CN104462845A - Space object orbit anomaly analysis method and device - Google Patents
Space object orbit anomaly analysis method and device Download PDFInfo
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- CN104462845A CN104462845A CN201410809151.1A CN201410809151A CN104462845A CN 104462845 A CN104462845 A CN 104462845A CN 201410809151 A CN201410809151 A CN 201410809151A CN 104462845 A CN104462845 A CN 104462845A
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Abstract
The invention discloses a space object orbit anomaly analysis method and device. The space object orbit anomaly analysis method comprises the following steps of S1 obtaining historical orbit data of a space object required to be analyzed, S2 calculating anomaly criterion for judging orbit anomaly according to the historical orbit data and S3 calculating judgment parameters of a current orbit of the space object, comparing the judgment parameters with the anomaly criterion and judging whether the orbit of the space object is abnormal according to a comparative result. Preferably, the anomaly criterion is variable quantity of the orbit semi-major axis of the space object. The space object orbit anomaly analysis method and device can quickly and effectively analyze the orbit anomaly of the space object and reduce the working amount of workers.
Description
Technical field
The invention belongs to spationautics field, be specifically related to space object track exception analysis method and device.
Background technology
The reason of satellite in orbit track exception is caused to mainly contain two kinds: a kind of is the orbit maneuver of manual control, another kind is the Orbit revolutionary of non-manual control, the Orbit revolutionary that such as satellite is caused by space object (such as fragment) shock or fuel leakage etc.Find that the track of satellite is abnormal significant timely and accurately, especially the track for non-artificial control is abnormal, immediately to abnormal factor may be caused to analyze, the state grasping space object can be conducive to after noting abnormalities, and the dangerous intersection event of analysis verification.Be no matter the Orbit revolutionary of manual control or non-artificial control, the change of semi-major axis of orbit is all the most significant.Therefore judging whether an object orbit the most direct abnormal method occurs is exactly analyze the change of semi-major axis of orbit.Therefore whether there is ANOMALOUS VARIATIONS by semi-major axis change size to the track of space object to differentiate.
Effective space object track exception analysis method can find out the space object that track exception occurs, and as fragment or spacecraft, can provide Data support for space junk collision warning systems analysis & verification collision accident.
Summary of the invention
(1) technical matters that will solve
Technical matters to be solved by this invention is that existing space object track exception analysis method accurately can not judge exception, or needs the shortcoming of a large amount of manual procedure.
(2) technical scheme
For solving the problems of the technologies described above, the present invention proposes a kind of space object track exception analysis method, comprises the steps: S1, obtains the historical orbit data of the space object of Water demand; S2, calculate Anomaly criterion for differentiating track exception according to historical orbit data; S3, calculate the discriminant parameter of the current orbit of described space object, and itself and Anomaly criterion are compared, judge whether this space object track occurs according to comparative result abnormal.
According to a kind of embodiment of the present invention, described Anomaly criterion is the variable quantity of the semi-major axis of orbit of described space object.
According to a kind of embodiment of the present invention, described step S2 comprises: S21, reject described historical orbit data abnormal point and outlier, to obtain Anomaly criterion sample data; S22, according to Anomaly criterion sample data calculate Anomaly criterion.
According to a kind of embodiment of the present invention, in described step S21, the semi-major axis of historical orbit is calculated according to described historical orbit data, and in chronological sequence order does difference between two, calculate a series of semi-major axis change absolute value | Δ a|, all to what obtain | Δ a| value carries out statistical study, rejects | the data that Δ a| is larger, using remaining | Δ a| value is as Anomaly criterion sample data.
According to a kind of embodiment of the present invention, in described step S21, described in rejecting | 20% data larger in Δ a| value, to remain 80% | Δ a| value is as Anomaly criterion sample data.
According to a kind of embodiment of the present invention, in described step S22, described Anomaly criterion sample data is calculated, obtains its average and standard deviation, utilize this average and standard deviation to calculate Anomaly criterion.
According to a kind of embodiment of the present invention, in described step S22, to described Anomaly criterion sample data | Δ a| carries out statistical study, calculates its average
and standard deviation sigma | Δ a|, then calculates Anomaly criterion
According to a kind of embodiment of the present invention, described step S3 comprises: S31, calculate the orbit parameter corresponding with Anomaly criterion sample data of the current orbit of described space object; S32, the orbit parameter obtained by step S31 and Anomaly criterion compare, and judge whether this space object track occurs abnormal according to comparative result.
According to a kind of embodiment of the present invention, described step S31 is: calculate the semi-major axis a of described space object current orbit value and with the value of last semi-major axis of orbit a, and do and differ from and take absolute value, obtain current orbit | Δ a| value, make described orbit parameter.
According to a kind of embodiment of the present invention, described step S32 is: by current orbit | Δ a| and Anomaly criterion δ compares, as | it is abnormal that Δ a| > δ then judges that this space object, in moment epoch of these data, track occurs, as | there is not track extremely in moment epoch of these data in Δ a|≤δ then this space object.
The present invention also proposes a kind of space object track anomaly analysis device, comprises as lower module: acquisition module, for obtaining the historical orbit data of the space object of Water demand; Anomaly criterion computing module, for calculating the Anomaly criterion for differentiating track exception according to historical orbit data; Judge module, for calculating the discriminant parameter of the current orbit of described space object, and compares itself and Anomaly criterion, judges whether this space object track occurs abnormal according to comparative result.
According to a kind of embodiment of the present invention, described Anomaly criterion is the variable quantity of the semi-major axis of orbit of described space object.
According to a kind of embodiment of the present invention, described Anomaly criterion computing module comprises: reject module, for rejecting described historical orbit data abnormal point and outlier, to obtain the module of Anomaly criterion sample data; Computing module, for calculating Anomaly criterion according to Anomaly criterion sample data.
According to a kind of embodiment of the present invention, described rejecting module calculates the semi-major axis of historical orbit according to described historical orbit data, and in chronological sequence order does difference between two, calculate a series of semi-major axis change absolute value | Δ a|, all to what obtain | Δ a| value carries out statistical study, reject | the data that Δ a| is larger, using remaining | Δ a| value is as Anomaly criterion sample data.
According to a kind of embodiment of the present invention, described in described rejecting module is rejected | 20% data larger in Δ a| value, to remain 80% | Δ a| value is as Anomaly criterion sample data.
According to a kind of embodiment of the present invention, described computing module calculates described Anomaly criterion sample data, obtains its average and standard deviation, utilizes this average and standard deviation to calculate Anomaly criterion.
According to a kind of embodiment of the present invention, described computing module is to described Anomaly criterion sample data | and Δ a| carries out statistical study, calculates its average
and standard deviation sigma | Δ a|, then calculates Anomaly criterion
According to a kind of embodiment of the present invention, described judge module comprises: orbit parameter computing module, for calculating the orbit parameter corresponding with Anomaly criterion sample data of the current orbit of described space object; Abnormal judge module, compares for the orbit parameter and Anomaly criterion orbit parameter being calculated the acquisition of mould port, judges whether this space object track occurs abnormal according to comparative result.
According to a kind of embodiment of the present invention, described orbit parameter computing module calculate the semi-major axis a of described space object current orbit value and with the value of last semi-major axis of orbit a, and do and differ from and take absolute value, obtain current orbit | Δ a| value, make described orbit parameter.
According to a kind of embodiment of the present invention, described abnormal judge module, by current orbit | Δ a| and Anomaly criterion δ compares, as | it is abnormal that Δ a| > δ then judges that this space object, in moment epoch of these data, track occurs, as | there is not track extremely in moment epoch of these data in Δ a|≤δ then this space object.
(3) beneficial effect
The space object track exception analysis method that the present invention proposes can the track of fast and effeciently analysis space object abnormal, and can labor workload be reduced.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of space object track anomaly analysis of the present invention.
Embodiment
Fig. 1 is the process flow diagram of space object track anomaly analysis of the present invention.As shown in Figure 1, the space object track exception analysis method that the present invention proposes comprises the steps:
The historical orbit data of the space object of S1, acquisition Water demand.
First, determine the space object of Water demand, then obtain the orbital data of this object within the previous time period of analysis time as sample, this time period is such as 3 months.Then, historical orbit data are in chronological sequence arranged.
S2, calculate Anomaly criterion for differentiating track exception according to historical orbit data.
This step can adopt different account forms, in the present invention be preferably using the variable quantity of the semi-major axis of track as Anomaly criterion, comprise the steps:
S21, reject described historical orbit data abnormal point and outlier, to obtain Anomaly criterion sample data.
Foregoing reason, is preferably and rejects historical orbit data abnormal point and outlier according to the change of semi-major axis of orbit.
Specifically, the semi-major axis of historical orbit is calculated according to historical orbit data, and in chronological sequence order does difference between two, calculate a series of semi-major axis change absolute value | Δ a|, all to what obtain | Δ a| value carries out statistical study, reject | the data that Δ a| is larger, using remaining | Δ a| value is as Anomaly criterion sample data.
Such as reject | 20% data larger in Δ a| value, to remain 80% | Δ a| value is as Anomaly criterion sample data.
S22, according to Anomaly criterion sample data calculate Anomaly criterion.
Anomaly criterion sample data is calculated, obtains its average and standard deviation, utilize this average and standard deviation to calculate Anomaly criterion.
Such as to Anomaly criterion sample data | Δ a| carries out statistical study, calculates its average
and standard deviation sigma | Δ a|.Then Anomaly criterion is calculated
S3, calculate the discriminant parameter of the current orbit of described space object, and itself and Anomaly criterion are compared, judge whether this space object track occurs according to comparative result abnormal.
Described discriminant parameter is the orbit parameter corresponding with Anomaly criterion sample data.Preferably, step S3 of the present invention specifically comprises:
S31, calculate the orbit parameter corresponding with Anomaly criterion sample data of the current orbit of described space object.
This orbit parameter is correspondingly preferably the change of semi-major axis of orbit.Particularly, by calculate the semi-major axis a of described space object current orbit value and with the value of last semi-major axis of orbit a, and do and differ from and take absolute value, obtain current orbit | Δ a| value.
S32, the orbit parameter obtained by step S31 and Anomaly criterion compare, and judge whether this space object track occurs abnormal according to comparative result.
Particularly, by current | Δ a| and Anomaly criterion δ compares, as | it is abnormal that Δ a| > δ then judges that this space object, in moment epoch of these data, track occurs, as | there is not track extremely in moment epoch of these data in Δ a|≤δ then this space object.
The present invention also proposes a kind of space object track anomaly analysis device, comprises as lower module: acquisition module, for obtaining the historical orbit data of the space object of Water demand; Anomaly criterion computing module, for calculating the Anomaly criterion for differentiating track exception according to historical orbit data; Judge module, for calculating the discriminant parameter of the current orbit of described space object, and compares itself and Anomaly criterion, judges whether this space object track occurs abnormal according to comparative result.
For making the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with specific embodiment, the present invention is described in further detail.This embodiment, for certain satellite, specifically describes each step as follows:
The historical orbit data of S1, acquisition satellite.
First determine the satellite of Water demand, then choose the orbital data of this satellite before analysis time in 3 months as sample, and in chronological sequence arrange.Here Water demand be satellite 25544 is 05:20:43 on July 1st, 2014 in its moment epoch of Article 1 TLE data on July 1st, 2014.Then can choose 25544 on April 1st, 2014 to the orbital data in three months on the 30th June as sample, through extract statistics, have 461 data.
S2, calculate Anomaly criterion for differentiating track exception according to historical orbit data.
S21, reject described historical orbit data abnormal point and outlier, to obtain Anomaly criterion sample data
The semi-major axis of orbit of sample data is calculated, and by sample order do between two difference calculate a series of semi-major axis change absolute value | Δ a|.The present embodiment has 460 | Δ a| data.To these | Δ a| value sorts and removes maximum 20%, for the present embodiment remove 92 maximum | Δ a|.Then remaining 368 | Δ a| data are exactly Anomaly criterion sample data.
S22, according to Anomaly criterion sample data calculate Anomaly criterion.
To Anomaly criterion sample data | Δ a| carries out statistical study, calculates its average
and standard deviation sigma | Δ a|.Calculate Anomaly criterion
the present embodiment obtains
σ | Δ a|=0.008513km, then δ=0.109413km.
The discriminant parameter of the current orbit of S3, calculating satellite, and itself and Anomaly criterion are compared, judge whether this space object track occurs according to comparative result abnormal.
The orbit parameter corresponding with Anomaly criterion sample data of the current orbit of S31, calculating satellite.
Choose and need to carry out the orbital data analyzed, calculate the value of its semi-major axis a and do with a value of last bar orbital data and differ from and take absolute value, obtain these data | Δ a| value.The present embodiment obtains the Article 1 TLE data on July 1st, 2014 | and Δ a| is 0.013956km.
The orbit parameter that step S31 obtains and Anomaly criterion compare, and judge whether satellite track occurs abnormal according to comparative result.
By these data | Δ a| and Anomaly criterion δ compares, as | it is abnormal that Δ a| > δ then judges that this space object, in moment epoch of these data, track occurs, as | there is not track extremely in moment epoch of these data in Δ a|≤δ then this space object.For the present embodiment | Δ a| < δ, therefore differentiates the Article 1 TLE data of satellite 25544 on July 1st, 2014, track does not occur abnormal when moment epoch is 05:20:43 on the 1st July in 2014.
Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; be understood that; the foregoing is only specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (20)
1. a space object track exception analysis method, is characterized in that, comprises the steps:
The historical orbit data of the space object of S1, acquisition Water demand;
S2, calculate Anomaly criterion for differentiating track exception according to historical orbit data;
S3, calculate the discriminant parameter of the current orbit of described space object, and itself and Anomaly criterion are compared, judge whether this space object track occurs according to comparative result abnormal.
2. space object track exception analysis method as claimed in claim 1, it is characterized in that, described Anomaly criterion is the variable quantity of the semi-major axis of orbit of described space object.
3. space object track exception analysis method as claimed in claim 2, it is characterized in that, described step S2 comprises:
S21, reject described historical orbit data abnormal point and outlier, to obtain Anomaly criterion sample data;
S22, according to Anomaly criterion sample data calculate Anomaly criterion.
4. space object track exception analysis method as claimed in claim 3, it is characterized in that, in described step S21, the semi-major axis of historical orbit is calculated according to described historical orbit data, and in chronological sequence order does difference between two, calculates a series of semi-major axis change absolute value | Δ a|, all to what obtain | Δ a| value carries out statistical study, reject | the data that Δ a| is larger, using remaining | Δ a| value is as Anomaly criterion sample data.
5. space object track exception analysis method as claimed in claim 4, is characterized in that, in described step S21, described in rejecting | 20% data larger in Δ a| value, to remain 80% | Δ a| value is as Anomaly criterion sample data.
6. space object track exception analysis method as claimed in claim 4, is characterized in that, in described step S22, calculate, obtain its average and standard deviation to described Anomaly criterion sample data, utilizes this average and standard deviation to calculate Anomaly criterion.
7. space object track exception analysis method as claimed in claim 6, is characterized in that, in described step S22, to described Anomaly criterion sample data | Δ a| carries out statistical study, calculates its average
and standard deviation sigma | Δ a|, then calculates Anomaly criterion
8. space object track exception analysis method as claimed in claim 2, it is characterized in that, described step S3 comprises:
S31, calculate the orbit parameter corresponding with Anomaly criterion sample data of the current orbit of described space object;
S32, the orbit parameter obtained by step S31 and Anomaly criterion compare, and judge whether this space object track occurs abnormal according to comparative result.
9. space object track exception analysis method as claimed in claim 8, it is characterized in that, described step S31 is: calculate the semi-major axis a of described space object current orbit value and with the value of last semi-major axis of orbit a, and do and differ from and take absolute value, obtain current orbit | Δ a| value, make described orbit parameter.
10. space object track exception analysis method as claimed in claim 9, it is characterized in that, described step S32 is: by current orbit | Δ a| and Anomaly criterion δ compares, as | it is abnormal that Δ a| > δ then judges that this space object, in moment epoch of these data, track occurs, as | there is not track extremely in moment epoch of these data in Δ a|≤δ then this space object.
11. 1 kinds of space object track anomaly analysis devices, is characterized in that, comprise as lower module:
Acquisition module, for obtaining the historical orbit data of the space object of Water demand;
Anomaly criterion computing module, for calculating the Anomaly criterion for differentiating track exception according to historical orbit data;
Judge module, for calculating the discriminant parameter of the current orbit of described space object, and compares itself and Anomaly criterion, judges whether this space object track occurs abnormal according to comparative result.
12. space object track anomaly analysis devices as claimed in claim 11, it is characterized in that, described Anomaly criterion is the variable quantity of the semi-major axis of orbit of described space object.
13. space object track anomaly analysis devices as claimed in claim 12, it is characterized in that, described Anomaly criterion computing module comprises:
Reject module, for rejecting described historical orbit data abnormal point and outlier, to obtain the module of Anomaly criterion sample data;
Computing module, for calculating Anomaly criterion according to Anomaly criterion sample data.
14. space object track anomaly analysis devices as claimed in claim 13, it is characterized in that, described rejecting module calculates the semi-major axis of historical orbit according to described historical orbit data, and in chronological sequence order does difference between two, calculate a series of semi-major axis change absolute value | Δ a|, all to what obtain | Δ a| value carries out statistical study, rejects | the data that Δ a| is larger, using remaining | Δ a| value is as Anomaly criterion sample data.
15. space object track anomaly analysis devices as claimed in claim 14, is characterized in that, described in described rejecting module is rejected | 20% data larger in Δ a| value, to remain 80% | Δ a| value is as Anomaly criterion sample data.
16. space object track anomaly analysis devices as claimed in claim 14, it is characterized in that, described computing module calculates described Anomaly criterion sample data, obtains its average and standard deviation, utilizes this average and standard deviation to calculate Anomaly criterion.
17. space object track anomaly analysis devices as claimed in claim 16, is characterized in that, described computing module is to described Anomaly criterion sample data | Δ a| carries out statistical study, calculates its average
and standard deviation sigma | Δ a|, then calculates Anomaly criterion
18. space object track anomaly analysis devices as claimed in claim 12, it is characterized in that, described judge module comprises:
Orbit parameter computing module, for calculating the orbit parameter corresponding with Anomaly criterion sample data of the current orbit of described space object;
Abnormal judge module, compares for the orbit parameter that obtained by orbit parameter computing module and Anomaly criterion, judges whether this space object track exception occurs according to comparative result.
19. space object track anomaly analysis devices as claimed in claim 18, it is characterized in that, described orbit parameter computing module calculate the semi-major axis a of described space object current orbit value and with the value of last semi-major axis of orbit a, and do and differ from and take absolute value, obtain current orbit | Δ a| value, make described orbit parameter.
20. space object track anomaly analysis devices as claimed in claim 19, it is characterized in that, described abnormal judge module, by current orbit | Δ a| and Anomaly criterion δ compares, as | it is abnormal that Δ a| > δ then judges that this space object, in moment epoch of these data, track occurs, as | there is not track extremely in moment epoch of these data in Δ a|≤δ then this space object.
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| CN104765964A (en) * | 2015-04-15 | 2015-07-08 | 北京空间飞行器总体设计部 | Space environment sensitive parameter screening method |
| CN109582029A (en) * | 2018-12-28 | 2019-04-05 | 西安四方星途测控技术有限公司 | A method of GEO satellite track condition is determined by TLE data |
| CN112623283A (en) * | 2020-12-30 | 2021-04-09 | 苏州三六零智能安全科技有限公司 | Space object abnormity detection method, device, equipment and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104765964A (en) * | 2015-04-15 | 2015-07-08 | 北京空间飞行器总体设计部 | Space environment sensitive parameter screening method |
| CN104765964B (en) * | 2015-04-15 | 2017-12-08 | 北京空间飞行器总体设计部 | A kind of space environment sensitive parameter screening technique |
| CN109582029A (en) * | 2018-12-28 | 2019-04-05 | 西安四方星途测控技术有限公司 | A method of GEO satellite track condition is determined by TLE data |
| CN112623283A (en) * | 2020-12-30 | 2021-04-09 | 苏州三六零智能安全科技有限公司 | Space object abnormity detection method, device, equipment and storage medium |
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Application publication date: 20150325 |