A method and system for autonomous pointing calibration and optimization of laser payload

Abstract

The application provides a laser load autonomous pointing mark calibration optimization method and system, and belongs to the technical field of space aurora communication. The method comprises the following steps: obtaining an orbit normal vector according to the position and speed of a star in a J2000 coordinate system, establishing an orbit coordinate system, and performing target star screening; according to the screened star table, sequentially open-loop pointing target stars; when the target star is in the field of view of a star-sensitive camera, the target star spot centroid position and the tracking point deviation are used to calculate the miss distance x and y, and then the motor theoretical pointing is calculated. The autonomous calibration scheme provided by the application has an autonomous target star screening algorithm, does not need ground constant star data, and greatly improves the calibration efficiency and data reliability.

Description

Technical Field

[0001] This invention proposes a method and system for autonomous pointing calibration and optimization of laser payloads, belonging to the field of space laser communication technology. Background Technology

[0002] Laser terminal pointing error calibration includes three calibration methods: stellar calibration, inter-satellite calibration, and inter-satellite calibration. The calibration light source is a star, a ground calibration station, or a pre-calibrated counterpart laser terminal.

[0003] Compared to other methods, stellar calibration offers advantages such as high precision, practicality, independence, and long-term stability. The positions of stars in the universe are known and relatively stable, making them suitable as precise calibration light sources. Stellar calibration does not rely on external equipment and is less affected by environmental factors, allowing for independent calibration testing.

[0004] The invention described in "A Ground-Based Star Calibration Test System and Method for Spaceborne Laser Communication Payloads" (202211316762.3) provides a test system comprising an on-board component and a ground component. This system allows for pre-testing of the feasibility of star calibration for laser communication payloads on the ground, enabling the selection of calibrable stars in advance and the establishment of a ground-based calibration process to ensure smooth on-orbit calibration. The method involves first selecting a target star, inputting its position information to a simulation center, and then framing the data for transmission to the laser communication payload. Based on the input position, the payload points towards the target star and calculates the required azimuth and elevation angles of the turntable according to calibration principles. A correction matrix is ​​then calculated using the actual pointing angle after stable tracking. The correction matrix is ​​then uploaded, and star calibration is performed again. Summary of the Invention

[0005] This invention provides a method and system for autonomous pointing calibration and optimization of laser payloads, to solve the problems mentioned in the background section above:

[0006] This invention proposes an autonomous pointing calibration and optimization method for laser payloads, characterized in that the method includes:

[0007] S1. Based on the position and velocity of the satellite in the J2000 coordinate system, calculate the orbital normal vector, establish the orbital coordinate system, and then select target satellites.

[0008] S2. Based on the selected star catalog, point to the target star in an open loop in sequence;

[0009] S3. When the target star is within the field of view of the star-sensor camera, the miss distance x and y are calculated based on the deviation between the target star's spot centroid position and the tracking point, and then the theoretical direction of the motor is calculated in reverse.

[0010] S4. Calculate the error installation correction matrix using the actual and theoretical directions of the motor;

[0011] S5. Repeat steps S1-S4 until all target stars have been calibrated;

[0012] S6. Take the average of all error installation matrices to obtain the correction matrix;

[0013] S7. Add the correction matrix and recalibrate the previously calibrated target star. If the target star falls on the tracking point of the star-sensor camera after pointing to the target star, the calibration is considered successful. If the error is large, the calibration process is repeated.

[0014] Furthermore, S1 includes:

[0015] S11. Obtain the precise position data and velocity vector of this satellite in the J2000 coordinate system; the position and velocity include longitude, latitude, and altitude;

[0016] S12. Utilize ephemeris data and navigation algorithms to update the position and velocity information of the satellite in real time, ensuring the accuracy and timeliness of the data;

[0017] S13. Based on the position and velocity of the local star, calculate the normal vector of the orbital plane using the principles of orbital mechanics, determine the direction of the orbital normal vector, and use it as the negative direction of the Z-axis of the orbital coordinate system;

[0018] S14. Using the local star as the origin, select the X-axis and Y-axis to construct the orbital coordinate system;

[0019] S15. Verify the correctness of the coordinate system, ensuring that all axes are perpendicular to each other and satisfy the right-hand screw rule;

[0020] S16. Call the star database to obtain the position information of all stars to be screened in the J2000 coordinate system, and calculate the angle between the vector of each star and the -Z direction of the orbital coordinate system.

[0021] S17. Calculate the angle between the stellar vector and the solar vector to ensure that the star is not blocked by the Earth and is far away from the Sun to avoid light interference.

[0022] S18. Add the stars that meet the conditions to the target star list as calibration targets.

[0023] Furthermore, S2 includes:

[0024] S21. Sort the target star list according to relevant factors of the stars, including brightness and observation angle;

[0025] S22. The control system calculates and outputs pointing instructions based on the position information of the target star. After receiving the instructions, the laser payload adjusts its attitude so that the optical axis points to the target star.

[0026] S23. Monitor the attitude adjustment process in real time.

[0027] Furthermore, S23 includes:

[0028] S231. Before attitude adjustment, the current attitude of the laser payload is accurately measured using a high-precision gyroscope and accelerometer to obtain initial attitude data.

[0029] S232. Compare the initial attitude data with the preset ideal attitude, calculate the attitude deviation, and perform preliminary attitude calibration based on the deviation results.

[0030] S233. The attitude of the laser payload is dynamically tracked by using a Kalman filter or an extended Kalman filter, and data from multiple sensors are fused in real time.

[0031] S234. Based on the star's trajectory and the laser payload's current attitude, use celestial navigation algorithms to predict the target star's position over a future period.

[0032] S235. Based on the predicted star position and the current attitude deviation, dynamically adjust the attitude adjustment strategy. Adaptively optimize the attitude adjustment strategy using a fuzzy logic algorithm. Dynamically adjust the control parameters based on real-time feedback attitude data and changes in the external environment.

[0033] S236. During the attitude adjustment process, a closed-loop control strategy is adopted to compare the deviation between the actual attitude and the target attitude in real time, and the attitude adjustment is controlled by a PID controller.

[0034] S237. During the attitude adjustment process, the performance indicators of the laser load are continuously monitored. If any abnormality is found, the abnormality handling mechanism is immediately triggered.

[0035] Furthermore, S3 includes:

[0036] S31. Capture images of the target star using a star-sensitive camera; extract the centroid position of the target star's light spot using image processing algorithms;

[0037] S32, Extract the centroid position coordinates ( , ) and preset tracking point coordinates ( , Compare and calculate the preliminary horizontal (X-axis) deviation. Deviation in the vertical direction (Y-axis) ;

[0038] S33. Preset a deviation threshold, for example , The deviation threshold is used to determine whether the deviation is within an acceptable range. If |ΔX|≤ And |ΔY|≤ If the initial assessment is that the miss distance is small, then proceed directly to the next step.

[0039] S34. If the deviation exceeds the threshold, further judgment and processing shall be carried out.

[0040] If the deviation is too large, check for external interference factors and take appropriate measures, including waiting for the interference to disappear and adjusting the camera exposure.

[0041] If there is no external interference or the interference has been dealt with, but the deviation is still large, then dynamic adjustment will be performed;

[0042] S35. Analyze the motion trend of the target star and predict its position in the next frame image;

[0043] S36. Dynamically adjust the camera's tracking point and exposure parameters based on the predicted position, capture the image again, and extract the centroid position.

[0044] S37. Repeat the above steps until the deviation is within an acceptable range;

[0045] S38. After the deviation is acceptable, calculate the miss distance x and y based on the final extracted centroid position and tracking point coordinates; miss distance x = final ΔX, miss distance y = final ΔY.

[0046] S39. Record the off-target amounts x and y calculated in this process, as well as any abnormalities or special handling situations encountered during the process, and feed the off-target amount data back to the control system.

[0047] Furthermore, S31 includes:

[0048] The star-sensor camera is calibrated and its parameters are initialized; a search strategy is formulated within the star-sensor camera's field of view based on the predicted position of the target star.

[0049] The camera is activated, and images of the target star are captured according to preset parameters. Image enhancement technology is applied in real time to improve image quality.

[0050] Image stability analysis algorithms are used to monitor minute jitter or motion blur in image sequences, and image registration techniques are used to remove or compensate for these effects.

[0051] Image segmentation and morphological processing techniques are used to identify and remove occlusions in the image. The degree of occlusion is determined by setting a threshold, and a recapture mechanism or adjustment of the observation angle is triggered based on the preset threshold.

[0052] Based on the characteristics of the target star's light spot, the feature extraction algorithm is optimized to locate the outline and centroid of the target star's light spot.

[0053] Furthermore, S4 includes:

[0054] S41. Detect the current status of the laser terminal, including the real-time position and speed of the motor and whether there are any abnormal alarms; if there is an abnormality, enter the fault handling process, record the error and attempt automatic or manual repair.

[0055] S42. Extract the centroid position of the target star spot captured by the star-sensitive camera; at the same time, read the pre-set tracking point coordinates;

[0056] S43. Compare the position of the center of mass with the coordinates of the tracking point, calculate the miss distance in the azimuth and pitch directions respectively, and record it.

[0057] S44. Based on the miss distances x and y, and the mechanical characteristics of the laser terminal, calculate the amount of rotation that the azimuth motor and the pitch motor need to compensate for.

[0058] S45. Using the calculated motor compensation amount, update the theoretical pointing of the laser terminal; compare the theoretical pointing with the actual pointing and evaluate the difference between the two.

[0059] S46. Convert the actual pointer and the theoretical pointer into the corresponding direction cosine matrix; the matrix corresponding to the actual pointer is denoted as err_co, and the matrix corresponding to the theoretical pointer is denoted as co;

[0060] S47. Based on the direction cosine matrices err_co and co, the PAT installation matrix is ​​calculated using the principle of matrix transformation.

[0061] S48. Verify the calculated PAT installation matrix. If the verification result does not meet the requirements, return to the above steps to recalculate and adjust until a satisfactory installation matrix is ​​obtained.

[0062] S49. Repeat the above steps for all selected target stars. Update the correction matrix once for each target star after calibration. Continuously monitor the status and performance parameters of the laser terminal throughout the calibration process.

[0063] Furthermore, S6 includes:

[0064] S61. Store the error installation correction matrix calculated in each calibration process. After all the selected target stars have been calibrated, average all the error installation correction matrices to obtain a preliminary average correction matrix.

[0065] S62. Using the preliminary average correction matrix, recalibrate some or all of the calibrated target stars; compare the difference between the recalibrated results and the initial calibration results, and evaluate the accuracy and stability of the average correction matrix;

[0066] S63. If the calibration result meets the preset accuracy requirements, the average correction matrix is ​​considered valid and proceed to the next step; otherwise, re-examine the calibration process, analyze the reasons, and adjust the screening conditions or correction matrix calculation method as needed.

[0067] S64. If the average correction matrix is ​​found to be insufficient during the verification process, the correction matrix shall be optimized. After optimization, the verification shall be performed again until the average correction matrix that meets the accuracy requirements is obtained.

[0068] S65. Store the final verified average correction matrix and label it with relevant verification information. Apply the average correction matrix to subsequent laser load pointing control.

[0069] Furthermore, S7 includes:

[0070] S71. Using the correction matrix above, perform another calibration test on the previously calibrated target star. Observe the position of the target star's spot centroid using a star-sensitive camera to determine whether it accurately falls on the tracking point.

[0071] If the centroid of the target star's spot coincides with the tracking point or the error is within an acceptable range, the calibration is considered successful.

[0072] If the calibration result has a large error, that is, the deviation between the target star's spot centroid position and the tracking point exceeds the acceptable range, then error analysis should be performed.

[0073] S72. Analyze the sources of error, including errors in stellar position data, orbital parameters, camera tracking accuracy, and motor control accuracy; based on the error analysis results, adjust the correction matrix or re-perform the relevant steps in the calibration process;

[0074] S73. After completing the error adjustment, repeat steps S2-S6 to recalibrate the target star and verify the calibration results again until the predetermined accuracy requirements are met.

[0075] S74. Record the results of each calibration, error analysis, adjustment measures, and final verification results.

[0076] The system proposed in this invention is characterized by comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0077] Memory, used to store computer programs;

[0078] A processor, when executing a program stored in memory, implements any of the steps described above.

[0079] The beneficial effects of this invention are as follows: The autonomous calibration scheme provided by this invention possesses an algorithm for autonomously selecting target stars, eliminating the need for ground-based stellar data injection, thus greatly improving calibration efficiency and data reliability. Simultaneously, the selection process ensures both the feasibility of the calibration process and the safety of the laser terminal during calibration. Attached Figure Description

[0080] Figure 1 This is a diagram illustrating the steps of the method described in this invention;

[0081] Figure 2 This is a flowchart of the method described in this invention. Detailed Implementation

[0082] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0083] One embodiment of the present invention, such as Figure 1 and Figure 2 The present invention discloses an autonomous pointing calibration and optimization method for a laser payload, the method comprising:

[0084] S1. Based on the position and velocity of the local star in the J2000 coordinate system, calculate the orbital normal vector and establish the orbital coordinate system. Select stars whose stellar vector has an angle of less than 90 degrees with the -z direction of the orbital coordinate system and an angle of greater than 90 degrees with the solar vector as target stars, i.e., those stars that are not obscured by the Earth and whose optical axes are far from the Sun;

[0085] S2. Based on the selected star catalog, point to the target star in an open loop in sequence;

[0086] S3. When the target star is within the field of view of the star-sensor camera, the miss distance x and y are calculated based on the deviation between the target star's spot centroid position and the tracking point, and then the theoretical direction of the motor is calculated in reverse.

[0087] S4. Calculate the error installation correction matrix using the actual and theoretical directions of the motor;

[0088] S5. Repeat steps S1-S4 until all target stars have been calibrated;

[0089] S6. Take the average of all error installation matrices to obtain the correction matrix;

[0090] S7. Add the correction matrix and recalibrate the previously calibrated target star. If the target star falls on the tracking point of the star-sensor camera after pointing to it, the calibration is considered successful. If the error is large, the calibration process is repeated. The specific calibration principle is as follows:

[0091] In one embodiment, the position of the local satellite must first be obtained. velocity, and the positions of other stars to be screened. and solar vector Position in the J2000 coordinate system.

[0092] Find the angle between the star and the orbital coordinate system in the -z direction. =

[0093] Find the angle between the vectors of the star and the sun. =

[0094] Will satisfy <90° and Stars with an angle greater than 90° are added to the calibration data source.

[0095] Based on the calibration data source, after the laser terminal is pointed open-loop at the target star, the star-sensitive camera can detect the target star's light spot and center of mass position. By obtaining the azimuth miss distance x and pitch miss distance y from the center of mass position and the tracking point, the required compensation movement of the laser terminal motor can be calculated as follows:

[0096] Azimuth motor compensation amount:

[0097] Pitch motor compensation amount:

[0098] The theoretical direction of the laser terminal is obtained based on the azimuth motor compensation, pitch motor compensation, and the current direction of the laser terminal. Both the actual and theoretical directions are converted into corresponding direction cosine matrices: the direction cosine matrix corresponding to the actual motor direction is errco, and the direction cosine matrix corresponding to the theoretical motor direction is co. Next, the PAT installation matrix is ​​calculated:

[0099] Rotation around the z-axis =Azimuth motor compensation amount

[0100] Rotation around the y-axis =Pitch motor compensation amount

[0101] Calculate the rotation about the x-axis according to the 312 revolution order.

[0102]

[0103] =

[0104] The working principle of the above technical solution is as follows: Based on the satellite's position and velocity information in the J2000 (a commonly used inertial reference coordinate system), the satellite's orbital normal vector is calculated, and an orbital coordinate system is established based on this to determine the satellite's precise position and attitude in space. By filtering through a stellar database, stars whose vectors form an angle of less than 90 degrees with the orbital coordinate system's -z direction (usually pointing towards the Earth's center) and a greater than 90 degrees with the solar vector (i.e., the star's optical axis is far from the Sun, avoiding sunlight interference) are selected as target stars. According to the selected target star list, the laser payload begins to attempt to point at these stars in a certain order (e.g., brightness, position), performing preliminary alignment through this step. The payload's attitude is adjusted to roughly align with the target star. Once the target star enters the field of view of the star-sensitive camera (a high-precision astronomical camera used for observing and locating stars), the deviation between the target star's center of mass position and the camera's preset tracking point (miss distances x and y) is calculated, and the theoretical pointing angle that the motor (the mechanism controlling the payload's attitude) should achieve is deduced. Next, the difference between the actual and theoretical pointing of the motor is compared, and an error installation correction matrix is ​​calculated. This correction matrix reflects the deviation between the current load attitude and the ideal attitude and is used for subsequent corrections. The above steps (S1-S4) are repeated to calibrate all selected target stars. Through multiple iterations, the error in the load attitude can be gradually reduced, improving pointing accuracy. The final correction matrix is ​​obtained by averaging all error installation matrices. This correction matrix contains the accumulated error information from all calibration processes and is a key parameter for optimizing load pointing accuracy. The correction matrix is ​​uploaded to the load's control system, and the pointing accuracy of the previously calibrated target stars is verified again. If the target star accurately lands on the tracking point of the star-sensor camera, the calibration is successful, and the load's pointing accuracy has been effectively improved. If the verification results show that the error is still large, the calibration process may need to be repeated until the accuracy requirements are met.

[0105] The above technical solution achieves the following results: by selecting suitable target stars (i.e., stars not obscured by Earth and whose optical axes are far from the Sun) and performing precise calibration, the pointing accuracy of the laser payload can be significantly improved. This method enables autonomous calibration of the laser payload, reducing reliance on ground control stations. In space missions, due to the limited and expensive nature of ground control resources, autonomous calibration significantly enhances mission flexibility and reliability. Through iterative calibration, errors can be gradually reduced and the correction matrix optimized, leading to more efficient improvement in payload pointing accuracy. Furthermore, reduced reliance on ground control shortens the calibration cycle and improves mission execution efficiency. This method dynamically selects suitable target stars for calibration based on the satellite's real-time position and velocity information. It can operate at different orbital altitudes, inclinations, and velocities, exhibiting strong adaptability and flexibility. Through multiple calibration and verification processes, the pointing accuracy of the laser payload can be ensured to meet predetermined requirements. Simultaneously, averaging the error correction matrix further improves the stability and reliability of the correction matrix. Autonomous calibration reduces the need for ground control resources, thereby lowering mission costs. Furthermore, by improving pointing accuracy and efficiency, other costs during mission execution (such as fuel consumption and time costs) can be indirectly reduced. The application and continuous optimization of this technology will drive the development and advancement of autonomous pointing technology for laser payloads. With the maturity of the technology and its widespread application, it will provide more reliable and efficient means for future space exploration and scientific research.

[0106] In one embodiment of the present invention, S1 includes:

[0107] S11. Obtain the precise position data and velocity vector of this satellite in the J2000 coordinate system; the position and velocity include longitude, latitude, and altitude;

[0108] S12. Utilize ephemeris data and navigation algorithms to update the position and velocity information of the satellite in real time, ensuring the accuracy and timeliness of the data;

[0109] S13. Based on the position and velocity of the local star, calculate the normal vector of the orbital plane using the principles of orbital mechanics, determine the direction of the orbital normal vector, and use it as the negative direction of the Z-axis of the orbital coordinate system;

[0110] S14. Using the local star as the origin of the coordinate system, select appropriate X-axis and Y-axis (for example, the X-axis points to a fixed star, and the Y-axis is determined according to the right-hand rule) to construct the orbital coordinate system;

[0111] S15. Verify the correctness of the coordinate system, ensuring that all axes are perpendicular to each other and satisfy the right-hand screw rule;

[0112] S16. Call the star database to obtain the position information of all stars to be screened in the J2000 coordinate system, and calculate the angle between the vector of each star and the -Z direction of the orbital coordinate system (i.e., the Earth direction).

[0113] S17. Calculate the angle between the stellar vector and the solar vector to ensure that the star is not blocked by the Earth (angle < 90°) and is far away from the Sun to avoid light interference (angle > 90°).

[0114] S18. Add stars that meet the criteria to the target star list as potential calibration targets.

[0115] The working principle of the above technical solution is as follows: the system acquires the precise position data and velocity vector of the satellite (i.e., "the local satellite") in the J2000 coordinate system. This data includes longitude, latitude, and altitude (in the space environment, altitude usually refers to the distance relative to a reference ellipsoid, such as the WGS-84 ellipsoid), but more accurately, the data should be the coordinates (X, Y, Z) and velocity vector (V) in three-dimensional space. x V y V z The system utilizes ephemeris data and navigation algorithms (such as those from satellite navigation systems like GPS, GLONASS, and Galileo, or autonomous navigation algorithms based on star charts) to update satellite position and velocity information in real time. Based on the satellite's real-time position and velocity, the system calculates the normal vector to the satellite's orbital plane using orbital mechanics principles. This normal vector is perpendicular to the orbital plane and points outwards. When constructing the orbital coordinate system, the direction of this normal vector is defined as the negative direction of the Z-axis (because the Earth's direction is usually considered downwards). Using the satellite as the origin, appropriate X-axis and Y-axis are selected to construct the complete orbital coordinate system. The X-axis can be chosen arbitrarily, typically pointing to a fixed star or a specific direction (such as a point on the Earth's equatorial plane). The Y-axis is determined according to the right-hand rule, meaning it is perpendicular to both the X and Z axes, and the three axes form a right-handed screw system. After constructing the coordinate system, the system verifies its correctness, including checking whether the axes are perpendicular to each other and whether the right-hand screw rule is satisfied. The system then calls upon a star database to obtain the position information of all stars to be screened in the J2000 coordinate system. For each star, the system calculates the angle between its vector and the Z-direction of the orbital coordinate system (i.e., the Earth direction), as well as the angle between the star's vector and the Sun's vector. To filter out stars that are neither obscured by Earth (angle < 90°) nor too far from the Sun to avoid light interference (angle > 90°), stars meeting these criteria are added to a target star list as potential calibration targets.

[0116] The above technical solution achieves the following results: By acquiring precise position data and velocity vectors of the local satellite in the J2000 coordinate system, including longitude, latitude, and altitude (although three-dimensional coordinates and velocity vectors are typically used in space), it ensures a high degree of accuracy in the foundational data for subsequent calculations. This is crucial for constructing an accurate orbital coordinate system and selecting suitable target satellites. Real-time updates of the local satellite's position and velocity information using ephemeris data and advanced navigation algorithms not only guarantee data timeliness but also improve the accuracy and reliability of subsequent calibration processes. Real-time updates can handle minor changes and disturbances in the satellite's orbit, ensuring the continuity and accuracy of the calibration process. The orbital coordinate system is constructed based on the accurate calculation of the orbital plane normal vector using orbital mechanics principles. This physics-based construction method ensures the scientific validity and accuracy of the coordinate system. Furthermore, selecting appropriate X-axis and Y-axis directions and adhering to the right-hand rule makes the coordinate system more standardized and easier to understand. By accessing a stellar database and calculating the angle between each star and the Z-direction of the orbital coordinate system (i.e., the Earth's direction), as well as the angle between the star and the Sun's vector, the selected target stars were ensured to be neither obscured by Earth nor too far from the Sun to avoid light interference. These stringent selection criteria significantly improved the success rate and reliability of the calibration process. By selecting suitable target stars as potential calibration targets, the number and time of invalid calibrations were reduced, improving the efficiency of the calibration process. Furthermore, because the selection of target stars was based on precise calculations and selection criteria, the calibration results were more accurate and reliable. This technical solution enabled autonomous pointing calibration of the laser payload, reducing reliance on ground control stations. It allowed the system to perform calibration operations independently without ground support, improving its autonomy and flexibility. Moreover, because the calibration process could be iterated and optimized multiple times according to actual conditions, the system possessed high adaptability and scalability. The application of autonomous pointing calibration technology reduced the demand for ground control resources, thereby lowering mission costs. In addition, by improving calibration accuracy and reliability, the risk of mission failure due to pointing errors was reduced, ensuring the smooth execution and successful completion of the mission.

[0117] In one embodiment of the present invention, S2 includes:

[0118] S21. Based on relevant factors of the stars, including brightness and observation angle, sort the target star list and prioritize easily observable stars.

[0119] S22. The control system calculates and outputs pointing instructions based on the position information of the target star. After receiving the instructions, the laser payload adjusts its attitude so that the optical axis points to the target star.

[0120] S23. Monitor the attitude adjustment process in real time.

[0121] The working principle of the above technical solution is as follows: Stars are sorted according to relevant factors, including brightness and observation angle. Brightness determines the visibility of a star in the night sky, while the observation angle affects the difficulty and accuracy of the laser payload's observation of the star; priority is given to easily observable stars. During the sorting process, stars with higher brightness and more favorable observation angles are given priority. The control system calculates and generates pointing instructions based on the already sorted target star position information. These instructions contain the attitude information that the laser payload needs to adjust to ensure that the optical axis is accurately pointed to the target star. After receiving the pointing instructions, the laser payload adjusts its attitude according to the instructions. This includes adjusting the satellite's pointing mechanism and stabilization platform to ensure that the optical axis is precisely aligned with the target star. Real-time monitoring of the attitude adjustment process ensures that the laser payload can accurately adjust its attitude according to the predetermined instructions and ultimately point the optical axis to the target star. The monitoring content includes, but is not limited to, the speed, accuracy, and stability of attitude adjustment. Real-time monitoring can promptly detect and correct any possible deviations or problems, ensuring the smooth progress of attitude adjustment. If deviations or problems are detected during the monitoring process, the control system will provide timely feedback and may recalculate the pointing command or adjust the attitude adjustment strategy to ensure that the final optical axis pointing meets the requirements.

[0122] The advantages of the above technical solution are as follows: By sorting the target star list according to the star's brightness and observation angle, the system can prioritize pointing to stars that are easy to observe. This significantly improves observation efficiency and reduces the time and resources wasted on difficult-to-observe stars. Prioritizing stars with higher brightness and better observation angles helps the laser payload to more accurately lock onto the target and perform calibration. Brighter stars have a higher signal-to-noise ratio during observation, while optimized observation angles reduce the influence of atmospheric interference and other factors, thus improving calibration accuracy. This technical solution achieves autonomous pointing control of the laser payload, reducing the need for manual intervention. The control system can automatically calculate and output pointing commands based on the target star's position information, enabling the laser payload to autonomously adjust its attitude, improving the system's autonomy and intelligence. Real-time monitoring of the attitude adjustment process can promptly detect and correct any possible deviations or problems, ensuring that the laser payload can stably point to the target star. The real-time monitoring mechanism helps improve the system's stability and reliability, reducing errors and malfunctions caused by improper attitude adjustment. This technical solution can adapt to different observation conditions and variations in stellar characteristics. Whether facing low-brightness stars or limited observation angles, the system employs flexible sorting and pointing strategies to ensure a smooth calibration process. By optimizing observation efficiency and calibration accuracy, this technology helps improve the success rate of missions such as laser communication and astronomical observation. Furthermore, the improved efficiency and accuracy contribute to reduced operating costs. Minimizing investment in ineffective observations and redundant calibrations allows for more efficient use of limited resources, thereby enhancing overall economic benefits.

[0123] In one embodiment of the present invention, step S23 includes:

[0124] S231. Before attitude adjustment, the current attitude of the laser payload is accurately measured using a high-precision gyroscope and accelerometer to obtain initial attitude data.

[0125] S232. Compare the initial attitude data with the preset ideal attitude, calculate the attitude deviation, and perform preliminary attitude calibration based on the deviation results; wherein, the attitude deviation involves comparing the current attitude quaternion (or rotation matrix) with the ideal attitude quaternion (or rotation matrix), and the attitude deviation calculation method based on quaternions is as follows:

[0126] Let the current quaternion be The quaternion of the ideal posture is Quaternions of attitude deviation Calculated using the following formula:

[0127]

[0128] in, It is the inverse of the current attitude quaternion, which is calculated using the following formula:

[0129]

[0130] Quaternion dot product The calculation is performed using the following formula:

[0131]

[0132] The norm (modulus) of a quaternion The calculation is performed using the following formula:

[0133]

[0134] S233. The attitude of the laser payload is dynamically tracked by using a Kalman filter or an extended Kalman filter, and data from multiple sensors (such as gyroscopes, star-sensor cameras, etc.) are fused in real time.

[0135] S234. Based on the star's trajectory and the laser payload's current attitude, use celestial navigation algorithms to predict the target star's position over a future period.

[0136] S235. Based on the predicted star position and the current attitude deviation, dynamically adjust the attitude adjustment strategy, including the optimization of adjustment speed, acceleration and adjustment path. Adaptively optimize the attitude adjustment strategy through fuzzy logic algorithm. Dynamically adjust control parameters based on real-time feedback attitude data and changes in the external environment (such as wind speed, temperature, etc.).

[0137] S236. During the attitude adjustment process, a closed-loop control strategy is adopted to compare the deviation between the actual attitude and the target attitude in real time, and to perform fine control of the attitude adjustment through a PID controller.

[0138] S237. During the attitude adjustment process, the performance indicators of the laser load are continuously monitored (such as attitude accuracy, motor current, temperature, etc.). If any abnormality is found (such as excessive attitude deviation, motor overheating, etc.), the abnormality handling mechanism is immediately triggered, including automatically adjusting control parameters, issuing an alarm to notify the operator, or performing an emergency shutdown.

[0139] The working principle of the above technical solution is as follows: High-precision gyroscopes and accelerometers are used to accurately measure the current attitude of the laser payload, acquiring initial attitude data. This data is used for subsequent attitude adjustment. The initial attitude data is compared with a preset ideal attitude to calculate the attitude deviation. This attitude deviation reflects the difference between the current attitude and the desired attitude of the laser payload. Based on the calculated attitude deviation, preliminary attitude calibration is performed on the laser payload to reduce the initial deviation, laying the foundation for subsequent dynamic tracking and adjustment. A Kalman filter or extended Kalman filter is used to fuse data from multiple sensors, such as gyroscopes and star-sensor cameras, in real time. Based on the trajectory of the star and the current attitude of the laser payload, a celestial navigation algorithm is used to predict the position of the target star within a certain period. An attitude adjustment strategy is formulated based on the predicted star position and the current attitude deviation. This adjustment strategy includes determining the adjustment speed, acceleration, and optimizing the adjustment path. A fuzzy logic algorithm is used to adaptively optimize the attitude adjustment strategy. This optimization method can dynamically adjust control parameters based on real-time feedback attitude data and changes in the external environment (such as wind speed and temperature), making the attitude adjustment process more accurate and efficient. During attitude adjustment, a closed-loop control strategy is employed. By comparing the deviation between the actual attitude and the target attitude in real time, a PID controller is used for fine-tuning the attitude adjustment. This control strategy ensures that the laser payload remains on the desired trajectory throughout the adjustment process. Various performance indicators of the laser payload are continuously monitored, including attitude accuracy, motor current, and temperature. Upon detecting any anomalies (such as excessive attitude deviation or motor overheating), an anomaly handling mechanism is immediately triggered. This mechanism may include automatically adjusting control parameters, issuing alarms to operators, or implementing emergency shutdown measures to ensure the safety and stability of the laser payload.

[0140] The above technical solution achieves the following effects: It accurately measures the current attitude of the laser payload using high-precision gyroscopes and accelerometers, compares it with a preset ideal attitude, calculates the attitude deviation, and performs preliminary calibration, ensuring the accuracy of the starting point for attitude adjustment and laying a solid foundation for subsequent adjustments. It utilizes Kalman filters or extended Kalman filters to fuse data from multiple sensors in real time, improving the accuracy and robustness of attitude estimation. The comprehensive use of multi-source data effectively reduces the impact of single sensor errors on overall attitude estimation. Based on the trajectory of the star and the current attitude of the laser payload, a celestial navigation algorithm predicts the position of the target star within a future period. This predictive capability allows the laser payload to plan its attitude adjustment strategy in advance, enhancing the system's dynamic adaptability. The attitude adjustment strategy is adaptively optimized using fuzzy logic algorithms, dynamically adjusting control parameters based on real-time feedback attitude data and changes in the external environment (such as wind speed and temperature). This adaptive mechanism enables the system to flexibly cope with various complex situations, ensuring smooth attitude adjustment. A closed-loop control strategy is employed to compare the deviation between the actual attitude and the target attitude in real time, and a PID controller provides fine-grained control of the attitude adjustment. The control method can quickly respond to attitude deviations and make corresponding adjustments, ensuring that the laser payload always stays on the desired trajectory. It continuously monitors various performance indicators of the laser payload, and immediately triggers an anomaly handling mechanism upon detecting any abnormality. This monitoring and handling mechanism effectively ensures the stability and safety of the system, avoiding attitude adjustment failures or equipment damage due to abnormal conditions. The attitude adjustment strategy is dynamically adjusted based on the predicted star position and the current attitude deviation, including adjusting speed, acceleration, and optimizing the adjustment path. This optimization strategy reduces unnecessary attitude adjustment actions and time consumption, improving overall performance and efficiency. By improving the accuracy, stability, and flexibility of attitude adjustment, this technical solution helps improve the success rate of tasks such as laser communication and astronomical observation. Accurate attitude pointing and stable tracking capabilities provide strong guarantees for the successful execution of these tasks.

[0141] In one embodiment of the present invention, S233 includes:

[0142] First, ensure that data from multiple sensors, including high-precision gyroscopes, accelerometers, magnetometers, and (optionally) GPS receivers, are strictly synchronized in time. High-precision clock synchronization technology ensures that the data from each sensor has a consistent time reference during fusion processing.

[0143] The collected multi-source sensor data is preprocessed, including noise filtering, outlier removal and data interpolation, and outliers are identified and removed using statistical methods and machine learning algorithms.

[0144] Based on the system model and sensor characteristics, the key parameters of the Kalman filter are initialized, including the state vector, covariance matrix, process noise, and measurement noise.

[0145] Based on the system model, the current state vector and its covariance matrix are predicted according to the estimated value of the previous moment. Combined with the measurement values ​​of multiple sensors, the state vector and covariance matrix are updated by weighted fusion using the Kalman gain matrix, and multi-source data are fused in real time.

[0146] For systems involving highly nonlinear dynamics (such as the attitude changes of a laser payload in a complex space environment), an extended Kalman filter (EKF) is introduced; in the EKF, the nonlinear model is linearized through Taylor series expansion;

[0147] By monitoring the statistical characteristics of sensor data in real time, the covariance matrix of process noise and measurement noise is dynamically adjusted. Through Bayesian inference or model probability update mechanism, the optimal model is selected or the model is switched based on real-time data.

[0148] Establish a performance evaluation mechanism for attitude estimation, and evaluate the accuracy and stability of the fusion results periodically or as needed; based on the evaluation results, dynamically adjust the filter parameters, model selection strategy, or data preprocessing method to form a closed-loop optimization loop.

[0149] The working principle of the above technical solution is as follows: High-precision clock synchronization technology ensures strict time consistency among multiple sensors, such as gyroscopes, accelerometers, magnetometers, and (if available) GPS receivers, during data acquisition. The acquired multi-source sensor data is then preprocessed, including noise filtering (such as low-pass filtering), outlier removal (identifying and deleting significant deviations from the normal range), and data interpolation (filling in missing data points). Statistical methods and machine learning algorithms are then used to further identify and remove potential outliers. Based on the system model and sensor characteristics, the key parameters of the Kalman filter are initialized. These key parameters include the state vector (representing the system state), the covariance matrix (representing the uncertainty of state estimation), process noise (representing the uncertainty of system dynamics), and measurement noise (representing the uncertainty of sensor measurements). Based on the system model, the Kalman filter uses the previous time-instance estimate to predict the current time-instance state vector and its covariance matrix. Then, the measurements from multiple sensors are combined and weighted and fused using the Kalman gain matrix. For systems with highly nonlinear dynamics, traditional Kalman filters may not be directly applicable. At this point, an extended Kalman filter (EKF) is introduced to linearize the nonlinear model through Taylor series expansion. The covariance matrix of process noise and measurement noise is dynamically adjusted by monitoring the statistical characteristics of sensor data in real time. Using Bayesian inference or a model probability update mechanism, the optimal model is selected or a model switch is performed based on real-time data. A performance evaluation mechanism for attitude estimation is established to periodically or as needed assess the accuracy and stability of the fusion results. Based on the evaluation results, the filter parameters, model selection strategy, or data preprocessing method are dynamically adjusted to form a closed-loop optimization circuit.

[0150] The above technical solution achieves the following effects: High-precision clock synchronization ensures strict temporal consistency of multi-source sensor data, eliminating errors caused by data time differences and improving attitude estimation accuracy. Preprocessing steps such as noise filtering, outlier removal, and data interpolation, along with the use of statistical methods and machine learning algorithms to identify and remove outliers, enhance data reliability and accuracy, providing a high-quality data source for subsequent fusion processing. Real-time fusion processing of multi-source sensor data is achieved using a Kalman filter (and its extended version, EKF). By updating the state vector and covariance matrix in real time based on the previous time-to-time estimate and the current time-to-time measurement, continuous tracking and prediction of the system state are possible. For nonlinear systems, EKF linearizes the nonlinear model through Taylor series expansion, ensuring real-time performance and accuracy under nonlinear environments. By monitoring the statistical characteristics of sensor data in real time, it dynamically adjusts the covariance matrix of process noise and measurement noise, and selects the optimal model or switches models based on real-time data, enabling the system to adapt to different environments and operating conditions, thus improving the adaptability and robustness of attitude estimation. A performance evaluation mechanism for attitude estimation is established to periodically or as needed assess the accuracy and stability of the fusion results. This allows for timely detection and correction of potential problems, ensuring consistently high-quality attitude estimation. Based on the evaluation results, filter parameters, model selection strategies, or data preprocessing methods are dynamically adjusted to form a closed-loop optimization loop. This closed-loop control mechanism continuously optimizes the performance of attitude estimation, improving the overall system efficiency. This technical solution supports the fusion processing of multiple sensors, including gyroscopes, accelerometers, magnetometers, and (optionally) GPS receivers, allowing for flexible selection and configuration of sensors according to actual needs.

[0151] In one embodiment of the present invention, S235 includes:

[0152] A comprehensive analysis of initial attitude deviation and star position prediction error is conducted, and the contribution of different factors (such as sensor noise, environmental interference, and accuracy of star motion model) to deviation and error is evaluated using statistical methods and machine learning models (such as neural networks or random forests).

[0153] Based on comprehensive analysis results, a dynamic adjustment strategy generation mechanism is implemented. This mechanism can automatically generate or select the optimal attitude adjustment strategy according to different scenarios (such as tracking highly dynamic stars, traversing radiation belts, etc.) and real-time deviation / error conditions; the adjustment strategy includes adjustment rate, acceleration limitation, and path planning.

[0154] The generated attitude adjustment strategy is further optimized by fuzzy logic algorithm. Fuzzy logic can handle uncertainty and fuzziness. By defining appropriate fuzzy sets and fuzzy rules, complex nonlinear relationships are mapped into an easy-to-handle fuzzy inference process. Based on real-time feedback attitude data, changes in the external environment (such as solar wind intensity and Earth's magnetic field disturbances), and prediction errors, the fuzzy rules and control parameters are dynamically adjusted.

[0155] The working principle of the above technical solution is as follows: Multiple data sources are collected, including initial attitude deviation, stellar position prediction error, sensor noise, environmental interference, and the accuracy of stellar motion models. These data are then comprehensively analyzed using statistical methods and machine learning models (such as neural networks and random forests) to assess the contribution of different factors to the deviation and error. Based on real-time data and predefined scene patterns (such as tracking highly dynamic stars or traversing radiation belts), the current scene is identified. Based on scene identification and comprehensive analysis results, a dynamic adjustment strategy generation mechanism automatically generates or selects the optimal attitude adjustment strategy. The strategy includes adjustment rate, acceleration limits, and path planning. Real-time feedback attitude data, external environmental changes (such as solar wind intensity and Earth's magnetic field disturbances), and prediction errors are fuzzified to define appropriate fuzzy sets and fuzzy rules. Fuzzy logic algorithms are used for reasoning, mapping complex nonlinear relationships into easily processed fuzzy reasoning processes. Based on the results of fuzzy reasoning, the fuzzy rules and control parameters are dynamically adjusted.

[0156] The above technical solution achieves the following effects: By comprehensively analyzing initial attitude deviations and stellar position prediction errors, it can fully consider the impact of multiple factors on system performance; by utilizing statistical methods and machine learning models (such as neural networks and random forests) to evaluate the contribution of different factors, it improves the accuracy and reliability of the evaluation; the mechanism can automatically generate or select the optimal attitude adjustment strategy based on different scenarios (such as tracking highly dynamic stars, traversing radiation belts, etc.) and real-time deviation / error conditions; the fuzzy logic algorithm can handle the uncertainties and fuzziness in the system, mapping complex nonlinear relationships into easily manageable fuzzy inference processes by defining appropriate fuzzy sets and fuzzy rules; and it can adjust attitude based on real-time feedback attitude data and changes in the external environment (such as solar wind). By dynamically adjusting fuzzy rules and control parameters based on factors such as intensity, Earth's magnetic field disturbances, and prediction errors, the system's response speed and stability are improved. Through comprehensive analysis and dynamic adjustment strategies, the impact of initial attitude deviations and star position prediction errors on system performance is effectively reduced. The optimized attitude adjustment strategy further enhances the system's tracking accuracy and stability, ensuring reliable operation in highly dynamic and complex environments. This technical solution can cope with the challenges of various complex environments (such as radiation belts and strong magnetic fields), ensuring the normal operation of the system under these extreme conditions. Through the optimization of dynamic adjustment strategies and fuzzy logic algorithms, the system possesses a certain degree of fault tolerance, and can tolerate the influence of adverse factors such as sensor noise and environmental interference to a certain extent.

[0157] In one embodiment of the present invention, S236 includes:

[0158] During the attitude adjustment process, the current attitude data of the laser payload is acquired in real time through high-precision sensors (such as gyroscopes, accelerometers, etc.) and accurately compared with the target attitude to calculate the real-time attitude deviation.

[0159] For the specific dynamic characteristics and external environmental conditions of the laser load, intelligent optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) are used to optimize the proportional (P), integral (I), and derivative (D) parameters of the PID controller offline or online.

[0160] Establish a dynamic adjustment mechanism for PID parameters, and dynamically adjust the PID parameters based on real-time feedback attitude data, environmental parameters, and system performance indicators;

[0161] Based on PID control, a predictive control strategy is integrated. Based on the future star position information predicted by the celestial navigation algorithm and combined with the dynamic model of the laser payload, the attitude change trend in the future period is predicted, and the control input is adjusted in advance.

[0162] The working principle of the above technical solution is as follows: High-precision sensors such as gyroscopes and accelerometers are used to acquire the current attitude data of the laser payload in real time; the acquired attitude data is accurately compared with the target attitude to calculate the real-time attitude deviation; intelligent optimization algorithms such as genetic algorithms and particle swarm optimization are used to optimize the proportional (P), integral (I), and derivative (D) parameters of the PID controller offline or online, based on the specific dynamic characteristics and external environmental conditions of the laser payload; the PID parameters are adjusted through optimization algorithms to reduce attitude deviation; a dynamic adjustment mechanism for PID parameters is established, dynamically adjusting the PID parameters based on real-time feedback attitude data, environmental parameters (such as temperature and vibration), and system performance indicators (such as attitude stability and tracking accuracy); future star position information is predicted based on celestial navigation algorithms to provide accurate reference for attitude adjustment; the attitude change trend over a future period is predicted by combining the dynamic model of the laser payload; and the control input is adjusted in advance based on the prediction results to cope with upcoming attitude changes.

[0163] The effects of the above technical solution are as follows: Real-time acquisition of the laser payload's current attitude data via high-precision sensors, and precise comparison with the target attitude, allows for timely detection and correction of minute attitude deviations, thereby improving the accuracy of attitude adjustment. Intelligent optimization algorithms are used to optimize the PID controller parameters offline or online, automatically adjusting control parameters based on the specific dynamic characteristics of the laser payload and external environmental conditions. This makes the control system more adaptable to complex and changing environments, improving the stability of attitude adjustment. A dynamic adjustment mechanism for PID parameters is established, dynamically adjusting PID parameters based on real-time feedback of attitude data, environmental parameters, and system performance indicators, ensuring the control system maintains optimal control performance under various conditions. The integrated application of predictive control strategies enables the system to predict future stellar position information based on celestial navigation algorithms and the laser payload's dynamic model, allowing for advance adjustments to the control input. This improves system robustness and reduces the risk of attitude loss of control due to sudden changes in the external environment or internal system faults. The acquisition and comparison of real-time attitude data, intelligent optimization of PID parameters, and the establishment of a dynamic adjustment mechanism collectively enhance the system's response speed and adjustment accuracy, enabling the laser payload to reach the target attitude more quickly and maintain stability. The application of predictive control strategies enables the system to plan control inputs in advance, reducing energy consumption and mechanical wear caused by frequent attitude adjustments, thereby improving the overall performance and operational efficiency of the system. Due to the system's high adaptability and robustness, it can automatically cope with various complex and changing scenarios and external environmental conditions, thus reducing the frequency and complexity of maintenance required due to system failures or performance degradation. At the same time, the application of intelligent optimization algorithms and dynamic adjustment mechanisms also reduces the reliance on manual intervention, making system maintenance more convenient and efficient.

[0164] In one embodiment of the present invention, S3 includes:

[0165] S31. Capture target star images using a star-sensitive camera and extract the centroid position of the target star's light spot using image processing algorithms (such as the centroid method).

[0166] S32, Extract the centroid position coordinates ( , ) and preset tracking point coordinates ( , Compare and calculate the preliminary horizontal (X-axis) deviation. Deviation in the vertical direction (Y-axis) ;

[0167] S33. Preset a deviation threshold, for example , The deviation threshold is used to determine whether the deviation is within an acceptable range. If |ΔX|≤ And |ΔY|≤ If the initial assessment is that the miss distance is small, then proceed directly to the next step.

[0168] S34. If the deviation exceeds the threshold, further judgment and processing shall be carried out.

[0169] If the deviation is too large, check for external interference factors (such as cloud cover, solar radiation interference, etc.) and take appropriate measures, including waiting for the interference to disappear and adjusting the camera exposure.

[0170] If there is no external interference or the interference has been dealt with, but the deviation is still large, then dynamic adjustment will be performed;

[0171] S35. Analyze the motion trend of the target star (based on the position and velocity information in the stellar database) and predict the possible position of the target star in the next frame image.

[0172] S36. Dynamically adjust the camera's tracking point and exposure parameters based on the predicted position, capture the image again, and extract the centroid position.

[0173] S37. Repeat the above steps until the deviation is within an acceptable range;

[0174] S38. After the deviation is acceptable, calculate the miss distance x and y based on the final extracted centroid position and tracking point coordinates; miss distance x = final ΔX, miss distance y = final ΔY.

[0175] S39. Record the off-target amounts x and y calculated in this process, as well as any abnormalities or special handling situations encountered during the process, and feed the off-target amount data back to the control system.

[0176] The working principle of the above technical solution is as follows: A star-sensitive camera captures images of the target star, ensuring the images are clear and unobstructed. As a high-precision sensor, the star-sensitive camera can capture faint light signals from the night sky; image processing algorithms (such as the centroid method) are used to process the captured images and extract the centroid position of the target star's light spot. The centroid method determines the centroid position by calculating the brightness-weighted average of the pixels within the light spot; the extracted centroid position coordinates (X... c ,Y c ) and preset tracking point coordinates (X t ,Y t Compare these values ​​and calculate the initial horizontal (X-axis) deviation ΔX = X. c -Y t The deviation from the vertical direction (Y-axis) is ΔY = Y. c -Y t These two deviation values ​​reflect the difference between the current tracking state and the target state. A preset deviation threshold (e.g., ...) is used. , This is used to determine whether the deviation is within an acceptable range. If |ΔX|≤ And |ΔY|≤ If the initial assessment is that the miss distance is small, the process can proceed directly to the next step. If the deviation exceeds the threshold, further judgment and processing are required.

[0177] If the deviation is too large, first check if there are any external interference factors (such as cloud cover, solar radiation interference, etc.) and take appropriate measures (such as waiting for the interference to disappear, adjusting the camera exposure, etc.).

[0178] If there is no external interference or the interference has been dealt with, but the deviation is still large, then dynamic adjustment will be performed.

[0179] Analyzing the target star's motion trend and using its position and velocity information from a stellar database, the possible position of the target star in the next frame is predicted. The camera's tracking point and exposure parameters are dynamically adjusted based on the predicted position, and the image is captured again to extract the centroid position. This process is iterated to gradually reduce the deviation. The above steps (S32 to S36) are repeated until the deviation is within an acceptable range. This process is a closed-loop control process, continuously optimizing the tracking effect through feedback and adjustment. After the deviation is acceptable, the miss distances x and y are calculated based on the final extracted centroid position and tracking point coordinates. Miss distance x = final ΔX, miss distance y = final ΔY. The calculated miss distances x and y, as well as any anomalies or special handling situations encountered during the process, are recorded. The miss distance data is fed back to the control system for subsequent motor pointing adjustments and error correction matrix calculations. By continuously optimizing the control parameters and algorithms, the tracking accuracy and stability can be further improved.

[0180] The above technical solution achieves the following results: It extracts the centroid position of the target star's light spot using high-precision image processing algorithms (such as the centroid method) and compares it with the preset tracking point coordinates to calculate a precise deviation value. This ensures the foundation for tracking accuracy and provides reliable data support for subsequent adjustments. When the deviation exceeds a preset threshold, the system can automatically perform dynamic adjustments, including checking for external interference, adjusting camera exposure parameters, and predicting the target star's position based on its motion trend. This dynamic adjustment mechanism allows the system to adapt to environmental changes in real time, maintaining tracking stability and accuracy. The system can automatically detect and handle external interference factors, such as cloud cover and solar radiation interference. By waiting for the interference to disappear or adjusting camera exposure parameters, the system can maintain stable tracking of the target star in complex environments. The entire technical solution is designed to consider various possible abnormal situations and provides corresponding handling strategies. This robust design enables the system to maintain a certain level of stability and reliability when facing unforeseen challenges. The technical solution employs a closed-loop control strategy, continuously comparing the actual deviation with a preset threshold and automatically adjusting tracking parameters to achieve precise tracking of the target star. This automated control method reduces the need for manual intervention and improves work efficiency. Based on the position and velocity information in the stellar database, the system can intelligently predict the target star's motion trend and adjust the camera's tracking point and exposure parameters accordingly. This intelligent prediction and adjustment mechanism allows the system to prepare in advance, ensuring the continuity and accuracy of tracking. The system records in detail the miss distance calculated each time, as well as any anomalies or special handling situations encountered during the process. This data is crucial for subsequent analysis and optimization, helping to identify potential problems and formulate corresponding improvement measures. The miss distance data is fed back to the control system in a timely manner for subsequent motor pointing adjustments and error correction matrix calculations. This feedback mechanism forms a closed-loop optimization process, enabling continuous improvement in system performance.

[0181] In one embodiment of the present invention, step S31 includes:

[0182] Perform precise calibration on the star-sensitive camera, including lens distortion correction, focal length adjustment, and sensor pixel alignment, and initialize camera parameters such as exposure time, gain settings, and white balance; develop efficient search strategies within the star-sensitive camera's field of view based on known target star position predictions (such as from stellar databases or previous observation data), such as using spiral search or grid scanning methods to quickly locate the target star region.

[0183] The camera is activated to capture images of the target star according to preset parameters, and image enhancement techniques, such as histogram equalization, contrast stretching, or noise suppression algorithms, are applied in real time to improve image quality.

[0184] Image stability analysis algorithms are used to monitor minute jitter or motion blur in image sequences, and image registration techniques are used to remove or compensate for these effects.

[0185] Image segmentation and morphological processing techniques are used to identify and remove occlusions in images, such as clouds, satellite structure shadows, or cosmic dust belts; the degree of occlusion is determined by setting a threshold, and a recapture mechanism or adjustment of the observation angle is triggered based on the preset threshold;

[0186] Based on the characteristics of the target star's light spot, the feature extraction algorithm is optimized to locate the outline and centroid of the target star's light spot.

[0187] The working principle of the above technical solution is as follows: the distortion of the camera lens is corrected by a mathematical model to ensure that the geometric shape in the image is accurate and undistorted; the focal length of the camera is adjusted according to the observation requirements to obtain the best image clarity and resolution. To ensure proper pixel alignment on the camera sensor and reduce image quality issues caused by pixel misalignment, the system employs the following techniques: Setting parameters such as exposure time, gain, and white balance to adapt to different observation environments and lighting conditions; predicting the approximate position of the target star within the star-sensitive camera's field of view based on a stellar database or previous observation data; using efficient algorithms such as spiral search or grid scanning to quickly locate the target star region within the field of view, reducing search time and resource consumption; starting the camera according to preset parameters to capture the target star image; applying algorithms such as histogram equalization, contrast stretching, or noise suppression to improve image quality, enhancing image visibility and clarity; monitoring for minute jitter or motion blur in the image sequence using image stability analysis algorithms; removing or compensating for these effects using image registration techniques to ensure the stability and consistency of the image sequence; separating different objects or regions in the image using image segmentation techniques; identifying occlusions (such as clouds, shadows, dust bands, etc.) in the image using morphological processing techniques and setting thresholds to determine the degree of occlusion. If the degree of occlusion exceeds a preset threshold, triggering a recapture mechanism or adjusting the observation angle to avoid the occlusion; and optimizing feature extraction algorithms based on the target star's light spot characteristics. In addition to the traditional centroid method, advanced technologies such as edge detection, Hough transform, or deep learning algorithms (such as convolutional neural networks CNN) are combined to accurately locate the outline and centroid position of the target star's light spot.

[0188] The effects of the above technical solutions are as follows: By correcting lens distortion, adjusting focal length, and aligning sensor pixels in the star-sensor camera, high precision and consistency of the camera are ensured, thereby improving the quality of captured images; real-time application of image enhancement techniques, such as histogram equalization, contrast stretching, or noise suppression algorithms, effectively improves image visibility and clarity, making subsequent processing more accurate; based on known target star position prediction, efficient search strategies (such as spiral search or grid scanning) are formulated to quickly locate the target star region, reducing search time and resource consumption; by setting thresholds and triggering mechanisms, the system can automatically determine the degree of occlusion and adjust the observation angle or trigger a recapture mechanism, improving the flexibility and efficiency of the search; through image stability analysis algorithms and image registration techniques, it is possible to monitor and remove or compensate for defects in the image sequence. To minimize minute jitter or motion blur, the system ensures image stability and consistency. Image segmentation and morphological processing techniques are employed to effectively identify and remove occlusions such as clouds, satellite shadows, or cosmic dust belts, reducing the impact of external factors on observation results. Optimized feature extraction algorithms are used to target the star's light spot characteristics, combined with various techniques (such as edge detection, Hough transform, or deep learning algorithms) for comprehensive analysis, enabling more accurate localization of the target star's light spot contour and centroid position, improving tracking accuracy and reliability. This technical solution achieves a high degree of automation, from camera calibration and image capture to feature extraction, reducing the need for manual intervention and improving work efficiency. By introducing advanced technologies such as deep learning, the system can intelligently process and analyze image data, improving processing accuracy and intelligence.

[0189] In one embodiment of the present invention, step S4 includes:

[0190] S41. Detect the current status of the laser terminal, including the real-time position and speed of the motor and whether there are any abnormal alarms (such as overheating, overcurrent, etc.); if there is an abnormality, enter the fault handling process, record the error and attempt automatic or manual repair.

[0191] S42. Accurately extract the centroid position of the target star spot captured by the star-sensitive camera; at the same time, read the pre-set tracking point coordinates;

[0192] S43. Compare the position of the center of mass with the coordinates of the tracking point, calculate the miss distance (i.e., the deviation in the x and y directions) in the azimuth and pitch directions respectively, and record them.

[0193] The miss distances in the azimuth and pitch directions are obtained in the following ways:

[0194] set up: The coordinates of the extracted spot centroid position;

[0195] The coordinates of the preset tracking point;

[0196] Azimuth Miss And pitch miss distance Calculated using the following formula:

[0197]

[0198]

[0199] S44. Based on the miss distances x and y, and the mechanical characteristics of the laser terminal (such as gear ratio, motor resolution, etc.), calculate the required compensation rotation amounts for the azimuth and pitch motors; the compensation rotation amounts for the azimuth and pitch motors are obtained as follows:

[0200] and These are the miss distances in the azimuth and elevation directions, respectively (obtained from step S43);

[0201] and Optical system magnification (unit: angle / pixel) in azimuth and elevation directions, respectively;

[0202] and These are the angular resolutions (in degrees per step) for the azimuth and pitch motors, respectively.

[0203] Azimuth motor compensation rotation amount and pitch motor compensation rotation Calculated using the following formula:

[0204]

[0205]

[0206] S45. Using the calculated motor compensation amount, update the theoretical pointing of the laser terminal; compare the theoretical pointing with the actual pointing (i.e., the current actual position of the motor) and evaluate the difference between the two.

[0207] S46. Convert the actual pointer and the theoretical pointer into the corresponding direction cosine matrix; the matrix corresponding to the actual pointer is denoted as err_co, and the matrix corresponding to the theoretical pointer is denoted as co;

[0208] S47. Based on the direction cosine matrices err_co and co, and using the principle of matrix transformation, calculate the PAT (Pointing Adjustment and Testing) installation matrix; the PAT installation matrix is ​​obtained as follows:

[0209] set up: The direction cosine matrix is ​​the actual direction it points to;

[0210] The direction cosine matrix is ​​the theoretically pointed direction;

[0211] PAT Installation Matrix Calculated using the following formula:

[0212]

[0213] in, express The inverse matrix, the direction cosine matrix is ​​a 3 A 3-matrix represents a rotation from one coordinate system to another in three-dimensional space;

[0214] The direction cosine matrix C is represented as:

[0215]

[0216] in, , , , , , , , , It is the direction cosine, representing the cosine value of the angle between each axis of the carrier coordinate system and each axis of the inertial coordinate system;

[0217] Calculate the inverse matrix Obtain it through the following steps:

[0218] A1. Calculate the matrix The determinant value;

[0219] A2. Calculate the matrix The adjoint matrix;

[0220] A3. Divide each element of the adjoint matrix by the determinant value;

[0221] Obtain it using the following formula:

[0222]

[0223] in, yes The determinant, yes The adjoint matrix.

[0224] Calculated by matrix multiplication In practical applications, numerical computing libraries (such as NumPy) are typically used to handle these matrix operations.

[0225] S48. Verify the calculated PAT installation matrix. If the verification result does not meet the requirements, return to the above steps to recalculate and adjust until a satisfactory installation matrix is ​​obtained.

[0226] S49. Repeat the above steps for all selected target stars. After each target star is calibrated, update the correction matrix (which can be the weighted average of all previous correction matrices or the latest value). Throughout the calibration process, continuously monitor the status and performance parameters of the laser terminal.

[0227] The working principle of the above technical solution is as follows: First, the system detects the current state of the laser terminal, including the real-time position and speed of the motors and whether there are any abnormal alarms (such as overheating, overcurrent, etc.). If an abnormality is detected, the system enters the fault handling process, records the error, and attempts to repair it automatically or manually. The system captures the target star's light spot using a star-sensitive camera and accurately extracts the centroid position of the light spot. Simultaneously, it reads the pre-set tracking point coordinates; compares the centroid position with the tracking point coordinates, calculates the miss distances (i.e., deviations in the x and y directions) in the azimuth and pitch directions, and records them. Based on the miss distances x and y, and the mechanical characteristics of the laser terminal (such as gear ratio, motor resolution, etc.), the system calculates the amount of rotation that the azimuth and pitch motors need to compensate for. Using the calculated motor compensation amounts, the theoretical pointing of the laser terminal is updated. Then, the theoretical pointing is compared with the actual pointing (i.e., the current actual position of the motors), and the difference between the two is evaluated. The actual pointing and theoretical pointing are converted into corresponding direction cosine matrices. The construction of the direction cosine matrix needs to be accurate to multiple decimal places. Based on the direction cosine matrices err_co and co, the PAT installation matrix is ​​calculated using the principle of matrix transformation. During the calculation process, the symmetry and orthogonality of the installation matrix are considered, and matrices that do not meet the conditions are corrected or recalculated. The calculated PAT installation matrix is ​​verified to ensure that it can effectively reduce the miss distance. If the verification result does not meet the requirements, the above steps are returned for recalculation and adjustment until a satisfactory installation matrix is ​​obtained. The above steps are repeated for all selected target stars. After each target star is calibrated, the correction matrix is ​​updated (it can be a weighted average of all previous correction matrices or the latest value). Throughout the calibration process, the status and performance parameters of the laser terminal are continuously monitored to ensure the stability and reliability of the system.

[0228] The effects of the above technical solution are as follows: By calculating the required rotational compensation for the azimuth and pitch motors and considering the physical limitations of the motors, the accuracy and accessibility of the compensation are ensured, thereby significantly improving the pointing accuracy of the laser terminal; throughout the calibration process, the status and performance parameters of the laser terminal are continuously monitored to ensure stable system operation and reduce pointing errors caused by equipment failure or performance degradation; the system can automatically detect the current status of the laser terminal and enter the fault handling process when an anomaly is detected, attempting automatic or manual repair, thus improving the system's automation level and fault response speed; using the direction cosine matrix and matrix transformation principle, the PAT installation matrix is ​​intelligently calculated, reducing human intervention and calculation errors, and improving the system's intelligence level; the calibration steps are repeated for all selected target stars, and the correction matrix is ​​updated after each target star is calibrated, enabling the system to adapt to different target stars and observation requirements, thus improving the system's flexibility; this technical solution has good scalability, allowing the addition of new functional modules or optimization of existing algorithms as needed to meet future pointing requirements with higher precision; the calculated PAT installation matrix is ​​rigorously verified to ensure that it can effectively reduce the miss distance. By employing various verification methods (such as applying to other calibrated target stars and using simulation software for verification), the reliability and accuracy of the system were improved. Recording and analyzing data such as the difference between actual and theoretical pointing, and miss distances, provided strong data support for system optimization and improvement. Automated and intelligent calibration processes reduced manual intervention and repetitive work, improving the efficiency of calibration. Timely detection and repair of equipment faults reduced downtime and maintenance costs. Simultaneously, improved pointing accuracy and stability reduced resource waste and cost increases caused by pointing errors.

[0229] In one embodiment of the present invention, step S6 includes:

[0230] S61. Store the error installation correction matrix calculated in each calibration process. After all the selected target stars have been calibrated, average all the error installation correction matrices to obtain a preliminary average correction matrix.

[0231] S62. Using the preliminary average correction matrix, recalibrate some or all of the calibrated target stars; compare the difference between the recalibrated results and the initial calibration results, and evaluate the accuracy and stability of the average correction matrix;

[0232] S63. If the recalibration result meets the preset accuracy requirements (e.g., the pointing error is less than a certain threshold), the average correction matrix is ​​considered to be valid and proceed to the next step; otherwise, re-examine the calibration process, analyze possible reasons, such as the accuracy of star position data, sensor errors, etc., and adjust the screening conditions or correction matrix calculation method as needed.

[0233] S64. If, during the verification process, certain aspects of the average correction matrix are found to be deficient, such as poor correction effects in certain specific directions, the correction matrix shall be optimized. The optimization includes adjusting the calculation weights of the correction matrix, introducing more complex mathematical models (such as least squares method, Kalman filtering, etc.) to optimize the solution process of the correction matrix, or making local fine-tuning of the correction matrix based on the calibration results. After optimization, the verification shall be performed again until an average correction matrix that meets the accuracy requirements is obtained.

[0234] S65. Store the final verified average correction matrix and label it with relevant verification information (such as verification time, verification results, etc.). Apply the average correction matrix to subsequent laser load pointing control.

[0235] The working principle of the above technical solution is as follows: During each calibration process, the system calculates an error installation correction matrix for the current target star. After all selected target stars have been calibrated, the system averages these correction matrices to obtain a preliminary average correction matrix. Combining the calibration results of all target stars, a correction matrix reflecting the overall error characteristics is obtained. The averaging calculation can be a weighted average or a simple arithmetic average, depending on the characteristics of the correction matrix and actual needs. Using the preliminary average correction matrix, the system recalibrates some or all of the calibrated target stars. The purpose of the recalibration is to evaluate the accuracy and stability of the average correction matrix by comparing the recalibration results with the initial calibration results. If the recalibration results can significantly reduce the pointing error, and this reduction is stable (i.e., consistent across different target stars), the average correction matrix is ​​preliminarily considered effective. The system will then verify the accuracy of the recalibration results to check whether they meet the preset accuracy requirements (e.g., pointing error less than a certain threshold). If the requirements are met, the average correction matrix is ​​considered valid and can proceed to the next step. If the requirements are not met, the calibration process needs to be re-examined, and possible causes analyzed, such as the accuracy of stellar position data or sensor errors. Based on the analysis results, it may be necessary to adjust the screening criteria or the calculation method of the correction matrix, and then recalibrate and recalculate the average. If the average correction matrix is ​​found to be deficient in certain aspects during the verification process (such as poor correction effect in certain specific directions), the correction matrix needs to be optimized. Optimization methods may include adjusting the calculation weights of the correction matrix, introducing more complex mathematical models (such as least squares method, Kalman filtering, etc.) to optimize the solution process of the correction matrix, or making local fine-tuning of the correction matrix based on the recalibration results. The optimized correction matrix will be verified again until an average correction matrix that meets the accuracy requirements is obtained; the final verified average correction matrix will be stored and labeled with relevant verification information (such as verification time, verification results, etc.).

[0236] The above technical solution achieves the following effects: By averaging the error correction matrices obtained during the calibration of all selected target stars, a preliminary average correction matrix is ​​obtained that comprehensively reflects the overall error characteristics of the system. This comprehensive correction method allows for a more complete consideration of errors under different target stars and observation conditions, thereby improving pointing accuracy and stability. The preliminary average correction matrix is ​​used to recalibrate some or all of the calibrated target stars, and the accuracy and stability of the average correction matrix are evaluated by comparing the recalibrated results with the initial calibration results. This ensures the effectiveness and reliability of the correction matrix in practical applications. During the verification process, if deficiencies are found in the average correction matrix, such as poor correction effects in specific directions, the system will optimize it. Optimization includes adjusting the calculation weights of the correction matrix and introducing more complex mathematical models to improve its adaptability and accuracy. The system can locally fine-tune or recalculate the correction matrix based on the recalibration results and verification information to adapt to different observation conditions and error situations. This adaptive adjustment capability enables the system to maintain high pointing accuracy and stability in practical applications. Through automated calibration and verification processes, the system can efficiently process large amounts of data and calculate accurate correction matrices. The system reduces manual intervention and repetitive work, improving efficiency. The entire calibration and verification process is repeatable, meaning the system can be recalibrated and verified when needed in the future, ensuring the accuracy and effectiveness of the correction matrix. Through a rigorous verification process, the system ensures that the final verified average correction matrix is ​​reliable and effective. Reliability verification reduces pointing errors and potential risks caused by inaccurate correction matrices. When using correction matrices for laser load pointing control, the system continuously monitors the accuracy and stability of the correction matrix, ensuring it always operates in a safe and reliable state. The system's storage and annotation methods for correction matrices simplify subsequent maintenance and management. When updates or adjustments to the correction matrix are needed, the system can quickly locate and replace the old correction matrix.

[0237] In one embodiment of the present invention, step S7 includes:

[0238] S71. Using the correction matrix above, perform another calibration test on the previously calibrated target star. Observe the position of the target star's spot centroid using a star-sensitive camera to determine whether it accurately falls on the tracking point.

[0239] If the centroid of the target star's spot coincides with the tracking point or the error is within an acceptable range, the calibration is considered successful.

[0240] If the calibration result has a large error, that is, the deviation between the target star's spot centroid position and the tracking point exceeds the acceptable range, then error analysis should be performed.

[0241] S72. Analyze the sources of error, including errors in stellar position data, orbital parameters, camera tracking accuracy, and motor control accuracy; based on the error analysis results, adjust the correction matrix or re-perform relevant steps in the calibration process; such as re-selecting target stars or recalculating motor compensation amounts.

[0242] S73. After completing the error adjustment, repeat steps S2-S6 to recalibrate the target star and verify the calibration results again until the predetermined accuracy requirements are met.

[0243] S74. Record the results of each calibration, error analysis, adjustment measures, and final verification results.

[0244] The working principle of the above technical solution is as follows: First, the correction matrix obtained through step S6 is used to recalibrate the previously calibrated target star. The position of the target star's centroid is observed using a star-sensitive camera and compared with a preset tracking point to determine whether the centroid accurately falls on the tracking point or whether the deviation is within an acceptable range. If the centroid position coincides with the tracking point or the error is within an acceptable range, the calibration is considered successful and the correction matrix is ​​effective. If the calibration result has a large error, i.e., there is a significant deviation between the target star's centroid position and the tracking point, error analysis is required to determine the root cause of the problem. During the error analysis phase, the system considers various possible sources of error, including the accuracy of stellar position data, errors in orbital parameters, camera tracking accuracy, and motor control accuracy. By comprehensively analyzing these factors, the system can identify the main causes of calibration errors. Based on the results of the error analysis, the system takes corresponding adjustment measures, including adjusting certain parameters of the correction matrix, re-selecting target stars to ensure their uniformity and representativeness, and recalculating motor compensation to optimize control performance. After error adjustment, the system repeats steps S2 to S6, namely, re-selecting target stars, calibrating, calculating and verifying the correction matrix. Through multiple iterations of calibration and verification, the system gradually approaches its optimal performance state, ensuring that the laser load pointing control system maintains high accuracy and stability in practical applications. Throughout the calibration process, the system meticulously records the results of each calibration, the error analysis, the adjustment measures taken, and the final verification results.

[0245] The above technical solution achieves the following effects: By retesting the calibrated target star using the corrected matrix, the accuracy and effectiveness of the corrected matrix can be double-verified. This double-verification mechanism significantly improves the accuracy and reliability of calibration, reducing the risk of calibration failure due to errors in a single test. When a large error is found in the calibration result, the error analysis in step S72 can accurately identify the source of the error, such as errors in star position data, orbital parameters, camera tracking accuracy, or motor control accuracy. Targeted adjustments based on the error analysis results, such as adjusting the corrected matrix, re-selecting target stars, or recalculating motor compensation, can effectively reduce errors and improve calibration accuracy. The iterative calibration process in step S73 allows the system to gradually approach its optimal performance state through continuous adjustment and optimization. By repeatedly executing steps S2 to S6, the system can make necessary adjustments to the corrected matrix and calibration process based on the results of the previous calibration and the error analysis, thereby gradually reducing calibration errors and improving pointing accuracy. The system can adaptively adjust the relevant parameters and steps in the corrected matrix and calibration process based on actual test results and error analysis. The adaptive adjustment capability enables the system to cope with different observation conditions and error scenarios, maintaining high pointing accuracy and stability. Step S74 requires detailed recording of the results of each calibration, error analysis, adjustment measures, and final verification results. These records not only provide important reference information for subsequent maintenance and upgrades but also contribute to the continuous optimization and improvement of system performance. By recording and summarizing the calibration process, the system can identify common error patterns and optimization opportunities, providing experience and improvement directions for future calibration work. This continuous improvement cycle helps to continuously enhance the system's pointing accuracy and reliability. Through rigorous calibration and verification procedures, the system can ensure high pointing accuracy and stability in practical applications. This stability is crucial for laser payload pointing control systems, ensuring normal operation in various complex environments. Detailed recording and summarization make system maintenance easier and more efficient. Maintenance personnel can quickly locate problems based on the recorded information and take corresponding measures for repair and optimization. This enhanced maintainability reduces system maintenance costs and downtime, improving the overall efficiency of the system.

[0246] An embodiment of the present invention provides a system characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0247] Memory, used to store computer programs;

[0248] A processor, when executing a program stored in memory, implements any of the steps described above.

[0249] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for laser payload autonomous pointing calibration optimization, characterized in that, The method comprises: S1, according to the position and velocity of the star in J2000 coordinate system, the orbit normal vector is calculated, and the orbit coordinate system is established; and target star screening is carried out; the star meeting the condition is added to the target star list as the calibration target; S2, according to the screened star table, the target star is open-loop directed in sequence; S3, when the target star is in the field of view of the star sensor camera, the deflection of the target star light spot centroid position and the tracking point is calculated, and then the motor theoretical direction is calculated; S4, the error installation correction matrix is calculated by using the actual direction of the motor and the theoretical direction; S5, steps S1-S4 are repeated until all target stars are calibrated; S6, the average correction matrix is obtained by averaging all error installation matrices; the preliminary average correction matrix is used to re-calibrate part or all of the calibrated target stars; if the re-calibration result meets the preset accuracy requirement, the average correction matrix is considered effective; otherwise, the average correction matrix is optimized, and after optimization, the average correction matrix meeting the accuracy requirement is obtained through verification again; S7, the average correction matrix is loaded, and the previously calibrated target stars are calibrated again; if the target star falls on the tracking point of the star sensor camera after being directed, it is determined that the calibration is successful; if the error is large, the calibration process is restarted.

2. The method of claim 1, wherein, The S1 comprises: S11, accurate position data and velocity vector of the star in J2000 coordinate system are obtained; the position data comprises longitude, latitude and height; S12, the position and velocity information of the star is updated in real time by using ephemeris data and navigation algorithm, and the accuracy and timeliness of the data are ensured; S13, the normal vector of the orbit plane is calculated by using the position and velocity of the star and the principle of orbit mechanics, the direction of the orbit normal vector is determined, and the negative direction of the Z axis of the orbit coordinate system is taken as the direction of the orbit normal vector; S14, the orbit coordinate system is constructed by taking the star as the coordinate origin and selecting X axis and Y axis; S15, the correctness of the coordinate system is verified to ensure that the axes are perpendicular to each other and meet the right-hand screw rule; S16, the position information of all stars to be screened in J2000 coordinate system is obtained by calling the star database, and the angle between the vector of each star and the Z direction of the orbit coordinate system is calculated; S17, the angle between the star vector and the sun vector is calculated; S18, the star meeting the condition is added to the target star list as the calibration target.

3. The method of claim 1, wherein, The S2 comprises: S21, the target star list is sorted according to the related factors of the star; S22, the control system calculates and outputs the pointing instruction according to the position information of the target star, and the laser load receives the instruction, adjusts the attitude, and points the optical axis to the target star; S23, the attitude adjustment process is monitored in real time.

4. The method of claim 3, wherein, The S23 comprises: S231, before the attitude adjustment, the current attitude of the laser load is accurately measured by using the high-precision gyroscope and accelerometer, and the initial attitude data is obtained; S232, the initial attitude data is compared with the preset ideal attitude, the attitude deviation is calculated, and the attitude is preliminarily calibrated based on the deviation result; S233, dynamically track the attitude of the laser payload through a Kalman filter or an extended Kalman filter, and fuse data from multiple sensors in real time; S234, based on the motion trajectory of the star and the current attitude of the laser payload, predict the position of the target star in the future period of time using celestial navigation algorithms; S235, dynamically adjust the attitude adjustment strategy according to the predicted star position and the current attitude deviation, and use fuzzy logic algorithms to adaptively optimize the attitude adjustment strategy, dynamically adjust the control parameters according to the real-time feedback of the attitude data and the changes of the external environment; S236, during the attitude adjustment process, adopt a closed-loop control strategy to compare the deviation between the actual attitude and the target attitude in real time, and use a PID controller to control the attitude adjustment; S237, during the attitude adjustment process, continuously monitor the performance indicators of the laser payload, and if an abnormality is found, immediately trigger the abnormality handling mechanism.

5. The method of claim 1, wherein, The S3 comprises: S31, capture the target star image through the star sensor camera; use image processing algorithms to extract the centroid position of the target star light spot; S32, Extract the centroid position coordinates ( , ) and preset tracking point coordinates ( , Compare and calculate the preliminary horizontal (X-axis) deviation. Deviation in the vertical direction (Y-axis) ; S33, preset a deviation threshold value: and , determine whether the deviation is within an acceptable range by the deviation threshold value, if |ΔX|≤ and |ΔY|≤ , preliminarily consider that the off-target amount is small, then directly enter the next step; S34, if the deviation exceeds the threshold, further judgment and processing are performed; If the deviation is too large, check whether there is external interference factor, and perform corresponding processing, which includes waiting for the interference to disappear and adjusting the camera exposure; If there is no external interference or the interference has been handled, but the deviation is still large, dynamic adjustment is performed; S35, analyze the motion trend of the target star to predict the position of the target star in the next frame of image; S36, dynamically adjust the tracking point and exposure parameters of the camera according to the predicted position, capture the image again and extract the centroid position; S37, repeat the above steps until the deviation is within an acceptable range; S38, after the deviation is acceptable, calculate the miss distance x and y according to the final extracted centroid position and tracking point coordinates; miss distance x = final ΔX, miss distance y = final ΔY; S39, record the miss distance x and y calculated this time, as well as the abnormalities or special handling situations encountered in the process, and feed back the miss distance data to the control system.

6. The method of claim 5, wherein, The S31 comprises: Calibrate the star sensor camera and initialize the camera parameters; based on the known target star position prediction, develop a search strategy within the field of view of the star sensor camera; Start the camera, capture the target star image according to the preset parameters, and improve the image quality in real time by applying image enhancement technology; Through image stability analysis algorithm, monitor the slight jitter or motion blur in the image sequence, and use image registration technology to remove or compensate these influences; Use image segmentation and morphological processing technology to identify and remove the occlusions in the image, judge the occlusion degree by setting a threshold, and trigger the recapture mechanism or adjust the observation angle based on the preset threshold; Optimize the feature extraction algorithm according to the light spot characteristics of the target star, and locate the contour and centroid position of the target star light spot.

7. The method of claim 1, wherein, The S4 comprises: S41, detect the current state of the laser terminal, including the real-time position and speed of the motor, and whether there is any abnormal alarm; if there is an abnormality, enter the fault handling process, record the error and try to repair it automatically or manually; S42, the target star light spot captured by the star sensor is used to extract the centroid position of the light spot; at the same time, the preset tracking point coordinates are read; S43, the centroid position is compared with the tracking point coordinates, the off-target amount in the azimuth direction and the elevation direction is respectively calculated, and is recorded; S44, the off-target amount x and y and the mechanical characteristics of the laser terminal are used to calculate the rotation amount of the azimuth motor and the elevation motor respectively; S45, the calculated motor compensation amount is used to update the theoretical pointing direction of the laser terminal; the theoretical pointing direction is compared with the actual pointing direction, and the difference between the two is evaluated; S46, the actual pointing direction and the theoretical pointing direction are converted into corresponding direction cosine matrices; S47, based on the direction cosine matrix, the principle of matrix transformation is used to calculate the PAT installation matrix; S48, the calculated PAT installation matrix is verified, if the verification result does not meet the requirements, the above steps are returned to recalculate and adjust until a satisfactory installation matrix is obtained; S49, the above steps are repeated for all selected target stars, and the correction matrix is updated once for each target star calibration, and the state and performance parameters of the laser terminal are continuously monitored during the entire calibration process.

8. The method of claim 1, wherein: The S6 comprises: S61, the error installation correction matrix calculated in each calibration process is stored, and when all selected target stars are calibrated, the error installation correction matrix is averaged to obtain a preliminary average correction matrix; S62, the preliminary average correction matrix is used to re-calibrate part or all of the calibrated target stars; the difference between the re-calibration result and the initial calibration result is compared to evaluate the accuracy and stability of the average correction matrix; S63, if the re-calibration result meets the preset accuracy requirement, the average correction matrix is considered effective, and the next step is entered; otherwise, the calibration process is rechecked, the reason is analyzed, and the selection condition or the correction matrix calculation method is adjusted as needed; S64, if the average correction matrix is found to have deficiencies in the verification process, the correction matrix is optimized, and after optimization, the average correction matrix is verified again until an average correction matrix meeting the accuracy requirement is obtained; S65, the average correction matrix that passes the final verification is stored, and the related verification information is marked, and the average correction matrix is applied to subsequent laser load pointing control.

9. The method of claim 1, wherein, The S7 comprises: S71, the previously noted correction matrix is used to calibrate the previously calibrated target stars again, and the star sensor is used to observe the light spot centroid position of the target star to determine whether it is accurately located on the tracking point; If the light spot centroid position of the target star coincides with the tracking point or the error is within an acceptable range, the calibration is considered successful; If the calibration result error is large, that is, the light spot centroid position of the target star deviates from the tracking point beyond the acceptable range, error analysis is performed; S72, the error sources are analyzed, the error sources include star position data error, orbit parameter error, camera tracking accuracy and motor control accuracy; according to the error analysis result, the correction matrix is adjusted or the related steps in the calibration process are re-performed; S73, after the error adjustment is completed, repeating the steps of S2-S6 to calibrate the target star again, and verifying the calibration result again until the predetermined accuracy requirement is met; S74, recording the result of each calibration, error analysis, adjustment measure and final verification result.

10. A laser payload autonomous pointing calibration optimization system, characterized by, The system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used for storing a computer program; The processor is used for executing the program stored in the memory, and realizing the steps of the method in any one of claims 1-9.

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