Satellite angular rate identification method and related device

Abstract

The application discloses a satellite angular velocity identification method and related equipment, and relates to the technical field of rocket launching. The method comprises the following steps: acquiring a rocket telemetry video frame sequence after satellite-rocket separation; determining a dynamic image area of a target satellite based on a static area mask for each frame image of the rocket telemetry video frame sequence; extracting binary descriptors from dynamic image areas of adjacent two frame images based on a feature detection algorithm; matching corresponding feature point pairs between the adjacent two frame images based on the binary descriptors; estimating a rotation matrix between the adjacent two frame images through an iterative optimization algorithm based on the corresponding feature point pairs; and determining an angular velocity of the target satellite according to the rotation matrix and a time interval of the adjacent two frame images. The application realizes fast and high-precision identification of the satellite angular velocity, solves the problem of high delay and dependence on indirect data in the traditional technology, can instantly determine the success or failure of a task, and improves the evaluation efficiency of a launch task.

Description

Technical Field

[0001] This application relates to the field of rocket launch technology, and in particular to a method and related equipment for identifying satellite angular rate. Background Technology

[0002] In rocket launch missions, the satellite's angular rate after separation from the rocket is a key indicator for determining mission success. Traditional methods rely on the rocket's inertial navigation system to obtain the angular rate before separation, but after separation, because the satellite detaches from the rocket, it is impossible to directly obtain the satellite's angular rate information through traditional sensors.

[0003] Existing technologies typically estimate satellite orbits indirectly through telemetry data from ground-based tracking stations or tracking ships, thereby assessing angular rates. However, this method has drawbacks: firstly, it requires a long time to accumulate data, resulting in insufficient real-time performance; secondly, if a satellite malfunctions during the delay period, the attribution of responsibility is difficult to determine, leading to disputes between the satellite operator and the launch vehicle operator. Furthermore, the complexity of orbit estimation further reduces the timeliness and accuracy of the assessment. Therefore, a method for identifying satellite angular rates is urgently needed to address the aforementioned technical problems. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solutions, nor is it intended to determine the scope of protection of the claimed technical solutions.

[0005] In a first aspect, this application provides a method for identifying satellite angular rate, the method comprising:

[0006] Obtain the rocket telemetry video frame sequence after the satellite-rocket separation;

[0007] For each frame of the rocket telemetry video frame sequence, the dynamic image region of the target satellite is determined based on a static region mask;

[0008] Based on feature detection algorithms, binary descriptors are extracted from dynamic image regions of two adjacent frames;

[0009] Based on binary descriptors, match corresponding feature point pairs between two adjacent image frames;

[0010] Based on corresponding feature point pairs, the rotation matrix between two adjacent frames is estimated through an iterative optimization algorithm.

[0011] The angular rate of the target satellite is determined based on the rotation matrix and the time interval between two adjacent frames.

[0012] In some implementations, binary descriptors are extracted from dynamic image regions of two adjacent frames based on feature detection algorithms, including:

[0013] oFAST key points in dynamic image regions were detected using the ORB algorithm.

[0014] Based on oFAST keypoints, rBRIEF binary descriptors for two adjacent frames are generated.

[0015] In some implementations, the iterative optimization algorithm is a random sample consensus algorithm, which estimates the rotation matrix between two adjacent image frames based on corresponding feature point pairs, including:

[0016] A predetermined number of point pairs are randomly selected from the corresponding feature point pairs, and the fundamental matrix is ​​calculated;

[0017] Select interior points from the fundamental matrix and iteratively optimize the fundamental matrix based on the number of interior points;

[0018] The optimized fundamental matrix is ​​decomposed to obtain the rotation matrix.

[0019] In some implementations, the angular rate of the target satellite is determined based on the rotation matrix and the time interval between two adjacent frames, including:

[0020] Convert the rotation matrix to Euler angles;

[0021] The relative angular rate of the target satellite is calculated based on the Euler angles and the time interval between two adjacent frames.

[0022] The relative angular rate is smoothed using a moving average filter to obtain the actual angular rate of the target satellite;

[0023] The actual angular rate is corrected based on the rocket's preset angular rate to generate the target satellite's angular rate.

[0024] In some implementations, for each frame of the rocket telemetry video frame sequence, the dynamic image region of the target satellite is determined based on a static region mask, including:

[0025] Based on the fixed position information of the satellite separation mechanism, a static region mask matching the resolution of the rocket telemetry video frame is generated;

[0026] Based on static region masking, the pixel region corresponding to the satellite separation mechanism in each frame of image is masked to obtain the masked image.

[0027] Based on the masked image, a dynamic image region containing only the target satellite is identified.

[0028] In some implementations, it also includes:

[0029] Multiple sets of adjacent two-frame images are selected from the rocket telemetry video frame sequence, and the angular rate corresponding to each set of adjacent two-frame images is determined.

[0030] Arrange the angular velocities corresponding to each pair of adjacent frames in chronological order to generate an angular velocity sequence for the target satellite.

[0031] In some implementations, it also includes:

[0032] Based on the angular rate sequence, determine whether the rocket launch mission was successful and output alarm information.

[0033] Secondly, this application proposes a satellite angular rate identification device, comprising:

[0034] The video sequence acquisition unit is used to acquire the rocket telemetry video frame sequence after the separation of the satellite and the rocket.

[0035] The satellite region determination unit is used to determine the dynamic image region of the target satellite based on a static region mask for each frame of the rocket telemetry video frame sequence.

[0036] The feature detection and extraction unit extracts binary descriptors from the dynamic image regions of two adjacent frames based on the feature detection algorithm.

[0037] The feature point pair matching unit, based on binary descriptors, matches corresponding feature point pairs between two adjacent frames of images;

[0038] The rotation matrix estimation unit estimates the rotation matrix between two adjacent frames of images based on corresponding feature point pairs through an iterative optimization algorithm.

[0039] The satellite rate determination unit is used to determine the angular rate of the target satellite based on the rotation matrix and the time interval between two adjacent frames.

[0040] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the satellite angular rate identification method of any of the first aspects.

[0041] Fourthly, this application proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the satellite angular rate identification method of any one of the first aspects.

[0042] In summary, this application uses a predefined static region mask to occlude the satellite separation mechanism in each frame of telemetry image, directly extracting dynamic satellite regions for feature analysis, thus improving the real-time performance and accuracy of angular rate recognition. This application avoids the latency problem caused by traditional technologies relying on ground-based telemetry and control data. By combining frame-by-frame masking with computer vision algorithms, it effectively eliminates the influence of fixed interference objects, ensuring the robustness of feature point matching and rotation matrix estimation. Simultaneously, the real-time generated angular rate sequence provides direct criteria for determining the success or failure of rocket launch missions, solving the problem of ambiguous responsibility allocation after satellite-rocket separation, and possesses high reliability and engineering application value. Attached Figure Description

[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0044] Figure 1 A schematic flowchart of a satellite angular rate identification method provided in an embodiment of this application;

[0045] Figure 2 This is a schematic diagram of different frames of images after the separation of the satellite and rocket, provided in an embodiment of this application.

[0046] Figure 3 A schematic diagram of a satellite angular rate identification device provided in an embodiment of this application;

[0047] Figure 4 This is a schematic diagram of the structure of an electronic device for identifying satellite angular rate, provided in an embodiment of this application. Detailed Implementation

[0048] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0049] Please see Figure 1 This is a schematic flowchart of a satellite angular rate identification method provided in an embodiment of this application, which may specifically include:

[0050] S110. Obtain the rocket telemetry video frame sequence after the separation of the satellite and the rocket;

[0051] For example, after the satellite separates from the rocket, the telemetry equipment on board the rocket continuously captures and transmits video data, recording the satellite's real-time dynamics after separation. Obtaining this video frame sequence is fundamental to angular rate identification, providing a raw visual information source for subsequent analysis. By extracting continuous video frames after separation, it is possible to capture the satellite's attitude changes at the moment of separation and in its subsequent motion, laying the data foundation for image processing-based angular rate calculations.

[0052] Acquiring the video frame sequence requires combining rocket flight timing information to accurately locate the separation time point and extract multiple frames before and after the corresponding time. These images not only contain independent motion information of the satellite, but also record the inter-frame interval through timestamps, providing key time parameters for subsequent time-based angular rate derivation and ensuring that the calculation process is synchronized with the actual physical motion.

[0053] S120. For each frame of the rocket telemetry video frame sequence, determine the dynamic image region of the target satellite based on a static region mask;

[0054] For example, in telemetry videos after satellite-rocket separation, the positions of the satellite separation mechanism (such as a fixed support) and the cosmic background are typically static, while the satellite itself is in motion. By using a pre-generated static region mask, fixed areas in the video frame (such as the separation mechanism and background) can be precisely masked, retaining only the dynamic image areas of the satellite. This operation effectively eliminates static interference, ensuring that subsequent feature extraction and calculation target only the moving parts of the satellite, thus improving the focus of the analysis.

[0055] The static region mask is generated based on the known location of the satellite separation mechanism and the video resolution. Real-time occlusion processing is performed on each frame of the image using a preset template. After masking, the dynamic satellite region is completely preserved, and its motion trajectory and attitude changes become the core input for subsequent feature detection, providing a clean and high signal-to-noise ratio image data foundation for angular rate identification.

[0056] S130. Based on the feature detection algorithm, extract binary descriptors from the dynamic image region of two adjacent frames;

[0057] For example, in dynamic image regions, key feature points (such as edges or areas with significant texture) on the satellite surface are identified using feature detection algorithms, and corresponding binary descriptors are generated. These descriptors represent the local image characteristics of the feature points in the form of compact binary strings, providing a data foundation for subsequent inter-frame feature matching and ensuring the effective association of motion information.

[0058] Binary descriptor extraction focuses on feature points within dynamic regions, rapidly encoding their grayscale distribution and spatial structure using algorithms. The generated descriptors are computationally efficient and fast in matching, supporting real-time processing requirements and providing input conditions for subsequent estimation of satellite attitude changes using feature points from adjacent frames.

[0059] S140. Based on binary descriptors, match corresponding feature point pairs between two adjacent frames of images;

[0060] For example, by comparing the similarity of binary descriptors in two adjacent image frames, a one-to-one correspondence of feature points within a dynamic region is filtered out. This process uses Hamming distance to measure the difference between descriptors, selects the feature point pair with the smallest distance as the matching result, establishes the correlation of satellite motion between two frames, and provides reliable input data for subsequent attitude change analysis.

[0061] The matching process focuses on feature point pairs within dynamic regions. By quickly comparing the similarities and differences in binary strings, incorrect matches are eliminated, and high-confidence corresponding points are retained. This step ensures the continuity of the satellite's trajectory, providing support for subsequent estimation of the rotation matrix based on feature point pairs, while balancing real-time performance and computational efficiency.

[0062] S150. Based on corresponding feature point pairs, estimate the rotation matrix between two adjacent frames using an iterative optimization algorithm;

[0063] For example, based on matched feature point pairs, an iterative optimization algorithm (such as RANSAC) is used to filter out interior points that meet geometric constraints from a large number of possible point pairs, and the fundamental matrix between two image frames is calculated. By decomposing the fundamental matrix, a rotation matrix characterizing satellite attitude changes can be derived, providing core motion parameters for angular rate calculation. This process, through multiple iterations of optimization, effectively suppresses noise and mismatch interference, ensuring the robustness of the rotation matrix.

[0064] The core of the iterative optimization algorithm lies in gradually approximating the optimal solution through probabilistic sampling and verification, thereby accurately describing the relative rotational motion of the satellite between adjacent frames. This step lays a high-precision mathematical foundation for the subsequent calculation of angular rates, while also considering algorithm efficiency and anti-interference capabilities, ensuring the robustness and accuracy of the model.

[0065] S160. Determine the angular rate of the target satellite based on the rotation matrix and the time interval between two adjacent frames.

[0066] For example, by characterizing the satellite attitude change using a rotation matrix and combining it with the time interval between adjacent frames, the satellite's angular rate per unit time can be derived. The rotation matrix reflects the relative rotation of the satellite between two frames. It is decomposed into Euler angles through mathematical transformation, and the angular rate component is calculated based on the time difference, thus realizing the mapping from image motion to physical angular rate.

[0067] The process further smooths the instantaneous angular rate using techniques such as moving average filtering to suppress fluctuations caused by image noise or calculation errors. Finally, combined with the rocket's own angular rate correction, the actual angular rate after satellite separation is generated, providing highly reliable dynamic parameters for real-time determination of the launch mission status.

[0068] In summary, this application's embodiments utilize real-time image processing technology based on rocket telemetry video to directly calculate the satellite's angular rate after separation from the rocket, overcoming the real-time limitations and ambiguous responsibility assignment issues caused by traditional methods relying on ground-based telemetry and control data. Static region masks are used to quickly eliminate fixed background interference and accurately locate dynamic satellite regions. Feature detection and binary descriptor matching technologies are combined to efficiently extract motion features, significantly improving the targeting and computational efficiency of data processing. Iterative optimization algorithms are used to screen high-confidence feature point pairs and estimate the rotation matrix, effectively suppressing noise and mismatch interference and ensuring robustness against attitude changes. By combining time intervals and mathematical transformation models, the rotation matrix is ​​mapped to physical angular rates, and through moving average filtering and rocket angular rate correction, a high-precision, low-noise sequence of the satellite's actual angular rates is output. This application does not rely on external telemetry data, generating angular rate results immediately after separation, providing real-time technical support for mission success or failure determination, and simplifying the responsibility tracing process, thus possessing significant engineering application value.

[0069] Please see Figure 2 This is a schematic diagram of different frames after the separation of the star and rocket provided in an embodiment of this application.

[0070] In some instances, for each frame of a rocket telemetry video frame sequence, the dynamic image region of the target satellite is determined based on a static region mask, including:

[0071] Based on the fixed position information of the satellite separation mechanism, a static region mask matching the resolution of the rocket telemetry video frame is generated;

[0072] Based on static region masking, the pixel region corresponding to the satellite separation mechanism in each frame of image is masked to obtain the masked image.

[0073] Based on the masked image, a dynamic image region containing only the target satellite is identified.

[0074] For example, such as Figure 2 The image shown is a 4-frame image from the telemetry video sequence after satellite-rocket separation. The cosmic background is black, the white support is the satellite separation mechanism, and the blue cuboid is the separated satellite. Since the position of the satellite separation mechanism (white support) is fixed in the rocket structure design, its coordinate information is pre-matched to the video frame resolution. Based on this fixed position information, a static region mask template is generated, which covers the pixel area where the separation mechanism is located (e.g., ...). Figure 2 (The white support section in the middle). The size of the mask template is strictly consistent with the video frame resolution to ensure accurate matching of the position of the separation mechanism in each frame of the image during subsequent processing, and to avoid accidental occlusion of dynamic satellite areas.

[0075] In specific implementation, such as Figure 2As shown, after each frame of telemetry image input, a static region mask is loaded and superimposed on the current frame. Pixels marked as occluded areas in the mask (corresponding to white supports) are set to invalid values ​​or filled with the background color (such as black), thereby eliminating interference from the separation mechanism in the image. For example, Figure 2 After the white support area is masked, only the blue satellite body and its surrounding dynamic area are retained. This step is achieved through real-time pixel-level operation to ensure that the processed mask image contains only the moving part of the satellite (blue cuboid) and the cosmic background, avoiding interference from static structures in subsequent feature extraction.

[0076] After masking, the dynamic image regions of the satellite are fully preserved, while the separation mechanism and fixed background are effectively filtered out. Through image segmentation or thresholding, the boundary coordinates of the dynamic regions are further extracted from the masked image to define the region of interest (ROI) of the target satellite. This region contains all the satellite's motion information, such as attitude changes, surface texture, and edge features, providing high signal-to-noise ratio input data for subsequent ORB feature detection algorithms, ensuring the accuracy and stability of feature point extraction.

[0077] Figure 2 The four frames further demonstrate the satellite's attitude changes between different frames, proving that masking can consistently and stably eliminate fixed interference. Accurate localization of dynamic regions relies on strict alignment between the mask and the video frames, ensuring the satellite remains within the ROI throughout its movement. Through this embodiment, interference from static backgrounds and separation mechanisms is completely eliminated, providing a clean data foundation for subsequent feature matching and rotation matrix estimation, thus improving the accuracy and real-time performance of angular rate calculation.

[0078] In some instances, based on feature detection algorithms, binary descriptors are extracted from dynamic image regions in two adjacent frames, including:

[0079] oFAST key points in dynamic image regions were detected using the ORB algorithm.

[0080] Based on oFAST keypoints, rBRIEF binary descriptors for two adjacent frames are generated.

[0081] For example, in a dynamic image region, the oFAST keypoint detection module of the ORB algorithm identifies feature points through local grayscale differences. Specifically, 16 pixels within a circular neighborhood of radius 3 are selected, centered on a single pixel, with a threshold set to 20% of the center pixel's value. If 12 consecutive pixels within the neighborhood have grayscale values ​​higher or lower than the threshold, that point is identified as a corner point. Further, by calculating the intensity centroid direction of pixels within the feature point's neighborhood, the orientation angle of the feature point is determined, thereby generating an oFAST keypoint set with directional information.

[0082] For the detected oFAST keypoints, the rBRIEF algorithm is used to generate binary descriptors. First, the image is Gaussian filtered to reduce noise. Then, 512 pairs of pixels are randomly sampled within a specified neighborhood window centered on the keypoint, following an isotropic Gaussian distribution. Based on the orientation angle of the feature point, the sampled pairs are rotated to align with the orientation angle, ensuring rotation invariance of the descriptor. By comparing the grayscale values ​​of each rotated pixel pair, a 512-bit binary string is generated (1 if the former is greater than the latter, 0 otherwise). Finally, each oFAST keypoint corresponds to an rBRIEF binary descriptor, providing standardized, highly discriminative data input for feature matching between adjacent frames.

[0083] In some instances, based on binary descriptors, corresponding feature point pairs between two adjacent image frames are matched, including:

[0084] For example, the difference between binary descriptors in two adjacent frames of images is measured using Hamming distance to filter out the one-to-one correspondence of feature points within a dynamic region. Specifically, for each feature point in the first frame, the Hamming distance (i.e., the number of different bits after an XOR operation of two binary strings) between its binary descriptor and the binary descriptors of all feature points in the second frame is calculated, and the feature point with the smallest Hamming distance is selected as the matching point. This process uses a brute-force matching algorithm to traverse all possible feature point pairs, ensuring that each feature point is associated with its most similar target point, forming a preliminary matching result.

[0085] To further improve matching accuracy, a Hamming distance threshold is set to eliminate invalid matches with excessively large discrepancies. If the Hamming distance between two descriptors exceeds the preset threshold, it is judged as an incorrect match and excluded. The final retained matching point pairs must satisfy the bidirectional consistency condition, that is, when point A in the first frame matches point B in the second frame, the reverse match of point B should also point to point A. Through the above filtering mechanism, a set of corresponding feature point pairs with high confidence is obtained, providing robust motion correlation data for subsequent rotation matrix estimation.

[0086] In some instances, the iterative optimization algorithm is a random sample consensus algorithm, which estimates the rotation matrix between two adjacent image frames based on corresponding feature point pairs, including:

[0087] A predetermined number of point pairs are randomly selected from the corresponding feature point pairs, and the fundamental matrix is ​​calculated;

[0088] Select interior points from the fundamental matrix and iteratively optimize the fundamental matrix based on the number of interior points;

[0089] The optimized fundamental matrix is ​​decomposed to obtain the rotation matrix.

[0090] For example, the Random Sample Consensus (RANSAC) algorithm first randomly selects a predetermined number of point pairs (e.g., 8 pairs) from the matched feature point pairs. Based on these 8 pairs, a system of linear equations is constructed using the eight-point method to solve for the fundamental matrix describing the geometric relationship between the two frames of images. The fundamental matrix reflects the epipolar constraint relationship between the images, and its calculation is accomplished by minimizing the projection error of corresponding point pairs. The purpose of random sampling is to reduce the interference of noise and mismatched points, ensuring that the initial estimate of the fundamental matrix has high reliability.

[0091] After calculating the fundamental matrix, the algorithm iterates through all matching point pairs and filters inliers using epipolar geometric constraints. Specifically, for each point pair, the distance to its corresponding epipolar line is calculated; if the distance is less than a preset threshold, it is determined to be an inlier. The current number of inliers is counted, and the proportion of inliers is recorded. If the proportion of inliers does not reach the preset condition (e.g., exceeding 90%), the above random sampling and calculation process is repeated, iteratively updating the fundamental matrix. In each iteration, the fundamental matrix with the largest number of inliers is retained as the current optimal solution until the termination condition is met.

[0092] After obtaining the optimal fundamental matrix, it is decomposed into an essential matrix using singular value decomposition (SVD), further separating the rotation matrix and translation vector. Since the satellite's motion in a short period of time can be approximated as pure rotation, the influence of the translation component is ignored, and the rotation matrix is ​​finally extracted as the core parameter characterizing the satellite's attitude change between two frames. The decomposition of the rotation matrix must satisfy the constraints of orthogonality and a determinant of 1 to ensure that its physical meaning conforms to the characteristics of rigid body rotation.

[0093] The core of the RANSAC algorithm lies in its gradual approximation of the optimal solution through probabilistic sampling and verification. This process effectively suppresses the interference of mismatched points and noise on the calculation of the fundamental matrix, ensuring the stability and reliability of the rotation matrix. The final output rotation matrix accurately describes the relative rotational motion of the satellite between adjacent frames, providing a high-precision mathematical basis for subsequent angular rate calculations, while balancing algorithm efficiency and anti-interference capabilities.

[0094] In some instances, the angular rate of the target satellite is determined based on the rotation matrix and the time interval between two adjacent frames, including:

[0095] Convert the rotation matrix to Euler angles;

[0096] The relative angular rate of the target satellite is calculated based on the Euler angles and the time interval between two adjacent frames.

[0097] The relative angular rate is smoothed using a moving average filter to obtain the actual angular rate of the target satellite;

[0098] The actual angular rate is corrected based on the rocket's preset angular rate to generate the target satellite's angular rate.

[0099] For example, the three-dimensional spatial attitude changes of a satellite, represented by a rotation matrix, are converted into Euler angle parameters using matrix decomposition techniques. Specifically, the rotation matrix is ​​decomposed into rotational angular components about three orthogonal coordinate axes (such as the pitch, yaw, and roll axes), i.e., Euler angles. This conversion process mathematically determines the absolute rotation angle of the satellite about each axis between two adjacent image frames, providing the fundamental physical quantity for angular rate calculation.

[0100] The relative angular rate of the satellite around each coordinate axis is calculated based on the time interval between two adjacent frames and the change in Euler angles. Specifically, the change in each Euler angle component between two frames is divided by the time interval to obtain the satellite's rotational speed around the corresponding axis per unit time. For example, if the change in pitch angle is... If the time interval is Δt, then the pitch rate is: This step converts the geometric rotation into physical motion parameters, directly reflecting the satellite's instantaneous dynamic characteristics.

[0101] The calculated relative angular velocities are processed using a moving average filter to suppress transient noise interference. Specifically, a fixed-length data window is set, and the angular velocities at consecutive time points within the window are weighted and averaged. When new data replaces old data, the average value is updated to smooth the output results. This processing effectively filters out abnormal fluctuations caused by mismatched image feature points or environmental interference, improving the stability and continuity of the angular velocities.

[0102] Based on the rocket's preset angular rate (provided by the rocket's inertial navigation system), the satellite's actual angular rate is inversely compensated. Since the satellite's angular rate includes residual motion components after rocket separation, the influence of rocket motion is eliminated through vector superposition or algebraic correction, ultimately yielding the satellite's absolute angular rate in the inertial coordinate system. For example, if the satellite's relative angular rate is ω1 and the rocket's angular rate is ω2, then the satellite's actual angular rate is ω1 + ω2. This step ensures the independence of the angular rate data and avoids misjudgments caused by systematic errors.

[0103] After the above steps, the corrected actual angular rate of the target satellite is generated. This angular rate accurately characterizes the satellite's independent rotational motion after separation, providing a key indicator for real-time assessment of its attitude stability. Through continuous time-point angular rate sequence analysis, it is possible to immediately determine whether the satellite exceeds a preset safety threshold, providing direct evidence for successful rocket launch mission evaluation and fault alarm.

[0104] In some instances, it also includes:

[0105] Multiple sets of adjacent two-frame images are selected from the rocket telemetry video frame sequence, and the angular rate corresponding to each set of adjacent two-frame images is determined.

[0106] Arrange the angular velocities corresponding to each pair of adjacent frames in chronological order to generate an angular velocity sequence for the target satellite.

[0107] For example, multiple sets of adjacent pairs of images are repeatedly selected from the rocket telemetry video frame sequence based on a preset time interval. Specifically, consecutive image frame pairs are extracted from the video stream at fixed time steps (e.g., 0.1 seconds), with each pair containing the current frame and its next frame. During the selection process, it is necessary to ensure that the time intervals of the frame pairs are consistent to maintain the uniformity of the time base for angular rate calculation and provide standardized input data for the subsequent generation of continuous angular rate sequences.

[0108] For each pair of adjacent frames, a process of feature point extraction, matching, rotation matrix estimation, and angular rate calculation is performed to obtain the instantaneous angular rate of the satellite within that time period. Specifically, after obtaining the rotation matrix through ORB feature detection and RANSAC optimization algorithm, the angular rate components around each axis are calculated in conjunction with the inter-frame time interval, and finally the actual angular rate of the satellite corresponding to that frame pair is output. This process independently processes each frame pair to ensure that the angular rate calculations at each time point do not interfere with each other, and the results are independent and reliable.

[0109] The angular velocities calculated from multiple adjacent frame pairs are arranged in chronological order to form a continuous angular velocity sequence. For example, if the angular velocity of the first set of images corresponding to time t1 to t2 is ω1, and the angular velocity of the second set corresponding to t2 to t3 is ω2, then they are arranged sequentially along the time axis as ω1, ω2, etc. This process ensures the continuity and consistency of the angular velocity sequence through temporal alignment technology, forming a complete time series data of satellite rotation motion. This sequence fully characterizes the dynamic rotation characteristics of the satellite after separation. By analyzing the trend of angular velocity changes in the time dimension, it provides a high-resolution data foundation for real-time determination of mission status, triggering alarms, and tracing faults.

[0110] In some instances, it also includes:

[0111] Based on the angular rate sequence, determine whether the rocket launch mission was successful and output alarm information.

[0112] For example, based on the generated angular rate sequence, the angular rate components along each axis of the satellite are compared in real time within a preset safety threshold range: if the angular rate components at all time points in the sequence remain within the threshold range, the rocket launch mission is considered successful; if the angular rate component at any moment exceeds the threshold or exhibits abnormal fluctuations (such as continuous increase, sudden changes, etc.), the mission is considered a failure and an alarm is triggered. The alarm information includes the abnormal time point, the out-of-limit angular rate component and its deviation value, and is linked with the original video frames and calculation logs to form a traceable fault report. This report is transmitted to the ground control center in real time via a preset communication protocol and triggers audible and visual warning devices to ensure immediate response from operators. This judgment mechanism, by continuously monitoring the dynamic changes in angular rate, solves the delay problem of traditional methods relying on post-event analysis, providing a highly timely and reliable technical basis for mission status assessment and responsibility allocation.

[0113] Please see Figure 3 The diagram below illustrates the structure of a satellite angular rate identification device according to an embodiment of this application, including:

[0114] The video sequence acquisition unit 21 is used to acquire the rocket telemetry video frame sequence after the separation of the satellite and the rocket.

[0115] Satellite region determination unit 22 is used to determine the dynamic image region of the target satellite based on a static region mask for each frame of the rocket telemetry video frame sequence.

[0116] The feature detection and extraction unit 23 extracts binary descriptors from the dynamic image regions of two adjacent frames based on the feature detection algorithm.

[0117] Feature point pair matching unit 24, based on binary descriptors, matches corresponding feature point pairs between two adjacent frames of images;

[0118] The rotation matrix estimation unit 25 estimates the rotation matrix between two adjacent frames based on corresponding feature point pairs using an iterative optimization algorithm.

[0119] The satellite rate determination unit 26 is used to determine the angular rate of the target satellite based on the rotation matrix and the time interval between two adjacent frames.

[0120] Please see Figure 4 This application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any method for identifying satellite angular rate.

[0121] Since the electronic device described in this embodiment is the device used to implement a satellite angular rate identification device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application is within the scope of protection of this application.

[0122] In practice, when the computer program 311 is executed by the processor, it can implement any of the embodiments corresponding to the first aspect.

[0123] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0124] Those skilled in the art will understand that embodiments of this application can provide methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0125] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform... Figure 1 The flowchart of a satellite angular rate identification method in the corresponding embodiment.

[0129] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions may be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium may be any usable medium that a computer can store or a data storage device such as a server or data center that integrates one or more usable media. The usable medium may be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in the form of hardware and / or software functional units.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, magnetic disks, or optical disks.

[0135] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0136] Although preferred embodiments have been described in this specification, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this specification.

[0137] Obviously, those skilled in the art can make various modifications and variations to this specification without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims and their equivalents, this specification is also intended to include such modifications and variations.

Claims

1. A method of identifying a satellite angular rate, characterized in that, include: Obtain the rocket telemetry video frame sequence after the satellite-rocket separation; For each frame of the rocket telemetry video frame sequence, the dynamic image region of the target satellite is determined based on a static region mask, including: Based on the fixed position information of the satellite separation mechanism, a static region mask matching the resolution of the rocket telemetry video frame is generated; Based on the static region mask, the pixel region corresponding to the satellite separation mechanism in each frame image is masked to obtain a masked image. Based on the masked image, a dynamic image region containing only the target satellite is determined; Based on the feature detection algorithm, binary descriptors are extracted from the dynamic image region of two adjacent frames; Based on the binary descriptor, match the corresponding feature point pairs between two adjacent frames of images; Based on the corresponding feature point pairs, the rotation matrix between the two adjacent frames is estimated by an iterative optimization algorithm; The angular rate of the target satellite is determined based on the rotation matrix and the time interval between two adjacent frames, including: Convert the rotation matrix into Euler angles; The relative angular rate of the target satellite is calculated based on the Euler angles and the time interval between two adjacent frames. The relative angular rate is smoothed using a moving average filter to obtain the actual angular rate of the target satellite; The actual angular rate is corrected based on the rocket's preset angular rate to generate the target satellite's angular rate.

2. The method of claim 1, wherein, The feature detection algorithm extracts binary descriptors from the dynamic image region of two adjacent frames, including: oFAST key points in the dynamic image region were detected using the ORB algorithm. Based on the oFAST key points, rBRIEF binary descriptors for two adjacent frames are generated.

3. The method of claim 1, wherein, The iterative optimization algorithm is a random sampling consensus algorithm. The step of estimating the rotation matrix between two adjacent image frames based on the corresponding feature point pairs using the iterative optimization algorithm includes: A predetermined number of point pairs are randomly selected from the corresponding feature point pairs, and the fundamental matrix is ​​calculated; Filter interior points from the corresponding feature point pairs, and iteratively optimize the fundamental matrix based on the number of interior points; The optimized fundamental matrix is ​​decomposed to obtain the rotation matrix.

4. The method according to any one of claims 1 to 3, characterized in that, Also includes: Multiple sets of adjacent two-frame images are selected from the rocket telemetry video frame sequence, and the angular rate corresponding to each set of adjacent two-frame images is determined. Arrange the angular velocities corresponding to each pair of adjacent frames in chronological order to generate an angular velocity sequence for the target satellite.

5. The method according to claim 4, characterized in that, Also includes: Based on the angular rate sequence, determine whether the rocket launch mission was successful and output alarm information.

6. A satellite angular rate identification device, used to implement the method according to any one of claims 1 to 5, characterized in that, include: The video sequence acquisition unit is used to acquire the rocket telemetry video frame sequence after the separation of the satellite and the rocket. The satellite region determination unit is used to determine the dynamic image region of the target satellite based on a static region mask for each frame of the rocket telemetry video frame sequence. The feature detection and extraction unit is used to extract binary descriptors from the dynamic image region of two adjacent frames of images based on a feature detection algorithm. The feature point pair matching unit is used to match corresponding feature point pairs between two adjacent frames of images based on the binary descriptor. A rotation matrix estimation unit is used to estimate the rotation matrix between two adjacent frames of images based on the corresponding feature point pairs through an iterative optimization algorithm; The satellite rate determination unit is used to determine the angular rate of the target satellite based on the rotation matrix and the time interval between two adjacent frames.

7. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program stored in the memory to implement the steps of the satellite angular rate identification method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for identifying satellite angular rate as described in any one of claims 1 to 5.

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