Optical observation correlation method based on mixed multi-hour ephemeris

Abstract

The invention discloses an optical observation correlation method based on mixed multi-hour ephemeris, and belongs to the technical field of space target monitoring. Aiming at the problem that the correlation performance is reduced when the ephemeris quality is unstable due to dependence on a single ephemeris in the prior art, the method comprises the following steps of: firstly, constructing a mixed set containing multi-source ephemeris; secondly, intelligently selecting a leading ephemeris and a lagging ephemeris which are closest in time for each observation arc section and each target; then, the two ephemeris are used for association respectively, and union sets of results are obtained, so that an initial association set is expanded; and for the generated fuzzy correlation, performing primary disambiguation based on the comprehensive confidence, and performing secondary accurate disambiguation on the residual fuzzy condition by comparing the weighted angular distance residual error root-mean-square value of the whole arc section. According to the method, the dependence on single-term and single-source ephemeris precision is effectively reduced, available information is fully utilized, and the correlation robustness and accuracy of optical observation data under the complex ephemeris condition are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of space target monitoring and aerospace telemetry and control technology, specifically to a method for associating and matching space targets using optical observation data, and in particular to a hybrid multi-time ephemeris association method that can effectively address problems such as unstable ephemeris data quality and inconsistent timeliness. Background Technology

[0002] When using optical observation methods to track, catalog, and assess the situation of low-Earth orbit targets (such as satellites and debris), one of the core steps is to correlate the measurement arcs acquired by optical sensors with the predicted ephemeris of known targets. The accuracy of this correlation directly affects the reliability of target identification, orbit determination, and collision warning.

[0003] Currently, correlation operations typically rely on ephemeris data from a single source and at a single epoch, such as the newly released TLE (two-row root) ephemeris. However, in practical applications, the quality of ephemeris data exhibits significant instability and limitations: (1) Timeliness issues: Public ephemeris (such as TLE) updates are delayed, and the update frequencies for different targets are inconsistent, resulting in ephemeris available at the correlation time potentially being severely outdated, leading to large prediction errors; (2) Accuracy differences: Ephemeris from different sources (such as public ephemeris and commercial high-precision ephemeris) have vastly different accuracy levels, and using only a single-source ephemeris will waste high-precision information or be hampered by low-precision data; (3) Local failures: Ephemeris for some targets may be missing or contain gross errors for some reason.

[0004] Existing technologies have failed to systematically solve the above problems. Simply using the latest ephemeris may result in missed correlations due to outdated target ephemeris data; attempting correlations with all ephemeris within a fixed time window will generate a large number of fuzzy correlations and impose a heavy computational burden. Therefore, there is an urgent need for a robust correlation method that can adaptively utilize multi-source heterogeneous ephemeris data. Summary of the Invention

[0005] (I) Purpose of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an optical observation correlation method based on hybrid multi-epoch ephemeris. This method effectively improves the correlation success rate and accuracy under conditions of unstable ephemeris quality by intelligently fusing ephemeris data from different sources and at different epochs and designing a multi-level disambiguation strategy.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention adopts the following technical solution: an optical observation correlation method based on hybrid multi-temporal ephemeris, comprising the following steps:

[0009] Step 1: Constructing a hybrid ephemeris set

[0010] For the startup data to be processed, associated time windows are used to collect ephemeris data from multiple sources to form a hybrid ephemeris set.

[0011] Step 2: Match the nearest neighbor ephemeris to the observed arc segment

[0012] For each optical observation arc to be associated, based on the center time or key time of the arc, search and obtain at least one leading ephemeris and one lagging ephemeris that are closest in time to each potential associated target from the hybrid ephemeris set.

[0013] Step 3: Perform a two-term association and union operation.

[0014] Independent association assessments are performed using the advanced ephemeris and the lagging ephemeris obtained in step two, respectively; any association result deemed successful in either of the two assessments is included in the initial successful association set.

[0015] Step 4: Initial disambiguation based on confidence level

[0016] If the same observation arc segment is associated with multiple targets from different ephemeris sources or different times in the initial successful association set, the confidence levels of these association results are compared; the association result with the highest confidence level is selected as the preferred association, and other association results with low confidence levels are marked as pending.

[0017] Step 5: Secondary disambiguation based on residual RMS

[0018] For arc segments that still have multiple candidate associations after step four, calculate the weighted angular distance residuals of each observation point within the arc segment relative to different candidate targets, and obtain the root mean square value for each. If the ratio between the root mean square values ​​of the weighted angular distance residuals of different candidate targets exceeds a preset first threshold and the absolute difference exceeds a preset second threshold, then the candidate target with the smallest root mean square value of the weighted angular distance residual is selected as the final association result.

[0019] (III) Advantages

[0020] ① Significantly improves correlation robustness: By using a hybrid multi-source ephemeris and dual-time (leading / lagging) matching strategy, the dependence on the timeliness and accuracy of a single ephemeris is greatly reduced, and a high correlation coverage can still be maintained even when some ephemeris data is outdated or missing.

[0021] ② Intelligent defuzzification capability: The designed two-level disambiguation strategy (confidence priority, residual RMS verification) can efficiently and accurately handle the correlation fuzziness problem introduced by multi-ephemeris, effectively suppressing the growth of false correlations while expanding the correlation "fishing net".

[0022] ③ Maximize information utilization: This method systematically integrates ephemeris information from different sources (public / commercial) and at different times (new / old), transforming heterogeneous data into a coherent advantage and enhancing the overall spatial situation.

[0023] Data utilization efficiency and performance lower limit of the sensing system. Attached Figure Description

[0024] Figure 1 This is an overall flowchart of the method of the present invention.

[0025] Figure 2 This is a schematic diagram of matching leading and lagging ephemeris for a single observation arc segment in step two.

[0026] Figure 3 This is a schematic diagram of the disambiguation process performed in step five based on the weighted angular distance residual RMS of the entire arc segment. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0028] Example: Correlating a 10-minute observation arc of a ground-based optical telescope.

[0029] 1. Constructing the collection: Collect TLE ephemeris files up to the observation date, as well as Starlink satellite commercial ephemeris obtained from commercial channels (updated every 8 hours).

[0030] 2. Matching ephemeris: The center time T0 of the observation arc is 2023-10-01 12:00:00 UTC. For the satellite with target ID "STARLINK-1234":

[0031] Find the ephemeris with the time tag 11:58:30 from the commercial ephemeris (90 seconds ahead).

[0032] • Find the ephemeris with the time tag 12:01:15 from the commercial ephemeris (75 seconds behind).

[0033] • For the other target "DEBRIS-5678", there is only a TLE source. The latest TLE epoch time is found to be 11:30:00 (1800 seconds ahead). This is taken as the ahead ephemeris. The lagging ephemeris does not exist.

[0034] 3. Union join:

[0035] • The "STARLINK-1234" was successfully linked to both the leading and lagging commercial ephemeris (with small residuals).

[0036] • The association was successful using the advanced TLE ephemeris of “DEBRIS-5678”. Due to the older ephemeris, the residuals were large, but still within an acceptable threshold.

[0037] • This arc segment is initially associated with two targets.

[0038] 4. Confidence Disambiguation: Calculate the confidence score. "STARLINK-1234" uses high-precision commercial ephemeris and both correlation residuals are very small, resulting in a confidence score of 95. "DEBRIS-5678" uses outdated TLE and its residuals are close to the upper threshold, resulting in a confidence score of 65. The confidence scores differ significantly (>20 points), therefore, the arc segment is initially determined to belong to "STARLINK-1234," and the result for "DEBRIS-5678" is marked as low-confidence pending.

[0039] 5. Residual RMS Verification (Step 4 has largely solved this in this example; this step demonstrates another scenario): Assume there are two candidates with similar confidence levels (both 80). Calculate the weighted angular distance residual RMS for the entire 10-minute arc (60 observation points). The RMS of candidate A is 15 arcseconds, and the RMS of candidate B is 120 arcseconds. The ratio between the two is 8 (>5), and the absolute difference is 105 arcseconds (>100). Therefore, candidate A (RMS = 15 arcseconds) is selected as the final association result, and candidate B is excluded.

Claims

1. An optical observation correlation method based on hybrid multi-temporal ephemeris, characterized in that, Includes the following steps: Step 1: Construct a hybrid ephemeris set For the startup data to be processed, associated time windows are used to collect ephemeris data from multiple sources to form a hybrid ephemeris set. Step 2: Match the nearest neighbor ephemeris to the observed arc segment For each optical observation arc to be associated, based on the center time or key time of the arc, search and obtain at least one leading ephemeris and one lagging ephemeris that are closest in time to each potential associated target from the hybrid ephemeris set. Step 3: Perform a two-term association and union operation. Independent association assessments are performed using the advanced ephemeris and the lagging ephemeris obtained in step two, respectively; any association result deemed successful in either of the two assessments is included in the initial successful association set. Step 4: Initial disambiguation based on confidence level If the same observation arc segment is associated with multiple targets from different ephemeris sources or different times in the initial successful association set, the confidence levels of these association results are compared; the association result with the highest confidence level is selected as the preferred association, and other association results with low confidence levels are marked as pending. Step 5: Secondary disambiguation based on residual RMS For arc segments that still have multiple association candidates after step four, calculate the weighted angular distance residuals of each observation point within the arc segment relative to different candidate targets, and obtain the root mean square value for each. If the ratio between the root mean square values ​​of the weighted angular distance residuals of different candidate targets exceeds the preset first threshold and the absolute difference exceeds the preset second threshold, then the candidate target with the smallest root mean square value of the weighted angular distance residual is selected as the final association result.

2. The method according to claim 1, characterized in that, Step one involves ephemeris data from multiple sources, including public and commercial ephemeris from different publishing organizations or with varying degrees of precision.

3. The method according to claim 2, characterized in that, The prescribed public ephemeris includes TLE ephemeris, and the prescribed commercial ephemeris includes ephemeris for use provided by low-Earth orbit satellite operators.

4. The method according to claim 1, characterized in that, In step two, the nearest ephemeris in terms of time is searched and obtained, and the search range is limited to the reference time relative to the default maximum time interval.

5. The method according to claim 1, characterized in that, The confidence level specified in step four is calculated by combining one or more parameters from the normalized residual value output by the association evaluation method, the historical association success rate, and the inherent accuracy level of the ephemeris source.

6. The method according to claim 1, characterized in that, The first threshold value specified in step five is 5 times, and the second threshold value is 100 arcseconds.

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