A dynamic digital imaging simulation method for complex scenes of small space targets in space

Abstract

The application relates to a kind of space-based space small target complex scene dynamic digital imaging simulation methods, and relates to space imaging simulation technical field.The method comprises the following steps: target imaging model design is carried out according to simulation system parameters;Pixel-level plane plotting is carried out on the target digital library, and the target contour mask of target under specific ground pixel resolution is obtained;The stars in the simulation field of view are mapped to the target digital image;The target digital image of the diversity space light background is generated;A variety of transient noise is sequentially superimposed on the imaging original drawing, and then fixed noise fusion processing is carried out to generate the target digital image containing a variety of noise;When the space imaging simulation image time sequence is generated through comprehensive motion simulation, the application can carry out general digital modeling on various space targets through designing the target digital library construction method based on two-dimensional shape feature point description method and the imaging analysis model, and the imaging process simulation of the space camera is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of space imaging simulation, in particular to a space-based small target complex scene dynamic digital imaging simulation method. BACKGROUND

[0002] In recent years, the number of spacecrafts in orbit has shown a growing trend, especially after the widespread application of small satellite technology, the number of spacecrafts in orbit has increased significantly. As a result, the occupancy rate of space orbits has increased, the number of space debris and abandoned satellites has increased, and the risk of collision between spacecrafts in orbit has increased, posing a serious threat to the safe operation of normal satellites in orbit. Therefore, it is urgent to equip satellites and other spacecrafts with the ability to automatically perceive the surrounding space targets, and the space target detection technology based on images has great engineering application value.

[0003] Space target detection technology includes two technical approaches based on artificial intelligence and traditional image processing, both of which rely heavily on image data for detection. In particular, artificial intelligence methods based on deep learning require a large amount of image data input for model training. Due to the difficulty of obtaining space-based imaging data, and the fact that most space-based observer image data involves certain military sensitivity and is not easily accessible, the space-based space target imaging data set available for space target monitoring system design and verification is very scarce, limiting the optimization and development of detection algorithms for actual application needs.

[0004] Space target imaging simulation technology is one of the best ways to provide images for detection, and is often used for ground-based verification of equipment and algorithms in technical research and engineering implementation. After years of technical research, the design framework of space target imaging simulation has gradually become clear, and the simulation results are closer to actual imaging. However, the existing simulation images still have problems such as single imaging simulation element, limited imaging background, and insufficient dynamic imaging simulation, and lack of robustness testing capability for target detection algorithms. SUMMARY

[0005] The present application solves the technical problems in the prior art and provides a space-based small target complex scene dynamic digital imaging simulation method.

[0006] To solve the above technical problems, the technical solution of the present application is as follows:

[0007] A space-based small target complex scene dynamic digital imaging simulation method, comprising the following steps:

[0008] Step 1: Design the target imaging model according to the simulation system parameters, realize the theoretical mapping of the position and brightness information of the three-dimensional coordinate space object to the two-dimensional coordinate space image, and simulate the camera optical system imaging process;

[0009] Step two, construct a target imaging digital library by a two-dimensional shape feature point description method, use a target data imaging analysis model to draw a pixel-level plane for the target imaging digital library, and obtain a target contour mask under a specific ground pixel resolution; then input to a target imaging model to simulate a space camera imaging the target and generate a target digital image;

[0010] Step three, select the Hipparcos star catalog as a data source for space background star simulation, convert the inertial coordinate system, image coordinate system, and pixel coordinate system to each other, map the stars in the simulation field of view to the target digital image, obtain real star data in the camera field of view, and finally simulate the target imaging model to image the stars in the field of view, and add sub-pixel level modeling to achieve a more realistic imaging effect of the star point target position;

[0011] Step four, generate target digital images of diverse space light backgrounds through edge gradual change spot background simulation modeling, edge steep change spot background simulation modeling, and linear non-uniform weak light background simulation modeling;

[0012] Step five, sequentially superimpose various transient noises on the imaging original image, and then generate a target digital image containing multiple noises through fixed noise fusion processing.

[0013] Step six, generate a space imaging simulation image time sequence through comprehensive motion simulation.

[0014] In the above technical solution, the transient noise in step five includes readout noise, photon shot noise, dark current noise, hot pixels, and cosmic ray noise.

[0015] In the above technical solution, the space imaging simulation image time sequence in step six includes target, star background, background light, noise, and motion elements.

[0016] In the above technical solution, the target imaging model design in step one includes the following steps:

[0017] Based on the physical imaging characteristics and parameters of the space camera, a theoretical simulation of the space camera imaging process describing the size and brightness characteristics of the target is established through imaging resolution calculation, coordinate system conversion, pixel gray value calculation, and imaging dispersion operation.

[0018] In the above technical solution, the two-dimensional shape feature point description method in step two includes the following steps:

[0019] The rough outline of the target in the view is decomposed into several basic shapes.

[0020] A feature description matrix containing real size, position, shape, and grouping information is designed for each basic shape.

[0021] Comprehensive construction is the target view profile of the target imaging digital library.

[0022] In the above technical solution, in step two, the target data imaging analysis model performs pixel-level plane drawing on the target digital library to obtain a target profile mask of the target under a specific ground sample resolution, and specifically includes the following steps:

[0023] When analyzing a certain target digital library, the width and height of the target imaging container rectangle are obtained by analyzing the first row of the target digital library with a serial number of 1;

[0024] The coordinate calculation is performed on all point coordinates in the target digital library with other serial numbers to realize the conversion of the feature point description coordinate system to the imaging container pixel coordinate system;

[0025] According to the attribute values in the target digital library, the minimum traversal range of each attribute in the imaging container pixel coordinate system is determined;

[0026] The mask generation criterion of each attribute is determined, and finally the target profile mask of the target under a specific ground sample resolution GSD is obtained.

[0027] In the above technical solution, the comprehensive motion simulation in step six includes the following steps:

[0028] The frame unit calculation modeling is performed on all time-varying variables of the target position and the target overall gray scale, the change parameters of each type of background light, the noise parameters of transient and special space working conditions in the target imaging model, the motion model of the target curve displacement, the gray dynamic change, the imaging tailing and the platform jitter is added, the image simulation element state information is updated according to the simulation time stamp, and the dynamic digital image of the space target under different working conditions is constructed.

[0029] The present application has the following advantages:

[0030] The space-based small target complex scene dynamic digital imaging simulation method of the present application can generally model various space targets by designing a target digital library construction method based on the two-dimensional shape feature point description method and an imaging analysis model.

[0031] The space-based small target complex scene dynamic digital imaging simulation method of the present application can meet the robustness test of the space target detection algorithm for multi-element scenes by modeling various light backgrounds and noises under space imaging environment to construct a simulation simulation library of space complex light scenes.

[0032] The digital imaging simulation system established by the space-based small target complex scene dynamic digital imaging simulation method has modeling of target dynamic gray scale, curve motion, imaging tailing and imaging platform jitter, and the simulation system framework design has extensibility and good compatibility for subsequent possible access of an orbit coordinate model and a target texture imaging model.

[0033] The space digital imaging model and the simulation system designed by the space-based small target complex scene dynamic digital imaging simulation method are dynamically adjustable, can generate space target dynamic digital images under different working conditions by setting imaging simulation parameters, and can be used for ground end development verification of traditional space target detection algorithms and provide data set support for network model training of artificial intelligence detection algorithms such as deep learning. BRIEF DESCRIPTION OF DRAWINGS

[0034] The application will be further described in detail below in combination with the drawings and specific embodiments.

[0035] Figure 1 The design flowchart of the space-based small target complex scene dynamic digital imaging simulation method.

[0036] Figure 2 The typical satellite orthographic projection rough outline decomposition diagram constructed by the two-dimensional shape feature point description method in the space-based small target complex scene dynamic digital imaging simulation method.

[0037] Figure 3 The target mask schematic diagram obtained by calculating the target data imaging analysis model in the space-based small target complex scene dynamic digital imaging simulation method.

[0038] Figure 4 The space target imaging simulation image obtained by integrating the target mask and the imaging model in the space-based small target complex scene dynamic digital imaging simulation method.

[0039] Figure 5 The digital image obtained by point target sub-pixel level modeling in the space-based small target complex scene dynamic digital imaging simulation method.

[0040] Figure 6 The image obtained by modeling a plurality of space illumination backgrounds in the space-based small target complex scene dynamic digital imaging simulation method.

[0041] Figure 7 The target digital image obtained by modeling a plurality of space imaging noise simulations in the space-based small target complex scene dynamic digital imaging simulation method.

[0042] Figure 8 The uniform weak light background space target comprehensive imaging simulation image output by the digital imaging simulation system established by the space-based small target complex scene dynamic digital imaging simulation method.

[0043] Figure 9 The background slowly-varying light spot space target comprehensive imaging simulation image output by the digital imaging simulation system established by the space-based small target complex scene dynamic digital imaging simulation method. DETAILED DESCRIPTION

[0044] The inventive idea of the application is:

[0045] The space-based small target complex scene dynamic digital imaging simulation method of the application maximally simulates the imaging effect of a space-based observation platform in a real space scene, and provides data input for the design of a space target monitoring system and the research of a detection algorithm.

[0046] The application is directed to the imaging simulation research of a space target in a complex light scene, takes a visible light camera as a simulation simulation object, and takes a complex light space small target dynamic imaging image as a simulation target. A space small target imaging modeling method based on a two-dimensional shape feature point description method and an imaging analysis model is proposed. A sub-pixel level point target gray imaging position modeling is implemented to realize high-precision background star imaging simulation. A plurality of dynamic light background models are established, and a noise calculation model is introduced to generate a digital simulation sequence image with a dynamic change of a target local signal-to-noise ratio. In the comprehensive motion simulation, a target curve displacement, a gray dynamic change, an imaging tailing, and a platform jitter model are added to design, which maximally simulates the imaging effect of a space-based observation platform in a real space scene, and provides data input for the design of a space target monitoring system and the research of a detection algorithm.

[0047] Step one, the target imaging model is designed according to the simulation system parameters, the three-dimensional coordinate space object position and brightness information are theoretically mapped to the two-dimensional coordinate space of the image, and the camera optical system imaging process is simulated.

[0048] Step two, the two-dimensional shape feature point description method is used to construct a target imaging digital library, the target data imaging analysis model is used to draw a plane of the target imaging digital library at a pixel level to obtain a target contour mask of the target under a specific ground pixel resolution, and then the target imaging model is input to simulate the space camera imaging of the target and generate a target digital image.

[0049] Step three, select the Hipparcos-2 as the data source of the spatial background star simulation, through the inertial coordinate system, image coordinate system, pixel coordinate system mutual conversion, the stars in the simulation field of view are mapped to the target digital image, the real star data in the camera field of view is obtained, finally through the target imaging model simulation, the imaging of the stars in the field of view is simulated, and the sub-pixel level modeling is realized to realize the imaging effect more in line with the real position of the star point target.

[0050] Step four, through the edge gradual change spot background simulation modeling, the edge steep change spot background simulation modeling and the linear non-uniform weak light background simulation modeling, the target digital image of the diversity spatial light background is generated.

[0051] Step five, a variety of transient noise is superimposed on the imaging original drawing in sequence, and then fixed noise fusion processing is carried out, so that the target digital image containing a variety of noise is generated.

[0052] Step six, the spatial imaging simulation image time sequence is generated through comprehensive motion simulation. The spatial imaging simulation image time sequence contains the elements of target, star background, background light and noise and motion. The whole digital imaging simulation system is designed to be dynamically adjustable, and different working conditions of space target dynamic digital image can be generated by setting the imaging simulation parameters.

[0053] The space-based space small target complex scene dynamic digital imaging simulation method of the application will be described in detail below with reference to the drawings, as shown in the figure, the method comprises the following steps: Figure 1

[0054] Step one, the target imaging model is designed according to the simulation system parameters, the theoretical mapping of the position and brightness information of the three-dimensional coordinate space object to the two-dimensional coordinate space of the image is realized, and the imaging process of the camera optical system is simulated.

[0055] The target imaging model in this step is based on the physical imaging characteristics and parameters of the space camera, describes the camera imaging process of the target size and brightness characteristics, and obtains the target digital image output by the simulation image sensor. The specific steps of the target imaging model design include: based on the physical imaging characteristics and parameters of the space camera, through imaging resolution calculation, coordinate system conversion, pixel gray value calculation and imaging dispersion operation, the theoretical simulation of the space camera imaging process describing the target size and brightness characteristics is established.

[0056] The imaging mapping of the target size is mainly related to the spatial resolution GSD of the camera: L is the distance from the target to the optical lens of the camera; P is the pixel size of the camera image sensor; f is the focal length of the camera;

[0057] ​According to the input target profile mask, a mapping model of target brightness to target imaging gray scale value is established. For a small target in a far distance, the brightness of the target is described by a star magnitude, and the apparent magnitude of the target is set as m , the irradiance E m (kw / m 2 ) of the target outside the optical system of the camera is E m =E s ·10 0.4(-26.73-m) , E s is the solar irradiance outside the earth's atmosphere, and the apparent magnitude of the sun at this time is -26.73.

[0058] Supposing that the clear aperture of the optical system is D, the transmittance is tau, and the camera simulation exposure time is T, the total energy Q (J) received by the camera optical system generated by the target is:

[0059] Supposing that the energy of a single photon E ph is: The Planck constant h = 6.6260693 x 10 -34 J·s, the speed of light c = 3 x 10 8 m / s, and the average wavelength of visible light lambda is 550 nm. In the camera simulation exposure time T, the total number of photons N ph received by the image sensor is:

[0060] Supposing that the quantum efficiency of the image sensor at lambda is eta, and the image sensor gain is K (e - / DN), the total digital quantity D e generated is:

[0061] Wherein, eta represents the quantum efficiency of the image sensor, that is, the average number of photoelectrons generated per unit time at a specified wavelength divided by the number of incident photons.

[0062] At a long distance, the digital image formed by the space target usually occupies several to tens of pixel units, which is a small target feature, so the digital quantity generated by a single target pixel can be approximately represented by an average digital quantity. Supposing that the digital image of the target on the image plane has n pixels, the digital quantity D i generated by a single pixel is:

[0063] Due to the influence of circular hole diffraction, the image formed by the target on the camera focal plane will have a dispersion phenomenon, and a two-dimensional Gaussian filter is used to perform convolution operation on the original image to realize the dispersion effect of the optical system imaging.

[0064] Step two, construct the target imaging digital library by two-dimensional shape feature point description method, use the target data imaging analysis model to draw the target contour mask under the specific ground pixel resolution.

[0065] In this step, first, the space target is constructed into a target imaging digital library by two-dimensional shape feature point description method, then the target contour mask under the specific ground pixel resolution is obtained by using the target data imaging analysis model, and finally the target digital image is generated by inputting the target contour mask into the target imaging model.

[0066] In this step, the two-dimensional shape feature point description method includes the following steps:

[0067] The rough contour of the target in the view is decomposed into several basic shapes; a feature description matrix containing real size, position, shape and grouping information is designed for each basic shape; and a digital library of the target view contour is constructed.

[0068] The description information of each feature point in the digital library is represented by a row, i.e. each serial number points to specific feature point information. The architecture of the digital library is shown in Table 1.

[0069] Table 1 Digital library architecture based on two-dimensional shape feature point description

[0070]

[0071] The target data imaging analysis model draws the target digital library at the pixel level, including the following steps:

[0072] When analyzing a certain target digital library, first analyze the first row of the target digital library with serial number 1 to obtain the width W and height H of the target imaging container rectangle;

[0073] The coordinates of all points with other serial numbers in the target digital library are calculated to realize the conversion of the feature point description coordinate system to the imaging container pixel coordinate system;

[0074] According to the attribute values in the target digital library, determine the minimum traversal range of each attribute in the imaging container pixel coordinate system;

[0075] Determine the mask generation criterion of each attribute, and finally obtain the target contour mask of the target under the specific pixel resolution GSD, i.e. a two-dimensional pixel matrix with pixel point value 1 in the target area and 0 in the remaining part.

[0076] In this step, a typical satellite is taken as an example to design a digital model for its front view with obvious shape features. For example, Figure 2As shown in , the rough outline of the target in the view is decomposed into 9 basic shapes, such as Figure 2 As shown in the identification numbers 1-9 in the figure, there are two circles, three rectangles, and four parallelograms. According to Table 1, a feature description matrix containing the actual size, position, shape, and grouping information is established for each basic shape. Finally, a digital library of the target view outline is constructed. The description information of each feature point in the digital library is represented by a row, that is, each serial number points to a specific feature point information.

[0077] After obtaining the target digital library, parse the first row of the target digital library with serial number 1 to obtain the width W and height of the target imaging container rectangle H ;

[0078] Calculate the coordinates of all points of other serial numbers in the target digital library to realize the conversion from the feature point description coordinate system to the imaging container pixel coordinate system. The mathematical model of the imaging container pixel coordinate coefficient is: and

[0079] Among them, C k , R k are the column and row coordinate values ​​of the imaging container pixel coordinate system respectively; k ,y k are the XY coordinate values ​​of the feature points in the feature point description coordinate system; N offset is the edge offset value; r c It is the radius of the basic circular shape in the coordinate system described by the feature point. UpInt(·) indicates rounding up.

[0080] In this embodiment, the attributes in the target digital library include circle, rectangle and parallelogram. The minimum traversal range of the circle, rectangle and parallelogram in the imaging container pixel coordinate system can be determined according to the calculation formula. The mathematical model is:

[0081] round

[0082] rectangle

[0083] parallelogram

[0084] Then the mask generation criteria for circle, rectangle and parallelogram are determined:

[0085] round

[0086] Rectangular T model (i,j)=1R min ≤i≤R max ,C min ≤j≤C max 、

[0087] parallelogram

[0088] When GSD is 0.5m, we get Figure 2 The target digital library constructed is analyzed and obtained Figure 3 Target mask shown.

[0089] Comprehensive target mask and imaging model, set the target magnitude m They are 3rd and 5th order respectively. When the distance L is 100km, the target digital image obtained by imaging simulation is as follows Figure 4 As shown. Among them, P is 2.5μm, f is 70mm, T is 10ms, η is 0.65, and K is 2e - / DN, τ is 0.8, D is 90mm, the image quantization level is 8bit, the Gaussian convolution kernel size is 5×5, and the W×H of the target imaging container rectangle is 58m×26m.

[0090] Step 3: Select the Hipparcos-2 catalog as the data source for space background star simulation. After converting between the inertial coordinate system, image coordinate system, and pixel coordinate system, map the stars in the simulated field of view into the target digital image to obtain the real star data in the camera field of view. Finally, use the target imaging model to simulate the imaging of the star targets in the field of view, and incorporate sub-pixel modeling to achieve an imaging effect that is more consistent with the actual position of the star point target.

[0091] In this step, the formula The spherical coordinates of the star catalog represented by right ascension and declination are converted into rectangular coordinates represented by the XYZ axis, where the XY plane of the rectangular coordinate system coincides with the celestial equatorial plane, the X axis passes through the intersection of 0° right ascension and 0° declination (i.e. the vernal equinox), the Z axis points to the north celestial pole, and the Y axis, X axis, and Z axis form a right-handed rectangular coordinate system, thereby obtaining the inertial coordinates representing the star position. And the camera optical axis is Z C Axis, the center position of the optical system is the origin O C Establish simulation camera coordinate system O C -X C Y C Z C The position coordinates [XYZ] in the inertial system are transformed by the rotation transformation matrix. T Convert to the position coordinates in the camera coordinate system [X C Y C Z C ] T , thus giving the star position in the camera coordinate system with the optical axis pointing downward.

[0092] Repass Convert the rectangular coordinates of the camera coordinate system to spherical coordinates. The diagonal field of view of the camera is: where P is the image sensor pixel size, N x M is the image sensor pixel number, and f is the camera focal length. The star data to be simulated for imaging is determined according to θ'≤FOV / 2. The star coordinates in the camera coordinate system that satisfy θ'≤FOV / 2 are marked as [X C ',Y C ',Z C ] T According to the camera coordinate system to image coordinate system conversion formula The star coordinates [X I ,Y I ] data in the rectangular region inscribed in the circular field of view of the simulated camera can be obtained in the image coordinate system, and then the star coordinates [X P ,Y P ] data in the pixel coordinate system are obtained through the formula

[0093] Since the star is far away from the camera, it appears as a point on the image, and becomes a point spot after dispersion through the camera optical system. When performing star imaging simulation modeling, the energy distribution function of the dispersed spot can be approximated by a Gaussian distribution function. Assuming that the numerical coordinates of the star are (9.9556, 10.1257) and the total digital quantity is 1024, the dispersed neighborhood size used for sub-pixel level modeling is all 7 x 7. Taking the star coordinates [X P ,Y P ] data as the center point and taking all the pixel grid center coordinates in the dispersed neighborhood as the dispersed processing point coordinates, the pixel digital quantity data is obtained through the imaging model, and then the dispersed processing is performed using the Gaussian distribution function, so as to realize the sub-pixel level modeling of the star point target as shown in Figure 5 . In the above formula, the pixel grid positions in the neighborhood are described using the pixel coordinates in the pixel coordinate system, and the pixel grid center coordinates and the star coordinate data are described using the numerical coordinates in the pixel coordinate system.

[0094] Step four, generate digital images of diverse spatial light background through edge gradual change spot background simulation modeling, edge steep change spot background simulation modeling, and linear non-uniform weak light background simulation modeling.

[0095] In this step, the modeling of the spot is based on a two-dimensional Gaussian function model. In order to simulate the case that the background light spot gray level slowly extends from the center to the periphery when spatial imaging, a time-varying edge gradual change spot simulation imaging mathematical model is established:

[0096]

[0097] where K G (t) is the total energy of the time-varying spot, and σ G ​is the time-varying radius of the light spot; x0(t) and y0(t) are the coordinates of the center of the light spot that change with time.

[0098] In order to simulate the special lighting background with concentrated energy at the edge of the light spot and large grayscale gradient, a mathematical model for the simulation imaging of the light spot with steep time-varying changes is established:

[0099] G(x,y,t)=F(x,y,t)*w(s,τ)

[0100]

[0101] Where * represents convolution; w(s,τ) represents the Gaussian filter kernel, H gray (t) represents the grayscale value of the light spot imaging that changes with time; H offset Bias grayscale value for the image background.

[0102] To simulate the non-uniform weak light background, the gray gradient direction is set to be horizontal, and the initial gray value of the left edge of the image is set to be H L,0 , the initial grayscale value of the left edge is H R,0 , the time-varying linear non-uniform background mathematical model is established as:

[0103]

[0104] Where K L , K R Respectively represent the time-varying coefficients of the grayscale values ​​of the left and right edges of the image; M represents the number of image columns.

[0105] According to actual needs, you can generate Figure 6 Simulated digital images of various types of spatial lighting backgrounds, including slowly changing edge spot background simulation images, abruptly changing edge spot background simulation images, and linear non-uniform weak light background simulation images.

[0106] Step 5: Transient noises such as readout noise, photon shot noise, dark current noise, hot pixel noise and cosmic ray noise are superimposed on the original image in sequence, and then a digital image containing multiple noises is generated through fixed noise fusion processing.

[0107] The order of noise modeling superposition is: readout noise, photon shot noise, dark current noise, hot pixel and cosmic ray noise, and fixed noise. The specific modeling steps are as follows:

[0108] Based on the camera grayscale bias, Get the biased electron number, establish the mean as the biased electron number, and the variance is Gaussian distribution probability model of pixel electron number

[0109]

[0110] Then by Simulate camera readout noise after converting grayscale values;

[0111] Calculate the average signal of each non-zero grayscale pixel in the original image at exposure time T (expressed in grayscale value), establish the mean and variance are Poisson distribution model Simulating photon shot noise;

[0112] Set n e (x,y), by Based on the exposure time T, n dark is the Poisson probability distribution model with mean and variance, substitute After conversion to grayscale values, dark current noise is simulated;

[0113] The thermal pixel and cosmic ray noise models are represented by discrete impulse functions;

[0114] Fixed noise is spatially fixed noise. Pixel-distributed FPN is similar to hot pixel noise but has a limited maximum grayscale. Row-column distributed FPN creates positional noise with random intensity in rows and columns.

[0115] The additive noise is sequentially superimposed on the original image, and the multiplicative noise is finally fused to generate the final noisy digital image.

[0116] Set the parameter values: K is 2e - / DN, image quantization level is 8bit, I offset Take 50 (ie 100e - ),σ read Take 20e - ; Calculated by imaging the original image Here we take 8.0957, that is Take 8.0957; camera exposure time T is 10ms, n e (x,y) average 500e - / pixel / s; the maximum number of FPN electrons is 10e - , using the row distribution type; the maximum PRNU is 2%. The final generated noise imaging simulation image is as follows Figure 7 As shown. Step 6: Generate a time-series sequence of space imaging simulation images through integrated motion simulation. Specifically, integrated motion simulation generates a time-series sequence of space imaging simulation images that includes elements such as the target, stellar background, background illumination and noise, and motion. The entire digital imaging simulation system is designed to be dynamically adjustable, allowing dynamic digital images of space targets to be generated under different operating conditions by setting imaging simulation parameters.

[0117] In this step, when comprehensive motion simulation is performed, the following steps are included: frame unit calculation modeling of all time-varying variables in the target position and overall gray scale of the target in the target imaging model, each type of background light change parameter, transient and special space working condition noise parameter, etc., and adding motion models such as target curve displacement, gray scale dynamic change, imaging tailing and platform jitter, updating image simulation element state information according to simulation time stamp, and constructing dynamic digital images of space targets under different working conditions.

[0118] Specifically, for target curve displacement modeling, the method of the application designs a target motion model based on increments for the specified target initial position.

[0119] Suppose the origin of the imaging container pixel coordinate system is at the initial position (x c ,y r ) of the pixel coordinate system, the target moving speed is v T (unit: pixel / ms), and the camera exposure time is T (unit: ms), then the coordinates (x(k), y(k)) of the target in the pixel coordinate system at the kth frame satisfy:

[0120]

[0121] wherein x(0) = x c , y(0) = y c , and θ(k) satisfies:

[0122] In the formula, θ(k) represents the speed angle of the target at the kth frame; w f is the initial speed angle of the target; w e is the speed angle at the end of the target simulation; the speed angle takes the pixel coordinate system X P axis as the starting point and rotates in the clockwise direction around the coordinate origin as the increasing direction.

[0123] For gray scale dynamic change modeling, according to the total digital quantity D e of the target imaging, suppose the target gray scale change amplitude is A e (unit: %), and the change frequency is w e (unit: ° / frame), then the total digital quantity D e (k) of the target imaging at the kth frame is:

[0124] For target motion tailing modeling, the method of the application takes 1 pixel as the unit of displacement, divides the target displacement within a single exposure, obtains the displacement pixel grid number, takes the displacement pixel grid number as the microframe number, takes 1 pixel / microframe as the target moving speed, and performs microframe imaging modeling on the target. Among them, the speed angle increment of each microframe and the target gray scale are the average values of the same quantities for the microframe number.

[0125] For imaging platform jitter modeling, when performing imaging simulation, set the edge extension value to expand the edge of the original image size (N*M). At this time, the imaging target resolution remains unchanged, only the field of view is expanded, and the single frame image generated by substituting it into the imaging model is recorded as the cache frame I buff , set the image cropping window to the original image size (N*M) and record it as I out , use I out to I buff Slide along the frame sequence to take pictures, and the output image sequence will produce a jitter effect. out The origin of the pixel coordinate system is I buff The pixel coordinates in (x d ,y d ), the maximum jitter offset range radius is l max (Unit: pixel), the jitter frequency is w d , the sliding image acquisition model is established as:

[0126]

[0127] Comprehensive target, background stars, illumination background, noise and dynamic motion imaging model, the star gazing mode imaging simulation is performed on the sky area where the camera optical axis points (declination 52.358°, right ascension 175.128°), and the total imaging simulation time is T total The exposure time T of the camera is 10ms. Figure 8 Target magnitude shown m The target is level 7, the distance L is 500km, the background light is set to uniform weak light, and the target moving speed is v T These are the 100th, 300th, 500th, 700th, and 900th imaging simulation images at 0.08 pixel / ms. Figure 9 The following image shows an imaging simulation with the illumination background set to a gradually changing spot at the edge of a strong background light. P is set to 2.5μm, f is set to 70mm, N is set to 1024, and M is set to 2048. The remaining imaging simulation parameters are the same as those set previously.

[0128] The present invention provides a method for simulating dynamic digital imaging of small space targets in complex scenes. By designing a target digital library construction method based on a two-dimensional shape feature point description method and its imaging analysis model, general digital modeling of various space targets can be performed. Then, through imaging simulation methods such as target digital imaging, field of view star selection, imaging coordinate conversion, and point target sub-pixel modeling, the imaging process of a space camera is simulated.

[0129] The space-based small target complex scene dynamic digital imaging simulation method of the application builds a simulation simulation library of a complex space illumination scene by modeling various illumination backgrounds and noises under a space imaging environment, and can satisfy robustness testing of a multi-element scene by a space target detection algorithm.

[0130] The digital imaging simulation system built by the space-based small target complex scene dynamic digital imaging simulation method of the application includes modeling of target dynamic gray scale, curve motion, imaging tailing and imaging platform jitter, and the simulation system framework design has extensibility and good compatibility for subsequent possible access of an orbital coordinate model and a target texture imaging model.

[0131] The space digital imaging model built by the space-based small target complex scene dynamic digital imaging simulation method of the application and the simulation system designed are dynamically adjustable, can generate space target dynamic digital images under different working conditions by setting imaging simulation parameters, and can be used for ground end development verification of a traditional space target detection algorithm and can provide a data set support for network model training of a deep learning and other artificial intelligence detection algorithm.

[0132] Obviously, the above embodiments are merely examples for clear illustration, and are not limitations on the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments need not and cannot be exhausted. The changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A space-based small target complex scene dynamic digital imaging simulation method, characterized by: The following steps are included: Step 1: Design the target imaging model based on the simulation system parameters, realize the theoretical mapping of the object position and brightness information in the three-dimensional coordinate space to the two-dimensional coordinate space of the image, and simulate the imaging process of the camera optical system; Step 2: Build a target imaging digital library using a two-dimensional shape feature point description method, and use the target data imaging analytical model to perform pixel-level planar mapping on the target imaging digital library to obtain the target contour mask at a specific ground pixel resolution; then input the mask into the target imaging model to simulate the space camera imaging of the target and generate a target digital image; Step 3: Select the Hipparcos catalog as the data source for the space background star simulation. After converting between the inertial coordinate system, image coordinate system, and pixel coordinate system, map the stars in the simulated field of view to the target digital image to obtain the real star data in the camera field of view. Finally, simulate the imaging of the star targets in the field of view through the target imaging model, and add sub-pixel modeling to achieve an imaging effect that is more consistent with the actual position of the star point target. Step 4: Generate a target digital image with diverse spatial illumination backgrounds through simulation modeling of a slowly changing edge spot background, a steeply changing edge spot background, and a linear non-uniform weak light background. Step 5: Superimpose multiple transient noises on the original image in sequence, and then perform fixed noise fusion processing to generate a target digital image containing multiple noises; Step 6: Generate a time sequence of spatial imaging simulation images through comprehensive motion simulation.

2. The space-based small target complex scene dynamic digital imaging simulation method according to claim 1 is characterized in that: The transient noise in step 5 includes: readout noise, photon shot noise, dark current noise, hot pixel and cosmic ray noise.

3. The space-based small target complex scene dynamic digital imaging simulation method according to claim 1 is characterized in that: The spatial imaging simulation image time series in step six includes: target, star background, background illumination and noise, and motion elements.

4. The space-based small target complex scene dynamic digital imaging simulation method according to any one of claims 1 to 3, characterized in that: Designing the target imaging model in step 1 includes the following steps: Based on the physical imaging characteristics and parameters of the space camera, a theoretical simulation of the space camera imaging process that describes the target size and brightness characteristics is established through imaging resolution calculation, coordinate system conversion, pixel grayscale value calculation, and imaging diffusion operation.

5. The space-based small target complex scene dynamic digital imaging simulation method according to any one of claims 1 to 3, characterized in that: The two-dimensional shape feature point description method in step 2 includes the following steps: Decompose the rough outline of the target in the view into several basic shapes; For each basic shape, a feature description matrix containing real size, position, shape and grouping information is designed; A digital library of target imaging is comprehensively constructed as target view profiles.

6. The space-based small target complex scene dynamic digital imaging simulation method according to any one of claims 1 to 3, characterized in that: In step 2, the target data imaging parsing model performs pixel-level planar mapping on the target digital library to obtain the target contour mask at a specific ground pixel resolution. Specifically, the following steps are included: When parsing a target digital library, first parse the first row of the target digital library with serial number 1 to obtain the width and height of the target imaging container rectangle; Calculate the coordinates of all points of other serial numbers in the target digital library to achieve the conversion from the feature point description coordinate system to the imaging container pixel coordinate system; According to the attribute values ​​in the target digital library, the minimum traversal range of each attribute in the pixel coordinate system of the imaging container is determined; Determine the mask generation criteria for each attribute, and finally obtain the target contour mask at a specific pixel resolution GSD.

7. The space-based small target complex scene dynamic digital imaging simulation method according to any one of claims 1 to 3, characterized in that: The comprehensive motion simulation in step 6 includes the following steps: The target position and overall grayscale of the target, various types of background illumination change parameters, transient and special spatial working condition noise parameters in the target imaging model are calculated and modeled in frame units. The motion models of target curve displacement, grayscale dynamic change, imaging tailing and platform jitter are added. The state information of the image simulation elements is updated according to the simulation timestamp to construct dynamic digital images of space targets under different working conditions.

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