A system and method for video-in-the-loop simulation for surveillance satellites

Abstract

The application discloses a video injection type simulation system for a surveillance satellite and a method thereof, and belongs to the technical field of spacecraft simulation. The system comprises a target and scene editing module, a real-time rendering module and a simulation image injection module. The target and scene editing module generates a full-link simulation model by establishing an infrared intrinsic radiation model and integrating a target model, a background model, an atmosphere model and a detection system model; the real-time rendering module performs real-time radiation calculation and image rendering on the full-link simulation model based on an OSG+GPU architecture to generate a two-dimensional grayscale image sequence; and the simulation image injection module processes the image sequence by a direct injection or a fusion injection mode and outputs the image sequence to a system under test. The application solves the problems that a traditional optical signal injection simulation cannot simulate a full-trajectory and lacks confidence evaluation by full-digital modeling and video stream injection, and realizes omnidirectional ground testing of an optical tracking system of a surveillance satellite.

Description

A system and method for video injection simulation of surveillance satellites Technical Field

[0001] This application relates to the field of spacecraft simulation technology, and more specifically, to a system and method for video-injected simulation of surveillance satellites. Background Technology

[0002] With the rapid iteration of aerospace equipment and the upgrading of evaluation requirements, space-based surveillance satellites have gradually become core nodes for full-process monitoring, on-orbit target inspection, and space situational awareness. Compared with ground-based optoelectronic telemetry and control equipment, space-based platforms have advantages such as a wide observation angle and no weather or geographical limitations. However, once its optical tracking and identification subsystem is in orbit, it faces the dilemma of being unable to perform physical retesting: on-orbit testing opportunities are scarce, the risks are high, the costs are significant, and it is difficult to reproduce boundary conditions under complex airspace, lighting, and radiation conditions. Therefore, how to verify the detection, tracking, and identification link performance of space-based surveillance satellites completely, realistically, and repeatably on the ground, and establish a quantitative evaluation system covering the entire chain of targets, background, atmosphere, and detectors, has become a key link in shortening satellite development cycles, reducing on-orbit failure rates, and improving the overall effectiveness of equipment testing and evaluation.

[0003] In existing technologies, hardware-in-the-loop simulation and injection-based testing methods for ground-based optoelectronic theodolites, radars, and seekers are relatively mature. For example, a patent document proposes a "light signal injection simulation" scheme. The idea is to calculate the spatial mapping relationship between the target and the equipment based on the motion trajectory of the simulated target and the tracking performance of the theodolite under test. A physical beam of light or image is projected using a target simulator, and a target / scene database is established after the theodolite takes actual photos. During formal testing, motion blur processing is performed according to the pre-stored image sequence and relative velocity, and then the video is synchronously injected into the theodolite's video acquisition processor to complete the tracking performance evaluation. This method has achieved good results in ground-based small field-of-view, small-range, and low-dynamic scenarios and is widely used in the assembly, commissioning, and delivery stages of optoelectronic measurement and control systems.

[0004] However, with the continuous improvement of the technical specifications of surveillance satellites, the aforementioned traditional "optical signal injection" scheme has revealed significant shortcomings when facing applications with "large field of view, high dynamic range, and weak radiation contrast":

[0005] Limited projection scale: Due to the limitations of optical projector aperture, focal length and image size, the target simulator can only generate "small scenes" on the scale of meters to tens of meters in ground-based laboratories. It cannot cover the pixel ratio of real targets at monitoring distances of hundreds to thousands of kilometers, and it cannot simulate the high dynamic changes of angular velocity and angular acceleration of targets during the process of crossing the field of view.

[0006] Limited target / background types: Existing methods mostly use static film or fixed digital image sequences, making it difficult to construct complex radiation backgrounds in real time based on conditions such as season, sea conditions, day and night, atmospheric turbulence, and ground-atmosphere clutter; at the same time, they lack the ability to quickly model and expand databases for the characteristics of new problems such as hypersonic speeds, maneuvering and orbit changes, and multi-target clusters.

[0007] Lack of confidence assessment: Traditional solutions do not introduce model verification and error propagation analysis based on measured data in the "projection-real shooting-injection" link, which makes it impossible to quantitatively trace the consistency between simulated images and the radiation, geometry and motion characteristics of real targets, making it difficult to support the stringent requirements of "data credibility" for qualification-level tests.

[0008] Weak closed-loop testing capability: Most existing injection devices are in "open-loop playback" mode, which cannot collect key back-transmission parameters such as optical axis pointing, frame angular velocity, tracking error, and recognition confidence of the satellite platform under test in real time. Therefore, they cannot perform dynamic performance evaluation of the entire process of "target loss-reacquisition-retracking" and cannot generate traceable test regression reports.

[0009] System-level expansion is difficult: Traditional optical signal injection devices rely on high-power visible / infrared projectors, large-aperture collimating light tubes, and precision mechanical turntables. Their size, power consumption, and heat dissipation performance cannot meet the flexible deployment requirements of various scenarios such as satellite assembly plants, environmental testing stations, and launch site technical positions.

[0010] Therefore, a system and method for video-injected simulation of surveillance satellites are needed to solve one of the aforementioned technical problems. Summary of the Invention

[0011] The purpose of this application is to provide a system and method for video injection simulation of surveillance satellites, which can solve at least one of the technical problems mentioned above. The specific solution is as follows:

[0012] According to a specific embodiment of this application, this application provides a system for video injection simulation of surveillance satellites, comprising:

[0013] The target and scene editing module is used to integrate target, background, atmospheric and detection system models to generate a full-link simulation model;

[0014] The real-time rendering module is used to perform real-time radiation calculations and image generation based on the full-link simulation model.

[0015] The simulation image injection module is used to perform fusion injection processing on the image frames output by the real-time rendering module, or to perform direct injection processing on real sampled data, so as to generate video for satellite injection simulation.

[0016] The target and scene editing module, real-time rendering module, and simulation image injection module work together to form a complete simulation chain from model building and physical rendering to injected image generation, enabling the output of simulation images for monitoring satellite image trackers.

[0017] Furthermore, the real-time rendering module includes:

[0018] The application layer processing unit is used to load the full-link simulation model and update the spatial position and attitude of the camera and scene objects according to the motion equation or external control commands.

[0019] The vertex shading unit is used to perform coordinate transformations, normal calculations, and related geometric property processing on the geometric data of scene objects in the programmable rendering pipeline;

[0020] The fragment shading unit is used to perform radiation equation calculations, atmospheric effect calculations, and observation geometry calculations based on vertex data, camera information, light source information, and lookup table textures to obtain fragment-level radiance.

[0021] Rasterization units are used to interpolate, perform pixel-level transformations and occlusion processing on the transformed primitives to generate fragment data to be processed.

[0022] The image quantization unit is used to quantize the fragment-level radiance output by the fragment coloring processing unit to form a two-dimensional grayscale image sequence for simulation.

[0023] Furthermore, the target and scene editing module includes:

[0024] The target modeling unit is used to construct a high-fine-grained static target model, a target surface material library, and a target interference 3D solid model, and to establish an infrared intrinsic radiation model.

[0025] Background modeling unit is used to collect and construct environmental background image data to form a background model;

[0026] The axial modeling unit is used to radially axially model a static target based on typical target characteristics.

[0027] The fusion modeling unit is used to integrate the target model, background model, atmospheric model, and detector model to generate a full-link simulation model.

[0028] Furthermore, the simulated image injection module includes:

[0029] The direct injection unit is used to perform format adjustment and consistency processing on real target scene data to form an injectable two-dimensional image sequence.

[0030] The fusion injection unit is used to fuse real-time rendered images with target measured data and orbital data, and generate a fused two-dimensional image sequence through edge smoothing and grayscale consistency processing.

[0031] Furthermore, the target modeling unit,

[0032] Based on the target's three-dimensional solid structure, surface material properties, and flow field and temperature field models under typical working conditions;

[0033] Generate the target base data used to construct the infrared intrinsic radiation model;

[0034] Based on the target data, the infrared intrinsic radiation model is generated by performing radiative transfer calculations.

[0035] The calculation results of the infrared intrinsic radiation model are compared with the field test data. Based on the comparison results, the parameters of the model are calibrated, and the confidence evaluation index of the model is output.

[0036] Furthermore, the radiative transfer calculation includes:

[0037] Radiation equations are calculated, spectral lines are looked up, and related radiation physical quantities are solved to obtain the radiation intensity distribution and radiance distribution of the target in different surface regions.

[0038] Furthermore, the pull-off modeling unit,

[0039] The radiation intensity distribution and radiance distribution were calculated based on the infrared intrinsic radiation model.

[0040] By combining the working state of typical targets, perturbation is applied to the key radiation parameters of the static target model to generate a dynamic target model for dynamic rendering;

[0041] The dynamic target model simulates the fluctuations in target characteristics and the boundary conditions of complex environments.

[0042] Furthermore, the fusion modeling unit,

[0043] Receive the dynamic target model processed by the aforementioned pull-off modeling unit;

[0044] The dynamic target model, the background model, the atmospheric model, and the detector model are encapsulated and linked according to a unified data structure and calling interface.

[0045] Generate and output a structurally unified end-to-end simulation model to the real-time rendering module.

[0046] Furthermore, the fusion injection unit,

[0047] Used to calculate the projection position of the target in the detector imaging plane of the image output by the real-time rendering module;

[0048] Based on the projection position, the target and background image are fused together;

[0049] Output the final fused image sequence.

[0050] According to a specific embodiment of this application, this application also provides a method for video injection simulation for surveillance satellites, comprising:

[0051] Integrate target, background, atmospheric and detection system models, establish and calibrate infrared intrinsic radiation model, generate dynamic target model, and integrate them to form a full-link simulation model;

[0052] Real-time radiation calculation and image processing are performed based on the full-link simulation model to generate a two-dimensional grayscale image sequence.

[0053] The two-dimensional grayscale image sequence and the real sampled data are subjected to format conversion or image fusion processing to output an injected image sequence.

[0054] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects:

[0055] 1. This application provides a system and method for video injection simulation of surveillance satellites. By establishing a full-link digital model through the target and scene editing module, it breaks through the technical bottleneck of traditional optical signal injection being limited by the optical projection range. It can completely simulate the entire trajectory from target launch to orbit insertion and various complex environmental conditions, solving the core problem of existing technology that "cannot simulate the full ballistic trajectory of an aircraft".

[0056] 2. This application provides a system and method for video injection simulation of surveillance satellites. It adopts a technical approach that combines quantitative modeling and random biasing. It establishes a high-confidence infrared intrinsic radiation model through calibration using field test data, and uses biasing to process the fluctuations in the simulated target characteristics and boundary conditions. This overcomes the shortcomings of existing technologies that "lack the ability to construct models of various types of simulation targets and complex environments" and "cannot perform confidence assessment".

[0057] 3. This application provides a system and method for video injection simulation of surveillance satellites. It generates high-precision simulation images through a real-time rendering module and combines direct injection and fusion injection modes of the simulation image injection module. This effectively solves the engineering problems of existing technologies that "rely on expensive actual combat mission assessments and have few test samples", and greatly improves testing efficiency and coverage. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0059] Figure 1 is a flowchart illustrating the workflow of a video injection simulation system for surveillance satellites, as shown in an embodiment of this application.

[0060] Figure 2 is a flowchart illustrating the real-time rendering module in an embodiment of this application.

[0061] Figure 3 is a flowchart of the target and scene editing module shown in an embodiment of this application.

[0062] Figure 4 is a flowchart illustrating the operation of the simulation image injection module in an embodiment of this application. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0065] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0066] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0067] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0068] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0069] This application proposes a video injection simulation system for surveillance satellites, which is mainly used to simulate space-based targets such as satellites and their motion scenarios. By generating simulated video streams, the system directly injects them into the optical tracking and recognition system of the surveillance satellite to replace traditional field tests and achieve a comprehensive, repeatable, and high-confidence assessment of satellite tracking and recognition capabilities.

[0070] This application provides a system for video injection simulation of surveillance satellites. In this embodiment, the system is implemented on a high-performance HP Z8G4 graphics workstation from HP Inc., with a Windows 7 64-bit development environment, a Microsoft Visual Studio 2010 compilation environment, 3ds Max and Photoshop CS modeling software, and the interactive graphics program OpenSceneGraph (OSG) and OpenGL 3.0 rendering environment. This embodiment uses a ground demonstration prototype of a two-dimensional tracking platform for space-based surveillance satellites as the device under test to explain the working principle of the injection simulation system for surveillance satellites. The system injects a simulated video stream into the device under test, i.e., the ground demonstration prototype of the two-dimensional tracking platform for space-based surveillance satellites, through the CameraLink-Medium interface (1280×1024@50Hz). This embodiment uses the injection of a generated medium-wave / long-wave simulated video stream into the device under test as an example for illustration.

[0071] Figure 1 illustrates the internal relationships and workflow of a video injection simulation system for surveillance satellites. It shows the process of starting from raw data and physical principles, transforming them into a computable model through numerical modeling, and finally generating a simulation image that can be injected through real-time rendering.

[0072] As shown on the left side of Figure 1, the target and scene editing module is responsible for integrating target, background, atmospheric, and detection system models to ultimately generate a full-link simulation model. Specifically, it includes:

[0073] Target Modeling Unit: Based on the three-dimensional solid structure, surface material properties, and flow and temperature field models under typical operating conditions of typical targets such as rockets and satellites, this unit generates fundamental data for constructing an infrared intrinsic radiation model. Through radiation equation calculation, spectral line lookup, and solution of relevant radiation physical quantities, it establishes a model of the radiation intensity and radiance distribution of the target in different surface regions using the inverse Monte Carlo method and the CG spectral line method. After calibration with field test data, this model has radiation polarization capability and can output confidence assessment indicators. Background Modeling Unit: This unit constructs a complete background model including ground background (DEM + texture + material), sky background (based on the MODTRAN atmospheric model), and cloud background, achieving quantitative modeling of background radiation. Polarization Modeling Unit: Based on typical target characteristics, this unit applies random perturbations to key radiation parameters of the static target model to generate a dynamic target model for dynamic rendering, simulating fluctuations in target radiation characteristics and complex environmental boundary conditions. Fusion Modeling Unit: This unit encapsulates and links the dynamic target model, background model, atmospheric model, and detector model according to a unified data structure and calling interface, forming a full-link simulation model for use by the real-time rendering module.

[0074] The real-time rendering module, as shown in the lower part of Figure 1, is based on a full-link simulation model and uses an OSG+GPU architecture to perform real-time radiometric calculations and image generation. The specific process includes: Application layer processing (CPU side): Loading the full-link simulation model and updating the spatial position and pose of the camera and scene objects according to motion equations or external control commands. Geometry and raster processing (GPU side): Performing coordinate transformation and normal vector calculation on geometric data through vertex shading units; performing interpolation and occlusion processing on primitives through rasterization units to generate fragment data. Radiometric calculation (GPU side): Based on the interpolated vertex data, camera information, light source information, and pre-calculated lookup table textures (such as blackbody radiance, atmospheric transmittance, and path radiance lookup tables), the fragment shading unit performs intrinsic radiation equation calculations, atmospheric effect calculations, and observation geometry calculations to obtain fragment-level radiance. Image quantization (GPU side): Quantizing the fragment-level radiance into 16-bit grayscale values ​​to generate a two-dimensional grayscale image sequence. Image data is output frame-by-frame to form a dynamic simulation video stream.

[0075] The simulation image injection module, as shown in Figure 1, is responsible for format processing and fusion of the rendered image sequence to generate a video stream that can be directly injected. It includes two working modes: Direct Injection Unit: This unit adjusts the format and performs consistency processing on the field measurement data, removing useless signals to form an injectable two-dimensional image sequence. Fusion Injection Unit: This unit calculates the projection position of the target on the detector's imaging plane, uses an image edge smoothing algorithm to fuse the real-time rendered image with the target's measured data and orbital data, and performs grayscale consistency processing to generate the fused two-dimensional image sequence. The system achieves a closed-loop modeling, rendering, and injection process from left to right and top to bottom: Modeling Stage: Integrates target, background, atmospheric, and detector models, forming a unified simulation model through biasing and scheduling; Rendering Stage: Based on this model, it generates a two-dimensional image sequence containing radiation, atmospheric, and detector effects through the GPU real-time rendering pipeline; Injection Stage: Outputs a 16-bit grayscale image sequence, which is injected into the tested satellite system through interfaces such as CameraLink, supporting both direct injection and fusion injection modes.

[0076] In this embodiment of the application, a system for video injection simulation of surveillance satellites includes:

[0077] The target and scene editing module integrates target, background, atmospheric, and detection system models to generate a full-link simulation model. The real-time rendering module performs real-time radiometric calculations and image generation based on the full-link simulation model. The simulation image injection module processes or fuses image frames or real sampled data output by the real-time rendering module into an injection format to obtain an injected simulation video. The target and scene editing module, real-time rendering module, and simulation image injection module work together to form a complete simulation chain from model building and physical rendering to injected image generation, enabling the output of simulated image and video for surveillance satellite image trackers.

[0078] This application embodiment also provides a preferred solution, which performs real-time rendering through a real-time rendering module. The specific loop workflow is shown in Figure 2: Initialization and Loading: After system startup, the simulation configuration file and parameters are loaded, the OSG rendering engine is initialized, and scene resources are loaded. Motion Calculation: Based on the motion equations or external control commands, the spatial position and pose of the camera and all objects in the scene are updated. GPU Parallel Rendering: Geometric Stage: Coordinate transformation and geometric parameter calculation are performed. Radiation Calculation Stage: Combining lookup tables and dynamic accuracy evaluation strategies, simplified intrinsic radiation equations are calculated in real time. Effect Simulation Stage: Atmospheric effects (attenuation and path radiation) calculations and infrared imaging system effects (noise, nonlinearity, etc.) simulations are performed sequentially. Quantization and Output: The calculated radiance values ​​are quantized into a 16-bit grayscale image. Loop Judgment: It is determined whether the simulation should continue. If it continues, it returns to render the next frame; otherwise, the process ends. This loop mechanism ensures the continuous generation of the dynamic simulation video stream.

[0079] Real-time rendering is divided into two stages: the application stage (CPU side) and the graphics processing unit (GPU side) rendering stage. It includes the entire process of computer imaging, including the application stage, geometry stage, and rasterization stage. The project adopts the OSG+GPU rendering mode, which uses a CPU and GPU collaborative architecture to improve rendering effects and real-time performance.

[0080] In this embodiment of the application, the real-time rendering module includes:

[0081] The application layer processing unit, which is the CPU in this embodiment, is responsible for data I / O and loading / releasing, loading the full-link simulation model, and updating the spatial position and attitude of the camera and scene objects according to the motion equations or external control commands.

[0082] The vertex shading processing unit, which is a GPU in this embodiment, performs coordinate transformation from the object's own coordinate system to the world coordinate system, calculates normal vectors, and processes related geometric attributes on the geometric data of scene objects in the programmable rendering pipeline.

[0083] The fragment shading processing unit, which is a GPU in this embodiment, performs intrinsic radiation equation calculation, atmospheric effect calculation, and observation geometry calculation based on the interpolated vertex data, camera information, light source information, and pre-calculated lookup table textures, including blackbody radiance lookup table and atmospheric transmittance and path radiation lookup table, to obtain fragment-level radiance.

[0084] In this embodiment, the fragment coloring processing unit is the core of the radiation calculation, which receives the interpolated fragment data, camera and light source information, and performs the following key steps:

[0085] Calculate geometric parameters such as observation distance, light source distance, reflection direction, and cosine of various included angles.

[0086] Obtaining radiation physical quantities: Using the above parameters as an index, look up the physical quantity lookup table texture of the radiation equation, such as the blackbody radiance lookup table, and combine it with direct calculation to obtain physical quantities such as blackbody radiance and solar irradiance.

[0087] Dynamic accuracy assessment and calculation: Based on parameters such as observation distance and time, the radiation accuracy is dynamically assessed, and the simplest intrinsic radiation equation is selected to calculate the radiance of the segment while ensuring accuracy.

[0088] The atmospheric effect texture is sampled according to the atmospheric index parameters, the average transmittance and path radiance lookup table are used, the total intrinsic radiance is multiplied by the transmittance and the path radiance is added to obtain the average apparent radiance of the segment.

[0089] The rasterization unit, which is a GPU in this embodiment, receives the transformed primitive triangles and automatically performs primitive assembly, interpolation, pixel-level transformation and depth occlusion processing. It discretizes continuous geometric primitives into fragments to be processed and generates interpolated fragment data containing coordinates, depth, normal vectors and other parameters.

[0090] The image quantization unit, located on the GPU in this embodiment, quantizes the fragment-level radiance output by the fragment shading processing unit to form a two-dimensional grayscale image sequence for simulation. In this embodiment, the image quantization unit uses a quantization scale to quantize the apparent radiance output by the fragment shading processing unit into 16-bit grayscale values. A 16-bit data image is used, where the G channel stores the high 8 bits and the B channel stores the low 8 bits, ultimately completing the output of the current frame's grayscale image. The system repeatedly executes the above process to form a dynamic scene until the simulation ends.

[0091] This application embodiment also provides a preferred embodiment: a target and scene editing module, as shown in Figure 3, is composed of dedicated simulation image editing software. It employs a simulation approach of quantitative modeling and random biasing to generate a full-link simulation model. The target and scene editing module specifically includes:

[0092] Target Modeling Unit: Constructs a high-fine-grained static target model and its infrared intrinsic radiation model. The modeling process begins with the analysis of the characteristics and operating conditions of a typical target, such as a satellite. First, a finite element three-dimensional solid model of the target is established. Based on its typical operating state, the improved Fluent calculation model and the GasTurb engine model are used to calculate the thermal flow field, obtaining the temperature field and flow field distribution on the target surface. Combining the emissivity parameters obtained from the surface material library, the inverse Monte Carlo method and the CG spectral line method are used to calculate radiative transfer, thereby generating the zero-line-of-sight radiance field model and radiative intensity field model of the target, thus constituting a complete infrared intrinsic radiation model. To ensure the model's confidence, the model calculation results are compared and calibrated with field test data. A quantitative confidence assessment index is output through the target characteristic similarity evaluation model, ultimately completing the construction of a high-confidence intrinsic radiation model.

[0093] Background Modeling Unit: Collects and constructs environmental background image data to form a complete background model including ground background, DEM, texture, and material; sky background, based on the MODTRAN atmospheric model; and cloud background.

[0094] The polarization modeling unit, based on the calibrated infrared intrinsic radiation model described above, applies controllable perturbations to key radiation parameters through a typical target polarization model to generate a static polarization model of a typical target, thereby obtaining a dynamic target model for dynamic rendering. The polarization modeling unit simulates the natural fluctuations, uncertainties, and extreme boundary conditions in complex environments of real target radiation characteristics, providing a rich sample covering typical and boundary states for simulation testing.

[0095] Fusion Modeling Unit: Integrates the target model, background model, atmospheric model, and detector model to generate a full-link simulation model.

[0096] This application embodiment also provides a preferred embodiment, wherein the simulation image injection module includes: a direct injection unit and a fusion injection unit. In this embodiment, the simulation image injection module is implemented through an interface protocol converter. Its working mode is shown in Figure 4. The interface protocol converter injects the simulation video stream into the device under test, specifically including: a direct injection unit (right path): performing format adjustment and consistency processing on the target data and background data measured in the field, directly forming a 16-bit image sequence conforming to the injection interface specification. A fusion injection unit (left path): performing image fusion with the image output from the simulation scene and the real target data, trajectory data, and background data. The fusion process calculates the target projection position and uses edge smoothing algorithms to achieve natural superposition of the target and background, ultimately generating a fused 16-bit image sequence.

[0097] The direct injection unit adjusts the format of the real target scene data, removing useless signals from the data file, such as superimposed characters. It performs consistency processing on the external field data under the current detector sampling parameters, forming a two-dimensional image sequence with selectable data bits, which is then directly output to the interface protocol converter in preparation for injection.

[0098] The fusion injection unit integrates the measured target data, orbital data, and simulated scene data. Based on the distance between the target and the detector, and the target's position and attitude, it calculates the target's projection onto the detector. An image edge smoothing algorithm is used to overlay the target onto the background. Consistency processing is performed on the grayscale measurement range of the sampled data and the simulated scene, and relevant biasing is applied. This forms a 16-bit two-dimensional image sequence, ready for injection into the image sequence.

[0099] This application proposes a video injection simulation system for surveillance satellites, which is mainly used to simulate space-based targets such as rockets, satellites and their motion scenarios. By generating simulated video streams, the system directly injects them into the optical tracking and recognition system of the surveillance satellite to replace traditional field live-fire tests and achieve a comprehensive, repeatable and high-confidence assessment of satellite tracking and recognition capabilities.

[0100] This application constructs a complete all-digital simulation testing system through the collaborative work of a target and scene editing module, a real-time rendering module, and a simulation image injection module. The target and scene editing module is responsible for establishing a high-confidence target infrared intrinsic radiation model, integrating complex background environments, and generating a full-link simulation model by processing simulated characteristic fluctuations through polarization. The real-time rendering module, based on an OSG+GPU architecture, converts the simulation model into a two-dimensional image sequence including atmospheric transmission and detector effects. The simulation image injection module injects the processed video stream into the system under test through two modes: direct injection and fusion injection. These three modules work sequentially and collaboratively, forming a technical closed loop from accurate modeling, physical rendering, to flexible injection. Together, they solve the technical challenges of traditional optical signal injection simulations, such as the inability to simulate full ballistic trajectories, lack of confidence assessment, limited scene selection, and reliance on field operations. This enables comprehensive, repeatable, and highly reliable ground testing of surveillance satellite tracking and identification systems.

[0101] This application also provides a method for injection-based simulation of surveillance satellites, including:

[0102] S1. Integrate target, background, atmospheric and detection system models, establish and calibrate infrared intrinsic radiation model, generate dynamic target model, and integrate to form a full-link simulation model.

[0103] In this embodiment, a combined approach of numerical modeling and injection simulation is adopted, integrating target, background, atmospheric, and detection system models. A full-link simulation model is generated through quantitative modeling and random biasing. Specifically, this includes: generating target baseline data for constructing an infrared intrinsic radiation model based on the target's three-dimensional solid structure, surface material properties, and flow and temperature field models under typical operating conditions. The infrared intrinsic radiation model is established by calculating the radiation equations, looking up spectral lines, and solving related radiation physical quantities using the inverse Monte Carlo method and the CG spectral line method to calculate the radiation intensity and radiance distributions of the target in different surface regions. The model calculation results are compared and calibrated with field test data, and the model's confidence evaluation index is output using a static model similarity check method. Based on the radiation intensity and radiance distributions combined with the target's operating conditions, perturbations are applied to the key radiation parameters of the static target model through biasing, generating a dynamic target model for dynamic rendering. The dynamic target model, background model, atmospheric model, and detector model are encapsulated and linked according to a unified data structure and calling interface to form a full-link simulation model.

[0104] S2. Based on the full-link simulation model, perform real-time radiation calculation and image processing to generate a two-dimensional grayscale image sequence.

[0105] In this embodiment, based on a full-link simulation model, a real-time radiometric calculation and image generation are performed using an OSG+GPU real-time rendering platform: the full-link simulation model is loaded through the application layer processing unit, and the spatial position and pose of the camera and scene objects are updated according to the motion equations or external control commands. The vertex shading processing unit performs geometric coordinate transformation and normal vector calculation from the object coordinate system to the world coordinate system in the programmable rendering pipeline. The fragment shading processing unit performs intrinsic radiometric equation calculation, atmospheric effect calculation, and observation geometry calculation based on vertex data, camera information, light source information, and pre-calculated lookup table textures to obtain fragment-level radiance. Full-link detector modeling is integrated to simulate detector effects such as detection nonlinearity, blind pixels, and dual-tone noise. The radiance is quantized in 16 bits to generate a two-dimensional grayscale image sequence.

[0106] S3. Perform format conversion or image fusion processing on the two-dimensional grayscale image sequence and the real sampled data to output the injected image sequence.

[0107] In this embodiment, the rendering results are processed using an injection format to form an injected image sequence: In direct injection simulation mode, the field measurement data is formatted and consistency-processed to remove unwanted signals. In fusion injection simulation mode, the projection position of the target in the detector imaging plane is calculated, and an image edge smoothing algorithm is used to fuse the target with the background image. Mid-wave and long-wave simulation video streams are output to the device under test via an interface protocol converter. Mid-wave infrared miss distance, long-wave infrared miss distance, and servo system status information are acquired in real time. Based on the acquired tracking status data, a performance evaluation report and charts are generated post-processed to quantitatively evaluate the tracking and identification capabilities of the surveillance satellite.

[0108] The technical solution of this application embodiment constructs a complete ground test solution for space-based surveillance satellites through the systematic connection and coordination of three key steps. First, by integrating target, background, atmospheric, and detection system models, an infrared intrinsic radiation model calibrated with field data is established, and a dynamic target model containing parameter perturbations is generated. Finally, these are integrated into a full-link simulation model, solving the problem of traditional methods having a single target model and lacking confidence assessment. Then, based on this full-link model, a two-dimensional grayscale image sequence containing atmospheric transmission and detector effects is generated through real-time radiation calculation and image processing, breaking through the physical limitations of optical projection simulation and realizing full ballistic scenario simulation. Finally, by converting the image sequence into a format or fusing it with measured data, a simulation video stream that can be directly injected is output, supporting both flexible direct injection and fused injection modes, overcoming the shortcomings of traditional methods that are limited by test scenarios and dependent on field operations.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0110] The units described in the embodiments of this application can be implemented in software or hardware. The names of the units are not, in some cases, limiting the scope of the unit itself.

Claims

1. A system for video injection simulation of surveillance satellites, characterized in that, include: The target and scene editing module is used to integrate target, background, atmospheric and detection system models to generate a full-link simulation model; The real-time rendering module is used to perform real-time radiation calculation and image generation based on the full-link simulation model. The simulation image injection module is used to perform fusion injection processing on the image frames output by the real-time rendering module, or to perform direct injection processing on real sampled data, so as to generate video for satellite injection simulation. The target and scene editing module includes: a target modeling unit for constructing a high-fine-grained static target model, a target surface material library, and a target interference 3D entity model, and establishing an infrared intrinsic radiation model; a background modeling unit for collecting and constructing environmental background image data to form a background model; a polarization modeling unit for performing radiation polarization processing on the static target model based on typical target characteristics; and a fusion modeling unit for integrating the target model, background model, atmospheric model, and detector model to generate the full-link simulation model. The polarization modeling unit calculates the obtained radiation intensity distribution and radiance distribution based on the infrared intrinsic radiation model. Combining the working state of typical targets, it applies perturbations to the key radiation parameters of the static target model to generate a dynamic target model for dynamic rendering. The dynamic target model simulates the fluctuation of target characteristics and the boundary conditions of complex environments.

2. The system according to claim 1, characterized in that, The real-time rendering module includes: an application layer processing unit for loading the full-link simulation model and updating the spatial position and pose of the camera and scene objects according to the motion equations or external control commands; a vertex shading processing unit for performing coordinate transformation, normal vector calculation, and related geometric attribute processing on the geometric data of scene objects in the programmable rendering pipeline; a fragment shading processing unit for performing radiation equation calculation, atmospheric effect calculation, and observation geometry calculation based on vertex data, camera information, light source information, and lookup table texture to obtain fragment-level radiance; a rasterization unit for interpolating, pixel-level converting, and occlusion processing on the transformed primitives to generate fragment data; and an image quantization unit for quantizing the fragment-level radiance into a two-dimensional grayscale image sequence.

3. The system according to claim 1, characterized in that, The simulation image injection module includes: a direct injection unit, used to adjust the format and perform consistency processing on real target scene data to form an injectable two-dimensional image sequence; and a fusion injection unit, used to fuse real-time rendered images with target measured data and orbit data, and generate a fused two-dimensional image sequence through edge smoothing and grayscale consistency processing.

4. The system according to claim 1, characterized in that, The target modeling unit includes: generating target basic data for constructing the infrared intrinsic radiation model based on the target's three-dimensional solid structure, surface material properties, and flow field and temperature field models under typical working conditions; generating the infrared intrinsic radiation model based on the target basic data by performing radiative transfer calculations; comparing the calculation results of the infrared intrinsic radiation model with field test data, calibrating the parameters of the model based on the comparison results, and outputting the confidence evaluation index of the model.

5. The system according to claim 4, characterized in that, The radiative transfer calculation includes: radiation equation calculation, spectral line lookup and solution of related radiation physical quantities, to obtain the radiation intensity distribution and radiance distribution of the target in different surface regions.

6. The system according to claim 5, characterized in that, The fusion modeling unit includes: receiving the dynamic target model processed by the bias modeling unit; encapsulating and linking the dynamic target model, the background model, the atmospheric model, and the detector model according to a unified data structure and calling interface; generating and outputting a structurally unified end-to-end simulation model to the real-time rendering module.

7. The system according to claim 3, characterized in that, The fusion injection unit includes: calculating the projection position of the target in the detector imaging plane of the image output by the real-time rendering module; fusing the target with the background image according to the projection position; and outputting the final fused image sequence.

8. A simulation method for a system based on injection-based simulation of a surveillance satellite as described in any one of claims 1 to 7, characterized in that, The simulation method includes: integrating target, background, atmosphere and detection system models, establishing and calibrating an infrared intrinsic radiation model, generating a dynamic target model, and integrating them into a full-link simulation model; performing real-time radiation calculation and image processing based on the full-link simulation model to generate a two-dimensional grayscale image sequence; and performing format conversion or image fusion processing on the two-dimensional grayscale image sequence and real sampled data to obtain an image sequence for injection simulation.

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