Satellite observation station information inversion method based on TLE

Abstract

The invention discloses a satellite observation station information inversion method based on TLE, which inverts observation station information of an enemy satellite observation station by analyzing public TLE data. The method comprises the following steps: S1, collecting TLE data; s2, extracting features including spatial features, time features and orbit height features; s3, classification inversion: in combination with an orbit classification statistics tracking frequency inversion monitoring strategy, generating a spatial distribution cluster by using a clustering algorithm so as to position a potential observation station position, and analyzing a working time sequence through a time feature scatter diagram; and S4, comprehensively verifying and outputting enemy monitoring system information. According to the method, low-cost and high-efficiency satellite observation station information inversion is realized through multi-dimensional feature fusion and an automatic algorithm, the method is suitable for space situation awareness and information analysis, and the detection capability of a hidden monitoring system is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of aerospace information inversion, and in particular to a satellite station information inversion method based on TLE. Background Art

[0002] A Two-Line Element Set (TLE) is a data format used to describe the orbital parameters of an artificial satellite in Earth orbit. A TLE consists of two lines of data, each containing a series of values describing the satellite's orbital parameters, allowing calculation of its position and velocity. Orbital calculation software, such as the Simplified General Perturbations 4 (SGP4) model, converts TLEs into longitude, latitude, and altitude.

[0003] Satellite stations are facilities on the ground that receive, store, and process information detected by the stations. These stations typically include antenna systems, receivers, and computing equipment, and serve various satellite systems, such as remote sensing satellites, navigation satellites, or communication satellites.

[0004] Inversion is the process of reverse inference, that is, to deduce the actual physical location, working hours and monitoring priority of the satellite station from the satellite information monitored by the satellite station.

[0005] This invention aims to utilize the orbital epoch time in the TLE element as the end time of tracking data to perform inversion analysis of satellite stations. This method effectively utilizes existing satellite orbit data and improves early warning capabilities for satellite stations. Accurate orbit prediction and analysis are particularly important in rapidly changing space environments. Summary of the Invention

[0006] Based on the above analysis, the present invention provides a satellite station information inversion method based on TLE, and its specific implementation scheme is as follows: A satellite station information inversion method based on TLE includes the following steps:

[0007] S1: Obtain TLE data of satellites monitored by satellite stations;

[0008] S2: Feature extraction:

[0009] Spatial characteristics: The six satellite orbital parameters are obtained through the TLE data and converted into longitude, latitude and altitude;

[0010] Time feature: extracting timestamp information from the TLE data and converting it into date and time format;

[0011] Orbital altitude characteristics: Calculate the satellite orbit altitude from the TLE data using a satellite orbit model;

[0012] S3: Classification inversion:

[0013] Device position inversion: Based on spatial characteristics, a clustering algorithm is used to generate satellite spatial distribution clusters;

[0014] Working time inversion: Based on time characteristics, a scatter plot of satellites at a certain time on a certain day is made;

[0015] Monitoring strategy inversion: Based on orbital altitude characteristics, satellites are classified according to their orbital altitude and the tracking frequency of each classified satellite is counted;

[0016] S4: Comprehensive inversion analysis:

[0017] Spatial analysis: obtaining the position of the satellite station through the satellite spatial distribution cluster;

[0018] Time analysis: obtaining the working time of the satellite station through the scatter plot of the satellite;

[0019] Priority analysis: The monitoring priority of each satellite classification by the satellite station is obtained by the tracking frequency of each satellite classification. The six orbital parameters in S2 refer to the semi-major axis, eccentricity, orbital inclination, periapsis argument, ascending node longitude, and true anomaly.

[0020] The six orbital numbers described in S2 are first converted into coordinates in the J2000 coordinate system, then the J2000 coordinate system is converted into the Earth-centered Earth-fixed coordinate system, and finally the Earth-centered Earth-fixed coordinates are converted into latitude, longitude and altitude.

[0021] The format of the date and time converted to a unified format described in S2 includes: the TLE timestamp uses the format of YYDDD.DDDDDDDD, where YY represents the last two digits of the year; DDD represents the day of the year, counting from 1; and the digits after the decimal point represent the time of day. This conversion method is conventional technology.

[0022] The satellite orbit model described in S2 adopts the SGP4 model.

[0023] Preferably, the clustering algorithm in S3 includes a K-means clustering algorithm and a hierarchical clustering algorithm.

[0024] Preferably, generating the satellite spatial distribution cluster in S3 includes drawing a cluster diagram with longitude as the X-axis, latitude as the Y-axis and altitude as the Z-axis, and using the cluster center point as the position of the satellite station.

[0025] Preferably, the scatter plot of the satellite at a certain time on a certain day in S3 includes a scatter plot with date as the horizontal axis and time as the vertical axis, and each point represents the working time of the satellite station.

[0026] Preferably, the tracking frequency of each classified satellite in S3 includes classifying the satellites according to orbital altitude and counting the number of each classified satellite, and dividing the number of each classified satellite by the total number of satellites to obtain the tracking frequency of each classified satellite.

[0027] The beneficial effects of the present invention are:

[0028] (1) This method uses TLE data to extract spatial, temporal, and orbital altitude features, combined with clustering algorithms and statistical analysis, to infer the physical location, operating hours, and monitoring priorities of satellite stations. This systematic inversion process significantly improves the accuracy and efficiency of information acquisition compared to traditional empirical judgment or manual analysis.

[0029] (2) Comprehensive analysis of the spatial distribution, operating hours, and monitoring strategies of satellite stations can provide important support for space environment monitoring. In particular, in rapidly changing space environments, accurate orbit prediction and station information inversion can help to promptly detect potential threats or abnormal situations, thereby improving early warning capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 It is the basic flow chart of the present invention;

[0032] Figure 2 This is a clustering diagram provided by the first embodiment of the present invention;

[0033] Figure 3 This is a working time diagram provided by the first embodiment of the present invention;

[0034] Figure 4 This is a satellite type diagram provided by the first embodiment of the present invention;

[0035] Figure 5 This is a clustering diagram provided by the second embodiment of the present invention;

[0036] Figure 6 This is a working time diagram provided by the second embodiment of the present invention;

[0037] Figure 7 This is a satellite type map provided by Example 2 of the present invention. DETAILED DESCRIPTION

[0038] S1: TLE data integration

[0039] A total of 25,483 satellite TLE data were collected from satellite stations.

[0040] S2: Feature Extraction

[0041] like Figure 1 As shown, feature extraction is performed:

[0042] Spatial features:

[0043] The six satellite orbital parameters, namely the semi-major axis, eccentricity, orbital inclination, argument of periapsis, longitude of ascending node, and true anomaly, are obtained from TLE data. These parameters are converted to the J2000 coordinate system, then to the Earth-centered, Earth-fixed coordinate system, and finally to geographic coordinates of longitude, latitude, and altitude, laying the foundation for subsequent inversion analysis.

[0044] Time characteristics:

[0045] Extract the data timestamps of each satellite in the TLE and convert them to a standard date and time format. TLE timestamps use the format YYDDD.DDDDDDDD, where YY represents the last two digits of the year, DDD represents the day of the year, and the number after the decimal point represents 0.X days.

[0046] If YY is less than 50, it is generally considered to be a year after 2000. For example, 21 represents 2021.

[0047] If YY is greater than or equal to 50, it is considered to be a year after 1900, for example, 80 represents 1980.

[0048] DDD represents the number of days from the beginning of the year. This value is used to determine a specific date. The digits after the decimal point represent the time of day. Conversion to a specific date takes into account the distribution of days in the month and whether it is a leap year. For example, the 275th day of 2021 is converted to 2021-10-02.

[0049] Track classification characteristics: In this embodiment, tracks are classified according to the following requirements:

[0050] Low-orbit satellites (orbit altitude <1000km);

[0051] Medium-orbit satellites (1000km<orbital altitude<20000km);

[0052] High-orbit satellite (orbit altitude > 20,000 km).

[0053] S3: Staged cluster inversion

[0054] Device position inversion:

[0055] Based on spatial features, a cluster diagram is drawn with longitude as the X-axis, latitude as the Y-axis, and altitude as the Z-axis. The K-means clustering algorithm is used to generate satellite spatial distribution clusters. The code is as follows:

[0056]

[0057] The longitude, latitude, and altitude of the cluster center are:

[0058] [-1.02554455e+02 5.94469836e+01 1.93315880e+06]

[0059] [1.61318888e+02 5.41496988e+01 4.04461259e+06]

[0060] [-9.16361267e+01 2.00975972e+01 5.17441877e+06]

[0061] [1.09727943e+02 -3.95159398e+00 2.73919710e+07]

[0062] [6.83090852e-01 4.06793805e+01 8.64880658e+06]

[0063] like Figure 2 As shown, the cluster center diagram is obtained, where different colors represent different clusters and the red '×' position is the cluster center.

[0064] Working hours inversion:

[0065] Make a scatter plot of satellite date and time to observe the working time period of monitoring equipment at a certain time on a certain day. Figure 3 As shown, the data points are the working time periods of the monitoring equipment, and the blank parts are where no monitoring equipment is working.

[0066] Surveillance Strategy Inversion:

[0067] like Figure 4 As shown, red represents low-orbit satellites: 21,081, green represents medium-orbit satellites: 1,852, and blue represents high-orbit satellites: 2,550. Calculation shows that the frequency of detecting low-orbit satellites is 82.73%, the frequency of detecting medium-orbit satellites is 7.27%, and the frequency of detecting high-orbit satellites is 10.00%.

[0068] S4: Comprehensive verification and output

[0069] Combined with the above analysis, the tracking device location in Example 1 is near the cluster point; the device detection time is concentrated from January 1, 2024 to January 5, 2024; the detection equipment focuses on detecting low-orbit satellites, and the detection time allocated to medium-orbit and high-orbit satellites is 10% or less.

[0070] Example 2:

[0071] S1: Multi-source TLE data integration

[0072] A total of 25,517 TLE data were collected from satellite stations.

[0073] S2: Multi-dimensional feature extraction

[0074] The spatial feature and temporal feature extraction process is the same as that in the first embodiment.

[0075] Track classification characteristics: In this embodiment, tracks are classified according to the following requirements:

[0076] Low-orbit satellites (orbit altitude <2000km);

[0077] Medium-orbit satellites (2000km<orbital altitude<36000km);

[0078] High-orbit satellite (orbit altitude > 36,000 km).

[0079] Device position inversion:

[0080] A hierarchical clustering algorithm is used to generate satellite spatial distribution clusters. The code is as follows:

[0081]

[0082] Cluster center longitude, latitude, and altitude:

[0083] [-1.03015856e+02 5.91876808e+01 1.49113302e-01]

[0084] [1.60733135e+02 5.92573635e+01 1.24367734e-01]

[0085] [-9.25186076e+01 1.85046505e+01 7.42624385e-01]

[0086] [1.20335906e+02 2.95459860e+00 2.59212724e+00]

[0087] [6.98087742e+00 3.86490013e+01 6.67347884e-01]

[0088] like Figure 5 As shown, the cluster center diagram is obtained, where different colors represent different clusters and the red '×' position is the cluster center.

[0089] Working sequence inversion:

[0090] Make a scatter plot of satellite date and time to observe the time period when monitoring equipment is working intensively at a certain time on a certain day.

[0091] like Figure 6 As shown, the data points are the working time periods of the monitoring equipment, and the blank parts are where no monitoring equipment is working.

[0092] Inverse tracking device priority and monitoring strategy simulation

[0093] like Figure 7 As shown, red represents low-orbit satellites: 21,115, green represents medium-orbit satellites: 3,816, and blue represents high-orbit satellites: 586. Calculation shows that the frequency of detecting low-orbit satellites is 82.75%, the frequency of detecting medium-orbit satellites is 7.30%, and the frequency of detecting high-orbit satellites is 9.94%.

[0094] S4: Comprehensive verification and output

[0095] Combined with the above analysis, the tracking device location in Example 2 is near the cluster point; the device detection time is concentrated from December 25, 2023 to December 31, 2023; the detection equipment focuses on detecting low-orbit satellites, and the detection time allocated to medium-orbit and high-orbit satellites is less than 10%.

[0096] After obtaining the tracking ratio of satellites of different categories based on the above embodiment, the satellite's spatial location, operating hours, and orbital altitude can be adjusted as needed. If the probability of a satellite being monitored needs to be reduced, the satellite can be moved to the area with the lowest tracking ratio.

[0097] The above is a detailed introduction to the embodiments of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core concept. At the same time, for those skilled in the art, according to the concept of the present invention, there may be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A satellite station information inversion method based on TLE, characterized in that: The following steps are involved: S1: Obtain TLE data of satellites monitored by satellite stations; S2: Feature extraction: Spatial characteristics: The six satellite orbital parameters are obtained through the TLE data and converted into longitude, latitude and altitude; Time feature: extracting timestamp information from the TLE data and converting it into date and time format; Orbital altitude characteristics: Calculate the satellite orbit altitude from the TLE data using a satellite orbit model; S3: Classification inversion: Device position inversion: Based on spatial characteristics, a clustering algorithm is used to generate satellite spatial distribution clusters; Working time inversion: Based on time characteristics, a scatter plot of satellites at a certain time on a certain day is made; Monitoring strategy inversion: Based on orbital altitude characteristics, satellites are classified according to their orbital altitude and the tracking frequency of each classified satellite is counted; S4: Comprehensive inversion analysis: Spatial analysis: obtaining the position of the satellite station through the satellite spatial distribution cluster; Time analysis: obtaining the working time of the satellite station through the scatter plot of the satellite; Priority analysis: The monitoring priority of the satellite station for each classified satellite is obtained through the tracked frequency of each classified satellite.

2. The TLE-based satellite station information inversion method according to claim 1, characterized in that: The six orbital parameters include semi-major axis, eccentricity, orbital inclination, periapsis argument, ascending node longitude, and true anomaly.

3. The satellite station information inversion method based on TLE according to claim 1 is characterized in that ,The conversion into longitude, latitude and altitude described in S2 includes: first converting the six orbital numbers into coordinates in the J2000 coordinate system, then converting the J2000 coordinate system into the Earth-centered Earth-fixed coordinate system, and finally converting the Earth-centered Earth-fixed coordinates into longitude, latitude and altitude.

4. The satellite station information inversion method based on TLE according to claim 1, characterized in that: The conversion into the date and time format in S2 includes: extracting time information from the TLE data, wherein the time information adopts the format of YYDDD.DDDDDDDD, where: YY represents the last two digits of the year; DDD represents the day of the year, counting starting from 1; and the digits after the decimal point represent the time of day.

5. The TLE-based satellite station information inversion method according to claim 1 and claim 2, characterized in that: The satellite orbit model described in S2 adopts the SGP4 model.

6. The TLE-based satellite station information inversion method according to claim 1, characterized in that: The clustering algorithms described in S3 include K-means clustering algorithm and hierarchical clustering algorithm.

7. The TLE-based satellite station information inversion method according to claim 1 and claim 2, characterized in that: Generating the satellite spatial distribution cluster in S3 includes drawing a cluster diagram with longitude as the X-axis, latitude as the Y-axis and altitude as the Z-axis, and using the cluster center point as the position of the satellite station.

8. The TLE-based satellite station information inversion method according to claim 1 and claim 2, characterized in that: The scatter plot of the satellite at a certain time on a certain day in S3 includes a scatter plot with the date as the horizontal axis and the time as the vertical axis, and each point represents the working time of the satellite station.

9. The satellite station information inversion method based on TLE according to claim 1 and claim 2, characterized in that: The tracking frequency of each classified satellite in S3 includes classifying the satellites according to the orbital altitude and counting the number of each classified satellite. The tracking frequency of each classified satellite is obtained by dividing the number of each classified satellite by the total number of satellites.

Images