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Anomaly Cleaning Method of Orbital Parameters Based on Expectation Maximization Estimation

A technology of orbital parameters and cleaning methods, which is applied in the field of orbital parameter anomaly cleaning, and can solve the problems of data "islands, inconsistencies, sparse orbital parameters, etc.

Active Publication Date: 2022-04-01
ZIJINSHAN ASTRONOMICAL OBSERVATORY CHINESE ACAD OF SCI +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there are such problems in practical applications: if there is a problem with the orbital parameters after it is released, it will be corrected later, and the orbital parameters with problems still exist in the catalog library; the orbital parameters of some targets are sparse, resulting in the existence of data "islands"; Some unknown events in the process of orbit determination caused abnormalities in the final orbital parameters, etc.
Because the a priori threshold for judging anomalies needs to be obtained by simulation, there are three significant problems in this technology: ① lack of theoretical support, which leads to unreasonable phenomena in judging anomalies; ② it cannot be applied to all objects in the space object catalog library, and its applicability is poor; ③The cleaning process of orbital parameters is cumbersome and inconsistent

Method used

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  • Anomaly Cleaning Method of Orbital Parameters Based on Expectation Maximization Estimation
  • Anomaly Cleaning Method of Orbital Parameters Based on Expectation Maximization Estimation
  • Anomaly Cleaning Method of Orbital Parameters Based on Expectation Maximization Estimation

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Experimental program
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Effect test

no. 1 Embodiment

[0077] Based on TLE data in the US NORAD catalog database, the threshold settings in the target 13025 time sequence, the threshold setting and window threshold settings in the window are set in the three combinations on an abnormal cleaning: Strategy 1: Window threshold 3 times standard deviation 3 times the three-fold standard deviation of the window; Strategy 2: window threshold is 2 times the standard deviation, the window threshold is 3 times the standard deviation; the Strategy 3: window threshold is 2 times the standard deviation, the window threshold is 2 times the standard deviation.

[0078] Take the target of the track tilt angle to clean up as an example, discuss the impact of the three strategies:

[0079] Step 1: Download the target 13025 All data, time from January 9, 1982 to November 20, 1988;

[0080] Step 2: Read the data, specify the correction observation threshold 0.5 rail cycles, and clean up;

[0081] Step 3: Set the number of observations within the sliding ...

no. 2 Embodiment

[0092] TLE data in the US NORAD cataloging database Threshold setting and window threshold setting three combinations: Strategy 1: Window threshold 3 times standard deviation, 3 times the window threshold; Strategy 2: window threshold 2 times standard deviation, window threshold 3 times standard deviation; Strategy3 : The window threshold is 2 times the standard deviation, and the window threshold is 2 times the standard deviation.

[0093] The NORAD number of satellite Jason-2 is 33105, and the time to be processed is January 1, 2019 January 11, 2021. After the correction observation is cleaned, the remaining 1820 observations are restored. Taking the track tilt as an example, the combined strategy cleaning results Figure 7 . The order of the polynomial is set to 3, and the number of forecast values ​​is 8. The track parameters are now introduced as follows:

[0094] Step 1: Combined strategy: n TLE = 200 and Strategy1, clean up 17 exceptions, see Figure 7 (A).

[0095] Step 2: C...

no. 3 Embodiment

[0108] 根据Lidtke等(Lidtke,A.A.,Gondelach,D.J.,&Armellin,R.Optimisingfiltering of two-line element sets to increase re-entry prediction accuracyfor GTO objects[J].Advances in Space Research,2018:1289-1317.)的研究Results, select the rocket target 14287 as a comparison of the cleaning algorithm. Figure 8 The cleaning effect of two algorithms is given. The graph is the cleaning effect of the present application algorithm. In the dotted line box, the box is the abnormality of the new algorithm decision; the effect of the right LIDTKE is clear, there is a significant exception. Therefore, the algorithm proposed by LIDTKE et al. Is unreasonable for some abnormal determination, and there is a lot of abnormalities.

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Abstract

The present invention is an orbital parameter abnormal cleaning method based on expectation maximization estimation, and the specific steps are as follows. Firstly, cleaning of corrected observations: determine the threshold for cleaning corrected observations based on the release time interval of statistical observations, or specify a priori cleaning threshold based on existing experience, in units of orbital periods; secondly, cleaning of orbital parameters: based on Polynomial regression and anomaly detection within the sliding window and detection of anomalies and space events outside the sliding window complete the anomaly cleaning of the orbital elements of the observations; if the target is close to the atmospheric re-entry time, all observations with negative ballistic coefficients are eliminated. This method can unify the orbital parameter anomaly cleaning process, and effectively clean up the orbital parameter anomalies of all objects in the spatial data cataloging library.

Description

Technical field [0001] The present invention relates to the field of small orbit parameter abnormal technologies present in the space target rail data corrugation under an astrody mechanical framework, which is specifically based on the EXPECTATION MAXIMIZATION estimation of the track parameter abnormal cleaning method. Background technique [0002] The space target track data catalog contains a large number of historical track parameters, which is the basis for collision warning, ballistic coefficient estimation, atmosphere re-entry forecast, spatial target association and other applications. However, there is such a problem in practical applications: If there is a problem after the track parameter is released, then the problematic track parameters still have a catalog library; some target track parameters are sparse, resulting in the existence of data "Island"; Some unknown events during the leading process cause the final rail parameter abnormality. In practical applications, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20
CPCG06F30/20G06F2119/14
Inventor 刘劲宏杜建丽徐劲
Owner ZIJINSHAN ASTRONOMICAL OBSERVATORY CHINESE ACAD OF SCI
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