l is the distance between the background or target and the entrance pupil, L is the radiation luminance of the background or target, A s is the surface area of the radiation source, and I is the radiation intensity before the entrance pupil. The expressions for the background radiation result (including the atmospheric path radiation in the limb observation part and the ground object radiation in the ground object observation part) and the target radiation result both adopt the above formula.

[0096] Furthermore, the method of the reverse ray tracing iteration in the background part is the same as that of the forward ray tracing iteration in the target part, except that the iteration starting points are different.

[0097] Please refer to Figure 3 , Figure 3 which is a schematic diagram of the propagation path of light rays in the stratified medium of the Earth's atmosphere provided by an embodiment of the present invention. In the figure, I and T are respectively the incident angle and the refraction angle of the light ray at the stratification, and the θ angle is the superposition of the deflection angles of the light ray in each layer. According to Snell's theorem, the total deflection angle of the atmosphere can be obtained.

[0098] Please refer to Figure 4 , Figure 4 which is a schematic diagram of the ray tracing algorithm for the stratified medium of the atmosphere provided by an embodiment of the present invention. The ray tracing algorithm in this embodiment uses the fourth-order Runge-Kutta algorithm. Figure 4The stratified data in green is the atmospheric data passed by the sampling points of the optical path obtained by the algorithm. The whole algorithm uses a fixed distance step size, combines the atmospheric data of the surrounding environment, and fits the next optical path point. Iteratively, starting from the intersection point group, it ends until it diverges out of the atmosphere or intersects with the ground surface. According to Fermat's principle, the basic equation of the light ray transmission vector in a three-dimensional variable refractive index medium can be expressed as:

[0099]

[0100] Among them, r is the position vector at the light ray trajectory point, ds is a small step on the light ray trajectory, n is the refractive index value corresponding to the light ray trajectory point, is the gradient of the refractive index value corresponding to the light ray trajectory point. After introducing the variable p, it can be converted into a first-order differential equation:

[0101]

[0102] According to the mean value theorem of differentiation, the fourth-order Runge-Kutta can obtain the solution of the fourth-order Runge-Kutta, and its local truncation error is an infinitesimal quantity. According to the empirical formula of the fourth-order Runge-Kutta algorithm, the light ray transmission formula can be transformed into the following form:

[0103]

[0104] Among them, is the unit vector of the light ray at the i+1 step, is the unit vector of the light ray at the i step, h is the light ray tracing step size, is the product of the light ray tangential vector and the refractive index distribution at the i+1 step, is the product of the light ray tangential vector and the refractive index distribution at the i step, and F1, F2, F3, F4, L1, L2, L3, L4 are all coefficients to be calculated, n is the refractive index at the preset position, is the refractive index gradient at the preset position. The refractive index and refractive index gradient of L1 take the values at r i at the position, the refractive index and refractive index gradient of L2 take the values at at the position, the refractive index and refractive index gradient of L3 take the values at at the position, the refractive index and refractive index gradient of L4 take the values at r i3 =r i +hF3 at the position.

[0105] Through this formula, the light ray trajectory point at the i+1 step can be calculated from the light ray trajectory point at the i step. When all the light rays are iteratively calculated, the path of the whole light ray and the atmospheric data at the sampling points can be obtained.

[0106] In this embodiment, the ray tracing algorithm is used to obtain the deflection routes of each radiation ray propagating in a long-distance inhomogeneous atmospheric medium, which makes up for the deficiency of the simulation in this aspect in the previous models, and can calculate the radiation data of the target and the background more accurately, obtaining better simulation results.

[0107] S5. Integrate the background radiation result and the target radiation result, and superimpose the detector effect, and convert the integrated imaging simulation radiation signal into a gray value to obtain the imaging simulation result.

[0108] Specifically, first, integrate and fuse the background radiation result and the target radiation result to obtain the final imaging simulation radiation result. Then, for the imaging simulation radiation result, it is also necessary to simulate the process from the radiation signal to the electrical signal and then to the gray value, and superimpose the detector effect. Finally, the final simulation imaging result will be obtained. In this process, it is necessary to simulate the transfer effects of the signal in the optical system and the detector system in the system and the final gray quantization; and it is necessary to consider the self-effects of the optical system and the detector, including the addition of noise signals and the non-uniform effects that affect the imaging result caused by their own structural properties, etc. Finally, the radiation signal will be displayed in the form of a gray-scale distribution image sequence to become the final simulation result.

[0109] Superimposing the detector effect and converting the integrated imaging simulation radiation signal into a gray value to obtain the imaging simulation result includes the steps of:

[0110] S51. Obtain the radiation power value on the detector surface after the integrated imaging simulation radiation signal passes through the optical system.

[0111] Please refer to Figure 5 , Figure 5 , which is the object-image space relationship diagram of the spatial point target imaging scenario provided by the embodiment of the present invention. According to the object-image relationship, the imaging size on the detector image plane can be obtained by the following formula:

[0112] A spix / f 2 =A s / l 2

[0113] where A spix is the imaging size on the image plane, and f is the focal length of the optical system.

[0114] According to the above formula, it can be deduced that the radiation power value received on the detector surface after the integrated imaging simulation radiation signal passes through the optical system can be approximately obtained by the following formula:

[0115] P spix =τ opt ×P opt =τ opt ×L×Apix ×A opt / f 2

[0116] Among them, P spix is the radiation power value received on the pixel, τ opt is the radiation transmittance of the optical system, P opt is the radiation power before the optical system, L is the radiation luminance value of the radiation source corresponding to the pixel, A pix is the pixel area, A opt is the entrance pupil area of the optical system, and f is the focal length of the optical system.

[0117] S52. Convert the radiation power value on the detector surface into the response voltage on a single pixel of the detector.

[0118] Specifically, after the signal light enters the detector pixel, the photoelectric effect will occur to form a signal voltage. The signal carrier is converted from light to electricity and finally quantized to form a gray-scale result, generating the final image result. In this embodiment, by analyzing the detector imaging effect that appears in the signal process, the conversion from the radiation signal distribution matrix to the two-dimensional gray-scale distribution image is realized.

[0119] For the photoelectric conversion process on a single pixel of the detector, its expression is as follows:

[0120]

[0121] Among them, U pixel is the response voltage on the pixel, with the unit V (volt), P in (λ) is the input radiation power on the detector pixel, with the unit W (watt), R(λ) is the detector responsivity, λ is the spectral wavelength, with the unit μm (micrometer), τ int is the detector imaging integration time, with the unit s (second).

[0122] S53. Perform Fourier transform on the response voltage in the spatial domain to obtain the response voltage in the frequency domain distribution, multiply the response voltage in the frequency domain distribution by the modulation transfer function to obtain the response voltage frequency domain distribution with the spatial transfer effect added, and then perform inverse Fourier transform on the response voltage frequency domain distribution with the spatial transfer effect added to obtain the response voltage with the spatial transfer effect superimposed.

[0123] In actual situations, due to the non-uniform response of the detector pixels, noise generated by the irregular changes of the circuit signals, etc., detector effects such as imaging blur and picture noise will occur.

[0124] For the spatial transfer effect that causes image blurring in the detection system, the degradation of imaging pixels caused by it is mainly described by the Modulation Transfer Function (MTF). According to the timing of physical effects, considering the spatial modulation characteristics of each physical effect module in the frequency domain, the total MTF of the system is equal to the product of the MTF values of each subsystem, and the MTF value of each module is less than 1. The expression of the MTF modulation transfer function is:

[0125] MTF sys = MTF optic ·MTF dect

[0126] Among them, MTF optic is the modulation transfer function of the optical system, used to add the spatial transfer effect of the optical system, and MTF dect is the modulation transfer function of the detector system, used to add the spatial transfer effect of the detector system. MTF is mainly obtained through parameters such as spatial frequency, cut-off frequency, and detector field of view in the detector parameters.

[0127] The process of using the MTF method for the spatial transfer effect is mainly processed in the frequency domain. Therefore, the frequency-domain distribution of the response voltage matrix can be obtained by Fourier transform of the response voltage matrix received by the detector from the spatial domain. Multiplying the response voltage matrix in the frequency domain by the MTF modulation transfer function can obtain the frequency-domain distribution of the response voltage matrix after adding the spatial transfer effect, and then performing an inverse Fourier transform on it can obtain the response voltage matrix with the spatial transfer effect superimposed.

[0128] S54. Add noise to the response voltage with the spatial transfer effect superimposed to obtain the response voltage after adding noise.

[0129] The noise includes one or more of thermal noise, dark current noise, and shot noise represented by the Gaussian noise model. It is mainly manifested as irregularly distributed noise particles on the screen. The main reason is the thermal effect of internal molecules in the material. Charged carriers inside the detector will have fluctuating motions that deviate from the potential, also known as thermal current noise. It is not related to the signal and is also called additive noise. During the simulation process, a function with a random number seed is usually used to simulate the random noise distribution of the detector. In this embodiment, the Gaussian noise (Gauss Noise) simulation model is used. The probability distribution function of Gaussian white noise can be expressed as:

[0130]

[0131] Among them, x represents the gray value of the imaging noise, μ is the average value of the noise gray value, and σ is the variance of the noise gray value.

[0132] In the simulation, according to the Noise Equivalent Flux Density (NEFD) of the detector, the root mean square of the noise can be obtained, and the simulation of Gaussian random noise can be carried out. The noise equivalent flux is the minimum detectable signal for the infrared system to detect a point source target, and its expression is as follows:

[0133]

[0134] In the formula, U noise is the root mean square value of the response voltage of the system noise, and U Δsignal is the output signal value when the difference in target background irradiance is E Δsignal . When the temperature of the background increases, the NEFD of the system will also increase.

[0135] The conversion formula between NEFD and the root mean square of the noise voltage is as follows:

[0136]

[0137] In the formula, τ opt is the transmittance of the optical system, D is the aperture of the optical system, and R is the detector responsivity.

[0138] Using the above formula, the voltage distribution of the noise can be obtained, so that the voltage distribution of the noise is added to the response voltage with the spatial transfer effect superimposed, and the response voltage after adding the noise is obtained.

[0139] S55. Convert the response voltage after adding the noise into a grayscale value to obtain the imaging simulation result.

[0140] Specifically, there are upper and lower bounds for the linear quantization of the voltage value of the output signal. The lower bound of the quantization is expressed as the noise equivalent voltage value of the detector, that is, U min = U noise , and the upper bound of the voltage calculation is related to the full well charge number n of the detector unit and the integration capacitance C of the detector unit:

[0141] U max = n·qC

[0142] Among them, q is the absolute value of the electron charge, q = 1.6×10 -19 C.

[0143] Taking the conversion of a common 8-bit grayscale image as a reference, the upper limit G max of the grayscale quantization is 255, and the lower limit is 0. Then the formula for the grayscale voltage conversion is as follows:

[0144]

[0145] Among them, G is the grayscale value of the imaging image.

[0146] Finally, the gray value of the imaging image can be obtained from this formula, and the imaging simulation result can be obtained.

[0147] In this embodiment, the ray tracing method is used to simulate the propagation of light in the long-distance atmosphere. Compared with the existing methods, not only the influence of the light deflection problem on the imaging simulation quality is considered, but also the influence received by the radiation light in the atmosphere and the radiation amount of the limb background are calculated more precisely. The imaging simulation of the target background in the near-space scene is refined, and the fidelity of the modeling and simulation effect is improved. And it synthesizes all the simulation elements of the signal from the radiation of the target and the background to the imaging gray level, and can simulate and analyze different satellite orbits, different target position states, different times, different bands, different detector parameters, etc. globally, with better universality. The method of this embodiment is an integrated simulation of the entire signal transmission process of transient high-speed targets, near-space scenes, limb and ground object backgrounds, space-based satellite detection, and detector imaging effects. The spatial and self-properties of the target, satellite, and detector can be flexibly set, and simulation results under various conditions can be obtained.

[0148] In summary, in view of the problem that the light is deflected in the long-distance atmospheric transmission path due to the non-uniform distribution of the atmosphere, resulting in the lack of imaging simulation accuracy of the space platform for small near-space targets, based on the ray tracing algorithm, this embodiment designs an infrared imaging precise modeling method for small near-space targets in a long-distance non-uniform atmosphere. This method solves the problem of the loss of simulation accuracy in the simulation process of the background and target caused by the atmospheric deflection of the light, effectively improves the reliability and confidence of the space scene ground remote sensing imaging simulation, comprehensively establishes a refined imaging simulation model of the target-background-atmospheric environment-sensor, and refines the imaging simulation of the target background in the near-space scene.

[0149] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, which should all be regarded as belonging to the protection scope of the present invention.

Claims

1. An infrared imaging modeling method for small targets in the near space of long-distance non-uniform atmosphere, characterized in that Including the steps: Establish atmospheric scene data; Determine the spatial position and orientation of the space exploration platform, as well as the spatial position and orientation of the target, and determine the ground object observation part and the limb observation part according to the spatial position and orientation of the space exploration platform; Perform reverse ray tracing iteration starting from the detector pixel surface, and calculate the atmospheric path radiation of the limb observation part and the background radiation distribution of the ground object observation part on the ray tracing path to obtain the background radiation result; Perform forward ray tracing iteration calculation of the ray tracing path starting from the target's pixel, obtain the position of the target on the detector pixel, and calculate the attenuated radiation amount in the ray tracing path to obtain the target radiation result; Fuse the background radiation result and the target radiation result, superimpose the detector effect, and convert the fused imaging simulation radiation signal into a gray value to obtain the imaging simulation result.

2. The infrared imaging modeling method for small targets in the long-distance non-uniform atmosphere near space according to claim 1, wherein Establish atmospheric scene data, including: For the atmospheric scene below the mesosphere, convert the atmospheric parameter data in the target data source for the atmospheric isobaric surface and altitude. For the atmospheric scene above the mesosphere, perform the conversion of the atmospheric isobaric surface and altitude according to the standard atmospheric model to obtain the atmospheric height distribution data; Calculate the temperature, relative humidity, atmospheric density, and atmospheric refractive index at different altitudes according to the atmospheric height distribution data.

3. The infrared imaging modeling method for small targets in the long-distance non-uniform atmosphere near space according to claim 2, characterized in that, The conversion formula for the atmospheric isobaric surface and altitude is: P = -7.19045×10 4 ×e -0.2538h +1.7210×10 5 ×e -0.1682h Where, P is the actual atmospheric pressure, and h is the altitude.

4. The infrared imaging modeling method for long-distance non-uniform atmospheric near-space small targets according to claim 2, wherein Calculate the temperature, relative humidity, atmospheric density, and atmospheric refractive index at different altitudes according to the atmospheric height distribution data, including: Use the three-dimensional linear interpolation method to interpolate the temperature and relative humidity in the atmospheric height distribution data to obtain the temperature and relative humidity at different altitudes; Calculate the atmospheric density at different altitudes using the temperature and atmospheric pressure at different altitudes: Where, ρ is the atmospheric density, P is the actual atmospheric pressure, P0 is the standard physical atmospheric pressure, T0 is the absolute zero, and T is the actual absolute temperature; Calculate the atmospheric refractive index at different altitudes using the atmospheric density at different altitudes: n = 1 + ρ×Kgd Where, D is a coefficient, usually taken as 0.00752, λ is the light wavelength, Kgd is the Gladstone Dale constant, and n is the atmospheric refractive index.

5. The infrared imaging modeling method for small targets in the long-distance non-uniform atmospheric near space according to claim 1, characterized in that, Determine the ground object observation part and the limb observation part according to the spatial position and orientation of the space exploration platform, including: Determine the intersection point group of the reverse rays of the detector image plane and the outer surface of the Earth's atmosphere according to the spatial position and orientation of the space exploration platform. Take the rays where the reverse rays of the detector image plane intersect the Earth's surface as the ground object observation part, and remove the ground object observation part from the intersection point group to obtain the limb observation part.

6. The infrared imaging modeling method for long-distance non-uniform small targets in the near space of the atmosphere according to claim 1, characterized in that, Calculate the atmospheric path radiation of the limb observation part and the background radiation distribution of the ground object observation part on the ray tracing path to obtain the background radiation result, including: Use atmospheric radiation calculation software to recursively calculate the atmospheric path radiation of the limb observation part on the ray tracing path; Extract the background radiation of the ground object from the ground object radiation database through longitude and latitude, and use the atmospheric radiation calculation software to calculate the attenuation of the background radiation of the ground object after atmospheric transmission, so as to obtain the background radiation distribution of the ground object observation part; Superimpose the atmospheric path radiation of the limb observation part and the background radiation distribution of the ground object observation part to obtain the background radiation result.

7. The infrared imaging modeling method for small targets in the near space of long-distance non-uniform atmosphere according to claim 1, characterized in that The expressions of the background radiation result and the target radiation result are both: P = I·A opt / l 2 = L·A s ·A opt / l 2 Wherein, P is the radiation power before the entrance pupil, A opt is the area of the entrance pupil of the optical system, l is the distance between the background or target and the entrance pupil, L is the radiance of the background or target, A s is the area of the radiation source surface, and I is the radiation intensity before the entrance pupil.

8. The infrared imaging modeling method for small targets in the long-distance non-uniform atmosphere near space according to claim 1, characterized in that Both the reverse ray tracing iteration and the forward ray pursuit iteration use the fourth-order Runge-Kutta algorithm, and their expressions are both: Wherein, is the light unit vector at the (i + 1)-th step, is the light unit vector at the i-th step, h is the light tracing step size, is the product of the light tangential vector and the refractive index distribution at the (i + 1)-th step, is the product of the light tangential vector and the refractive index distribution at the i-th step, F1, F2, F3, F4, L1, L2, L3, L4 are all coefficients to be calculated, n is the refractive index at a preset position, is the refractive index gradient at the preset position, the refractive index and the refractive index gradient of L1 take the values at r i position, the refractive index and the refractive index gradient of L2 take position values, the refractive index and the refractive index gradient of L3 take position values, the refractive index and the refractive index gradient of L4 take the values at r i3 = r i + hF3 position values.

9. The infrared imaging modeling method for small targets in the near space of long-distance non-uniform atmosphere according to claim 1, characterized in that Superimpose the detector effect, convert the fused imaging simulation radiation signal into gray values to obtain the imaging simulation result, including: Obtain the radiation power value on the detector surface after the fused imaging simulation radiation signal passes through the optical system: P spix = τ opt × P opt = τ opt × L × A pix × A opt / f 2 where P spix is the radiation power value received on the pixel, τ opt is the radiation transmittance of the optical system, P opt is the radiation power before the optical system, L is the radiation luminance value of the radiation source corresponding to the pixel, A pix is the pixel area, A opt is the area of the entrance pupil of the optical system, and f is the focal length of the optical system; Convert the radiation power value on the detector surface into the response voltage on a single pixel of the detector: Among them, U pixel is the response voltage on the pixel, P in (λ) is the input radiation power on the detector pixel, R(λ) is the detector responsivity, λ is the spectral wavelength, τ int is the detector imaging integration time; Perform Fourier transform on the response voltage in the spatial domain to obtain the response voltage in the frequency domain distribution, multiply the response voltage in the frequency domain distribution by the modulation transfer function to obtain the response voltage frequency domain distribution with the spatial transfer effect added, and then perform inverse Fourier transform on the response voltage frequency domain distribution with the spatial transfer effect added to obtain the response voltage with the spatial transfer effect superimposed; where, the modulation transfer function is: MTF sys = MTF optic ·MTF dect Among them, MTF optic is the modulation transfer function of the optical system, and MTF dect is the modulation transfer function of the detector system; Add noise to the response voltage with the spatial transfer effect superimposed to obtain the response voltage after adding noise; Convert the response voltage after adding noise into gray values to obtain the imaging simulation result: Among them, G is the gray value of the imaging image, U max = n·q / C, where n is the full well charge number of the detector unit, q is the absolute value of the electron charge, C is the integration capacitance of the detector unit, and U min is the noise equivalent voltage value of the detector, and U min = U noise , G max is the upper limit of gray quantization.

10. The infrared imaging modeling method for small targets in the long-distance non-uniform atmosphere near space according to claim 9, characterized in that, The noise includes one or more of thermal noise, dark current noise, and shot noise represented by the Gaussian noise model.

Images