Method and device for improving attitude measurement precision of star sensor

Abstract

The application discloses a method and device for improving attitude measurement precision of a star sensor. The method comprises the following steps: extracting star points of stars in a current frame image acquired by the star sensor, and calculating original positions and original signal-to-noise ratios of the star points; for each star point, matching is performed based on a triangle matching method and a star library to obtain a plurality of identified stars; for each identified star, it is judged whether the star corresponding to the identified star in the previous continuous frame image forms a tracking queue of a preset length; if yes, the target position and the target signal-to-noise ratio of the identified star are estimated based on the tracking queue; if no, the original position of the identified star is taken as the target position, and the original signal-to-noise ratio is taken as the target signal-to-noise ratio; according to the position error and the target signal-to-noise ratio of each identified star, a weight coefficient of each identified star is calculated; and based on the target position and the weight coefficient of each identified star, an attitude quaternion corresponding to a photographing moment of the current frame image is calculated. The application can significantly improve the attitude measurement precision of the star sensor, and the cost is low.

Description

Technical Field

[0001] This invention relates to the field of aerospace technology, and in particular to a method and apparatus for improving the attitude measurement accuracy of star sensors. Background Technology

[0002] Star sensors are core attitude measurement components for modern spacecraft, especially high-precision Earth observation, deep space exploration, and scientific experimental satellites. By capturing images of the night sky, they identify the vectors of stars in the celestial sphere, thereby calculating the spacecraft's three-axis absolute attitude in inertial space. Therefore, their measurement accuracy directly determines the accuracy of the spacecraft's "sense of direction," and thus has a decisive impact on the success of the entire mission.

[0003] Currently, related research mainly focuses on improving the measurement accuracy of star sensors through hardware methods such as increasing the angular resolution of the star sensor or increasing the optical aperture. However, the accuracy of star sensors used in orbit has already reached 0.1 arcseconds, and the optical aperture is already quite large. If we continue to improve measurement accuracy by improving hardware performance, it will significantly increase equipment costs, which is quite costly.

[0004] Therefore, there is an urgent need for a new method and device to improve the accuracy of star sensor attitude measurement and solve the above problems. Summary of the Invention

[0005] This invention provides a method and apparatus for improving the attitude measurement accuracy of a star sensor, which can significantly improve the attitude measurement accuracy of the star sensor at a low cost. The technical solution is as follows: On the one hand, a method for improving the attitude measurement accuracy of a star sensor is provided, the method comprising: Extract star points from the current frame image obtained by the star sensor, and calculate the original position and original signal-to-noise ratio of each star point; For each star point, a triangle matching method is used to match it with the star database to obtain multiple identified stars; For each identified star, it is determined whether the stars corresponding to the identified star in the preceding consecutive frames constitute a tracking queue of a preset length; if so, the target position and target signal-to-noise ratio of the identified star are estimated based on the tracking queue; if not, the original position of the identified star is used as the target position and the original signal-to-noise ratio is used as the target signal-to-noise ratio. Calculate the positional error between the target position of the identified star and the actual position of the reference star in the star database; Calculate the weight coefficient for each identification star based on its position error and target signal-to-noise ratio; Based on the target position and weight coefficient of each identified star, the QUEST algorithm is used to calculate the pose quaternion corresponding to the current frame image capture time.

[0006] On the other hand, an apparatus for improving the attitude measurement accuracy of a star sensor is provided, the apparatus comprising: The extraction unit is used to extract star points in the current frame image acquired by the star sensor and calculate the original position and original signal-to-noise ratio of each star point; The matching unit is used to match each star point with the star database based on the triangle matching method to obtain multiple identified stars; The determining unit is used to determine, for each identified star, whether the stars corresponding to the identified star in the preceding consecutive frames of images constitute a tracking queue of a preset length; if so, the target position and target signal-to-noise ratio of the identified star are estimated based on the tracking queue; if not, the original position of the identified star is used as the target position and the original signal-to-noise ratio is used as the target signal-to-noise ratio. The first calculation unit calculates the positional error between the target position of the identified star and the actual position of the reference star in the star database. The second calculation unit calculates the weight coefficient of each identified star based on the position error of each star and the target signal-to-noise ratio. The third calculation unit uses the QUEST algorithm to calculate the pose quaternion corresponding to the current frame image capture time, based on the target position and weight coefficient of each identified star.

[0007] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for executing the computer program stored in the memory to implement the steps of the method for improving the attitude measurement accuracy of a star sensor as described above.

[0008] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the steps of the method for improving the attitude measurement accuracy of a star sensor as described above.

[0009] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method for improving the attitude measurement accuracy of a star sensor as described above.

[0010] This invention provides a method for improving the attitude measurement accuracy of a star sensor. First, by estimating the position coordinates and signal-to-noise ratio (SNR) of each identified star in the image using a tracking queue, temporal errors (random errors) in the identification results can be eliminated, thereby improving the accuracy of the position and SNR calculations. Then, by calculating the weight coefficients of the identified stars using the optimized position and SNR, brighter stars can be assigned larger weights, while dimmer stars can be assigned smaller weights, further improving the accuracy of satellite attitude calculation. Therefore, this application can eliminate noise and temporal random errors at the software level, resulting in low cost and significantly improved attitude measurement accuracy of the star sensor. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a method for improving the attitude measurement accuracy of a star sensor according to an embodiment of the present invention; Figure 2 This is a structural diagram of a device for improving the attitude measurement accuracy of a star sensor according to an embodiment of the present invention; Figure 3 This is a hardware architecture diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] The specific implementation of the method in this application is described in detail below.

[0015] Please refer to Figure 1 The present invention provides a method for improving the attitude measurement accuracy of a star sensor, the method comprising: Step 100: Extract star points from the current frame image obtained by the star sensor, and calculate the original position and original signal-to-noise ratio of each star point; Step 102: For each star point, a matching method is used to match it with the star database to obtain multiple identified stars; Step 104: For each identified star, determine whether the stars corresponding to the identified star in the previous consecutive frames form a tracking queue of a preset length; if so, estimate the target position and target signal-to-noise ratio of the identified star based on the tracking queue; if not, take the original position of the identified star as the target position and the original signal-to-noise ratio as the target signal-to-noise ratio. Step 106: Calculate the positional error between the target position of the identified star and the actual position of the reference star in the star database; Step 108: Calculate the weight coefficient of each identification star based on the position error of each identification star and the target signal-to-noise ratio; Step 110: Based on the target position and weight coefficient of each identified star, the QUEST algorithm is used to calculate the pose quaternion corresponding to the current frame image capture time.

[0016] In this embodiment, firstly, by estimating the position coordinates and signal-to-noise ratio (SNR) of each identified star in the image using a tracking queue, temporal errors (i.e., random errors) in the identification results can be eliminated, thereby improving the accuracy of the position and SNR calculation results. Then, the weight coefficients of the identified stars are calculated using the optimized position and SNR, allowing brighter stars to have larger weights and dimmer stars to have smaller weights, further improving the accuracy of satellite attitude calculation. Therefore, this application can eliminate noise and temporal random errors at the software level, which is not only low-cost but also significantly improves the attitude measurement accuracy of the star sensor.

[0017] The following description Figure 1 The execution method for each step is shown.

[0018] First, regarding step 100: The original position and original signal-to-noise ratio of each star point are calculated as follows: The initial position of each star point in its image plane coordinate system is calculated based on the energy centroid method. Determine a pixel matrix of a preset size that surrounds the corresponding star points. Based on the mean and variance of the energy of the star points and the background in this pixel matrix, calculate the initial signal-to-noise ratio of the corresponding star points. The calculation formula is as follows: In the formula, This represents the original signal-to-noise ratio of the star points. For star point energy, This represents the noise corresponding to the star points. It should be noted that the star point extraction process is a standard technique and will not be elaborated upon here.

[0019] Regarding step 102: Before performing star point matching, the original position coordinates of each star point need to be transformed from the image plane coordinate system to the star sensor's body coordinate system. Then, the coordinates of each star point in the star sensor's body coordinate system are matched with a pre-stored star catalog. Unmatched star points are removed, and the successfully matched star points are retained to obtain the final identified stars.

[0020] Regarding step 104: In this step, following the first-in-first-out principle, each tracking queue stores the star time of multiple consecutive images that are closest to the current frame image, as well as the original position coordinates and original signal-to-noise ratio of the star point corresponding to the star to be estimated and identified in each frame image.

[0021] Specifically, when processing each frame of the image, a tracking queue is constructed for each identified satellite in the image. For example, for the first frame of the image, if there are 100 identified satellites, 100 tracking queues are constructed. Taking the tracking queue of satellite A as an example, its original position and original signal-to-noise ratio (SNR) in the first frame of the image, as well as the satellite time when the first frame of the image was captured, are first recorded. Then, it is determined whether satellite A appears in the second frame of the image. If so, its original position and original SNR in the second frame of the image, as well as the satellite time when the second frame of the image was captured, are recorded. This process is repeated to obtain the tracking queue for satellite A. In addition, the tracking queue follows the first-in, first-out (FIFO) principle, retaining only a preset number of image frames, such as 10 frames. Therefore, for satellite A in the 11th frame of the image, if satellite A also appears in the previous 10 consecutive frames, that is, if the tracking queue of satellite A constitutes a tracking queue of the preset length, then the position and SNR of satellite A in the 11th frame of the image are determined according to the tracking queue corresponding to satellite A (i.e., the position and SNR of satellite A in the previous 10 frames of the image). Conversely, the position and signal-to-noise ratio of satellite A in the 11th frame image are calculated according to the method in step 100.

[0022] In some implementations, the target position and target signal-to-noise ratio of the corresponding identification star are estimated based on the tracking queue in the following manner: A position coordinate model is constructed that varies with star time, and the position coordinate model is solved based on the star time of each frame in the tracking queue and the position coordinates of the corresponding star points in the image to obtain the final position coordinate fitting formula. A signal-to-noise ratio (SNR) model for star points varying with star time is constructed. The SNR model is then solved based on the star time and the corresponding star point SNR in each frame of the tracking queue to obtain the final SNR fitting formula. Substitute the star time corresponding to the current frame image into the position coordinate fitting formula to obtain the target position of the identified star; By substituting the star time corresponding to the current frame image into the signal-to-noise ratio fitting formula, the target signal-to-noise ratio of the identified star can be obtained.

[0023] In some implementations, the fitted formula for the position coordinates of the star point is: In the formula, , The coordinates of the star point on the image plane; The star time corresponding to the current frame; , , , , , , , These are the fitting parameters for the rational polynomial.

[0024] In some implementations, the signal-to-noise ratio fitting formula for the star points is: In the formula, For star point signal-to-noise ratio, The star time corresponding to the current frame; , , , These are the fitting parameters for the rational polynomial.

[0025] The above steps, by incorporating star information from multiple preceding images to calculate star information (including position and signal-to-noise ratio) in the current image, effectively reduce the impact of random errors on the measurement results, while simultaneously improving stellar dynamic tracking capabilities and measurement accuracy. Verification has shown that this scheme can improve the attitude measurement accuracy of star sensors by more than 40%.

[0026] For step 108: Calculate the weight coefficient for each identified star using the following formula: In the formula, For the first i The weight coefficients corresponding to each star point For the first i The mean signal-to-noise ratio corresponding to each star point. , , , These are the fitting parameters for the rational polynomial.

[0027] The above steps, based on the signal-to-noise ratio (SNR) star weight coefficient calculation method, reallocate the weight coefficients for each identified star in the image to determine its pose, assigning larger weight coefficients to brighter stars and smaller weight coefficients to darker stars. This solves the problem of insufficient differentiation of darker stars in the energy weighting method. Verification shows that this method improves accuracy by more than 16% compared to the traditional energy weighting method.

[0028] For step 110: Based on the target position and weight coefficient of each identified star, the QUEST algorithm is used to calculate the pose quaternion corresponding to the current frame image capture time.

[0029] In this step, firstly, based on the target position of each identified star, the unit vector of each identified star in the star sensor's body coordinate system is determined, denoted as . ; Then, based on the matching results between the identified star and the reference star in the star database, its unit vector in the inertial coordinate system is determined, denoted as . ; The inertial attitude array of the star sensor is: The QUEST algorithm is used to solve for the pose. It should be noted that the pose solution process is a conventional technique and will not be described in detail here.

[0030] This application requires no hardware modifications or excessive software overhead, making it an ideal solution for improving the attitude measurement accuracy of star sensors, star cameras, and observation cameras.

[0031] like Figure 2 , Figure 3 As shown, this invention provides a device for improving the attitude measurement accuracy of a star sensor. The device can be implemented in software, hardware, or a combination of both. From a hardware perspective, as... Figure 2 The diagram shown is a hardware architecture diagram of a computing device containing a device for improving the attitude measurement accuracy of a star sensor, as provided in an embodiment of the present invention. Except for... Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the computing device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the computing device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0032] Please refer to Figure 3 This invention provides an apparatus for improving the attitude measurement accuracy of a star sensor, the apparatus comprising: Extraction unit 300 is used to extract star points in the current frame image acquired by the star sensor and calculate the original position and original signal-to-noise ratio of each star point; The matching unit 302 is used to match each star point with the star database based on the triangle matching method to obtain multiple identified stars; The determining unit 304 is used to determine, for each identified star, whether the stars corresponding to the identified star in the previous consecutive frames of images constitute a tracking queue of a preset length; if so, the target position and target signal-to-noise ratio of the identified star are estimated based on the tracking queue; if not, the original position of the identified star is used as the target position and the original signal-to-noise ratio is used as the target signal-to-noise ratio. The first calculation unit 306 calculates the position error between the target position of the identified star and the actual position of the reference star in the star database. The second calculation unit 308 calculates the weight coefficient of each identification star based on the position error of each identification star and the target signal-to-noise ratio. The third calculation unit 310 calculates the attitude quaternion corresponding to the current frame image capture time using the QUEST algorithm, based on the target position and weight coefficient of each identified star.

[0033] In some implementations, following the first-in-first-out principle, each tracking queue stores the star time of multiple consecutive images that are closest to the current frame image, as well as the original position coordinates and original signal-to-noise ratio of the star point corresponding to the star to be estimated and identified in each frame image.

[0034] In some implementations, the target position and target signal-to-noise ratio of the corresponding identification star are estimated based on the tracking queue in the following manner: A position coordinate model is constructed that varies with star time, and the position coordinate model is solved based on the star time of each frame in the tracking queue and the position coordinates of the corresponding star points in the image to obtain the final position coordinate fitting formula. A signal-to-noise ratio (SNR) model for star points varying with star time is constructed. The SNR model is then solved based on the star time and the corresponding star point SNR in each frame of the tracking queue to obtain the final SNR fitting formula. Substitute the star time corresponding to the current frame image into the position coordinate fitting formula to obtain the target position of the identified star; By substituting the star time corresponding to the current frame image into the signal-to-noise ratio fitting formula, the target signal-to-noise ratio of the identified star can be obtained.

[0035] In some implementations, the position coordinate fitting formula is: In the formula, , The coordinates of the star point on the image plane; The star time corresponding to the current frame; , , , , , , , These are the fitting parameters for the rational polynomial.

[0036] In some implementations, the signal-to-noise ratio fitting formula is: In the formula, For star point signal-to-noise ratio, The star time corresponding to the current frame; , , , These are the fitting parameters for the rational polynomial.

[0037] In some implementations, the original position and original signal-to-noise ratio of each star point are calculated, including: The initial position of each star point in its image plane coordinate system is calculated based on the energy centroid method. Determine a pixel matrix of a preset size that surrounds the corresponding star points. Based on the mean and variance of the energy of the star points and the background in the pixel matrix, calculate the initial signal-to-noise ratio of the corresponding star points.

[0038] In some implementations, the weighting coefficient for each identification star is calculated using the following formula: In the formula, For the first i The weight coefficients corresponding to each star point For the first i The mean signal-to-noise ratio corresponding to each star point. , , , These are the fitting parameters for the rational polynomial.

[0039] It should be noted that the apparatus for improving the attitude measurement accuracy of star sensors provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus for improving the attitude measurement accuracy of star sensors provided in the above embodiments and the method embodiments for improving the attitude measurement accuracy of star sensors belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0040] Embodiments of this application also provide a computer device, please refer to... Figure 3The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the method for improving the attitude measurement accuracy of the star sensor provided in the above-described method embodiments.

[0041] Embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the method for improving the attitude measurement accuracy of a star sensor provided in the above-described method embodiments.

[0042] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform any of the methods for improving the attitude measurement accuracy of a star sensor described in the above embodiments.

[0043] For ease of description, the above systems or devices are described separately as various modules or units based on their functions. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components.

[0044] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0045] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0046] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for improving the attitude measurement accuracy of a star sensor, characterized in that, The method includes: Extract star points from the current frame image obtained by the star sensor, and calculate the original position and original signal-to-noise ratio of each star point; For each star point, a triangle matching method is used to match it with the star database to obtain multiple identified stars; For each identified star, it is determined whether the stars corresponding to the identified star in the preceding consecutive frames constitute a tracking queue of a preset length; if so, the target position and target signal-to-noise ratio of the identified star are estimated based on the tracking queue; if not, the original position of the identified star is used as the target position and the original signal-to-noise ratio is used as the target signal-to-noise ratio. Calculate the positional error between the target position of the identified star and the actual position of the reference star in the star database; Calculate the weight coefficient for each identification star based on its position error and target signal-to-noise ratio; Based on the target position and weight coefficient of each identified star, the QUEST algorithm is used to calculate the pose quaternion corresponding to the current frame image capture time. The target position and target signal-to-noise ratio of the corresponding identified satellite are estimated based on the tracking queue as follows: A position coordinate model is constructed that varies with star time, and the position coordinate model is solved based on the star time of each frame in the tracking queue and the position coordinates of the corresponding star points in the image to obtain the final position coordinate fitting formula. A signal-to-noise ratio (SNR) model for star points varying with star time is constructed. The SNR model is then solved based on the star time and the corresponding star point SNR in each frame of the tracking queue to obtain the final SNR fitting formula. Substitute the star time corresponding to the current frame image into the position coordinate fitting formula to obtain the target position of the identified star; By substituting the star time corresponding to the current frame image into the signal-to-noise ratio fitting formula, the target signal-to-noise ratio of the identified star is obtained.

2. The method according to claim 1, characterized in that, Following the first-in-first-out principle, each tracking queue stores the star time of multiple consecutive images that are closest to the current frame image, as well as the original position coordinates and original signal-to-noise ratio of the star point corresponding to the star to be estimated and identified in each frame image.

3. The method according to claim 1, characterized in that, The fitted formula for the position coordinates is: In the formula, , The coordinates of the star point on the image plane; The star time corresponding to the current frame; , , , , , , , These are the fitting parameters for the rational polynomial.

4. The method according to claim 1, characterized in that, The signal-to-noise ratio fitting formula is: In the formula, For star point signal-to-noise ratio, The star time corresponding to the current frame; , , , These are the fitting parameters for the rational polynomial.

5. The method according to claim 1, characterized in that, Calculate the original position and original signal-to-noise ratio for each star point, including: The initial position of each star point in its image plane coordinate system is calculated based on the energy centroid method. Determine a pixel matrix of a preset size that surrounds the corresponding star points. Based on the mean and variance of the energy of the star points and the background in the pixel matrix, calculate the initial signal-to-noise ratio of the corresponding star points.

6. The method according to claim 1, characterized in that, The weight coefficient for each identified star is calculated using the following formula: In the formula, For the first i The weight coefficients corresponding to each star point For the first i The mean signal-to-noise ratio corresponding to each star point. , , , These are the fitting parameters for the rational polynomial.

7. A device for improving the attitude measurement accuracy of a star sensor, characterized in that, The apparatus for implementing the method according to any one of claims 1-6, comprising: The extraction unit is used to extract star points in the current frame image acquired by the star sensor and calculate the original position and original signal-to-noise ratio of each star point; The matching unit is used to match each star point with the star database based on the triangle matching method to obtain multiple identified stars; The determining unit is used to determine, for each identified star, whether the stars corresponding to the identified star in the preceding consecutive frames of images constitute a tracking queue of a preset length; if so, the target position and target signal-to-noise ratio of the identified star are estimated based on the tracking queue; if not, the original position of the identified star is used as the target position and the original signal-to-noise ratio is used as the target signal-to-noise ratio. The first calculation unit calculates the positional error between the target position of the identified star and the actual position of the reference star in the star database. The second calculation unit calculates the weight coefficient of each identified star based on the position error of each star and the target signal-to-noise ratio. The third calculation unit uses the QUEST algorithm to calculate the pose quaternion corresponding to the current frame image capture time, based on the target position and weight coefficient of each identified star.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-6.

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