Satellite platform micro-vibration feature extraction method and device

Abstract

The invention relates to the field of inter-satellite laser communication, and provides a satellite platform micro-vibration feature extraction method and device, and the method comprises the steps: obtaining the sampling data of a laser link related assembly in a laser terminal, the sampling data of the related components of the laser link at least comprises photoelectric encoder measurement value data, fast reflecting mirror driving value data and capturing and tracking camera miss distance data; extracting time domain spectrums of different granularity pointing compensation from sampling data of laser link related components; carrying out superposition processing on the time-domain spectrums of the granularity pointing compensation to obtain a micro-vibration signal time-domain spectrum; and determining micro-vibration characteristics according to the micro-vibration signal time domain spectrum. According to the invention, the extraction precision and efficiency of micro-vibration features can be improved.

Description

Technical Field

[0001] This application relates to the field of inter-satellite laser communication, and in particular to a method and apparatus for extracting micro-vibration features of a satellite platform. Background Technology

[0002] In the existing technology, the methods for extracting micro-vibration features of satellite platforms include the following: extracting micro-vibration features of satellite platforms using accelerometers; extracting micro-vibration features of satellite platforms using frequency domain disturbance data of flywheels; setting up vibration isolation platforms to reduce micro-vibration of satellite platforms; adjusting the vibration isolation frequency based on sensor feedback; and the fifth method is to use linear accelerometers to measure the micro-vibration features of satellite platforms. Summary of the Invention

[0003] This application provides a method and apparatus for extracting micro-vibration features from a satellite platform, which addresses the problems of poor accuracy and low efficiency in existing micro-vibration feature extraction methods for satellite platforms.

[0004] To address the aforementioned technical problems, the first aspect of this application provides a method for extracting micro-vibration features from a satellite platform, comprising:

[0005] Acquire sampling data of laser link-related components in the laser terminal, wherein the sampling data of the laser link-related components includes at least photoelectric encoder measurement data, fast-reflection mirror drive data, and target miss distance data of the tracking camera;

[0006] By removing satellite platform motion trajectory data from the sampling data of laser link-related components, time-domain spectra with pointing compensation at various granularities are obtained;

[0007] The time-domain spectrum of the micro-vibration signal is obtained by superimposing the time-domain spectra of multiple particle size orientation compensations;

[0008] The micro-vibration characteristics are determined based on the time-domain spectrum of the micro-vibration signal.

[0009] A second aspect of this application provides a satellite platform micro-vibration feature extraction device, comprising:

[0010] The acquisition unit is configured to acquire sampling data of laser link-related components in the laser terminal, wherein the sampling data of the laser link-related components includes at least photoelectric encoder measurement data, fast-reflection mirror drive data, and tracking camera miss distance data;

[0011] The first extraction unit is configured to remove satellite platform motion trajectory data from the sampling data of laser link-related components to obtain time-domain spectra with multiple granularities of pointing compensation;

[0012] The superposition unit is configured to superimpose time-domain spectra of multiple granularity orientation compensations to obtain the time-domain spectrum of the micro-vibration signal;

[0013] The second extraction unit is configured to determine the micro-vibration characteristics based on the time-domain spectrum of the micro-vibration signal.

[0014] A third aspect of this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the methods described in the foregoing embodiments.

[0015] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer device, implements the methods described in the foregoing embodiments.

[0016] A fifth aspect of this application provides a computer program product comprising a computer program, wherein the computer program, when executed by a processor of a computer device, implements the method described in the foregoing embodiments.

[0017] The random access channel adaptive peak detection method and apparatus provided in this application acquires sampling data from a laser terminal on a satellite platform. The sampling data includes photoelectric encoder measurement data, fast-reflecting mirror drive data, and tracking camera miss distance data. Different granularity pointing compensation time-domain spectra are extracted from the various types of sampling data. The time-domain spectra of each granularity pointing compensation time-domain spectrum are superimposed to obtain the micro-vibration signal time-domain spectrum. Based on the micro-vibration signal time-domain spectrum, micro-vibration characteristics are determined. This method can obtain micro-vibration characteristics that reflect the vibration of each link in the laser link, improve the accuracy of micro-vibration characteristics, and eliminate the complex calculation process, thereby improving the efficiency of micro-vibration characteristic extraction.

[0018] To make the above and other objects, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] The elements and features described in one drawing or embodiment of this application may be combined with elements and features shown in one or more other drawings or embodiments. Furthermore, in the drawings, similar reference numerals denote corresponding parts in several drawings and can be used to indicate corresponding parts used in more than one embodiment.

[0020] The accompanying drawings, which form part of the specification, are used to provide a further understanding of the embodiments of this application and illustrate the implementation methods of this application, together with the textual description, to explain the principles of this application. Obviously, the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings:

[0021] Figure 1 A flowchart of a satellite platform micro-vibration feature extraction method according to an embodiment of this application is shown;

[0022] Figure 2 A flowchart illustrating the time-domain spectrum determination process for the first pointing compensation according to an embodiment of this application is shown;

[0023] Figure 3 A flowchart of a satellite platform micro-vibration feature extraction method according to an embodiment of this application is shown;

[0024] Figure 4 A schematic diagram of the composite capture and tracking scheme according to an embodiment of this application is shown;

[0025] Figure 5 A structural diagram of the star platform micro-vibration feature extraction device according to an embodiment of this application is shown;

[0026] Figure 6 A schematic diagram of the time-domain spectrum of the first pointing compensation of the photoelectric encoder measurement data according to an embodiment of this application is shown;

[0027] Figure 7 A schematic diagram of the time-domain spectrum of the second pointing compensation of the fast-reflection mirror drive value data according to an embodiment of this application is shown;

[0028] Figure 8 This diagram illustrates the residual time-domain spectrum of the third pointing compensation for the miss distance data of the tracking camera according to an embodiment of this application.

[0029] Figure 9 A schematic diagram of the time-domain spectrum (time-domain waveform) of the micro-vibration of a satellite platform according to an embodiment of this application is shown;

[0030] Figure 10 A schematic diagram of the frequency domain spectrum of satellite platform micro-vibration obtained by FFT Fourier transform according to an embodiment of this application is shown;

[0031] Figure 11 A schematic diagram of the frequency domain spectrum of satellite platform micro-vibration obtained by AR transformation according to an embodiment of this application is shown;

[0032] Figure 12 A schematic diagram of the time-domain spectrum of the second pointing compensation of the fast-reflection mirror drive value data according to an embodiment of this application is shown;

[0033] Figure 13 This diagram illustrates the residual time-domain spectrum of the third pointing compensation for the miss distance data of the tracking camera according to an embodiment of this application.

[0034] Figure 14 This paper illustrates a time-domain waveform diagram of the laser terminal data signal without eliminating environmental measurement errors according to an embodiment of this application.

[0035] Figure 15 This paper illustrates a frequency domain waveform diagram of the laser terminal data signal without eliminating environmental measurement errors according to an embodiment of this application.

[0036] Figure 16 This paper shows a schematic diagram of the time-domain waveform of the laser terminal data signal after removing environmental measurement errors according to an embodiment of this application.

[0037] Figure 17 This paper shows a schematic diagram of the frequency domain waveform of the laser terminal data signal after removing environmental measurement errors according to an embodiment of this application.

[0038] Figure 18 A schematic diagram of the configuration of a computer device according to an embodiment of this application is shown.

[0039] Explanation of symbols in the attached drawings:

[0040] 401. Primary Mirror;

[0041] 402. Two-dimensional rotary table;

[0042] 403. Photoelectric encoder;

[0043] 404, Quick-reflective mirror;

[0044] 405. Tracking camera;

[0045] 406. Laser;

[0046] 501. Acquisition Unit;

[0047] 502. First extraction unit;

[0048] 503. Overlay unit;

[0049] 504, Second Extraction Unit;

[0050] 1802. Computer equipment;

[0051] 1804, Processor;

[0052] 1806. Memory;

[0053] 1808. Drive mechanism;

[0054] 1810. Input / Output Module;

[0055] 1812. Input devices;

[0056] 1814. Output devices;

[0057] 1816. Presentation equipment;

[0058] 1818. Graphical User Interface;

[0059] 1820. Network interface;

[0060] 1822. Communication link;

[0061] 1824. Communication bus. Detailed Implementation

[0062] 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 some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] It should be noted that the terms "first," "second," etc., 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 of this application described herein can be implemented in orders other than those 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, apparatus, product, or device 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 devices.

[0064] In this specification, unless otherwise stated, "and / or" describes an association between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. Furthermore, in this disclosure, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0065] In this specification, the expressions "greater than" or "less than" may be used to determine whether a specific condition is met. However, this is only for illustrative purposes and is not intended to exclude statements of "above" or "below". A condition described as "above" may be replaced by "greater than", a condition described as "below" may be replaced by "less than", and a condition described as "above and less than" may be replaced by "greater than and below". Furthermore, hereinafter, "A" to "B" represent at least one of the elements from A (inclusive) to B (inclusive).

[0066] In the embodiments of this application, the singular forms "a," "the," etc., including the plural forms, should be broadly understood as "a kind" or "a class" rather than limited to the meaning of "an." Furthermore, the term "the" should be understood to include both the singular and plural forms, unless the context explicitly indicates otherwise. Additionally, the term "according to" should be understood as "at least partially based on…," and the term "based on" should be understood as "at least partially based on…," unless the context explicitly indicates otherwise.

[0067] This specification provides the operational steps of the methods described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel.

[0068] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0069] For ease of understanding, the technical terms involved in the embodiments of this application will be explained below.

[0070] Satellite Platform: The fundamental support and maintenance system for a satellite, it is the core component that, apart from the payload (such as communication antennas, remote sensing cameras, navigation signal transmitters, and other equipment performing specific tasks), provides the operating conditions for the payload and ensures the stable operation of the satellite. In some implementations, the satellite platform includes, but is not limited to, the following subsystems: structural and mechanical subsystem, thermal control subsystem, attitude and orbit control subsystem, power supply subsystem, telemetry, tracking, command and data management subsystem, and propulsion subsystem. The structural and mechanical subsystem supports the payload and other subsystems. The thermal control subsystem regulates the temperature of various satellite components. The attitude and orbit control subsystem controls the satellite's attitude and orbit. The power supply subsystem provides electrical energy to the satellite. The telemetry, tracking, tracking, command and data management subsystem is responsible for space-to-ground communication and satellite management; on the one hand, it receives instructions from the ground station and transmits satellite status data back to the ground; on the other hand, it manages the satellite's mission scheduling, fault detection, and autonomous recovery. The propulsion subsystem provides power for satellite orbit adjustment and attitude control.

[0071] Laser Terminal: Based on the requirements of inter-satellite laser link establishment, terminal equipment generally possesses high-frame-rate measurement data of angle measurement information from photoelectric encoders (PE), fine steering mirrors (FSM), and charge-coupled devices (CCD), which can be recorded and transmitted losslessly. Among them, the photoelectric encoder belongs to the coarse tracking subsystem and is used for coarse alignment. The FSM belongs to the fine tracking subsystem and is used for precise tracking. The charge-coupled device is used for acquisition and tracking.

[0072] Inter-satellite laser links are primarily used for high-speed information exchange within a constellation and high-precision inter-satellite distance measurement, providing support for high-speed inter-satellite information transmission, high-precision orbit determination, and time synchronization. The stable and reliable operation of inter-satellite links places extremely high demands on their stability and robustness; the micro-vibration level of the satellite platform is crucial for the design of the laser communication closed-loop tracking system.

[0073] Because the beam divergence angle and communication receiving field of view of the laser terminal on the satellite platform are extremely narrow, only tens of microradians, which are on the same order of magnitude as the micro-vibrations of the satellite platform, the micro-vibrations of the satellite platform will seriously affect the tracking accuracy of the laser terminal for the laser link. In severe cases, it will lead to increased link pointing loss, reduced terminal receiving power and stability, thereby affecting the robustness and stability of the laser link, and even causing link interruption. Therefore, the laser terminal must take effective measures to compensate for and suppress micro-vibrations.

[0074] To ensure the effectiveness of the micro-vibration suppression design and the adequacy of ground verification, it is necessary to accurately understand the amplitude and spectral characteristics of the satellite platform's micro-vibrations, providing design input for laser terminal design and ground verification. To provide accurate input conditions for laser terminal design and ground verification, it is essential to understand the micro-vibration spectral characteristics of each satellite platform.

[0075] In the field of micro-vibration feature extraction for satellite platforms, existing technologies cover a variety of approaches. Some focus on modeling and parameter identification of reaction flywheel disturbance forces in satellite attitude control, improving identification accuracy by considering modal changes; others study the high-frequency micro-vibration characteristics of control moment gyroscopes, establishing various dynamic models and conducting experimental verification; still others utilize blind source separation technology to achieve quantitative identification of micro-vibration sources, achieving the goal through processes such as reference kurtosis maximization blind deconvolution algorithms. Existing technologies have significant shortcomings. For example, in the data acquisition stage, analog accelerometer signals are easily interfered with, making it difficult to meet high-precision requirements, and increasing equipment and signal transmission channels leads to increased satellite weight, power consumption, and assembly difficulty. In terms of signal processing, the complexity of satellite structure and vibration source signal characteristics makes model building difficult, limiting the accuracy and efficiency of solving transfer functions, and using energy to characterize contribution cannot accurately reflect the real situation.

[0076] For example, methods for extracting micro-vibration features from satellite platforms include the following:

[0077] The first method uses accelerometers to extract micro-vibration features of the satellite platform. However, this method cannot extract micro-vibration features that are not related to acceleration, such as micro-vibrations caused by minute deformations of the internal structure of the satellite platform. If these vibrations are not directly converted into changes in acceleration, they are difficult to detect. Therefore, this method suffers from incomplete micro-vibration capture.

[0078] The second method involves extracting micro-vibration features of the satellite platform using frequency-domain perturbation data from the flywheel. This method is relatively complex, requiring significant computational resources and processing power, making it difficult to operate efficiently on satellite platforms with limited computing resources. Furthermore, this method can only extract micro-vibrations from the flywheel assembly and cannot extract micro-vibrations from other sources, thus limiting its practicality.

[0079] The third method is to set up a vibration isolation platform to reduce vibration. The structure of the vibration isolation platform is relatively complex, involving multiple components such as a voice coil motor module, vibration isolation end mounting plate, vibration end mounting plate, spring damper, and controller. This increases the weight and cost of the system, and the increased number of components leads to a higher failure rate.

[0080] The fourth method is based on sensor feedback to adjust the vibration isolation frequency. This method relies on precise sensor feedback and complex control algorithms. If the sensor malfunctions or the algorithm deviates, the vibration isolation frequency adjustment may be inaccurate, affecting the micro-vibration suppression effect. Furthermore, this system primarily focuses on vibration isolation; the extraction of micro-vibration characteristics is not its core function, indicating a deficiency in micro-vibration characteristic research.

[0081] The fifth method involves using linear accelerometers to measure the micro-vibration characteristics of the satellite platform. This method relies on linear accelerometers, and the measurement accuracy is limited by factors such as the accuracy of the linear accelerometer itself and its installation location. If the linear accelerometer experiences installation deviations or accuracy drift, it will significantly affect the final micro-angular vibration measurement results. Furthermore, the entire measurement system is relatively complex, involving multiple modules and data processing steps, increasing the potential risk of system failure.

[0082] In practice, dedicated micro-vibration sensors can be installed on the satellite platform to achieve on-orbit measurement of satellite platform micro-vibrations through high-frequency sampling. However, due to changes in sunlight and shadows, the movements of onboard mechanisms such as solar panels, drive motors, feed antennas, and thrusters can affect the satellite platform's micro-vibrations. Generally, the limited onboard storage capacity is insufficient for long-term sample acquisition, meaning that moments when micro-vibrations significantly impact the laser terminal may not be simultaneously captured. Furthermore, not all satellite platforms are equipped with dedicated micro-vibration sensors, making it difficult to acquire truly effective vibration characteristics of the satellite platform for the laser terminal.

[0083] In summary, existing satellite platform micro-vibration feature extraction suffers from poor accuracy and low efficiency.

[0084] Analysis of the target tracking data from the satellite platform's laser terminal reveals that the target pointing value is the sum of three parts: the measurement data from the photoelectric encoder, the drive data from the fast-reflecting mirror, and the miss distance data from the tracking camera. The photoelectric encoder measurement data includes azimuth and elevation pointing angles. The fast-reflecting mirror drive data includes azimuth and elevation pointing angles. The tracking camera miss distance data includes both azimuth and elevation pointing angles.

[0085] Let the azimuth and pitch pointing angles of the tracked target be A and B, respectively. T and E T The azimuth and elevation pointing angles corresponding to the micro-vibrations of the satellite platform are A. V and E V The azimuth and pitch pointing angles measured by the photoelectric encoder are A C and E C The measurement errors of the azimuth and pitch pointing angles of the photoelectric encoder are N, respectively. AC and N EC The azimuth and pitch pointing angles of the fast-reflecting mirror drive value are AF and E F The measurement errors of the azimuth and pitch pointing angles of the fast-reflecting mirror drive value are N, respectively. AF and N EF The azimuth and pitch pointing angles of the tracking camera's miss distance are respectively... and The measurement errors of the azimuth and pitch pointing angle of the tracking camera's miss distance are respectively and The measured values ​​of the azimuth and elevation pointing angles of the laser terminal target are:

[0086] ;

[0087] .

[0088] Among them, A V +A T E represents the measured bearing of the target. V +E T This represents the measured value of the target's pitch and pointing angle.

[0089] The measured values ​​of the laser terminal target's azimuth and elevation pointing angles are the sum of the photoelectric encoder readings and the fast-reflecting mirror drive values. Considering the actual operating state of the satellite platform, the measured values ​​of the micro-vibration signal mainly include the satellite platform's micro-vibration signal and its motion trajectory. Therefore, to obtain the satellite platform's micro-vibration signal, the satellite platform's motion trajectory must be discarded.

[0090] To address the aforementioned technical problems, some embodiments of this application provide a method for extracting micro-vibration features of a satellite platform based on the analysis of the micro-vibration signals, such as... Figure 1 As shown, it includes:

[0091] 101. Obtain sampling data of laser link-related components in the laser terminal.

[0092] In detail, the laser link-related components include at least an optical encoder, a fast-reflecting mirror, and a tracking camera. The sampling data of the laser link-related components includes at least the measurement data of the optical encoder, the drive value data of the fast-reflecting mirror, and the miss distance data of the tracking camera.

[0093] In some implementations, a high-frequency synchronous sampling method is used to sample the relevant components of each laser link, thereby ensuring the temporal consistency of the sampled data.

[0094] In some implementations, the sampling frequency of the laser link-related components is determined based on the micro-vibration frequency characteristics of the satellite platform and the response capability of the laser terminal. Specifically, the sampling frequency is determined based on the highest frequency of the satellite platform's micro-vibration and the maximum sampling capability of the laser terminal equipment, with the sampling frequency ranging from [specific lower limit value to specific upper limit value].

[0095] In some implementations, after acquiring the sampling data of the laser link-related components, the method further includes converting the sampling data of each laser link-related component into standard sampling data. For example, the sampling data is standardized to μrad angle units, laying a solid foundation for subsequent accurate analysis.

[0096] 102. By removing satellite platform motion trajectory data from the sampling data of laser link-related components, time-domain spectra with multiple granularities of pointing compensation are obtained.

[0097] In detail, the sampling data of each laser link-related component yields a granular pointing-compensated time-domain spectrum, and the granularity of the pointing-compensated time-domain spectrum obtained from the sampling data of various laser link-related components is different. The pointing-compensated time-domain spectrum of each granularity reflects the micro-vibration information of the laser link-related components by the satellite platform.

[0098] 103. The time-domain spectrum of the micro-vibration signal is obtained by superimposing the time-domain spectra of multiple particle size orientation compensations.

[0099] 104. Based on the time-domain spectrum of the micro-vibration signal, determine the micro-vibration characteristics.

[0100] In this step, the time-domain spectrum of the micro-vibration signal is first converted into the frequency-domain spectrum. Based on the time-domain and frequency-domain spectra, the micro-vibration characteristics are determined. In some implementations, the micro-vibration characteristics include the amplitude of the time-domain spectrum, the peak frequency in the frequency-domain spectrum, the amplitude of each frequency component, and the spectral energy distribution.

[0101] This embodiment has the following technical effects:

[0102] 1) By starting from the sampling data of laser link related components (including photoelectric encoders, fast reflectors and tracking cameras), the satellite platform micro-vibration feature extraction method can be run on ground computer equipment, thereby enabling the direct extraction of satellite platform micro-vibration features from the ground.

[0103] 2) The sampling data revolves around the operation of the laser link, and the correlation between the data is stronger. By coordinating the sampling data of the laser link-related components through steps 101 to 104, we can not only obtain the time-domain and frequency-domain characteristics of the micro-vibration of the satellite platform, but also clearly reflect the relationship between each link of the laser link and the micro-vibration. The extracted micro-vibration feature information is more comprehensive and systematic, which can fit the actual operating state of the satellite platform. It helps to deeply analyze the generation mechanism of satellite micro-vibration, and provides more targeted solutions for practical engineering problems such as the optimization design of the satellite platform and the improvement of laser communication performance, and has higher engineering practical value.

[0104] 3) By adopting a multi-component collaborative data processing algorithm, the extraction efficiency of micro-vibration features can be improved without the need for complex intermediate calculation steps.

[0105] 4) It does not rely on specific sensors or physical structure modifications and can be widely used in various satellite platforms equipped with laser links. It has good adaptability to satellites with different orbits and functions and can be widely used in various satellite micro-vibration feature extraction scenarios. It can achieve high-precision, comprehensive and universal extraction of satellite platform micro-vibration features, and provide reliable data support for satellite platform optimization design and laser communication performance improvement.

[0106] In some embodiments of this application, before step 101 obtains the sampling data of the laser link-related components, the method further includes:

[0107] Perform initialization and calibration operations on the photoelectric encoder, fast-reflecting mirror, and tracking camera.

[0108] In some implementations, initializing the photoelectric encoder, quick-reflection mirror, and tracking camera includes setting the sampling frequency, sampling time interval, and data storage format of the photoelectric encoder, quick-reflection mirror, and tracking camera.

[0109] Calibration of the photoelectric encoder, fast mirror, and tracking camera includes: calibrating the measurement accuracy and measurement range of the photoelectric encoder, fast mirror, and tracking camera; using a standard signal source to perform comparative calibration of the photoelectric encoder, fast mirror, and tracking camera; and adjusting the parameters of the photoelectric encoder, fast mirror, and tracking camera by comparing the standard signal source data measured by the photoelectric encoder, fast mirror, and tracking camera with the theoretical data of the standard signal source to improve measurement accuracy.

[0110] In some embodiments of this application, after obtaining the sampling data in step 101, the method further includes:

[0111] Outlier removal is performed on the standard sampling data of each laser link-related component.

[0112] In practice, the Wright criterion can be used to remove outliers from the standard sampled data of each laser link-related component. Specifically, the mean and standard deviation of the sampled data sequence of each laser link-related component are first calculated, and data points exceeding the mean ± 3 times the standard deviation are identified as outliers and removed.

[0113] This embodiment can eliminate the influence of gross errors (outliers) on micro-vibration signal extraction and improve the accuracy of micro-vibration signal extraction.

[0114] In one embodiment of this application, step 102 above removes satellite platform trajectory data from the sampling data of laser link-related components to obtain time-domain spectra with various granularity pointing compensations. This includes: fitting the sampling data and removing satellite platform trajectory data based on the characteristics of the sampling data. Specifically, this includes:

[0115] 1) The photoelectric encoder measurement data is smoothed to obtain smoothed data, and the satellite platform motion trajectory data is removed from the smoothed data to obtain the time domain spectrum of the first pointing compensation.

[0116] In detail, the first aspect of the compensation, the time-domain spectrum, reflects the compensation information of the photoelectric encoder for the micro-vibrations of the satellite platform. The satellite platform trajectory data is the DC component of smoothed data.

[0117] In some implementations, smoothing photoelectric encoder measurement data to obtain smoothed data includes: smoothing the photoelectric encoder measurement data using a moving average filter to obtain first processed data; and performing data enhancement processing on the first processed data using a Laplace smoothing algorithm to obtain smoothed data.

[0118] In some implementations, smoothing the photoelectric encoder measurement data to obtain smoothed data further includes: determining the window size of the moving average filter based on the sampling frequency and fluctuation parameters of the photoelectric encoder measurement data, wherein the window size ranges from [minimum window value to maximum window value]; and determining the smoothing coefficient of the Laplace smoothing algorithm based on the local characteristics of the photoelectric encoder measurement data, wherein the smoothing coefficient ranges from [minimum coefficient value to maximum coefficient value].

[0119] In some implementations, such as Figure 2 As shown, the time-domain spectrum of the first pointing compensation is obtained by removing the satellite platform motion trajectory data from the smoothed data, including:

[0120] 201. Based on the smoothed data, determine the range of the first amplitude transformation.

[0121] 202. Based on the first amplitude transformation range, determine the first rejection threshold for the satellite platform motion trajectory data.

[0122] 203. Based on the first elimination threshold, satellite platform motion trajectory data is removed from the smoothed data to obtain the first pointing-compensated time-domain spectrum. In this step, data within the first elimination threshold range is removed from the smoothed data, and the remaining data constitutes the first pointing-compensated time-domain spectrum.

[0123] 2) Fit the fast-reflection mirror driving value data, and remove the satellite platform motion trajectory data from the fitting results to obtain the time domain spectrum of the second pointing compensation.

[0124] In detail, the second-pointing compensation time-domain spectrum response fast mirror provides compensation information for micro-vibrations of the satellite platform.

[0125] In some implementations, fitting the fast-reflecting mirror drive value data includes: dynamically selecting a fitting function type based on the data characteristics of the fast-reflecting mirror drive value data; and fitting the fast-reflecting mirror drive value data using the selected fitting function type.

[0126] In some implementations, the second pointing compensation time-domain spectrum is obtained by removing the satellite platform motion trajectory data from the fitting results, including:

[0127] Based on the fitting results, determine the range of the second amplitude transformation;

[0128] Based on the second amplitude transformation range, determine the second rejection threshold for the satellite platform motion trajectory data;

[0129] Based on the second elimination threshold, the satellite platform motion trajectory data is removed from the smoothed data to obtain the second pointing compensation time domain spectrum.

[0130] 3) Fit the data of the target miss by the tracking camera to obtain the residual time-domain spectrum of the third pointing compensation, that is, the residual time-domain spectrum of the coarse and fine pointing compensation.

[0131] In detail, the residual time-domain spectrum of the third pointing compensation reflects the compensation residual of the satellite platform's micro-vibrations recorded by the tracking camera. The tracking camera's miss distance data contains comprehensive information on the impact of satellite platform micro-vibrations on laser pointing, but the raw data often suffers from noise interference and large data fluctuations. Therefore, it is necessary to process it using polynomial fitting or spline interpolation methods to obtain the residual time-domain spectrum of coarse and fine pointing compensation.

[0132] Specifically, in some implementations, the residual time-domain spectrum of the third pointing compensation is obtained by fitting the tracking camera miss data, including: fitting the tracking camera miss data using polynomial fitting or spline interpolation methods to obtain the residual time-domain spectrum of the third pointing compensation.

[0133] This embodiment employs a combination of moving average filter and Laplace smoothing algorithm to remove the DC component from the photoelectric encoder measurement data, an adaptive curve fitting algorithm to process the fast-reflecting mirror data, and a polynomial fitting or spline interpolation method to process the tracking camera data. This significantly improves the accuracy and efficiency of signal processing, thereby enabling efficient and accurate extraction of the micro-vibration characteristics of the satellite platform and powerfully promoting the development of satellite-related technologies.

[0134] In some embodiments of this application, such as Figure 3 As shown, the method for extracting micro-vibration features of a satellite platform includes:

[0135] 301. After the laser link is established, the sampling frequency is determined based on the frequency characteristics of the micro-vibration of the satellite platform and the response capability of the laser terminal. High-frequency synchronous sampling is performed on the photoelectric encoder, fast reflector and tracking camera of the laser terminal. Synchronous sampling ensures that the data of the three have time consistency.

[0136] The laser terminal control beam pointing mechanism enables the pointing, acquisition, and tracking (PAT) of the laser beam, solving the problems of laser link alignment and stable tracking. The beam pointing mechanism (PAT) typically includes a photoelectric encoder, a tracking fast-reflecting mirror, and a tracking camera.

[0137] To better achieve high-performance PAT (Portable Target Acquisition) processes, laser terminals typically employ a composite tracking scheme, nesting a fine tracking subsystem within a coarse tracking subsystem to compensate for residual errors. The coarse tracking subsystem handles inter-satellite laser communication acquisition, coarse aiming, and coarse tracking, ensuring the inter-satellite laser communication system can fulfill its communication missions. However, the positioning accuracy after coarse aiming is relatively low, and its impact on subsequent link stability is also minimal. The fine tracking subsystem, with its small range of motion, enables precise aiming and tracking of the target, offering high precision and high bandwidth. It is crucial for improving the tracking accuracy of inter-satellite laser communication systems and ensuring link stability.

[0138] The schematic diagram of the composite capture and tracking scheme is as follows: Figure 4 As shown, Figure 4 The primary mirror 401 receives external light signals with a large field of view for initial target acquisition. The two-dimensional turntable 402, with azimuth and pitch degrees of freedom, drives the primary mirror 401 to achieve coarse target tracking. An optical encoder 403, mounted on the two-dimensional turntable 402, measures the angular position of the turntable in real time. A fast-reflection mirror 404 compensates for residual angular errors during coarse tracking; its drive value is used for error compensation during the fine tracking stage. A tracking camera 405 receives target echo signals or tracking error signals; the miss distance data from the tracking camera characterizes the tracking angular error after two stages of composite tracking. A laser 406 emits laser signals for high-precision pointing or communication with the target.

[0139] Typically, the tracking antenna is a dual-axis antenna with azimuth and elevation, and a high-precision absolute photoelectric encoder is used to measure its azimuth and elevation angles, thus establishing a coarse tracking system. The fine tracking system uses a fast-reflecting mirror to control the deflection of the beam angle compensation; the tracking camera calculates the position of the beam centroid, and the output feedback value is used for the adjustment of the photoelectric encoder and the fast-reflecting mirror.

[0140] 302. Based on the measurement principles and conversion coefficients of the photoelectric encoder, quick-reflection mirror, and tracking camera, the measurement data of the photoelectric encoder, the driving data of the quick-reflection mirror, and the target miss distance data of the tracking camera are uniformly converted into data in μrad angle units.

[0141] 303. Outlier values ​​are removed from photoelectric encoder measurement data, fast-reflecting mirror drive data, and tracking camera miss distance data using the Wright criterion.

[0142] With an increase in the amount of measurement data from the photoelectric encoder, the fast-reflecting mirror drive data, and the tracking camera miss distance data, values ​​that suddenly change and vary significantly may appear due to instrument instability or external factors; these are outliers. The process of eliminating outliers based on the Wright criterion includes: calculating the mean and standard deviation of the photoelectric encoder measurement data, the fast-reflecting mirror drive data, and the tracking camera miss distance data respectively; and removing outliers exceeding the mean. Data points that are three times the standard deviation are identified as outliers and removed.

[0143] The following example, using a specific data measurement, details the implementation process of the Wright criterion:

[0144] Suppose that a certain data measurement value is measured n times with equal precision, resulting in n measurement values ​​x1, x2, ..., xn. n ;

[0145] Based on the measured values ​​x1, x2, L, x n Calculate the arithmetic mean and residuals: ;

[0146] Calculate the standard deviation of the measured values: ;

[0147] If a certain measured value x d residual satisfy Then we consider x d These are bad values ​​containing gross errors and should be discarded.

[0148] 304. The photoelectric encoder measurement data obtained in step 303 is smoothed by a combination of moving average filter and Laplace smoothing algorithm to obtain smoothed data. The first elimination threshold of DC component is determined according to the first amplitude transformation range of smoothed data. The DC component is extracted from the smoothed data according to the first elimination threshold of DC component to obtain the first pointing compensation time domain spectrum, i.e., CPA coarse pointing compensation time domain spectrum.

[0149] In this step, considering that the inter-satellite motion is a smooth trajectory, a smoothing fitting algorithm is adopted. Each photoelectric encoder reading value is regarded as a data point, and the data is smoothed by calculating the local average value of the data points, so that each data point is replaced by the weighted average value of its neighboring data points. This embodiment uses a moving average filter and a Laplace smoothing algorithm.

[0150] The method of smoothing the readings of the photoelectric encoder by combining the moving average filter and the Laplace smoothing algorithm specifically includes: first, using the moving average filter to smooth the measurement data of the photoelectric encoder, and then using the Laplace smoothing algorithm to further smooth the data after the previous smoothing step, so as to enhance the local features of the data.

[0151] The basic principle of a moving average filter is to calculate the average value of data points within a sliding window and replace the value at the center point of the window with this average value. The smoothing effect of a moving average filter depends on the size and shape of the window, and the degree of smoothing can be controlled by adjusting the window parameters.

[0152] Laplace smoothing is a probabilistic model-based smoothing method. Its basic principle is to assume that the changes between data points are smooth and to describe these changes using a probabilistic model. Laplace smoothing smooths the data by calculating the probability distribution of each data point and then using the probability distributions of its neighboring data points. The effectiveness of Laplace smoothing depends on the choice of the probabilistic model and the parameter settings. For satellite motion states, data acquisition parameters, etc., different window parameters are selected using a moving average filter algorithm to achieve trajectory fitting and elimination.

[0153] 305. The first fitting result is obtained by fitting the fast-reflection mirror drive value data using an adaptive curve fitting algorithm. The second pointing compensation time domain spectrum is determined based on the first fitting result, that is, the fast-reflection mirror fine pointing compensation time domain spectrum.

[0154] The process of fitting the fast-reflecting mirror driving value data using an adaptive curve fitting algorithm to obtain the first fitting result includes: dynamically selecting the fitting function type from multiple candidate models based on the fluctuation characteristics of the fast-reflecting mirror driving value data, wherein the candidate models include, but are not limited to, multinomial models, Fourier series models, Gaussian mixture models, and piecewise fitting models; and fitting the collected fast-reflecting mirror driving value data using the least squares method according to the selected fitting function type to obtain the fitting parameters.

[0155] In one specific implementation, the first fitting result obtained by fitting the fast-reflection mirror drive value data using an adaptive curve fitting algorithm includes:

[0156] 1) Preprocess the collected fast-reflection mirror drive value data to eliminate noise interference.

[0157] 2) Extract key features of the fast-reflecting mirror driving value data, such as fluctuation frequency, amplitude variation range, and signal smoothness. Based on these features, the system will intelligently screen among various candidate fitting function models, such as polynomial model, Fourier series model, Gaussian mixture model, and piecewise fitting model, to ensure that the selected fitting function model is highly matched with the data characteristics. For example, for slow and uniform changes, a linear function is selected for fitting; for periodic back-and-forth swings, a sine function is selected for fitting; and for complex multi-segment changes, a high-order polynomial is selected for fitting.

[0158] 3) After determining the fitting function model, the fitting function model parameters are dynamically optimized using an improved least squares method.

[0159] To address the dynamic changes in micro-vibration characteristics during satellite operation, a real-time monitoring and adaptive adjustment mechanism was designed. By evaluating the effectiveness of the current fitting function model, an automatic update process is triggered when a significant change in key features of the fast-reflection mirror driving data is detected. During the model switching process, a smooth transition technique is employed to ensure a seamless connection between the old and new fitting function models, avoiding analytical errors caused by abrupt changes in the fitting function model. Targeted optimization strategies are used for different types of fitting function models.

[0160] 306. The residual time-domain spectrum of the third pointing compensation is obtained by fitting the target miss data of the tracking camera using polynomial fitting or spline interpolation method, that is, the residual time-domain spectrum of coarse and fine pointing compensation.

[0161] In practical applications, the appropriate fitting method should be selected based on the characteristics of the miss distance data from the tracking camera. If the overall trend of the data shows a relatively regular curve shape and the fluctuations are relatively gentle, the polynomial fitting method can be selected; while when there are many local abrupt changes or irregular variations in the data, and the fitting accuracy of local details is required to be high, the spline interpolation method should be used first.

[0162] Both polynomial fitting and spline interpolation methods can effectively eliminate trend terms and background noise in the tracking camera's miss distance data, highlighting the subtle changes in the centroid shift of the laser spot caused by the micro-vibration of the satellite platform. This yields the residual time-domain spectrum of coarse and fine pointing compensation, which clearly reflects the impact of the satellite platform's micro-vibration in the azimuth and elevation directions on the laser pointing. This provides an accurate data foundation for the subsequent synthesis of the time-domain spectrum of the micro-vibration signal and frequency domain conversion, helping to extract the micro-vibration characteristics of the satellite platform more accurately. Consequently, it provides strong data support for the optimized design of the satellite platform and the improvement of laser communication performance.

[0163] 307. The time-domain spectrum of the first directional compensation, the time-domain spectrum of the second directional compensation, and the residual time-domain spectrum of the third directional compensation are added together to obtain the time-domain spectrum of the micro-vibration signal.

[0164] Specifically, the time-domain spectrum of the micro-vibration signal obtained in this step is the time-domain spectrum of the azimuth and elevation micro-vibration signals generated by the satellite platform on the laser terminal during the link establishment process.

[0165] Through steps 303 to 307 above, the recovered micro-vibration signal after eliminating measurement errors and motion trajectory is obtained. Its characteristic parameters mainly include signal amplitude, frequency, and power. Therefore, it is necessary to combine the signal time domain information and frequency domain information to analyze and extract parameters from two dimensions.

[0166] When analyzing the time-domain information of micro-vibration signals, their time-domain waveforms can intuitively reflect the envelope changes, amplitude jitter, and time-varying characteristics of the signal in the time domain.

[0167] For signal amplitude, the average peak value V can be used. p In measurement, the peak value is the difference between the highest or lowest value of a signal and its average value within one period, equal to the peak-to-peak value V. pp Half of the time-domain spectral signal, therefore, the process first averages the groups of the time-domain spectral signal, and after obtaining the difference between its maximum and minimum values, the peak value V is obtained. p It can be represented as:

[0168] ;

[0169] Where max(s) is the maximum peak value and min(s) is the minimum peak value.

[0170] 308. The time-domain spectrum of the micro-vibration signal is transformed by FFT Fourier transform or modern spectrum estimation algorithm AR model to obtain the frequency-domain spectrum of the micro-vibration signal, that is, the frequency-domain spectrum of the azimuth and elevation micro-vibration signal generated by the satellite platform to the laser terminal.

[0171] In this step, in order to analyze the frequency domain characteristics of the satellite platform vibration, angular vibration can be used to describe it, and power spectral density can be used to characterize its vibration characteristics.

[0172] To extract the spectral features of the micro-vibration platform signal, power spectral density (PSD) was used for spectral feature analysis. Specifically:

[0173] First, we define the power spectrum (PS) as the power of a signal at each frequency component, measured in dB (energy units). Then, the power spectral density characterizes the signal power within a unit frequency band, showing how the signal power changes with frequency, measured in dB / Hz. In radian signal analysis, its unit is μrad. 2 / Hz. The area covered by the power spectrum curve is numerically equal to the total power (i.e., energy) of the signal:

[0174] ;

[0175] Where X[k] are the discrete Fourier transform coefficients of x[k], k is the number of samplings, and N is the total number of sampling points.

[0176] That is, the sum of all frequency components of a signal equals its time-domain energy. The above is the expression for the power spectrum. Therefore, the power spectral density, which is the frequency energy per Hz, can be written as:

[0177] ;

[0178] Where S(f) is the power spectral density, f is the frequency, Fs is the sampling frequency, N is the number of sampling points, and x n For discrete-time signals, it represents the amplitude of the signal at the nth sampling point. The Discrete Fourier Transform (DFT) represents a complex sine wave with frequency f, used to sum the frequency of the signal x. n Perform correlation calculations to detect whether the signal contains the frequency component. This formula represents the calculation from the discrete-time signal x. n Starting with the standard method for estimating the power density S(f) it contains at different frequencies f, we can determine its power density S(f).

[0179] Since a single-sided power spectral density is used, it needs to be multiplied by two to convert it to a two-sided power spectral density. Because the total energy changes depending on the data acquisition time, during data processing, data from the same sampling time is typically used for analysis to facilitate comparative analysis.

[0180] To obtain a more accurate power spectral density, the most commonly used method is the Fast Fourier Transform (FFT) algorithm. However, it has drawbacks such as limited resolution and reduced complexity of traditional algorithms. Therefore, based on the FFT algorithm, we use the Autoregressive model (AR) parameter model power spectral estimation algorithm for spectrum analysis.

[0181] The basic principle of power spectrum estimation for AR parametric models assumes that the observed data x(n) is white noise. By using the response of a linear time-invariant system obtained from a discrete-time system, the parameters of the parametric model are estimated using the observed sample values, thus obtaining the frequency response and, consequently, the power spectrum of the stochastic process x(n). A classical power spectrum estimation algorithm is employed because the N observed values ​​x of the stochastic process x(n) are... n x(n) is a deterministic signal. The Fourier transform of x(n) is:

[0182] ;

[0183] According to the Passaval relation, the square of the modulus in the above equation is the energy spectrum of the signal. Dividing the energy spectrum by the duration N yields x. n The power spectrum estimate of (n) can be regarded as the power spectrum estimate of the random signal x(n), expressed as:

[0184] ;

[0185] The power spectrum estimate obtained using this method is also called the periodogram method. Because this power spectrum estimation method is obtained directly through the Fourier transform of the observed data, it is also called the direct method. In the calculation process, the fast Fourier transform is used for power spectrum estimation.

[0186] In summary, the accuracy of the FFT algorithm is limited by the number of FFT points and the size of the measurement dataset. When the data length is fixed, increasing the number of FFT points can improve the FFT computation resolution, thereby improving the estimation accuracy. However, increasing the number of FFT points essentially involves interpolating the spectrum to improve accuracy, and the upper limit of accuracy for this process is limited by the physical resolution of the signal, which is determined by the size of the measurement dataset. Therefore, improving signal resolution requires increasing the acquisition length of the measurement data; the larger the acquisition data length, the higher the physical resolution of the estimated frequency points.

[0187] Meanwhile, considering the high complexity of micro-vibration signals from satellite platforms, traditional spectrum estimation methods lose some information. By employing the modern spectrum estimation algorithm, the AR parameter model, based on the characteristics of the transfer function, accurate power spectrum estimation can be achieved. The modern power spectral density estimation AR model estimates the power spectral density by estimating the system parameters of the original data. Compared to FFT and other algorithms, the power spectral density map estimated by the AR model carries less detail in the frequency domain, reducing the impact of invalid information and noise on spectrum analysis, and is more conducive to the observation and extraction of key spectral characteristic parameters.

[0188] Based on the same inventive concept, this application also provides a satellite platform micro-vibration feature extraction device, as described in the following embodiments. Since the principle of the satellite platform micro-vibration feature extraction device in solving the problem is similar to that of the satellite platform micro-vibration feature extraction method, the implementation of the satellite platform micro-vibration feature extraction device can refer to the satellite platform micro-vibration feature extraction method, and repeated details will not be elaborated further.

[0189] The satellite platform is equipped with a laser terminal, and the link-related components in the laser terminal include: a photoelectric encoder, a fast-reflecting mirror, and a tracking camera.

[0190] like Figure 5 As shown, the satellite platform micro-vibration feature extraction device includes:

[0191] The acquisition unit 501 is configured to acquire sampling data of laser link-related components in the laser terminal, wherein the sampling data of laser link-related components includes at least photoelectric encoder measurement data, fast-reflecting mirror drive data, and tracking camera miss distance data.

[0192] The first extraction unit 502 is configured to remove satellite platform motion trajectory data from the sampling data of laser link related components to obtain time-domain spectra with multiple granularity pointing compensation.

[0193] The superposition unit 503 is configured to superimpose time-domain spectra of multiple particle size orientation compensations to obtain the time-domain spectrum of the micro-vibration signal.

[0194] The second extraction unit 504 is configured to determine the micro-vibration characteristics based on the time-domain spectrum of the micro-vibration signal.

[0195] This embodiment acquires sampling data from the laser terminal on the satellite platform, including photoelectric encoder measurement data, fast-reflecting mirror drive data, and target miss distance data from the tracking camera. It extracts time-domain spectra with different granularities of pointing compensation from each type of sampling data. The time-domain spectra of the pointing compensation at each granularity are then superimposed to obtain the time-domain spectrum of the micro-vibration signal. Based on the time-domain spectrum of the micro-vibration signal, micro-vibration characteristics are determined. This allows for the acquisition of micro-vibration characteristics that reflect the vibrations of each link in the laser link, improving the accuracy of the micro-vibration characteristics. Simultaneously, it eliminates the need for complex calculations, thus improving the efficiency of acquiring micro-vibration characteristics.

[0196] To more clearly illustrate the effects of the technical solution in this application, the following description, in conjunction with example diagrams, provides further insight:

[0197] Example 1: Micro-vibration feature extraction of satellite platform.

[0198] This embodiment provides a set of measured data collected by a laser terminal during the link establishment process to illustrate in detail the data analysis process of the satellite platform micro-vibration feature extraction method provided in this application.

[0199] The satellite platform normalizes and converts the data collected by the photoelectric encoder, fast reflector, and tracking camera in the laser terminal into μrad angle units based on the conversion relationship; the standard data is then sent to the ground computer equipment.

[0200] The micro-vibration features are extracted by executing the method described in any of the above embodiments using ground-based computer equipment.

[0201] It should be noted that since the absolute value of the photoelectric encoder measurement data is large when the DC component is not removed, and the data analysis process mainly considers its relative value transformation, the DC component of the motion trajectory is removed according to the transformation range of the photoelectric encoder reading (i.e., the first amplitude transformation range of the azimuth axis and pitch axis) to obtain the time domain spectrum of the first pointing compensation representing the coarse pointing compensation of the laser terminal.

[0202] The first fitting result is obtained by fitting the fast-reflecting mirror drive value data. After removing the satellite platform motion trajectory from the first fitting result, the time-domain spectrum of the second pointing compensation, representing the fine pointing compensation of the laser terminal, is obtained. The process of removing the satellite platform motion trajectory from the fast-reflecting mirror drive value data is the same as the process of removing the satellite platform motion trajectory from the photoelectric encoder measurement data.

[0203] The miss distance data from tracking cameras are typically in the range of a few μrad. However, as the acquisition time increases, large outliers appear. Therefore, the Wright criterion (3σ criterion) is used to remove these outliers. By fitting the miss distance data from the tracking cameras, the residual time-domain spectrum representing the third directional compensation for laser terminal removal can be obtained.

[0204] like Figure 6 As shown, Figure 6 The time-domain spectrum of the first pointing compensation of the azimuth and pitch axis measurements of the photoelectric encoder according to an embodiment of this application is shown. The time-domain spectrum of the first pointing compensation has eliminated the DC component of the motion trajectory. Figure 6 The horizontal axis represents time (ms), and the vertical axis represents the vibration amplitude (Amp, μrad).

[0205] like Figure 7 As shown, Figure 7 The time-domain spectrum of the second pointing compensation of the azimuth axis and pitch axis drive values ​​of the fast-reflecting mirror according to an embodiment of this application is shown. The time-domain spectrum of the second pointing compensation has eliminated the DC component of the motion trajectory. Figure 7 The horizontal axis represents time (ms), and the vertical axis represents the vibration amplitude (Amp, μrad).

[0206] like Figure 8 As shown, Figure 8 The residual time-domain spectrum of the azimuth and elevation spot centroid of the tracking camera according to an embodiment of this application is shown. The residual time-domain spectrum of the third pointing compensation has been freed of outliers. Figure 8 The horizontal axis represents time (ms), and the vertical axis represents the vibration amplitude (Amp, μrad).

[0207] The time-domain spectrum of the micro-vibration signal is obtained by adding the time-domain spectrum of the first-pointing compensation, the time-domain spectrum of the second-pointing compensation, and the residual time-domain spectrum of the third-pointing compensation. The frequency-domain spectrum of the micro-vibration signal is obtained based on the time-domain spectrum. The feature extraction of the micro-vibration signal is achieved by combining time-domain and frequency-domain analysis.

[0208] Figure 9 A schematic diagram of the time-domain spectrum of micro-vibration of a satellite platform according to an embodiment of this application is shown, with the horizontal axis representing time (ms) and the vertical axis representing vibration amplitude (Amp) (μrad). Figure 9 It can be seen that the vibration amplitude of the laser terminal in the first half is ≤ ±50μrad, and that in the second half is ≤ ±100μrad.

[0209] Figure 10 This diagram illustrates the frequency domain spectrum of satellite platform micro-vibration obtained by FFT Fourier transform according to an embodiment of this application. The horizontal axis represents frequency (Hz), and the vertical axis represents power spectral density (dB / Hz). Figure 10 It can be seen that the vibration peak points obtained from the analysis of this set of data are 0.35Hz and 11.34Hz.

[0210] Figure 11This diagram illustrates the frequency domain spectrum of satellite platform micro-vibration obtained through AR transformation according to an embodiment of this application. The horizontal axis represents frequency (ms), and the vertical axis represents power spectral density (dB / Hz). Figure 11 It can be seen that the vibration peak points obtained from the analysis of this set of data are 0.346Hz and 11.34Hz, which are basically consistent with the frequency of the frequency domain spectrum obtained by FFT transformation.

[0211] Example 2: Micro-vibration extraction from a ground platform.

[0212] This embodiment provides a set of micro-vibration measurement data collected on the ground using a laser terminal during the self-closed-loop stabilization process of a two-dimensional turntable simulating a satellite platform and a collimator, to illustrate in detail the data analysis process of the satellite platform micro-vibration feature extraction method provided in this application.

[0213] During testing, the two-dimensional turntable was kept stationary. A single-tone micro-vibration signal of 20Hz and 20μrad was added to the motor driven by the fast-reflecting mirror in the collimator to measure and acquire data from the laser terminal. The analysis process used time-domain waveform diagrams and frequency-domain power spectral density to analyze the signal characteristic parameters.

[0214] Based on the conversion relationships of data collected by the photoelectric encoder, fast-reflecting mirror, and tracking camera in the laser terminal, the collected data from the photoelectric encoder, fast-reflecting mirror, and tracking camera are normalized and converted to μrad angle units. Micro-vibration analysis is then performed on the micro-vibration signal added by the fast-reflecting mirror. Since no rotation of the two-dimensional turntable was added in the ground test, the change in the photoelectric encoder was 0. Only the measurement data from the fast-reflecting mirror and tracking camera needs to be analyzed. Their time-domain waveform curves are plotted as follows: Figure 12 and Figure 13 As shown, Figure 12 and Figure 13 The horizontal axis represents time (ms), and the vertical axis represents the vibration amplitude (μrad). According to... Figure 12 and Figure 13 It can be seen that the amplitude of the fast-reflection mirror data is around 5~10μrad, and the amplitude of the tracking camera data is around 1μrad. The tracking camera data fluctuates less, which is the residual error of fine tracking.

[0215] The normalized and transformed fast-reflection mirror drive value data and the target miss distance data of the tracking camera are added together to obtain the measured value of the micro-vibration data during the laser terminal's target pointing process. This measured value includes the micro-vibration signal to be extracted and the measurement error. The resulting data signal is the laser terminal data signal before removing environmental measurement errors, and its time-domain and frequency-domain waveforms are shown below. Figure 14 and Figure 15 As shown, Figure 14 The horizontal axis represents time (ms), the vertical axis represents vibration amplitude (μrad), and the small boxes show magnified details of the time-domain waveform. Figure 15 The horizontal axis represents frequency (Hz), and the vertical axis represents power spectral density (Power / Frequency (μrad)). 2 / Hz).

[0216] according to Figure 14 and Figure 15 It can be seen that the added vibration signal is a sine wave, specifically a sine signal around 20Hz. Both its azimuth and pitch axes exhibit low-frequency envelopes, with frequencies generally within the range of 0.1~2Hz. The frequency portion corresponding to these low-frequency envelopes is shown within the boxes in the frequency domain waveform diagram, which may be due to environmental interference. The corresponding environmental interference signals can be removed. The time-domain waveform and spectrum waveform after removing environmental measurement errors are shown below. Figure 16 and Figure 17 As shown, Figure 16 The horizontal axis represents time (ms), and the vertical axis represents the vibration amplitude (Amp, μrad). Figure 17 The horizontal axis represents frequency (Hz), and the vertical axis represents power spectral density (Power / Frequency (μrad)). 2 After eliminating environmental interference errors, the applied 20Hz, 20μrad single-tone micro-vibration signal can finally be extracted, and the method has been verified.

[0217] In one embodiment of this application, a computer device is also provided, such as... Figure 18 As shown, computer device 1802 may include one or more processors 1804, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Computer device 1802 may also include any memory 1806 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, memory 1806 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Furthermore, any memory may provide volatile or non-volatile retention of information. Furthermore, any memory may represent a fixed or removable component of computer device 1802. In one case, when processor 1804 executes associated instructions stored in any memory or combination of memories, computer device 1802 may perform any operation of the associated instructions. Computer device 1802 also includes one or more drive mechanisms 1808 for interacting with any memory, such as hard disk drive mechanisms, optical disk drive mechanisms, etc.

[0218] Computer device 1802 may also include an input / output module 1810 (I / O) for receiving various inputs (via input device 1812) and providing various outputs (via output device 1814). A specific output mechanism may include a presentation device 1816 and an associated graphical user interface 1818 (GUI). In other embodiments, the input / output module 1810 (I / O), input device 1812, and output device 1814 may be omitted, and the device may function solely as a computer device within a network. Computer device 1802 may also include one or more network interfaces 1820 for exchanging data with other devices via one or more communication links 1822. One or more communication buses 1824 couple the components described above together.

[0219] Communication link 1822 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 1822 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0220] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described method.

[0221] This application also provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to perform the methods described in the above embodiments.

[0222] The apparatus and methods described above in this application can be implemented in hardware or in combination with software. This application relates to a computer-readable program that, when executed by a logic component, enables the logic component to implement the apparatus or components described above, or to implement the various methods or steps described above. Logic components include, for example, field-programmable logic devices (FPGAs), microprocessors, and processors used in computers. This application also relates to storage media for storing the above programs, such as hard disks, magnetic disks, optical disks, DVDs, and flash memory.

[0223] The methods / apparatus described in conjunction with the embodiments of this application can be directly embodied in hardware, software modules executed by a processor, or a combination of both. For example, one or more and / or combinations of one or more functional block diagrams shown in the figures can correspond to various software modules in a computer program flow, or to various hardware modules. These software modules can correspond to the various steps shown in the figures, respectively. These hardware modules can be implemented, for example, using a field-programmable gate array (FPGA) to embed these software modules.

[0224] The software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. A storage medium can be coupled to the processor, enabling the processor to read information from and write information to the storage medium; or the storage medium can be an integral part of the processor. The processor and storage medium can reside in an ASIC. The software module can be stored in the memory of a mobile terminal or in a memory card that can be inserted into the mobile terminal. For example, if the device (such as a mobile terminal) uses a high-capacity MEGA-SIM card or a high-capacity flash memory device, the software module can be stored in the MEGA-SIM card or the high-capacity flash memory device.

[0225] One or more and / or one or more combinations of functional blocks described in the accompanying drawings can be implemented as a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or any suitable combination thereof for performing the functions described herein. One or more and / or one or more combinations of functional blocks described in the accompanying drawings can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0226] The present application has been described above with reference to specific embodiments. However, those skilled in the art should understand that these descriptions are exemplary and not intended to limit the scope of protection of the present application. Those skilled in the art can make various modifications and variations to the present application based on its spirit and principles, and these modifications and variations are also within the scope of the present application.

Claims

1. A method for extracting micro-vibration features of a satellite platform, wherein, The satellite platform is equipped with a laser terminal, including: Acquire sampling data of laser link-related components in the laser terminal, wherein the sampling data of the laser link-related components includes at least photoelectric encoder measurement data, fast-reflection mirror drive data, and target miss distance data of the tracking camera; By removing satellite platform motion trajectory data from the sampling data of laser link-related components, time-domain spectra with pointing compensation at various granularities are obtained; The time-domain spectrum of the micro-vibration signal is obtained by superimposing the time-domain spectra of the various particle size orientation compensations. The micro-vibration characteristics are determined based on the time-domain spectrum of the micro-vibration signal.

2. The method as described in claim 1, wherein, The sampling frequency of the sampling data of the laser link-related components is determined based on the micro-vibration frequency characteristics of the satellite platform and the response capability of the laser terminal.

3. The method as described in claim 1, wherein, Before acquiring the sampling data of the laser link-related components, the method further includes: Perform initialization and calibration operations on the photoelectric encoder, fast-reflecting mirror, and tracking camera.

4. The method of claim 3, wherein, Initialization settings for the photoelectric encoder, quick-reflection mirror, and tracking camera include: Configure the sampling frequency, sampling time interval, and data storage format of the photoelectric encoder, fast-reflecting mirror, and tracking camera.

5. The method of claim 1, wherein, After acquiring the sampling data of the laser link-related components, the method further includes: The sampling data of each laser link-related component is converted into standard sampling data.

6. The method of claim 5, wherein, After acquiring the sampled data, the method further includes: Outlier removal is performed on the standard sampling data of each laser link-related component.

7. The method of claim 1, wherein, By removing satellite platform motion trajectory data from the sampling data of laser link-related components, time-domain spectra with various granularities of pointing compensation are obtained, including: The photoelectric encoder measurement data is smoothed to obtain smoothed data, and the satellite platform motion trajectory data is removed from the smoothed data to obtain the first pointing compensation time domain spectrum; The fast-reflection mirror drive value data is fitted, and the satellite platform motion trajectory data is removed from the fitting result to obtain the second pointing compensation time domain spectrum; The residual time-domain spectrum of the third pointing compensation is obtained by fitting the data of the target miss distance of the tracking camera.

8. The method of claim 7, wherein, Smoothing the photoelectric encoder measurement data to obtain smoothed data includes: The photoelectric encoder measurement data is smoothed using a moving average filter to obtain the first processed data. The Laplace smoothing algorithm is used to perform data augmentation on the first processed data to obtain smoothed data.

9. The method of claim 8, wherein, The process of smoothing the photoelectric encoder measurement data to obtain smoothed data further includes: The window size of the moving average filter is determined based on the sampling frequency and fluctuation parameters of the photoelectric encoder measurement data. The smoothing coefficient of the Laplace smoothing algorithm is determined based on the local characteristics of the photoelectric encoder measurement data.

10. The method of claim 7, wherein, The first pointing-compensated time-domain spectrum is obtained by removing the satellite platform motion trajectory data from the smoothed data, including: Based on the smoothed data, determine the first amplitude transformation range; Based on the first amplitude transformation range, a first rejection threshold for the satellite platform motion trajectory data is determined; Based on the first elimination threshold, the satellite platform motion trajectory data is removed from the smoothed data to obtain the first pointing compensation time domain spectrum.

11. The method of claim 7, wherein, Fitting the fast-reflecting mirror drive value data includes: Based on the data characteristics of the fast-reflection mirror drive value data, dynamically select the fitting function type; The fast-reflection mirror drive value data are fitted using the selected fitting function type.

12. The method of claim 7, wherein, The second-pointing compensated time-domain spectrum is obtained by removing the satellite platform motion trajectory data from the fitting results, including: Based on the fitting results, determine the range of the second amplitude transformation; Based on the second amplitude transformation range, determine the second rejection threshold for the satellite platform motion trajectory data; Based on the second elimination threshold, satellite platform motion trajectory data is eliminated from the smoothed data to obtain the second pointing compensation time domain spectrum.

13. The method of claim 7, wherein, The residual time-domain spectrum of the third pointing compensation is obtained by fitting the miss distance data of the tracking camera, including: The residual time-domain spectrum of the third pointing compensation is obtained by fitting the miss distance data of the tracking camera using polynomial fitting or spline interpolation methods.

14. The method of claim 1, wherein, Based on the time-domain spectrum of the micro-vibration signal, the micro-vibration characteristics are determined, including: The time-domain spectrum of the micro-vibration signal is converted into the frequency-domain spectrum of the micro-vibration signal; The micro-vibration characteristics are calculated based on the time-domain spectrum and frequency-domain spectrum of the micro-vibration signal.

15. A device for extracting micro-vibration features of a satellite platform, wherein, The satellite platform is equipped with a laser terminal, including: The acquisition unit is configured to acquire sampling data of laser link-related components in the laser terminal, wherein the sampling data of the laser link-related components includes at least photoelectric encoder measurement data, fast-reflection mirror drive data, and tracking camera miss distance data; The first extraction unit is configured to remove satellite platform motion trajectory data from the sampling data of laser link-related components to obtain time-domain spectra with multiple granularities of pointing compensation; The superposition unit is configured to superimpose the time-domain spectra of the multiple granularity orientation compensations to obtain the time-domain spectrum of the micro-vibration signal; The second extraction unit is configured to determine the micro-vibration characteristics based on the time-domain spectrum of the micro-vibration signal.

16. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the computer program, it implements the method according to any one of claims 1 to 14.

17. A computer-readable storage medium storing a computer program, wherein, When the computer program is executed by the processor of the computer device, it implements the method according to any one of claims 1 to 14.

18. A computer program product, the computer program product comprising a computer program, wherein, When the computer program is executed by the processor of the computer device, it implements the method according to any one of claims 1 to 14.

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