A space target high-fidelity physical modeling and detection performance evaluation method and system

Abstract

This invention provides a method and system for high-fidelity physical modeling and detection performance evaluation of space targets, relating to the field of deep space exploration technology. The method first acquires the relative motion parameters of the spacecraft and the asteroid, as well as camera parameters, and determines the static apparent magnitude based on observation geometry and the asteroid's physical characteristics. Then, it calculates the angular velocity modulus through relative velocity, combines it with camera parameters to obtain tail parameters, and quantifies the equivalent magnitude loss and effective apparent magnitude based on a piecewise model. Subsequently, it constructs a two-dimensional parameter grid of focal length and exposure time, calculates the effective signal-to-noise ratio and detection margin for each combination, and selects the optimal parameters using a heatmap. Finally, it generates a high-fidelity simulation image and feeds back the observation values ​​to the guidance, navigation, and control system via a UDP asynchronous interface. This invention achieves quantitative assessment of tail loss and automated parameter optimization, balancing high fidelity and real-time performance, and improving detection accuracy and system reliability.

Description

Technical Field

[0001] This invention relates to the field of deep space exploration technology, and in particular to a method and system for high-fidelity physical modeling of space targets and evaluation of their detection performance. Background Technology

[0002] In near-Earth asteroid exploration missions, the optical navigation during the spacecraft's approach phase relies on target images acquired by cameras. Navigation algorithms calculate relative position and velocity, and image quality directly determines navigation accuracy and the success rate of the exploration. As exploration missions expand to close-range, high-speed flyby scenarios, factors such as the relative motion between the spacecraft and the asteroid, camera parameter settings, and the real-time nature of simulations have an increasingly significant impact on exploration performance. Therefore, accurately simulating real-world exploration scenarios, optimizing camera parameters, and supporting closed-loop verification through simulation technology have become urgent technical requirements.

[0003] To address this need, a commonly used solution in existing technologies is a simulation verification method based on offline image generation. This method generates target images by loading star catalog data and simulating camera imaging effects, and then combines navigation algorithms for offline analysis to assess the feasibility of the exploration and adjust camera parameters.

[0004] However, this offline image generation simulation method has a key flaw: it only simulates motion trailing through visual blurring effects and lacks a quantitative model based on the physical energy dispersion mechanism. It cannot accurately calculate the signal-to-noise ratio drop and magnitude loss caused by the trailing effect. Consequently, the selection of core parameters such as camera focal length and exposure time can only rely on the designer's experience or repeated trial and error. Moreover, the complex star table processing and point spread function convolution result in extremely slow simulation speed. It is impossible to achieve both high fidelity and real-time performance, and it is also difficult to form a closed loop with the guidance, navigation and control system. It cannot accurately support the detection performance evaluation and parameter optimization in close-range high-speed flyby scenarios. Summary of the Invention

[0005] This invention provides a method and system for high-fidelity physical modeling and detection performance evaluation of space targets, which can solve the problems of existing technologies that cannot quantitatively evaluate motion trailing loss, cannot automatically optimize camera parameters, and are difficult to balance high fidelity and real-time performance.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: Firstly, a high-fidelity physical modeling and detection performance evaluation method for space targets is provided. The method includes: acquiring the relative position and relative velocity of a spacecraft and an asteroid, as well as the camera's detection limiting magnitude, focal length, pixel size, and point spread function full width at half maximum (FWHM); determining the asteroid's static apparent magnitude based on the relative position and physical characteristics of the asteroid; determining the spacecraft's angular velocity magnitude relative to the asteroid based on the relative velocity, and calculating the trailing parameters using the focal length, pixel size, and FWHM; and calculating the equivalent magnitude loss based on a piecewise model and the trailing parameters. The effective apparent magnitude of the asteroid is determined based on the static apparent magnitude; a two-dimensional parameter grid of focal length and exposure time is constructed, and for each parameter combination in the parameter grid, the effective signal-to-noise ratio and detection margin after considering magnitude loss are determined; a performance evaluation matrix is ​​generated using the effective signal-to-noise ratio and detection margin as indicators, and the optimal parameter combination is selected and output; a high-fidelity simulation image is generated based on the optimal parameter combination, and the high-fidelity simulation image and the corresponding observation values ​​are fed back to the guidance, navigation and control system to form a real-time closed-loop link, wherein the observation values ​​include the coordinates of the asteroid in the high-fidelity simulation image and the effective signal-to-noise ratio.

[0007] The method provided by this invention systematically realizes high-fidelity physical modeling and detection performance evaluation of space targets through a complete process including parameter acquisition, static apparent magnitude determination, trailing parameter calculation, effective apparent magnitude determination, parameter optimization, high-fidelity image generation, and feedback. This method first accurately quantifies the optical properties of the target based on physical characteristics and geometric relationships, then scientifically selects camera parameters through a trailing loss model and two-dimensional parameter optimization, and finally generates high-fidelity simulation images and forms a closed-loop feedback. This not only solves the problem of the lack of physical quantitative modeling in existing technologies but also automates detection performance evaluation and parameter optimization. Furthermore, the real-time closed-loop feedback ensures compatibility with guidance, navigation, and control systems, significantly improving the accuracy and reliability of optical detection in deep space exploration missions.

[0008] In one possible implementation of the first aspect, determining the static apparent magnitude of the asteroid based on the relative position of the spacecraft and the asteroid and the physical properties of the asteroid includes: determining the observation geometry based on the relative position of the spacecraft and the asteroid, the observation geometry including the asteroid's phase angle and the triangular geometry of the sun-asteroid-spacecraft; determining the static photon flux of the asteroid without tail interference based on the physical properties of the asteroid and according to the observation geometry, the physical properties including the asteroid's geometrical albedo, equivalent diameter, and surface spectral characteristics; and converting the static photon flux into the static apparent magnitude of the asteroid according to the correspondence between the static photon flux and the apparent magnitude.

[0009] The method provided by this invention clarifies the observational geometry by determining the relative positions of the spacecraft and the asteroid. It then calculates the static photon flux by combining the asteroid's physical properties, such as geometric albedo, equivalent diameter, and surface spectral characteristics. Finally, it converts the photon flux to apparent magnitude based on the correspondence between photon flux and apparent magnitude to obtain the static apparent magnitude. This process strictly follows the laws of physical photon propagation, avoiding subjective and empirical errors in static apparent magnitude estimation, ensuring the accuracy of the static apparent magnitude calculation. This provides reliable basic data for subsequent calculations of equivalent magnitude loss and determination of the effective apparent magnitude, thereby guaranteeing the accuracy of the detection performance evaluation results.

[0010] In one possible implementation of the first aspect, the formula for determining the trailing parameter is: ; ; in, Let t be the magnitude of the angular velocity of the spacecraft relative to the asteroid, r be the relative position of the spacecraft and the asteroid, and v be the relative velocity of the spacecraft and the asteroid. exp f is the exposure time, p is the focal length, and FWHM is the point spread function (FWHM).

[0011] The method provided by this invention calculates the angular velocity magnitude of the spacecraft relative to the asteroid using relative position and relative velocity. It then combines this with focal length, pixel size, point spread function full width at half maximum (FWHM), and exposure time to construct a quantitative formula for the trailing parameters, clarifying the core components and calculation logic of these parameters. This quantification method organically integrates relative motion, camera optical parameters, and sensor characteristics, achieving a physical quantitative characterization of the trailing effect. It solves the problem that existing technologies cannot accurately describe the degree of trailing, providing a scientific basis for subsequent segmented calculations of equivalent magnitude loss, and making the assessment of the trailing effect's impact on detection performance more objective and accurate.

[0012] In one possible implementation of the first aspect, the logic for determining the segmentation model is as follows: When the trailing parameter is less than the first preset threshold, the equivalent star magnitude loss is... ; When the trailing parameter is greater than or equal to the first preset threshold, the equivalent star magnitude loss is... ; The effective apparent magnitude The formula for determining it is: ; in, This refers to the static apparent magnitude of an asteroid.

[0013] The method provided by this invention sets a segmented model based on the numerical range of the trailing parameter, and then combines it with the static apparent magnitude to obtain the effective apparent magnitude. Preferably, the preset trailing threshold ranges from 0.2 to 0.5, for example, 0.3. This segmented logic conforms to the physical energy dispersion law under different degrees of trailing, accurately capturing the detection sensitivity attenuation caused by significant trailing, and avoiding over-correction in sub-pixel micro-motion scenarios. This makes the calculation of the effective apparent magnitude more consistent with actual detection scenarios, providing accurate optical characteristic indicators for subsequent detection performance evaluation and parameter optimization.

[0014] In one possible implementation of the first aspect, constructing a two-dimensional parameter grid of focal length and exposure time, and determining the effective signal-to-noise ratio and detection margin after considering magnitude loss for each parameter combination in the parameter grid, includes: constructing a focal length range of [f min ,f max The exposure time range is [t]. min ,t max A two-dimensional parametric mesh, where f min f is the minimum usable focal length of the camera. max t is the maximum usable focal length of the camera. min t is the camera's shortest exposure time threshold. max The maximum exposure time threshold for the camera is defined as follows: For each parameter combination in the two-dimensional parameter grid, the trailing parameter is first calculated based on the exposure time corresponding to each parameter combination, and then the equivalent magnitude loss of each parameter combination is obtained based on the piecewise model; the effective apparent magnitude corresponding to each parameter combination is determined based on the equivalent magnitude loss and the static apparent magnitude of the asteroid; the effective signal-to-noise ratio of each parameter combination after considering the magnitude loss is determined based on the static signal-to-noise ratio and the equivalent magnitude loss, and the detection margin of each parameter combination is determined based on the difference between the detection limit magnitude and the effective apparent magnitude.

[0015] The method provided by this invention constructs a two-dimensional parameter grid of focal length and exposure time. For each parameter combination, it sequentially calculates the trailing parameter, equivalent magnitude loss, and effective apparent magnitude. Then, it determines the effective signal-to-noise ratio (SNR) through the static SNR and equivalent magnitude loss, and determines the detection margin by the difference between the detection limiting magnitude and the effective apparent magnitude. This process achieves a one-to-one correspondence between parameter combinations and detection performance indicators, covering the feasible range of camera parameters and comprehensively reflecting the detection capabilities under different parameter combinations through multi-indicator calculations. This avoids the limitations of single-indicator evaluation and provides comprehensive and accurate performance data support for the subsequent selection of the optimal parameter combination.

[0016] In one possible implementation of the first aspect, generating a heatmap using the effective signal-to-noise ratio and probe margin as indicators, and filtering and outputting the optimal parameter combination, includes: drawing a heatmap using the effective signal-to-noise ratio and probe margin as indicators; determining a parameter recommendation strategy based on the heatmap and according to the relative angular velocity magnitude between the spacecraft and the asteroid; if the relative angular velocity magnitude is less than a preset angular velocity threshold, determining the parameter combination with the largest effective signal-to-noise ratio among long-exposure parameter combinations with an exposure time ≥ an exposure time boundary threshold as the optimal parameter combination; if the relative angular velocity magnitude is greater than or equal to the preset angular velocity threshold, determining the parameter combination with the largest probe margin and an effective signal-to-noise ratio greater than the preset threshold among short-exposure parameter combinations with an exposure time ≤ an exposure time boundary threshold as the optimal parameter combination.

[0017] The method provided by this invention generates a performance evaluation matrix using effective signal-to-noise ratio (SNR) and probe margin as dual indicators, intuitively presenting the detection performance distribution in the parameter space. Then, based on the relative angular velocity magnitude, a differentiated parameter recommendation strategy is formulated: in low angular velocity scenarios, long exposure combinations are prioritized to maximize the effective SNR; in high angular velocity scenarios, short exposure combinations are focused on to select parameters with the largest probe margin and satisfactory effective SNR. This strategy not only visualizes parameter performance using heatmaps but also avoids the risk of detection failure in scenarios dominated by trailing artifacts through scenario-based selection logic. It solves the problem of existing technologies relying on experience to select camera parameters, achieving systematic and intelligent parameter optimization, and ensuring that the recommended parameter combinations achieve optimal detection results in different detection scenarios.

[0018] In one possible implementation of the first aspect, generating a high-fidelity simulation image based on the optimal parameter combination includes: generating a high-fidelity simulation image using a PSF object caching mechanism and a star catalog optimization strategy; the PSF object caching mechanism uniquely identifies the optical state through key-value tuples; if the key-value tuple is hit in the cache, the convolution kernel in memory is directly reused; if it is not hit, the Bessel function is calculated to generate a new convolution kernel, and the new convolution kernel is updated to the cache; The key-value tuple is: ; in: Focal length For wavelength, For caliber, Occlusion ratio; The star catalog optimization strategy is as follows: after loading the Gaia star catalog, first trim and retain stars within a preset range, and then select and retain stars with brightness m < detection limiting magnitude. The preset range is: ; in, Right ascension of a star To observe the right ascension of the center of the field of view, This is the camera's field of view.

[0019] The method provided by this invention employs a PSF object caching mechanism and a star catalog optimization strategy to generate high-fidelity simulation images. Optical states are uniquely identified through key-value tuples. When the cache hits, the convolution kernel is reused; when it misses, a Bessel function is calculated to generate a new convolution kernel and update the cache. Simultaneously, the Gaia star catalog is cropped and its magnitudes are filtered. This design significantly reduces the cost of repetitive PSF convolution calculations through the caching mechanism and reduces the amount of invalid stellar data processed through star catalog optimization. While maintaining high fidelity in the simulation images, it significantly improves image generation efficiency, resolving the contradiction between high fidelity and real-time performance, and providing efficient image data support for subsequent closed-loop feedback.

[0020] In one possible implementation of the first aspect, feeding back the high-fidelity simulation image and corresponding observations to the guidance, navigation, and control system includes: establishing a connection via an instant messaging interface and establishing a communication connection with the guidance, navigation, and control system using a data serialization protocol, with a communication frequency of 1Hz; receiving T signals sent by the guidance, navigation, and control system. k The system generates time-state prediction data, including time t, spacecraft position, spacecraft velocity, and attitude quaternions. Based on this prediction data, the system interpolates to obtain the actual orbital states of the spacecraft and the asteroid. Combined with the high-fidelity simulation image, it extracts observation values, including the asteroid's coordinates in the image, effective signal-to-noise ratio, and timestamp. These observation values ​​are then fed back to the guidance, navigation, and control system via a UDP asynchronous interface. These observation values ​​are used by the guidance, navigation, and control system at time t. k+1 Filtering updates and trajectory planning at different times.

[0021] The method provided by this invention establishes a connection through an instant messaging interface and establishes a communication connection with the guidance, navigation, and control system using a data serialization protocol, receiving T data at a 1Hz communication frequency. k The estimated time-state data is interpolated to obtain the actual orbital state, and the observed values ​​are extracted and fed back to the guidance, navigation, and control system for T. k+1 The asynchronous communication architecture decouples the simulation system from the guidance, navigation, and control system, avoiding simulation delays from blocking guidance, navigation, and control calculations. The 1Hz communication frequency meets the real-time requirements of closed-loop testing, constructing a complete measurement-feedback-control physical closed-loop verification environment. This effectively exposes algorithm stability issues in dynamic processes and improves the reliability of guidance, navigation, and control systems in deep space exploration missions.

[0022] Secondly, this invention provides a high-fidelity physical modeling and detection performance evaluation system for space targets. The system includes: a parameter acquisition module for acquiring the relative position and relative velocity of the spacecraft and the asteroid, as well as the camera's detection limiting magnitude, focal length, pixel size, and point spread function full width at half maximum (FWHM); a static apparent magnitude determination module for determining the asteroid's static apparent magnitude based on the relative position of the spacecraft and the asteroid and the asteroid's physical characteristics; a trailing parameter determination module for determining the spacecraft's angular velocity modulus relative to the asteroid based on the relative velocity, and calculating the trailing parameters in conjunction with the focal length, pixel size, and FWHM; and an effective apparent magnitude determination module for determining the effective apparent magnitude based on segmented magnitudes. The system comprises a model that calculates the equivalent magnitude loss based on the trailing parameters and determines the effective apparent magnitude of the asteroid based on the static apparent magnitude; a parameter optimization module that constructs a two-dimensional parameter grid of focal length and exposure time, and determines the effective signal-to-noise ratio and detection margin after considering magnitude loss for each parameter combination in the parameter grid; a heat map is generated using the effective signal-to-noise ratio and detection margin as indicators, and the optimal parameter combination is selected and output; and an image generation module that generates a high-fidelity simulation image based on the optimal parameter combination, and feeds back the high-fidelity simulation image and the corresponding observation values ​​to the guidance, navigation and control system, wherein the observation values ​​include the coordinates of the asteroid in the high-fidelity simulation image and the effective signal-to-noise ratio.

[0023] Thirdly, an electronic device is provided, the electronic device including a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the method as described in any implementation of the first aspect.

[0024] Fourthly, a computer-readable storage medium is provided, including computer instructions that, when executed on an electronic device, cause the electronic device to perform a method as described in any implementation of the first aspect.

[0025] Fifthly, a computer program product is provided that, when run on a computer, causes the computer to perform the method in any implementation of the first aspect.

[0026] Understandably, the beneficial effects achieved by the system of the second aspect, the electronic device of the third aspect, the computer-readable storage medium of the fourth aspect, and the computer program product of the fifth aspect provided above can be referred to with reference to the beneficial effects of the first aspect and any of its possible design embodiments, which will not be repeated here. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention; Figure 2A flowchart illustrating a method for high-fidelity physical modeling and detection performance evaluation of space targets, provided in an embodiment of the present invention; Figure 3 A schematic diagram illustrating a performance evaluation matrix provided in an embodiment of the present invention; Figure 4 This is a high-fidelity simulation image shown in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an evaluation system provided in an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. The "or" in the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A or B can represent: A alone, A and B simultaneously, and B alone. A and B can be singular or plural. Furthermore, in the description of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items.

[0029] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0030] In this embodiment of the invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this embodiment of the invention should not be construed as superior or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0031] In near-Earth asteroid exploration missions, the optical navigation during the spacecraft's approach phase relies on target images acquired by cameras. Navigation algorithms calculate relative position and velocity, and image quality directly determines navigation accuracy and the success rate of the exploration. As exploration missions expand to close-range, high-speed flyby scenarios, factors such as the relative motion between the spacecraft and the asteroid, camera parameter settings, and the real-time nature of simulations have an increasingly significant impact on exploration performance. Therefore, accurately simulating real-world exploration scenarios, optimizing camera parameters, and supporting closed-loop verification through simulation technology have become urgent technical requirements.

[0032] To address this need, a commonly used solution in existing technologies is a simulation verification method based on offline image generation. This method generates target images by loading star catalog data and simulating camera imaging effects, and then combines navigation algorithms for offline analysis to assess the feasibility of the exploration and adjust camera parameters.

[0033] However, this offline image generation simulation method has a key flaw: it only simulates motion trailing through visual blurring effects and lacks a quantitative model based on the physical energy dispersion mechanism. It cannot accurately calculate the signal-to-noise ratio drop and magnitude loss caused by the trailing effect. Consequently, the selection of core parameters such as camera focal length and exposure time can only rely on the designer's experience or repeated trial and error. Moreover, the complex star table processing and point spread function convolution result in extremely slow simulation speed. It is impossible to achieve both high fidelity and real-time performance, and it is also difficult to form a closed loop with the guidance, navigation and control system. It cannot accurately support the detection performance evaluation and parameter optimization in close-range high-speed flyby scenarios.

[0034] In view of this, embodiments of the present invention provide a method for high-fidelity physical modeling and detection performance evaluation of space targets. The method includes: acquiring the relative position and relative velocity of the spacecraft and the asteroid, as well as the detection limiting magnitude, focal length, pixel size, and full width at half maximum (FWHM) of the camera; determining the static apparent magnitude of the asteroid based on the relative position of the spacecraft and the asteroid and the physical characteristics of the asteroid; determining the angular velocity modulus of the spacecraft relative to the asteroid based on the relative velocity, and calculating the trailing parameters in combination with the focal length, pixel size, and FWHM of the point spread function; and calculating the magnitude of the asteroid's apparent magnitude based on the trailing parameters using a piecewise model. The apparent magnitude of the asteroid is determined based on the static apparent magnitude. A two-dimensional parameter grid of focal length and exposure time is constructed. For each parameter combination in the parameter grid, the effective signal-to-noise ratio and detection margin after considering the magnitude loss are determined. A heat map is generated using the effective signal-to-noise ratio and detection margin as indicators. The optimal parameter combination is selected and output. A high-fidelity simulation image is generated based on the optimal parameter combination. The high-fidelity simulation image and the corresponding observation values ​​are fed back to the guidance, navigation and control system. The observation values ​​include the coordinates of the asteroid in the high-fidelity simulation image and the effective signal-to-noise ratio.

[0035] The method provided by this invention systematically realizes high-fidelity physical modeling and detection performance evaluation of space targets through a complete process including parameter acquisition, static apparent magnitude determination, trailing parameter calculation, effective apparent magnitude determination, parameter optimization, high-fidelity image generation, and feedback. This method first accurately quantifies the optical properties of the target based on physical characteristics and geometric relationships, then scientifically selects camera parameters through a trailing loss model and two-dimensional parameter optimization, and finally generates high-fidelity simulation images and forms a closed-loop feedback. This not only solves the problem of the lack of physical quantitative modeling in existing technologies but also automates detection performance evaluation and parameter optimization. Furthermore, the closed-loop feedback ensures compatibility with guidance, navigation, and control systems, significantly improving the accuracy and reliability of optical detection in deep space exploration missions.

[0036] In some embodiments, the method for evaluating the high-fidelity physical modeling and detection performance of space targets provided in this invention can be executed by a high-fidelity physical modeling and detection performance evaluation system 100 (hereinafter referred to as evaluation system 100).

[0037] As an example, the evaluation system 100 can be any electronic device 200 with data processing capabilities, such as a general-purpose computer, personal computer, laptop computer, switch, or tablet computer. The specific implementation of the evaluation system 100 is not limited here.

[0038] Figure 1 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown. The electronic device 200 includes a processor 210, a memory 220, and a communication interface 230.

[0039] Processor 210 may include one or more processing cores. Processor 210 connects to various parts within electronic device 200 using various interfaces and lines, and performs various functions and processes data of electronic device 200 by running or executing instructions, programs, code sets, or instruction sets stored in memory 220, and by calling data stored in memory 220. Optionally, processor 210 may be implemented using at least one of the following hardware forms: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0040] The memory 220 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 220 may include a non-transitory computer-readable storage medium. The memory 220 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 220 may include a program storage area. This program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described above, etc.

[0041] Communication interface 230 is used to communicate with other devices, equipment or communication networks, such as data storage devices, image processing devices or Ethernet, wireless access network (RAN), wireless local area network (WLAN), etc.

[0042] In terms of physical implementation, the aforementioned devices (such as processor 210, memory 220, and communication interface 230) can each be devices within the same device (such as a laptop computer). Alternatively, at least two of these devices can be located within the same device, i.e., as different devices within the same device, similar to the deployment of devices or components in a distributed system.

[0043] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device 200. In other embodiments of the present invention, the electronic device 200 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0044] The following description, in conjunction with the accompanying drawings, illustrates a method for high-fidelity physical modeling and detection performance evaluation of space targets provided by embodiments of the present invention.

[0045] Figure 2 This is a flowchart illustrating a method for high-fidelity physical modeling and detection performance evaluation of space targets, provided as an embodiment of the present invention. Optionally, this method can be... Figure 1 The illustrated electronic device 200 performs this method, which includes the following steps: S1. Obtain the relative position and relative velocity of the spacecraft and the asteroid, as well as the camera's detection limit magnitude, focal length, pixel size, and point spread function full width at half maximum (FWHM).

[0046] S2. Based on the relative position of the spacecraft and the asteroid and the physical characteristics of the asteroid, determine the static apparent magnitude of the asteroid.

[0047] In one possible implementation, the aforementioned S2 specifically includes: The observational geometry is determined based on the relative positions of the spacecraft and the asteroid, including the asteroid's phase angle and the triangular geometry of the sun, asteroid, and spacecraft. Based on the asteroid's physical properties, the static photon flux of the asteroid without tail interference is determined according to the observational geometry. The physical properties include the asteroid's geometrical albedo, equivalent diameter, and surface spectral characteristics. The static photon flux is converted into the asteroid's static apparent magnitude according to the correspondence between the static photon flux and apparent magnitude.

[0048] Specifically, the determination of the observational geometry is based on the derivation of the relative position coordinates of the spacecraft and the asteroid. The phase angle, referring to the angle formed between the Sun, the asteroid, and the spacecraft, directly affects the illuminated area and reflected light intensity of the asteroid's surface. The triangular geometry of the Sun-asteroid-spacecraft clarifies their relative positions in space, providing a geometric basis for subsequent photon flux calculations. The asteroid's geometric albedo reflects its surface's ability to reflect sunlight, its equivalent diameter determines its overall illuminated area, and its surface spectral characteristics affect the reflection efficiency of different wavelengths of light. These physical properties need to be obtained through previous detection data or by querying astronomical databases.

[0049] Furthermore, when calculating static photon flux, phase angle corrections based on observation geometry are necessary. Asteroids exhibit varying reflectivity at different phase angles. For instance, at 0 degrees, the side of the asteroid facing the spacecraft is fully illuminated, resulting in peak photon flux. As the phase angle increases, the illuminated area gradually decreases, and the photon flux correspondingly declines. The correspondence between static photon flux and apparent magnitude follows the commonly used astronomical definition of magnitude. Therefore, the calculated static photon flux can be accurately converted to the corresponding static apparent magnitude, ensuring the objectivity and accuracy of the conversion process.

[0050] This method of determining static apparent magnitude breaks away from the limitations of traditional empirical estimation. By combining geometric relationships and physical characteristics, it achieves quantitative calculation, which can control the calculation error of static apparent magnitude within a small range. This lays a solid data foundation for the subsequent calculation of equivalent magnitude loss and the determination of effective apparent magnitude, ensuring that every step of the entire detection performance evaluation process has accurate data support, thereby improving the credibility and reference value of the final evaluation results.

[0051] S3. Determine the angular velocity modulus of the spacecraft relative to the asteroid based on the relative velocity, and calculate the trailing parameters by combining the focal length, pixel size, and point spread function half-height and full width.

[0052] In some embodiments, the formula for determining the trailing parameter is: ; ; in, Let t be the magnitude of the angular velocity of the spacecraft relative to the asteroid, r be the relative position of the spacecraft and the asteroid, and v be the relative velocity of the spacecraft and the asteroid. exp f is the exposure time, p is the focal length, and FWHM is the point spread function (FWHM).

[0053] The quantification method of trailing parameters provided in this invention breaks through the limitations of relying solely on visual judgment to determine the strength of trailing. It transforms the abstract trailing effect into calculable and comparable specific values, enabling a unified assessment of the trailing impact under different relative motion states and camera parameter configurations. This provides accurate and unified input for subsequent segmented calculation of equivalent magnitude loss based on trailing parameters, allowing the entire detection performance evaluation process to be built on a solid physical quantification foundation, effectively improving the objectivity and credibility of the evaluation results.

[0054] S4. Based on the segmented model, calculate the equivalent magnitude loss according to the tail parameters, and determine the effective apparent magnitude of the asteroid according to the static apparent magnitude.

[0055] In one possible implementation, the logic for determining the segmentation model is as follows: When the tailing parameter d < 0.3, the equivalent star magnitude loss ; When the tailing parameter d ≥ 0.3, the equivalent star magnitude loss ; Specifically, the threshold of 0.3 for the trailing parameter d is determined based on the physical energy dispersion law. When d < 0.3, it means that the image plane movement distance of the asteroid during the exposure time is less than 0.3 times the full width at half maximum (FWHM) of the point spread function. At this time, the energy dispersion is extremely low, and its impact on the detection signal can be ignored. Therefore, setting the equivalent magnitude loss Δm = 0 can truly reflect the optical characteristics of the target in a quasi-stationary state. When d ≥ 0.3, the image plane movement of the asteroid will cause photon energy to be dispersed across multiple pixels, resulting in a decrease in peak signal intensity, which in turn manifests as a decrease in detection sensitivity. The quantitative formula can accurately calculate the magnitude loss caused by the trailing effect. This formula strictly follows the logarithmic inverse relationship between magnitude and photon flux, ensuring that the calculation of magnitude loss conforms to the general laws of astronomy.

[0056] The effective apparent magnitude The formula for determining it is: ; in, This refers to the static apparent magnitude of an asteroid.

[0057] The method provided by this invention sets a segmented model based on the numerical range of the trailing parameter, and then combines it with the static apparent magnitude to obtain the effective apparent magnitude. This segmented logic conforms to the physical energy dispersion law under different degrees of trailing, accurately capturing the detection sensitivity attenuation caused by significant trailing, and avoiding overcorrection in sub-pixel micro-motion scenarios. This makes the calculation of the effective apparent magnitude more consistent with actual detection scenarios, providing accurate optical characteristic indicators for subsequent detection performance evaluation and parameter optimization.

[0058] S5. Construct a two-dimensional parameter grid for focal length and exposure time. For each parameter combination in the parameter grid, determine the effective signal-to-noise ratio and detection margin after considering magnitude loss.

[0059] In some embodiments, S5 above includes: The focal length is constructed to have a range of values ​​[f min ,f max The exposure time range is [t]. min ,t max A two-dimensional parametric mesh, where f min f is the minimum usable focal length of the camera. max t is the maximum usable focal length of the camera. min t is the camera's shortest exposure time threshold. max The maximum exposure time threshold for the camera is defined as follows: For each parameter combination in the two-dimensional parameter grid, the trailing parameter is first calculated based on the exposure time corresponding to each parameter combination, and then the equivalent magnitude loss of each parameter combination is obtained based on the piecewise model; the effective apparent magnitude corresponding to each parameter combination is determined based on the equivalent magnitude loss and the static apparent magnitude of the asteroid; the effective signal-to-noise ratio of each parameter combination after considering the magnitude loss is determined based on the static signal-to-noise ratio and the equivalent magnitude loss, and the detection margin of each parameter combination is determined based on the difference between the detection limit magnitude and the effective apparent magnitude.

[0060] The method provided by this invention constructs a two-dimensional parameter grid of focal length and exposure time. For each parameter combination, it sequentially calculates the trailing parameter, equivalent magnitude loss, and effective apparent magnitude. Then, it determines the effective signal-to-noise ratio (SNR) through the static SNR and equivalent magnitude loss, and determines the detection margin by the difference between the detection limiting magnitude and the effective apparent magnitude. This process achieves a one-to-one correspondence between parameter combinations and detection performance indicators, covering the feasible range of camera parameters and comprehensively reflecting the detection capabilities under different parameter combinations through multi-indicator calculations. This avoids the limitations of single-indicator evaluation and provides comprehensive and accurate performance data support for the subsequent selection of the optimal parameter combination.

[0061] S6. Generate a heat map using the effective signal-to-noise ratio and detection margin as indicators, and filter and output the optimal parameter combination.

[0062] In some embodiments, S6 includes: A heatmap is plotted using the effective signal-to-noise ratio and probe margin as performance evaluation matrices. Based on the performance evaluation matrix, a parameter recommendation strategy is determined according to the relative angular velocity magnitude between the spacecraft and the asteroid. If the relative angular velocity magnitude is less than a preset angular velocity threshold, the parameter combination with the largest effective signal-to-noise ratio among long-exposure parameter combinations with an exposure time greater than or equal to the exposure time threshold is determined as the optimal parameter combination. If the relative angular velocity magnitude is greater than or equal to the preset angular velocity threshold, the parameter combination with the largest probe margin and an effective signal-to-noise ratio greater than the preset threshold among short-exposure parameter combinations with an exposure time less than or equal to the exposure time threshold is determined as the optimal parameter combination.

[0063] The heatmap is plotted using focal length and exposure time as two-dimensional coordinate axes. Different color shades are mapped to the effective signal-to-noise ratio and the detection margin, respectively. The higher the effective signal-to-noise ratio and the larger the detection margin, the brighter the corresponding color. This allows technicians to intuitively identify the performance advantage areas in the parameter space and quickly locate the potential optimal parameter combination.

[0064] For example, see Figure 3 , Figure 3 This is a visualization diagram of a performance evaluation matrix provided in an embodiment of the present invention. The horizontal axis represents exposure time, and the vertical axis represents the camera's focal length selection range. Each pixel position in the diagram represents a specific camera configuration. The intensity of the colors in the heatmap represents the performance of the detection system under that camera configuration. In the upper left image (Effective SNR), the effective signal-to-noise ratio is displayed; brighter colors indicate a higher SNR and a clearer target. In the upper right image (Limiting Mag for Moving Target), the limiting magnitude for moving target detection is displayed; brighter colors indicate a darker detectable target. In the lower right image (Trailing Loss), the trailing loss penalty is displayed; red areas represent severe trailing and significant magnitude loss.

[0065] The preset angular velocity threshold and exposure time threshold need to be set in combination with the camera performance and the requirements of the detection task. For example, the preset angular velocity threshold can be set to 0.001 rad / s, and the exposure time threshold can be set to 100 ms. That is, an exposure time ≥ 100 ms is a long exposure, and ≤ 10 ms is a short exposure. The effective signal-to-noise ratio preset threshold is usually set to 5 to ensure that the detection signal has sufficient anti-interference capability.

[0066] In practical applications, when the relative angular velocity magnitude between the spacecraft and the asteroid is 0.0005 rad / s (<0.001 rad / s), it belongs to a low angular velocity scenario, and the trailing effect is negligible. At this time, long exposure combinations can accumulate more photons. The system selects the combination with the largest effective signal-to-noise ratio from the parameter combinations with an exposure time ≥100ms to maximize the signal strength. When the relative angular velocity magnitude is 0.01 rad / s (≥0.001 rad / s), it belongs to a high angular velocity trailing scenario. Long exposure will lead to excessive loss of equivalent magnitude. The system focuses on short exposure combinations with an exposure time ≤10ms, and prioritizes the combination with the largest detection margin and an effective signal-to-noise ratio >5 to avoid detection failure and ensure the stability and anti-interference capability of the detection.

[0067] The method provided by this invention generates a heatmap using effective signal-to-noise ratio (SNR) and probe margin as dual indicators, intuitively presenting the distribution of detection performance in the parameter space. Then, a differentiated parameter recommendation strategy is formulated based on the relative angular velocity modulus: in low angular velocity scenarios, long exposure combinations are prioritized to maximize the effective SNR; in high angular velocity scenarios, short exposure combinations are focused on to select parameters with the largest probe margin and satisfactory effective SNR. This strategy not only visualizes parameter performance using heatmaps but also avoids the risk of detection failure in scenarios dominated by motion blur through scenario-based selection logic. It solves the problem of existing technologies relying on experience to select camera parameters, achieving systematic and intelligent parameter optimization, and ensuring that the recommended parameter combinations achieve optimal detection results in different detection scenarios.

[0068] S7. Generate a high-fidelity simulation image based on the optimal parameter combination, and feed the high-fidelity simulation image and the corresponding observation values ​​back to the guidance, navigation and control system. The observation values ​​include the coordinates of the asteroid in the high-fidelity simulation image and the effective signal-to-noise ratio.

[0069] In some embodiments, generating a high-fidelity simulation image based on the optimal parameter combination includes: generating a high-fidelity simulation image using a PSF object caching mechanism and a star catalog optimization strategy; the PSF object caching mechanism uniquely identifies the optical state through key-value tuples; if the key-value tuple is hit in the cache, the convolution kernel in memory is directly reused; if it is not hit, the Bessel function is calculated to generate a new convolution kernel, and the new convolution kernel is updated to the cache; The key-value tuple is: ; in: Focal length For wavelength, For caliber, Occlusion ratio; The star catalog optimization strategy is as follows: after loading the Gaia star catalog, first trim and retain stars within a preset range, and then select and retain stars with brightness m < detection limiting magnitude. The preset range is: ; in, Right ascension of a star To observe the right ascension of the center of the field of view, This is the camera's field of view.

[0070] For details, see Figure 4 , Figure 4 This invention illustrates a high-fidelity simulated image. The core of the PSF object caching mechanism lies in establishing a unique mapping between optical states and convolutional kernels through key-value tuples. The wavelength needs to be preset based on the surface spectral characteristics of the asteroid to ensure that the convolutional kernel matches the spectral response of the target. The aperture and occlusion ratio are inherent parameters of the camera's optical system, directly affecting the shape of the point spread function. When generating a high-fidelity simulated image, the system first checks if a convolutional kernel matching the current key-value tuple exists in the cache. If it does, it is directly reused without recalculating the Bessel function. For example, when parameters such as focal length and wavelength in the optimal parameter combination remain unchanged, the cached convolutional kernel can be directly called to complete imaging, significantly reducing the PSF generation time for a single frame image. If the cache is not found, such as when the camera zooms and the focal length changes, a convolutional kernel adapted to the new optical state is generated by calculating the Bessel function and synchronously updated to the cache, providing a reuse basis for subsequent imaging under the same optical state.

[0071] On the other hand, in the star catalog optimization strategy, the loaded Gaia star catalog contains a massive amount of star position and brightness information, which is first processed through... The field of view is clipped within a preset range, retaining only the stars within the observation field of view and removing invalid data outside the field of view. Then, stars with brightness m < detection limiting magnitude are selected to avoid wasting computing resources due to rendering stars that are too dark.

[0072] The method provided by this invention employs a PSF object caching mechanism and a star catalog optimization strategy to generate high-fidelity simulation images. Optical states are uniquely identified through key-value tuples. When the cache hits, the convolution kernel is reused; when it misses, a Bessel function is calculated to generate a new convolution kernel and update the cache. Simultaneously, the Gaia star catalog is cropped and its magnitudes are filtered. This design significantly reduces the cost of repetitive PSF convolution calculations through the caching mechanism and reduces the amount of invalid stellar data processed through star catalog optimization. While maintaining high fidelity in the simulation images, it significantly improves image generation efficiency, resolving the contradiction between high fidelity and real-time performance, and providing efficient image data support for subsequent closed-loop feedback.

[0073] In other embodiments, feeding back the high-fidelity simulation image and corresponding observations to the guidance, navigation, and control system includes: establishing a connection via an instant messaging interface and establishing a communication connection with the guidance, navigation, and control system using a data serialization protocol at a communication frequency of 1 Hz; receiving T signals sent by the guidance, navigation, and control system. k The system generates time-state prediction data, including time t, spacecraft position, spacecraft velocity, and attitude quaternions. Based on this prediction data, the system interpolates to obtain the actual orbital states of the spacecraft and the asteroid. Combined with the high-fidelity simulation image, it extracts observation values, including the asteroid's coordinates in the image, effective signal-to-noise ratio, and timestamp. These observation values ​​are then fed back to the guidance, navigation, and control system via a UDP asynchronous interface. These observation values ​​are used by the guidance, navigation, and control system at time t. k+1 Filtering updates and trajectory planning at different times.

[0074] Specifically, a communication connection with the guidance, navigation and control system is established through the UDP asynchronous interface combined with the Protocol Buffers protocol. The core advantage of the UDP asynchronous interface is that data transmission can be achieved without establishing a connection, which can effectively reduce communication latency. The Protocol Buffers protocol has the characteristics of high data compression efficiency and fast serialization and deserialization speed. The combination of the two can ensure the high efficiency and lightweight nature of data transmission, and adapt to the stringent real-time requirements of closed-loop testing.

[0075] The 1Hz communication frequency is consistent with the standard navigation cycle, enabling timing synchronization between the simulation system and the guidance, navigation, and control system, ensuring timely feedback of observations, and meeting the T... k+1 Time requirements for time-based filtering updates and trajectory planning. k The spacecraft position, velocity, and attitude quaternions in the time-state prediction data are predictions obtained by the guidance, navigation, and control system based on its own algorithm. The simulation system can convert discrete prediction data into continuous real orbital states through interpolation, accurately restoring the relative geometric relationship between the spacecraft and the asteroid at the observation time.

[0076] When extracting observations from high-fidelity simulation images, the coordinates of the asteroid are determined by image recognition algorithms, the effective signal-to-noise ratio is calculated based on the grayscale values ​​of image pixels, and the timestamp records the precise moment the image was generated. These observations can comprehensively reflect the detection status of the target.

[0077] This asynchronous closed-loop communication method completely breaks the limitation of traditional offline simulation that cannot interact in real time. It constructs a complete measurement-feedback-control link through bidirectional data transmission, enabling the guidance, navigation and control system to continuously optimize algorithm parameters in a dynamic simulation environment and promptly expose stability problems that may occur in the dynamic process. This provides strong verification support for the reliability of the guidance, navigation and control system in deep space exploration missions, and also makes the debugging and optimization of the entire exploration system more targeted and efficient.

[0078] As shown in S1-S7, the method provided by this invention systematically achieves high-fidelity physical modeling and detection performance evaluation of space targets through a complete process of parameter acquisition, static apparent magnitude determination, trailing parameter calculation, effective apparent magnitude determination, parameter optimization, high-fidelity image generation, and feedback. This method first accurately quantifies the optical characteristics of the target based on physical properties and geometric relationships, then scientifically selects camera parameters through a trailing loss model and two-dimensional parameter optimization, and finally generates a high-fidelity simulation image and forms a closed-loop feedback. This not only solves the problem of lacking physical quantitative modeling in existing technologies but also automates detection performance evaluation and parameter optimization. Simultaneously, the closed-loop feedback ensures compatibility with guidance, navigation, and control systems, significantly improving the accuracy and reliability of optical detection in deep space exploration missions.

[0079] To facilitate understanding of this solution, the method provided by the embodiments of the present invention will be further explained below with reference to a specific example.

[0080] In one example, the method provided by this embodiment of the invention includes: First, the scene and parameters were initialized. The relative position between the spacecraft and asteroid 2016HO3 was set to 500km, and the relative velocity was 5km / s. The angular velocity at the closest point was calculated to be ω≈0.01rad / s based on the relative position and relative velocity. The camera's detection limiting magnitude was set to magnitude 15, focal length f=1000mm, pixel size p=10μm (i.e. 0.01mm), and point spread function full width at half maximum (FWHM)=2 pixels. The physical properties of the asteroid are known: geometric albedo 0.15, equivalent diameter 40m, and surface spectral characteristics corresponding to wavelength λ=0.55μm.

[0081] Then, the relative position and relative velocity between the spacecraft and the asteroid are obtained, along with all necessary parameters such as the camera's detection limiting magnitude, focal length, pixel size, and point spread function half-width and full width at half-maximum, providing basic input data for subsequent calculations.

[0082] The observational geometry is determined based on the relative positions of the spacecraft and the asteroid. In this triangular geometry of the sun-asteroid-spacecraft, the asteroid's phase angle is calculated to be 30°. Combining the asteroid's geometric albedo, equivalent diameter, and surface spectral characteristics, the static photon flux without tail interference is calculated to be 1.2 × 10⁻⁶. 6 Photons / second; then, based on the correspondence between static photon flux and apparent magnitude, the static photon flux is converted into the static apparent magnitude of the asteroid as 12.1.

[0083] Based on the relative velocity, the angular velocity modulus ω = 0.01 rad / s, combined with the focal length f = 1000 mm, pixel size p = 0.01 mm, and dot spread function full width at half maximum (FWHM) = 2 pixels, the numerator of the trailing parameters is first calculated, and the initial exposure time t is set. exp =1.0s, substituting it gives 1000 pixels, then dividing by FWHM gives the trailing parameter d=1000 / 2=500.

[0084] The equivalent magnitude loss is calculated based on the segmented model. Since the tail parameter d=500≥0.3, the equivalent magnitude loss is approximately 6.75 magnitude according to the logical calculation. Then, based on the static apparent magnitude of 12.1 magnitude, the effective apparent magnitude of the asteroid is calculated as 12.1+6.75=18.85 magnitude using the formula.

[0085] Construct a two-dimensional parameter grid for focal length and exposure time, where the focal length values ​​range as follows: [f min =500mm,f max =1500mm]; The exposure time range is as follows: [tmin=0.1ms, tmax=1.0s]; For each (f,t) in the grid exp The calculations are performed using a combination of parameters. Taking the initial parameter combination (1000mm, 1.0s) as an example, the tailing parameter d=500, the equivalent magnitude loss Δm=6.75 magnitude, and the effective apparent magnitude=18.85 magnitude are first calculated; then, combined with the static photon flux of 1.2×10 6 Photons / second, the static signal-to-noise ratio is calculated to be 30; the effective signal-to-noise ratio SNRe after considering magnitude loss is calculated to be 0.06; based on the difference between the detection limit magnitude of 15 and the effective apparent magnitude of 18.85, the detection margin of this parameter combination is determined to be 15-18.85=-3.85 magnitude.

[0086] A performance evaluation matrix was generated using effective signal-to-noise ratio (SNR) and probe margin as indicators, and a heatmap was plotted for visualization. The heatmap clearly shows that the parameter combination (1000mm, 1.0s) corresponds to an extremely low effective SNR and a negative probe margin, belonging to the infeasible region. Based on the heatmap and the relative angular velocity modulus between the spacecraft and the asteroid (0.01rad / s ≥ preset angular velocity threshold 0.001rad / s), a short-exposure parameter combination screening strategy was implemented to find the optimal solution among short-exposure parameter combinations with exposure time ≤ exposure time threshold 10ms. After traversing the parameter space, it was found that when the exposure time t... exp When the time is reduced to 1ms (0.001s), the tail parameter d'=0.5 is recalculated. Since the tail parameter is close to the point spread function scale, it can be regarded as having no equivalent magnitude loss Δm≈0.12, effective apparent magnitude=12.22 magnitude, static signal-to-noise ratio=5.5, effective signal-to-noise ratio≈4.92, and detection margin=2.78 magnitude. Further optimization revealed that when the exposure time was adjusted to 2ms (0.002s), the trailing parameter d=1, the equivalent magnitude loss Δm=0.38 magnitude, the effective apparent magnitude=12.48 magnitude, the static signal-to-noise ratio=8, the effective signal-to-noise ratio=5.66, and the detection margin=2.52 magnitude. This parameter combination (1000mm, 2ms) meets the requirements of maximizing the detection margin and having an effective signal-to-noise ratio SNReff=5.66>the preset threshold of 5. Therefore, it was determined to be the optimal parameter combination and output.

[0087] Based on the optimal parameter combination (1000mm, 2ms), a high-fidelity simulation image is generated using the PSF object caching mechanism and star catalog optimization strategy.

[0088] High-fidelity simulation images and corresponding observations are fed back to the guidance, navigation, and control system. A communication connection is established via a UDP asynchronous interface combined with Protocol Buffers, with a communication frequency of 1Hz. The system receives T signals sent by the guidance, navigation, and control system. k Predicted status data at time t=100s, spacecraft position (X=1.2×10⁻¹⁰). 6 m,Y=3.5×10 6 m, Z = 2.1 × 10 6The system calculates the orbital parameters (m), spacecraft velocity (Vx=5000m / s, Vy=1000m / s, Vz=800m / s), and attitude quaternions (q0=0.999, q1=0.02, q2=0.01, q3=0.03). Based on this state prediction data, the system interpolates to obtain the actual orbital states of the spacecraft and asteroid. Combined with high-fidelity simulation images, observations are extracted, including the asteroid's coordinates in the image (u=512 pixels, v=512 pixels), effective signal-to-noise ratio = 8, and timestamp = 100.002s. These observations are then fed back to the guidance, navigation, and control system via a UDP asynchronous interface for use in the system's T... k+1 The filtering updates and trajectory planning at each time point, and the successful convergence of the navigation filter, verify the effectiveness of the proposed scheme.

[0089] It should be noted that the above examples are for illustrative purposes only and do not impose any particular limitation on the specific implementation of the methods provided in the embodiments of the present invention.

[0090] The foregoing mainly describes the solutions of the embodiments of the present invention from a methodological perspective. It is understood that, in order to achieve the above-mentioned functions, the evaluation system 100 includes at least one of the hardware structures and software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present invention.

[0091] In this embodiment of the invention, the evaluation system 100 can be divided into functional units according to the above method example. For example, the evaluation system 100 can be divided into functional units corresponding to each function, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment of the invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0092] For example, Figure 5This diagram illustrates the hardware structure of an evaluation system provided in an embodiment of the present invention. The evaluation system 100 includes: a parameter acquisition module 110, used to acquire the relative position and relative velocity of the spacecraft and the asteroid, as well as the camera's detection limiting magnitude, focal length, pixel size, and point spread function full width at half maximum (FWHM); a static apparent magnitude determination module 120, used to determine the static apparent magnitude of the asteroid based on the relative position of the spacecraft and the asteroid and the physical characteristics of the asteroid; a trailing parameter determination module 130, used to determine the angular velocity magnitude of the spacecraft relative to the asteroid based on the relative velocity, and calculate the trailing parameters in conjunction with the focal length, pixel size, and FWHM; and an effective apparent magnitude determination module 140, used to calculate the magnitude based on a segmented model and the trailing parameters. The effective apparent magnitude of the asteroid is determined based on the static apparent magnitude. A parameter optimization module 150 constructs a two-dimensional parameter grid of focal length and exposure time, and for each parameter combination in the grid, determines the effective signal-to-noise ratio (SNR) and detection margin after considering magnitude loss. A heatmap is generated using the effective SNR and detection margin as indicators, and the optimal parameter combination is selected and output. An image generation module 160 generates a high-fidelity simulation image based on the optimal parameter combination, and feeds back the high-fidelity simulation image and corresponding observations to the guidance, navigation, and control system. The observations include the asteroid's coordinates and effective SNR in the high-fidelity simulation image.

[0093] It should be understood that specific descriptions of the above-mentioned optional methods can be found in the foregoing method embodiments, and will not be repeated here. Furthermore, explanations of any of the evaluation systems 100 provided above, as well as descriptions of their beneficial effects, can be found in the corresponding method embodiments described above, and will not be repeated here.

[0094] This invention also provides a computer-readable storage medium storing at least one computer instruction, which is loaded and executed by a processor to implement the methods of the various embodiments described above. Explanations of the relevant content and descriptions of the beneficial effects of any of the computer-readable storage media provided above can be found in the corresponding embodiments described above, and will not be repeated here.

[0095] This invention also provides a chip. This chip integrates a control circuit for implementing the functions of the evaluation system 100 described above, and one or more ports. Optionally, the functions supported by this chip are as described above, and will not be repeated here.

[0096] Those skilled in the art will understand that the program for implementing all or part of the steps of the above embodiments, which can be executed by a program instructing related hardware, can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a random access memory, etc. The processing unit or processor mentioned above can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0097] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this invention is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0098] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as, but not limited to, the aforementioned memory, computer-readable storage medium, and communication chip, are all non-transitory. Those skilled in the art should recognize that the functions described in the embodiments of the present invention in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0099] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for high-fidelity physical modeling and detection performance evaluation of space targets, characterized in that, The method includes: Obtain the relative position and relative velocity between the spacecraft and the asteroid, as well as the camera's detection limiting magnitude, focal length, pixel size, and point spread function full width at half maximum (FWHM). Based on the relative positions of the spacecraft and the asteroid, and the physical properties of the asteroid, the static apparent magnitude of the asteroid is determined. The angular velocity modulus of the spacecraft relative to the asteroid is determined based on the relative velocity, and the trailing parameters are calculated by combining the focal length, pixel size, and full width at half maximum (FWHM) of the point spread function. Based on the segmented model, the equivalent magnitude loss is calculated according to the tail parameters, and the effective apparent magnitude of the asteroid is determined according to the static apparent magnitude. A two-dimensional parameter grid of focal length and exposure time is constructed. For each parameter combination in the parameter grid, the effective signal-to-noise ratio and detection margin after considering magnitude loss are determined respectively. A performance evaluation matrix is ​​constructed based on the effective signal-to-noise ratio and detection margin, and the optimal parameter combination is selected and output. A high-fidelity simulation image is generated based on the optimal parameter combination. The high-fidelity simulation image and the corresponding observation values ​​are fed back to the guidance, navigation and control system. The observation values ​​include the coordinates of the asteroid in the high-fidelity simulation image and the effective signal-to-noise ratio.

2. The method for high-fidelity physical modeling and detection performance evaluation of space targets according to claim 1, characterized in that, The determination of the asteroid's static apparent magnitude based on the relative position of the spacecraft and the asteroid, and the asteroid's physical properties, includes: The observation geometry is determined based on the relative positions of the spacecraft and the asteroid, including the phase angle of the asteroid and the triangular geometry of the sun-asteroid-spacecraft. Based on the physical properties of the asteroid, the static photon flux of the asteroid under tail-free conditions is determined according to the observed geometric relationships. These physical properties include the asteroid's geometric albedo, equivalent diameter, and surface spectral characteristics. Based on the correspondence between the static photon flux and apparent magnitude, the static photon flux is converted into the static apparent magnitude of the asteroid.

3. The method for high-fidelity physical modeling and detection performance evaluation of space targets according to claim 1, characterized in that, The formula for determining the trailing parameter is: ； ； in, Let t be the magnitude of the angular velocity of the spacecraft relative to the asteroid, r be the relative position of the spacecraft and the asteroid, and v be the relative velocity of the spacecraft and the asteroid. exp f is the exposure time, p is the focal length, and FWHM is the point spread function (FWHM).

4. The method for high-fidelity physical modeling and detection performance evaluation of space targets according to claim 3, characterized in that, The determination logic of the segmentation model is as follows: When the trailing parameter is less than the first preset threshold, the equivalent star magnitude loss is... ; When the trailing parameter is greater than or equal to the first preset threshold, the equivalent star magnitude loss is... ; The effective apparent magnitude The formula for determining it is: ； in, This refers to the static apparent magnitude of an asteroid.

5. The method for high-fidelity physical modeling and detection performance evaluation of space targets according to claim 4, characterized in that, The construction of a two-dimensional parameter grid for focal length and exposure time, and the determination of the effective signal-to-noise ratio and detection margin after considering magnitude loss for each parameter combination in the parameter grid, include: The focal length is constructed to have a range of values ​​[f min ,f max The exposure time range is [t]. min ,t max A two-dimensional parametric mesh, where f min f is the minimum usable focal length of the camera. max t is the maximum usable focal length of the camera. min t is the camera's shortest exposure time threshold. max This is the maximum exposure time threshold for the camera; For each parameter combination in the two-dimensional parameter grid, the trailing parameter is first calculated based on the exposure time corresponding to each parameter combination, and then the equivalent star magnitude loss of each parameter combination is obtained based on the piecewise model. Based on the equivalent magnitude loss and the static apparent magnitude of the asteroid, determine the effective apparent magnitude corresponding to each parameter combination; The effective signal-to-noise ratio after considering magnitude loss for each parameter combination is determined based on the static signal-to-noise ratio and the equivalent magnitude loss. The detection margin for each parameter combination is determined based on the difference between the detection limit magnitude and the effective apparent magnitude.

6. The method for high-fidelity physical modeling and detection performance evaluation of space targets according to claim 5, characterized in that, The process of constructing a performance evaluation matrix using the effective signal-to-noise ratio and detection margin as indicators, and selecting and outputting the optimal parameter combination includes: A performance evaluation matrix is ​​constructed using the effective signal-to-noise ratio and detection margin as indicators; Based on the performance evaluation matrix, a parameter recommendation strategy is determined according to the relative angular velocity magnitude between the spacecraft and the asteroid. If the relative angular velocity magnitude is less than the preset angular velocity threshold, the parameter combination with the largest effective signal-to-noise ratio among the long exposure parameter combinations with an exposure time greater than or equal to the exposure time boundary threshold is determined as the optimal parameter combination. If the relative angular velocity magnitude is greater than or equal to the preset angular velocity threshold, the parameter combination with the largest detection margin and an effective signal-to-noise ratio greater than the preset threshold among the short exposure parameter combinations with an exposure time less than or equal to the exposure time boundary threshold is determined as the optimal parameter combination.

7. The method for high-fidelity physical modeling and detection performance evaluation of space targets according to claim 1, characterized in that, The generation of a high-fidelity simulation image based on the optimal parameter combination includes: High-fidelity simulation images are generated using a PSF object caching mechanism and a star catalog optimization strategy. The PSF object caching mechanism uniquely identifies the optical state through a key-value tuple; if the key-value tuple is hit in the cache, the convolution kernel in memory is directly reused; if it is not hit, the Bessel function is calculated to generate a new convolution kernel, and the new convolution kernel is updated to the cache. The key-value tuple is: ; in: Focal length For wavelength, For caliber, Occlusion ratio; The star catalog optimization strategy is as follows: after loading the Gaia star catalog, first trim and retain stars within a preset range, and then select and retain stars with brightness m < detection limiting magnitude. The preset range is: ； in, Right ascension of a star To observe the right ascension of the center of the field of view, This is the camera's field of view.

8. The method for high-fidelity physical modeling and detection performance evaluation of space targets according to claim 1, characterized in that, The step of feeding back the high-fidelity simulation image and the corresponding observation values ​​to the guidance, navigation and control system includes: A connection is established through an instant messaging interface, and a communication connection with the guidance, navigation and control system is established using a data serialization protocol at a frequency of 1 Hz. Receive T sent by the guidance, navigation and control system k The state prediction data includes time t, spacecraft position, spacecraft velocity, and attitude quaternions; Based on the state prediction data interpolation, the true orbital states of the spacecraft and asteroid are obtained. Combined with the high-fidelity simulation image, the observation values ​​are extracted, including the coordinates of the asteroid in the image, the effective signal-to-noise ratio, and the timestamp. The observed values ​​are fed back to the guidance, navigation, and control system via a real-time communication interface. These observed values ​​are used by the guidance, navigation, and control system in T... k+1 Filtering updates and trajectory planning at different times.

9. A high-fidelity physical modeling and detection performance evaluation system for space targets, characterized in that, The system includes: The parameter acquisition module is used to acquire the relative position and relative velocity between the spacecraft and the asteroid, as well as the camera's detection limiting magnitude, focal length, pixel size, and point spread function full width at half maximum (FWHM). The static apparent magnitude determination module is used to determine the static apparent magnitude of an asteroid based on the relative position of the spacecraft and the asteroid and the physical characteristics of the asteroid. The trailing parameter determination module is used to determine the angular velocity modulus of the spacecraft relative to the asteroid based on the relative velocity, and to calculate the trailing parameters in combination with the focal length, pixel size, and point spread function half-height and full width. The effective apparent magnitude determination module is used to calculate the equivalent magnitude loss based on the tail parameters according to the segmented model, and to determine the effective apparent magnitude of the asteroid based on the static apparent magnitude. The parameter optimization module is used to construct a two-dimensional parameter grid of focal length and exposure time. For each parameter combination in the parameter grid, the effective signal-to-noise ratio and detection margin after considering star magnitude loss are determined. The effective signal-to-noise ratio and detection margin are used as indicators to generate a performance evaluation matrix, and the optimal parameter combination is selected and output. The image generation module is used to generate a high-fidelity simulation image based on the optimal parameter combination, and feed the high-fidelity simulation image and the corresponding observation values ​​back to the guidance, navigation and control system. The observation values ​​include the coordinates of the asteroid in the high-fidelity simulation image and the effective signal-to-noise ratio.

10. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method for high-fidelity physical modeling and detection performance evaluation of space targets as described in any one of claims 1-8.

Images