Method, medium and device for solving the pointing of the rotation axis of a space debris using distributed laser ranging data

Abstract

The application provides a method, medium and equipment for solving the pointing direction of a rotating shaft of a space debris by using distributed laser ranging data, and belongs to the technical field of space debris characteristic research. The application is based on a station layout in which a laser signal is emitted by one station and echo signals are received by three stations, and the data of the three stations are combined to inversely calculate the pointing direction of the rotating shaft of the space debris. Compared with a station layout in which a laser signal is emitted and received by a single station, the convergence of space debris attitude calculation is improved. The application firstly performs observation data preprocessing, removes low-frequency trend items by high-order polynomial fitting, and realizes a preprocessing process of conjunction frequency data screening by local cycle extraction and Gaussian fitting screening. Then, the conjunction cycle data screened by the multiple stations are combined, and an epoch method is used to calculate the pointing direction of the rotating shaft. The application can effectively improve the convergence of the pointing direction calculation of the rotating shaft of the space debris.

Description

Technical Field

[0001] This invention belongs to the field of space debris characteristics research technology, specifically involving a method, medium, and device for calculating the rotation axis orientation of space debris using distributed laser ranging data. Background Technology

[0002] High-precision inversion of space debris rotation parameters is a crucial prerequisite for collision risk assessment, orbit avoidance, and on-orbit maintenance. Laser ranging, with its high precision and all-weather capabilities, has become an important tool for space debris detection. However, current research on rotation parameter inversion still has significant shortcomings: while traditional single-station time-series observations exhibit dynamic geometry with changes in target orbit and attitude, their overall configuration is simple, highly approximate, and has limited perspective dimensions, making it difficult to provide sufficient spatial constraints on the rotation axis, leading to symmetric spurious solutions in attitude inversion. The epoch method, based on the periodic characteristics of observation signals, has been widely used to calculate the rotation axis pointing and spin rate of space debris. The core idea of ​​the epoch method is to infer the inertial rotation axis pointing and stellar frequency of the debris by analyzing the synod frequency changes of the observation signals. The synod frequency is the frequency at which a rotating target completes one full rotation relative to a ground-based telescope, reflecting the apparent rotation characteristics of the target relative to the observation station; the stellar frequency refers to the frequency at which the target completes one full rotation relative to a distant star in inertial space, unaffected by the observation position. The changes in the observation data essentially stem from the changes in the reflecting surface during the fragment's rotation, which are determined by both the incident direction and the observation direction. The overall pattern of change is mainly constrained by the observation geometry.

[0003] However, traditional single-station observations often suffer from symmetry ambiguity and multiple sets of pseudo-solutions due to insufficient spatial constraints provided by the observation geometry for the rotation axis. Low-Earth orbit space debris targets are inherently constrained by the short visible arc on the ground, making it difficult to improve the richness of the observation geometry by extending the observation time of a single station. Therefore, distributed multi-station observations become the only effective way to compensate for the deficiencies in the observation geometry and improve attitude identification capabilities. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method, medium, and device for calculating the rotation axis orientation of space debris using distributed laser ranging data.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for calculating the rotation axis orientation of space debris using distributed laser ranging data, comprising: The transmitting station emits a laser signal to a space debris target, and the receiving station receives the echo signal and obtains observation data, including ranging data. The observation data is preprocessed, and the distance residual data between the reflection point and the target centroid is obtained by fitting with a high-order polynomial and removing low-frequency trend terms. This data contains only information about the target's rotational attitude. Local convergence frequencies of distance residual data are extracted and fitted and filtered to obtain filtered convergence frequency data. Using the filtered rendezvous frequency data, the rotation axis pointing is calculated using the epoch method.

[0007] Optionally, there are three receiving stations, one of which is the same as the transmitting station, and all three receiving stations receive ranging data from the echo signal.

[0008] Optionally, the calculation process for the distance residual data is as follows: A third-order polynomial is used to fit the sum of the distances between the equivalent reflection point of the target and the transmitting and receiving stations. The distance residual data is obtained by subtracting the fitting result from the ranging data in the observation data.

[0009] Optionally, the step of extracting the local convergence frequency of the distance residual data and fitting and filtering it specifically involves: The Jurkevich method was used to extract the local convergence frequency of distance residual data within the time window before and after each time point; Calculate the Eulerian distance between the Gaussian fitting function and the local convergence frequencies, and select the local convergence frequencies that are sorted from smallest to largest Eulerian distance as the filtered convergence frequency data.

[0010] Optionally, the time window is ±8 s; the filtered rendezvous frequency data selects the local rendezvous frequencies that are sorted from smallest to largest by Eulerian distance in the top 80%.

[0011] Optionally, the step of using the filtered rendezvous frequency data and employing an epochal method to calculate the rotation axis pointing specifically involves: Establish a goodness-of-fit function : ; In the formula, This represents the frequency corresponding to stars. and rotation axis pointing parameters goodness of fit, and These represent the pitch and azimuth angles of the rotation axis in the inertial coordinate system, respectively. This indicates the filtered rendezvous frequency data, with the subscript indicating the frequency. Indicates the data point sequence number. Indicates the number of data points. This indicates the parameter corresponding to the rotation axis pointing. stellar frequency, For the arc segment time corresponding to the observed data, the parameter pointing to the rotation axis The velocity of the azimuth angle of the bisector of the observed phase angle caused by the movement of the station; Throughout Within the domain, a goodness-of-fit function is searched using an angle-step grid. The minimum value; Bundle The minimum value corresponding to Substitution Find the corresponding best-fit stellar frequencies. and stellar cycle .

[0012] In a second aspect, the present invention provides a computer-readable storage medium storing a computer program that causes a computer to perform the method described in the first aspect for calculating the orientation of the rotation axis of space debris using distributed laser ranging data.

[0013] Thirdly, the present invention provides an electronic 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 for calculating the rotation axis orientation of space debris using distributed laser ranging data as described in the first aspect.

[0014] The beneficial effects of this invention are: by acquiring data from multiple observation geometries in a single launch, this invention enriches the response information of different line-of-sight directions to the rotation axis and rotation direction. Compared with the traditional single-station launch and single-station data calculation of rotation axis pointing, distributed multi-station observation introduces stronger parameter constraints, which can significantly suppress the symmetric solution problem that is prone to occur in single-station calculation, and significantly improve the convergence of rotation axis pointing calculation results. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of an observation scenario that uses distributed laser ranging data to calculate the rotation axis orientation of space debris.

[0016] Figure 2 It is the raw distance measurement data containing orbital information.

[0017] Figure 3 It is the distance residual data after removing the low-frequency trend term from the ranging data.

[0018] Figure 4 This is the result of data frequency extraction. The left figure shows the residual data after removing low-frequency trend terms; the middle figure shows the frequency data extracted by Jurkevich, where black represents the frequency data extracted by Jurkevich, medium gray represents the Gaussian fitted data, and light gray represents the peak frequency obtained from the Gaussian fitted data; the right figure shows the data distribution after phase folding the period of the Gaussian fitted peak frequency.

[0019] Figure 5 The data was selected based on Euler distance.

[0020] Figure 6 The direction of the rotation axis The rotation axis was calculated using data from different stations. The left figure shows the rotation axis pointing result calculated from single-station data, and the right figure shows the rotation axis pointing result calculated from three-station data. The white hollow circles represent the true value of the rotation axis pointing, and the white solid dots represent the calculated rotation axis pointing.

[0021] Figure 7 The direction of the rotation axis The rotation axis was calculated using data from different stations. The left figure shows the rotation axis pointing result calculated from single-station data, and the right figure shows the rotation axis pointing result calculated from three-station data. The white hollow circles represent the true value of the rotation axis pointing, and the white solid dots represent the calculated rotation axis pointing.

[0022] Figure 8 The direction of the rotation axis The rotation axis was calculated using data from different stations. The left figure shows the rotation axis pointing result calculated from single-station data, and the right figure shows the rotation axis pointing result calculated from three-station data. The white hollow circles represent the true value of the rotation axis pointing, and the white solid dots represent the calculated rotation axis pointing. Detailed Implementation

[0023] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0024] In one embodiment, the present invention proposes a method for calculating the rotation axis orientation of space debris using distributed laser ranging data, such as... Figure 1 The diagram illustrates an observation scenario where multi-station data are jointly used to calculate the rotation axis pointing. Data from multiple observation geometries is acquired through a single transmission, enriching the response information of different line-of-sight directions to the rotation axis and direction. First, systematic preprocessing is performed to extract the rendezvous period and filter high-confidence data, ensuring the accuracy of the rotation information calculation results. Then, based on the Epoch Method, data from three distributed stations are used to jointly calculate the target's rotation axis pointing. This method increases the constraint of single-station transmission and single-station reception data on rotation information, effectively improving the convergence of the target rotation axis pointing calculation.

[0025] The specific process of using distributed laser ranging data to calculate the orientation of the rotation axis of space debris is as follows: Step 1: Preprocess the observed data by fitting high-order polynomials and removing low-frequency trend terms, extracting local periods, and filtering by Gaussian fitting to achieve the preprocessing process of filtering the convergence frequency data.

[0026] The ranging data received by the station contains target orbit information, such as... Figure 2 As shown, it is crucial to eliminate the influence of orbital trends. A third-order polynomial is used to fit the sum of the distances between the target reflection point and the launching and receiving stations. Subtracting the fitting result from the original distance information yields the distance residual between the reflection point and the target's centroid, containing only the target's rotational attitude information. Figure 3 As shown, the Jurkevich method is used to extract the local convergence frequency of the distance residual data within a ±8 s window before and after each time step, as shown in the figure. Figure 4 As shown. A Gaussian function is fitted to the meeting frequencies extracted by Jurkevich. The Eulerian distance between the Gaussian fitted function and the results directly extracted by Jurkevich is calculated. Finally, only the top 80% of the meeting frequencies, sorted by Eulerian distance from smallest to largest, are selected for subsequent rotation axis pointing calculations, as shown. Figure 5 As shown, the dots represent the convergence frequencies extracted from all observation data, the circles represent the selected convergence frequency data, and the dashed line represents the Eulerian distance between the period extracted by the Jurkevich method and the Gaussian fitting function.

[0027] Step 2: Combine the synodal period data from multiple stations and use the epoch method to calculate the rotation axis direction.

[0028] The goodness-of-fit parameters are shown in the following formula: ; In the formula, This represents the frequency corresponding to stars. and rotation axis pointing parameters goodness of fit, and These represent the pitch and azimuth angles of the rotation axis in the inertial coordinate system, respectively. This indicates the filtered rendezvous frequency data, with the subscript indicating the frequency. Indicates the data point sequence number. Indicates the number of data points. This indicates the parameter corresponding to the rotation axis pointing. stellar frequency, For the arc segment time corresponding to the observed data, the parameter pointing to the rotation axis The velocity of the azimuth angle bisector of the observed phase angle due to the station's motion is determined by known time, orbital parameters, and assumptions. The calculation yielded the result.

[0029] At a given time, the rendezvous frequency observation can be obtained directly from the observation data using a period extraction method. If a certain group Calculated rendezvous frequency Sufficiently close to the rendezvous frequency observation So this group This refers to the target's spin state. Therefore, based on observational data and the assumed direction of the rotation axis... The frequency of stars can be calculated. .

[0030] The problem then becomes finding the best-fit spin axis orientation. To minimize the objective function Because this function is two-dimensional and highly nonlinear, analytical methods are not applicable; however, numerical methods can be used to solve the problem. Throughout... Within the domain, perform a grid search with fine angular step sizes to fit a goodness function. The minimum value. Derived from observational data, these are known quantities; Assuming the rotation axis points in Then it can be calculated; Combining observational data and the assumed direction of the rotation axis It can be calculated. Therefore, it can be obtained through calculation. exist Distribution in two-dimensional space The minimum value and the corresponding That is, the optimal spin axis orientation. The minimum value corresponding to Substitution Find the corresponding best-fit stellar frequencies. and stellar cycle ( ).

[0031] After multiple rounds of calculation and testing, the stellar frequencies obtained through single-station and multi-station calculations were determined. Approaching, the axis of rotation points There are discrepancies. For example, suppose a laser is emitted from Kunming station, and ranging data is received at Kunming station, Lijiang station, and Yao'an station. The observation time is from 21:03:30 to 21:08:00 UTC on June 1, 2021, and the target's orbital elements are as follows: 1 39679U 14021B21152.47432380.0000034300000-034606-4 09997 2 3967951.6156 287.1190 017065175.7415 286.2441 15.06979105391640 Three different sets of rotation axis pointing parameters were set up for simulation experiments. The rotation axis pointing was calculated using the simulation data, and the results are shown in the table below. Figure 6 , 7 As shown in Figure 8.

[0032] Table 1. Results of Rotation Axis Orientation Calculation

[0033] As shown in the table above, compared to solving the rotation axis orientation using three-station data, the multi-station joint solution results in fewer solutions, and the optimal solution is closer to the true value. This demonstrates that distributed multi-station observations introduce stronger parameter constraints, significantly suppressing the symmetric solution problem that easily occurs in single-station solutions, and significantly improving the convergence of the rotation axis orientation solution.

[0034] In another embodiment, the present invention provides a computer-readable storage medium storing a computer program that causes a computer to execute the method described in the foregoing embodiments for calculating the rotation axis orientation of space debris using distributed laser ranging data.

[0035] In another embodiment, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method of calculating the rotation axis orientation of space debris using distributed laser ranging data as described in the foregoing embodiment.

[0036] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CDROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0037] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0038] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for calculating the rotation axis orientation of space debris using distributed laser ranging data, characterized in that, include: The transmitting station emits a laser signal to a space debris target, and the receiving station receives the echo signal and obtains observation data, including ranging data. The observation data is preprocessed, and the distance residual data between the reflection point and the target centroid is obtained by fitting with a high-order polynomial and removing low-frequency trend terms. This data contains only information about the target's rotational attitude. Local convergence frequencies of distance residual data are extracted and fitted and filtered to obtain filtered convergence frequency data. Using the filtered rendezvous frequency data, the rotation axis pointing method is employed to calculate the rotation axis pointing. There are three receiving stations, one of which is the same as the transmitting station. All three receiving stations receive ranging data from the echo signal. The calculation process for the distance residual data is as follows: A third-order polynomial is used to fit the sum of the distances between the target reflection point and the transmitting and receiving stations. The distance residual data is obtained by subtracting the fitting result from the ranging data in the observation data. The extraction of local convergence frequencies from distance residual data and their fitting and filtering are specifically as follows: The Jurkecich method was used to extract the local convergence frequency of distance residual data within the time window before and after each time point; Calculate the Euclidean distance between the Gaussian fitting function and the local convergence frequencies, and select the local convergence frequencies that are sorted from smallest to largest Euclidean distance as the filtered convergence frequency data. The time window is ±8 s; the filtered rendezvous frequency data selects the top 80% of local rendezvous frequencies sorted by Eulerian distance from smallest to largest. The process of using the filtered rendezvous frequency data and employing the epoch method to calculate the rotation axis pointing is as follows: Establish a goodness-of-fit function : ； In the formula, This represents the frequency corresponding to stars. and rotation axis pointing parameters goodness of fit, and These represent the pitch and azimuth angles of the rotation axis in the inertial coordinate system, respectively. This indicates the filtered rendezvous frequency data, with the subscript indicating the frequency. Indicates the data point sequence number. Indicates the number of data points. This indicates the parameter corresponding to the rotation axis pointing. stellar frequency, For the arc segment time corresponding to the observed data, the parameter pointing to the rotation axis The velocity of the azimuth angle of the bisector of the observed phase angle caused by the movement of the station; Throughout Within the domain, a goodness-of-fit function is searched using an angle-step grid. The minimum value; Bundle The minimum value corresponding to Substitution Find the corresponding best-fit stellar frequencies. and stellar cycle .

2. A computer-readable storage medium storing a computer program, characterized in that, The computer program causes the computer to perform the method as described in any one of claims 1 for calculating the orientation of the rotation axis of space debris using distributed laser ranging data.

3. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method for calculating the rotation axis orientation of space debris using distributed laser ranging data as described in any one of claims 1.

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