Satellite angular rate identification method and related equipment

Abstract

The invention discloses a satellite angular rate identification method and related equipment, and relates to the technical field of rocket launching, and the method comprises the steps: obtaining a rocket telemetry video frame sequence after satellite-rocket separation; for each frame of image of the rocket telemetry video frame sequence, determining a dynamic image area of the target satellite based on the static area mask; based on a feature detection algorithm, binary descriptors are extracted from dynamic image areas of two adjacent frames of images; based on the binary descriptor, matching corresponding feature point pairs between two adjacent frames of images; estimating a rotation matrix between two adjacent frames of images through an iterative optimization algorithm based on the corresponding feature point pairs; and determining the angular rate of the target satellite according to the rotation matrix and the time interval between the two adjacent frames of images. According to the method, rapid and high-precision identification of the satellite angular rate is realized, the problem that the traditional technology depends on indirect data and is high in delay is solved, task success or failure can be judged in real time, and the launching task evaluation efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of rocket launch technology, and in particular to a method for identifying satellite angular velocity and related equipment. Background Art

[0002] In rocket launch missions, the angular velocity of the satellite after rocket-satellite separation is a key indicator of mission success. Traditional methods rely on the rocket's inertial system to obtain the angular velocity before separation. However, after separation, the satellite is separated from the rocket, making it impossible to directly obtain the satellite's angular velocity information through traditional sensors.

[0003] Existing technologies typically use telemetry data from ground-based tracking stations or tracking vessels to indirectly infer satellite orbits and subsequently estimate angular rates. However, these methods have drawbacks: first, they require a long wait for data accumulation, making them less real-time; second, if a satellite malfunctions during this delay, responsibility is difficult to determine, leading to disputes between the satellite and the carrier. Furthermore, the complexity of orbital calculations further reduces the timeliness and accuracy of the determination. Therefore, a method for identifying satellite angular rates is urgently needed to address these technical issues. Summary of the Invention

[0004] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

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

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

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

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

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

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

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

[0012] In some embodiments, extracting binary descriptors from dynamic image regions of two adjacent frames of images based on a feature detection algorithm includes:

[0013] Detect oFAST key points in dynamic image regions using the ORB algorithm;

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

[0015] In some embodiments, the iterative optimization algorithm is a random sampling consensus algorithm, which estimates the rotation matrix between two adjacent frames of images based on corresponding feature point pairs through the iterative optimization algorithm, including:

[0016] Randomly select a preset number of point pairs from the corresponding feature point pairs and calculate the basic matrix;

[0017] Filter inliers from the fundamental matrix and iteratively optimize the fundamental matrix based on the number of inliers;

[0018] Perform matrix decomposition on the optimized basic matrix to obtain the rotation matrix.

[0019] In some embodiments, determining the angular velocity of the target satellite based on the rotation matrix and the time interval between two adjacent image frames includes:

[0020] Convert the rotation matrix to Euler angles;

[0021] Calculate the relative angular rate of the target satellite based on the Euler angle and the time interval between two adjacent frames of images;

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

[0023] According to the preset angular velocity of the rocket, the actual angular velocity is corrected to generate the angular velocity of the target satellite.

[0024] In some embodiments, for each frame of the rocket telemetry video frame sequence, determining the dynamic image region of the target satellite based on the static region mask includes:

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

[0026] Based on the static area mask, the pixel area corresponding to the satellite separation mechanism in each frame of the image is masked to obtain a mask-processed image;

[0027] The image is processed based on the mask to determine the dynamic image area that contains only the target satellite.

[0028] In some embodiments, further comprising:

[0029] Selecting multiple sets of two adjacent frames from a rocket telemetry video frame sequence and determining the angular rate corresponding to each set of two adjacent frames;

[0030] The angular rates corresponding to each group of two adjacent frames of images are arranged in chronological order to generate an angular rate sequence of the target satellite.

[0031] In some embodiments, further comprising:

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

[0033] In a second aspect, the present application proposes a device for identifying satellite angular rate, comprising:

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

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

[0036] A feature detection and extraction unit extracts binary descriptors from the dynamic image region of two adjacent frames of images based on a feature detection algorithm;

[0037] 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 through an iterative optimization algorithm based on corresponding feature point pairs;

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

[0040] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the satellite angular rate identification method of any one of the first aspects when executing the computer program stored in the memory.

[0041] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, which, 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 area mask to block the satellite separation mechanism in each frame of telemetry image, directly extracts the dynamic satellite area for feature analysis, and improves the real-time and accuracy of angular rate recognition. This application avoids the delay problem caused by traditional technology relying on ground measurement and control data. By combining frame-by-frame mask processing with computer vision algorithms, it effectively eliminates the influence of fixed interference objects and ensures the robustness of feature point matching and rotation matrix estimation. At the same time, the real-time generated angular rate sequence provides a direct basis for determining the success or failure of the rocket launch mission, solving the problem of ambiguous responsibility after the separation of the satellite and the rocket, and has high reliability and engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0044] Figure 1 A schematic flow chart of a method for identifying satellite angular rate provided in an embodiment of the present application;

[0045] Figure 2 Schematic diagram of different frames of images after the separation of the satellite and the rocket provided in the embodiment of the present application;

[0046] Figure 3 A schematic diagram of the structure of a device for identifying satellite angular rate provided in an embodiment of the present application;

[0047] Figure 4 A schematic diagram of the structure of an electronic device for identifying satellite angular rate provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments.

[0049] See also Figure 1 , which is a flow chart of a method for identifying satellite angular rate provided in an embodiment of the present application, may specifically include:

[0050] S110, obtaining a rocket telemetry video frame sequence after the separation of the rocket and satellite;

[0051] For example, after separation, the rocket's onboard telemetry equipment continuously captures and transmits video data, recording the satellite's real-time movements after separation. Acquiring this sequence of video frames is the basis for angular rate identification, providing the raw visual information source for subsequent analysis. By capturing continuous video frames after separation, it is possible to capture the satellite's attitude changes at the moment of separation and during subsequent motion, laying the data foundation for angular rate calculations based on image processing.

[0052] Acquiring video frame sequences requires integrating the rocket's flight timing information to precisely locate and separate time points, and extract multiple frames around those moments. These images not only contain the satellite's independent motion information but also record the inter-frame intervals through timestamps. This provides critical timing parameters for subsequent time-series-based angular rate derivation, ensuring the calculation process is synchronized with actual physical motion.

[0053] S120, for each frame of the rocket telemetry video frame sequence, determining the dynamic image region of the target satellite based on the static region mask;

[0054] For example, in telemetry videos following rocket-satellite separation, the position of the satellite separation mechanism (such as the fixed bracket) and the cosmic background typically remain stationary, while the satellite itself is in motion. Pre-generated static region masks can be used to precisely mask out fixed regions of the video frame (such as the separation mechanism and background), retaining only the dynamic image region of the satellite. This effectively eliminates static interference, ensuring that subsequent feature extraction and calculations focus solely on the moving portion of the satellite, improving the targeted nature of the analysis.

[0055] Static region masks are generated based on the known position of the satellite separation mechanism and the video resolution, and each frame is masked in real time using a preset template. After masking, the dynamic satellite region is fully preserved, and its motion trajectory and attitude changes become the core input for subsequent feature detection, providing clean image data with a high signal-to-noise ratio for angular rate identification.

[0056] S130, extracting binary descriptors from the dynamic image regions of two adjacent image frames based on a feature detection algorithm;

[0057] For example, in dynamic image regions, a feature detection algorithm identifies key feature points on the satellite surface (such as edges or texture-significant areas) and generates corresponding binary descriptors. 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 effective association of motion information.

[0058] Binary descriptor extraction focuses on feature points within dynamic regions, using algorithms to rapidly encode their grayscale distribution and spatial structure. The resulting descriptors are computationally efficient and fast, supporting real-time processing needs and providing the input for subsequent estimation of satellite attitude changes using feature point pairs from adjacent frames.

[0059] S140, matching corresponding feature point pairs between two adjacent image frames based on the binary descriptor;

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

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

[0062] S150, estimating the rotation matrix between two adjacent image frames through an iterative optimization algorithm based on the corresponding feature point pairs;

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

[0064] The core of the iterative optimization algorithm lies in gradually approaching 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 angular rate calculation, while also balancing algorithmic efficiency and anti-interference capabilities, ensuring the robustness and accuracy of the model.

[0065] S160: Determine the angular velocity of the target satellite according to the rotation matrix and the time interval between two adjacent image frames.

[0066] For example, the satellite's angular velocity per unit time can be derived by combining the satellite's attitude changes, as represented by the rotation matrix, with the time interval between adjacent frames. The rotation matrix reflects the satellite's relative rotation between two frames. Through mathematical transformations, it is decomposed into Euler angles, and the angular velocity components are calculated based on the time difference, achieving a mapping from image motion to physical angular velocity.

[0067] This process further smooths the instantaneous angular rate using techniques such as sliding average filtering to suppress fluctuations caused by image noise or computational 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 launch mission status.

[0068] In summary, the embodiment of the present application directly calculates the angular rate of the satellite after the separation of the rocket and the satellite through real-time image processing technology based on rocket telemetry video, overcoming the problems of insufficient real-time performance and ambiguous responsibility division caused by the traditional method relying on ground measurement and control data. Static area masks are used to quickly eliminate fixed background interference and accurately locate dynamic satellite areas. Feature detection and binary descriptor matching technology are combined to efficiently extract motion features, significantly improving the pertinence and computational efficiency of data processing. High-confidence feature point pairs are screened and the rotation matrix is estimated through an iterative optimization algorithm, effectively suppressing noise and mismatch interference to ensure the robustness of attitude changes. Combining the time interval with the mathematical transformation model, the rotation matrix is mapped to the physical angular rate, and through sliding average filtering and rocket angular rate correction, a high-precision, low-noise sequence of actual satellite angular rates is output. This application does not need to rely on external telemetry data, and generates angular rate results immediately after the separation of the rocket and the satellite, providing real-time technical support for mission success or failure determination, while simplifying the responsibility tracing process, and has important engineering application value.

[0069] See also Figure 2 , which is a schematic diagram of different frame images after the separation of the satellite and the rocket provided in an embodiment of the present application.

[0070] In some examples, for each frame of a rocket telemetry video frame sequence, determining a dynamic image region of a target satellite based on a static region mask includes:

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

[0072] Based on the static area mask, the pixel area corresponding to the satellite separation mechanism in each frame of the image is masked to obtain a mask-processed image;

[0073] The image is processed based on the mask to determine the dynamic image area that contains only the target satellite.

[0074] For example, Figure 2 As shown in the figure, there are four frames of telemetry video sequence after the separation of rocket and satellite. The cosmic background is black, the white bracket is the satellite separation mechanism, and the blue rectangle is the separated satellite. Since the position of the satellite separation mechanism (white bracket) is fixed during the rocket structure design, its coordinate information is pre-matched with the video frame resolution. Based on this fixed position information, a static area mask template is generated. The mask covers the pixel area where the separation mechanism is located (such as Figure 2 The size of the mask template is strictly consistent with the video frame resolution, ensuring that the position of the separation mechanism in each frame is accurately matched in subsequent processing to avoid false occlusion of dynamic satellite areas.

[0075] In specific implementation, Figure 2As shown in the figure, after each frame of telemetry image is input, the static area mask is loaded and superimposed with the current frame. The pixels marked as occluded areas in the mask (corresponding to white brackets) will be set to invalid values or filled with background color (such as black), thereby eliminating the interference of the separation mechanism in the image. For example, Figure 2 The white support region in the center is masked, leaving only the blue satellite body and the surrounding dynamic area. This step is performed using real-time pixel-level operations, ensuring that the processed mask image only contains the moving part of the satellite (the blue cuboid) and the cosmic background, preventing static structures from interfering with subsequent feature extraction.

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

[0077] Figure 2 The four frames of images further demonstrate the changes in the satellite's attitude between different frames, proving that mask processing can continuously and stably eliminate fixed interference. Accurate positioning of dynamic areas depends on strict alignment of the mask and the video frame to ensure that the satellite is always within the ROI during movement. Through the embodiments of the present application, the interference between the static background and the separation mechanism is completely eliminated, providing a pure data foundation for subsequent feature matching and rotation matrix estimation, and improving the accuracy and real-time performance of angular rate calculation.

[0078] In some examples, binary descriptors are extracted from the dynamic image region of two adjacent frames based on a feature detection algorithm, including:

[0079] Detect oFAST key points in dynamic image regions using the ORB algorithm;

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

[0081] For example, in dynamic image regions, the oFAST keypoint detection module of the ORB algorithm identifies feature points based on local grayscale differences. Specifically, 16 pixels within a circular neighborhood with a radius of 3, centered at the pixel, are selected, and a threshold is set at 20% of the center pixel's value. If 12 consecutive pixels within the neighborhood have grayscale values above or below the threshold, the point is considered a corner. Furthermore, the direction of the feature point's intensity centroid is calculated by calculating the direction of the pixel's neighborhood, thereby determining the directional angle of the feature point and generating an oFAST keypoint set with directional information.

[0082] For detected oFAST keypoints, the rBRIEF algorithm is used to generate binary descriptors. The image is first Gaussian filtered to reduce noise. Then, 512 pairs of pixels are randomly sampled from an isotropic Gaussian distribution within a specified neighborhood window centered on the keypoint. Based on the orientation angle of the feature point, the sampled pairs are rotated to align with the orientation angle to ensure rotational invariance of the descriptor. By comparing the grayscale values of each pair of rotated pixels, a 512-bit binary string is generated (where the grayscale value is greater than the grayscale value, and otherwise, it is 0). Ultimately, each oFAST keypoint corresponds to an rBRIEF binary descriptor, providing standardized, highly discriminative data input for feature matching between adjacent frames.

[0083] In some examples, matching corresponding feature point pairs between two adjacent frames of images based on binary descriptors includes:

[0084] For example, the Hamming distance is used to measure the difference between binary descriptors in two adjacent image frames, and the one-to-one correspondence between feature points in the dynamic region is screened out. Specifically, for each feature point in the first frame, the Hamming distance (i.e., the number of different bits after the two binary strings are XORed) 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 significant discrepancies. If the Hamming distance between two descriptors exceeds the preset threshold, it is considered an incorrect match and is eliminated. The remaining matching point pairs must meet 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 must also point to point A. This screening mechanism generates a high-confidence set of corresponding feature point pairs, providing robust motion correlation data for subsequent rotation matrix estimation.

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

[0087] Randomly select a preset number of point pairs from the corresponding feature point pairs and calculate the basic matrix;

[0088] Filter inliers from the fundamental matrix and iteratively optimize the fundamental matrix based on the number of inliers;

[0089] Perform matrix decomposition on the optimized basic matrix to obtain the rotation matrix.

[0090] For example, the random sampling consensus algorithm (RANSAC) first randomly selects a preset number of point pairs (e.g., 8 pairs) from the matched feature point pairs. Based on these 8 pairs of points, a system of linear equations is constructed using the eight-point method to solve the basic matrix that describes the geometric relationship between the two frames. The basic matrix reflects the epipolar constraint relationship between the images, and its calculation process is completed by minimizing the projection error of the corresponding point pairs. The purpose of random sampling is to reduce the interference of noise and mismatched points to ensure that the initial estimate of the basic matrix has a high degree of credibility.

[0091] After calculating the fundamental matrix, the algorithm traverses all matching point pairs and selects inliers using epipolar geometric constraints. Specifically, for each point pair, the distance to the corresponding epipolar line is calculated. If the distance is less than a preset threshold, it is considered an inlier. The current number of inliers is counted, and the inlier ratio is recorded. If the inlier ratio does not meet the preset requirement (for example, exceeding 90%), the above random sampling and calculation process is repeated, iteratively updating the fundamental matrix. The fundamental matrix with the largest number of inliers is retained as the current optimal solution for each iteration until the termination condition is met.

[0092] After obtaining the optimal fundamental matrix, singular value decomposition (SVD) is used to decompose it into its essential matrix, further separating the rotation matrix and translation vector. Since satellite motion over short periods of time can be approximated as pure rotation, the influence of the translation component is ignored. Ultimately, the rotation matrix is extracted as the core parameter representing the change in satellite attitude between two image 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 gradually approaching the optimal solution through probabilistic sampling and verification. This process effectively suppresses the interference of mismatched points and noise on the basic matrix calculation, 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 foundation for the subsequent angular rate calculation, while also balancing algorithm efficiency and anti-interference capabilities.

[0094] In some examples, determining the angular velocity of the target satellite based on the rotation matrix and the time interval between two adjacent image frames includes:

[0095] Convert the rotation matrix to Euler angles;

[0096] Calculate the relative angular rate of the target satellite based on the Euler angle and the time interval between two adjacent frames of images;

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

[0098] According to the preset angular velocity of the rocket, the actual angular velocity is corrected to generate the angular velocity of the target satellite.

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

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

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

[0102] The satellite's actual angular velocity is inversely compensated based on the rocket's preset angular velocity (provided by the rocket's inertial system). Because the satellite's angular velocity includes residual motion after rocket separation, vector superposition or algebraic corrections are used to eliminate the effect of rocket motion, ultimately yielding the satellite's absolute angular velocity in the inertial coordinate system. For example, if the satellite's relative angular velocity is ω1 and the rocket's angular velocity is ω2, the satellite's actual angular velocity is ω1 + ω2. This step ensures the independence of the angular velocity data and avoids misjudgments due to systematic errors.

[0103] After the above steps, the corrected actual angular velocity of the target satellite is generated. This angular velocity accurately represents the satellite's independent rotational motion after separation, providing a key indicator for real-time determination of its attitude stability. By analyzing the angular velocity sequence at consecutive time points, it is possible to instantly determine whether the satellite exceeds the preset safety threshold, providing a direct basis for rocket launch mission success assessment and fault warning.

[0104] In some instances, this also includes:

[0105] Selecting multiple sets of two adjacent frames from a rocket telemetry video frame sequence and determining the angular rate corresponding to each set of two adjacent frames;

[0106] The angular rates corresponding to each group of two adjacent frames of images are arranged in chronological order to generate an angular rate sequence of the target satellite.

[0107] For example, multiple sets of two adjacent frames are repeatedly selected from a rocket telemetry video frame sequence based on a preset time interval. Specifically, consecutive image frame pairs are captured from the video stream at a fixed time step (e.g., 0.1 seconds), with each set consisting of the current frame and the next frame. During the selection process, the time intervals between the frame pairs must be consistent to maintain a consistent time base for angular rate calculations and provide standardized input data for the subsequent generation of a continuous angular rate sequence.

[0108] For each pair of adjacent frames, the system performs feature point extraction, matching, rotation matrix estimation, and angular rate calculation to determine the instantaneous angular rate of the satellite during that time period. Specifically, after obtaining the rotation matrix using ORB feature detection and the RANSAC optimization algorithm, the angular rate components about each axis are calculated based on the inter-frame time interval. Ultimately, the actual satellite angular rate corresponding to that frame pair is output. This process processes each frame pair independently, ensuring that the angular rate calculations at each time point do not interfere with each other and provide independent and reliable results.

[0109] The angular rates calculated from multiple sets of adjacent frame pairs are arranged in chronological order to form a continuous angular rate sequence. For example, if the angular rate from time t1 to t2 in the first set of images is ω1, and the angular rate from time t2 to t3 in the second set is ω2, then the angular rates are arranged in chronological order as ω1, ω2, and so on. This process uses timing alignment technology to ensure the continuity and consistency of the angular rate sequence, forming a complete time series data of the satellite's rotational motion. This sequence fully characterizes the dynamic rotation characteristics of the satellite after separation. By analyzing the angular rate change trend in the time dimension, it provides a high-resolution data foundation for real-time mission status determination, alarm triggering, and fault tracing.

[0110] In some instances, this also includes:

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

[0112] For example, based on the generated angular rate sequence, the angular rate components of each axis of the satellite are compared in real time through a preset safety threshold range: if the angular rate components at all time points in the sequence are continuously within the threshold range, the rocket launch mission is judged to be successful; if the angular rate components at any moment exceed the threshold or show abnormal fluctuations (such as continuous increase, sudden change, etc.), the mission is judged to have failed and an alarm message is triggered. The alarm information includes the abnormal time point, the out-of-limit angular rate component and its deviation value, and at the same time associates the original video frame with the calculation log to form a traceable fault report, which is transmitted to the ground control center in real time through the preset communication protocol and triggers the sound and light warning device to ensure that the operator responds immediately. This judgment mechanism solves the delay problem of traditional methods that rely on post-analysis by continuously monitoring the dynamic changes of angular rate, and provides a highly timely and reliable technical basis for mission status assessment and responsibility division.

[0113] See also Figure 3 , which is a schematic structural diagram of a satellite angular rate identification device provided in an embodiment of the present application, comprising:

[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] The satellite region determination unit 22 is configured to determine the dynamic image region of the target satellite based on the 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 region of two adjacent frames of images based on a feature detection algorithm;

[0117] The feature point pair matching unit 24 matches corresponding feature point pairs between two adjacent frames of images based on the binary descriptor;

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

[0119] The satellite velocity determination unit 26 is configured to determine the angular velocity of the target satellite according to the rotation matrix and the time interval between two adjacent image frames.

[0120] See also Figure 4 An embodiment of the present 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, the steps of any method for identifying satellite angular velocity are implemented.

[0121] Since the electronic device introduced in this embodiment is a device used to implement a satellite angular rate identification device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application will not be introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application falls within the scope of protection to be protected by this application.

[0122] During the specific implementation process, when the computer program 311 is executed by the processor, any implementation method of the embodiments corresponding to the first aspect can be implemented.

[0123] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

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

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0128] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes Figure 1 The flowchart of a method for identifying satellite angular rate 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, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted 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 can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium can be a magnetic medium, an optical medium or a semiconductor medium, etc.

[0130] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned 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 device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0132] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0133] In addition, the functional units in the various embodiments of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The above-mentioned integrated units may be implemented in the form of hardware and / or software functional units.

[0134] If the integrated unit is implemented in the form of 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 the present application, 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. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk, or an optical disk.

[0135] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0136] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.

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

Claims

1. A method for identifying satellite angular rate, characterized in that: include: Obtain the rocket telemetry video frame sequence after the separation of the rocket and satellite; For each frame of the rocket telemetry video frame sequence, determining a dynamic image region of the target satellite based on a static region mask; Extracting binary descriptors from the dynamic image region of two adjacent frames of image based on a feature detection algorithm; Matching corresponding feature point pairs between the two adjacent frames of image based on the binary descriptor; Based on the corresponding feature point pairs, estimating the rotation matrix between the two adjacent frames of image by an iterative optimization algorithm; The angular velocity of the target satellite is determined according to the rotation matrix and the time interval between the two adjacent frames of image.

2. The method according to claim 1, characterized in that The step of extracting binary descriptors from the dynamic image region of two adjacent frames of image based on a feature detection algorithm includes: Detecting oFAST key points of the dynamic image area by using the ORB algorithm; Based on the oFAST key points, rBRIEF binary descriptors of two adjacent frames of images are generated.

3. The method according to claim 1, characterized in that The iterative optimization algorithm is a random sampling consensus algorithm, and the iterative optimization algorithm is used to estimate the rotation matrix between the two adjacent frames of images based on the corresponding feature point pairs, including: Randomly select a preset number of point pairs from the corresponding feature point pairs to calculate a basic matrix; Screening inliers from the basic matrix, and iteratively optimizing the basic matrix according to the number of inliers; Perform matrix decomposition on the optimized basic matrix to obtain a rotation matrix.

4. The method according to claim 1, wherein Determining the angular velocity of the target satellite according to the rotation matrix and the time interval between two adjacent frames of image includes: Convert the rotation matrix into Euler angles; Calculating the relative angular velocity of the target satellite according to the Euler angle and the time interval between two adjacent frames of image; Smoothing the relative angular rate based on a sliding average filter to obtain an actual angular rate of the target satellite; The actual angular velocity is corrected according to the preset angular velocity of the rocket to generate the angular velocity of the target satellite.

5. The method according to claim 1, wherein Determining the dynamic image area of the target satellite based on the static area mask for each frame image of the rocket telemetry video frame sequence includes: generating a static area mask that matches the resolution of the rocket telemetry video frame based on the fixed position information of the satellite separation mechanism; Based on the static area mask, the pixel area corresponding to the satellite separation mechanism in each frame of the image is masked to obtain a mask-processed image; The image is processed based on the mask to determine a dynamic image region that only includes the target satellite.

6. The method according to any one of claims 1 to 5, characterized in that Also includes: Selecting a plurality of groups of two adjacent frames from the rocket telemetry video frame sequence, and determining an angular rate corresponding to each group of two adjacent frames; The angular rates corresponding to each group of two adjacent frames of images are arranged in time sequence to generate an angular rate sequence of the target satellite.

7. The method according to claim 6, characterized in that Also includes: According to the angular rate sequence, it is determined whether the rocket launch mission is successful and an alarm message is output.

8. A device for identifying satellite angular rate, characterized in that: include: A video sequence acquisition unit is used to acquire the rocket telemetry video frame sequence after the separation of the satellite and rocket; a satellite region determination unit, configured to determine, for each frame of the rocket telemetry video frame sequence, a dynamic image region of the target satellite based on a static region mask; A feature detection and extraction unit extracts binary descriptors from the dynamic image region of two adjacent frames of image based on a feature detection algorithm; A feature point pair matching unit, which matches corresponding feature point pairs between the two adjacent frames of images based on the binary descriptor; A rotation matrix estimation unit estimates the rotation matrix between the two adjacent frames of image 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 according to the rotation matrix and the time interval between the two adjacent frames of image.

9. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the satellite angular rate identification method according to any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying the satellite angular rate according to any one of claims 1 to 7 is implemented.

Images