CN105911524B - Supersparsity radar data associates matching process - Google Patents

Supersparsity radar data associates matching process Download PDF

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CN105911524B
CN105911524B CN201610152919.1A CN201610152919A CN105911524B CN 105911524 B CN105911524 B CN 105911524B CN 201610152919 A CN201610152919 A CN 201610152919A CN 105911524 B CN105911524 B CN 105911524B
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data
msubsup
matching
potential
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CN105911524A (en
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张淑琴
宋克章
何雨帆
杨涛
赵治
李永华
高景丽
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CHINA XI'AN SATELLITE CONTROL CENTER
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CHINA XI'AN SATELLITE CONTROL CENTER
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00

Abstract

The present invention provides a kind of supersparsity radar data to associate matching process, using the thought and " line-of-sight course " matching principle of " first matching again orbit determination to match again ", classified to data, first orbit determination, " line-of-sight course " Orbit Matching, the potential incidence set of step-sizing target, to achieve the purpose that data autocorrelation matching." line-of-sight course " matching process of supersparsity radar data proposed by the present invention, highly practical, matching accuracy height, the association matching problem for lacking extraterrestrial target multi-turn supersparsity radar data in the case of prior information is efficiently solved, has filled up blank of the China in terms of supersparsity radar multi-turn observes data correlation matching.

Description

Supersparsity radar data associates matching process
Technical field
The invention belongs to field of aerospace measurement and control, is related to a kind of data correlation matching process.
Background technology
Supersparsity radar data refers to that radar equipment observes the few sampling number obtained every time during single extraterrestrial target (no More than 4 points) measurement data.Such as the NAVSPASUR spaces fencing system in the U.S., extraterrestrial target every time through when leave Extremely short measurement segmental arc.The association matching of measurement data, mainly combines related according to the locus of measurement point and orbital characteristics Property require, easy to uniformly carry out analysis calculating by the data correlation met the requirements together.
Single segmental arc data are carried out orbit determination first, orbit determination result and priori are believed by traditional data correlation matching Cease database middle orbit information and carry out slightly association matching, on this basis, then measurement data is subjected to o-c associations and is matched, if Mix, be then known target associated data, if on not associated, being defined as " fresh target data ", such target frequently includes newly Launch target, or caused by target is disintegrated, target becomes the spatial events such as rail " fresh target " etc..And for owning " new mesh The not associated data of mark ", can only use each single segmental arc orbit determination as a result, progress autocorrelation matching.In short, it is using " first orbit determination is again The principle of matching ".
Supersparsity radar data list segmental arc is counted very little, can not utilize least square orbit determination, and two based on short arc points are determined Rail precision is too poor, so, traditional " first orbit determination matches again " method has been not suitable with.Because of the track without certain precision, cause to survey Amount data can not be matched correctly with known target database middle orbit information, and each segmental arc multi-revolution data also can not be according to track Autocorrelation matching is carried out, so that a large amount of " not associated data " are formed, and the association matching of multi-revolution data is the premise of improvement of orbit With the basis of target identification.
The content of the invention
For overcome the deficiencies in the prior art, the present invention provides a kind of supersparsity radar data association matching process, completes The autocorrelation matching of supersparsity radar observation data, determines for the extraterrestrial target track based on supersparsity radar data and target is known Indescribably for supporting.
The technical solution adopted by the present invention to solve the technical problems includes the following steps:
Step 1: select Arbitrary Digit strong point D in not associated data point complete or collected works { A }k, with any one data point D in { A }j Carry out potential associative classification;DkCorresponding observation time is t (Dk), locus is r (Dk), DjCorresponding observation time is t (Dj), locus is r (Dj);Δh、Δt、Height, time, the angle respectively set screens thresholding;
If 1)K ≠ j, then DjFor DkThe potential associated data of the first kind, add potential pass Connection collectionIn, wherein,For r (Dk)、r(Dj) angle, tΔFor DjAnd DkWhen relatively a certain observation station is located at same segmental arc, from r (Dk) arrive r (Dj) required theoretical time,B is observation station latitude, the flat movement velocity of trackμ draws for the earth The i of force constant, latitude argument u and orbit inclination angle meetsR(Rx,Ry,Rz) it is r (Dk)、r(Dj) institute it is in-orbit The unit normal direction in road face, and
If 2)K ≠ j, then DjFor DkThe potential associated data of the second class, add potential association CollectionIn, wherein, t 'ΔFor DjAnd DkIt is all rail lift or drop rail after relatively a certain observation station interval multi-turn During mode, from r (Dk) arrive r (Dj) required theoretical time, t 'Δ=sT, Round numbers afterwards;
If 3)Then DjFor DkThe potential associated data of three classes, add potential incidence setIn;
Step 2: respectively by DkWithIn each potential relating dot carry out just orbit determination, Draw corresponding track σ1m, wherein, σ1m=(a1m,e1m,i1m1m1m,M1m)T, be the semi-major axis of track, eccentricity, Inclination angle, the red footpath of ascending node, argument of perigee, mean anomaly;
Respectively by DkWithIn each potential relating dot carry out just orbit determination, draw orbital tracking σ3n, wherein, σ3n=(a3n,e3n,i3n3n3n,M3n)T, be track the red footpath of semi-major axis, eccentricity, inclination angle, ascending node, Argument of perigee, mean anomaly;
If setNon-NULL, respectively with σ1mOn the basis of screen σ3n, screening principle is such as Under:
Δ a, Δ i, Δ Ω are respectively the red footpath screening thresholding of semi-major axis of orbit, orbit inclination angle, the ascending node set;
Select σ3nIn with σ1mThe orbital tracking to match is as benchmark track radical
If setFor sky, then σ3nIn all orbital trackings be benchmark track root Number,
Step 3: the data point that all participation benchmark track radicals are determined establishes set That is the potential incidence set in the second level;
Step 4: according to potential associated 2 points of orbital characteristics, in the potential incidence set of the second class In, lookup is associated thirdly:
1) successively according to each corresponding data point of benchmark track radical Respectively willWith Pd2 (Dk) in all the points carry out orbit determination, draw orbital tracking σ2l, σ2l=(a2l,e2l,i2l2l2l,M2l)T, it is the half of track Major axis, eccentricity, inclination angle, the red footpath of ascending node, argument of perigee, mean anomaly;
2) by σ2lIt is extrapolated to matchedCorresponding radicalPrinciple of extrapolating is as follows:
Wherein, aeFor earth radius, C20=0.0010826;
3) by E-esinE=M andThe f of the true anomaly of track is calculated, wherein, M puts down for track Anomaly, E are eccentric anomaly, and e is eccentricity;
4) using line-of-sight course to true respectively from three data points in the first kind, the second class, the potential incidence set of three classes Two groups of fixed tracks are matched, and matching principle is as follows:
Δ a, Δ i, Δ Ω, Δ f are respectively semi-major axis of orbit, orbit inclination angle, the red footpath of ascending node, the true anomaly sieve set Select thresholding;
Step 5: by all orbital trackings for meeting matching principleWithIn used data point, establish the third level Potential incidence set Pd123(Dk)={ D1,D2…,Dx, then Pd123For DkFinal association matched data collection { P (Dk)};Make { A }= {A}-{P(Dk), return to step one, carries out the association matching of other data points, if data element number is less than 3 in { A }, With end.
The beneficial effects of the invention are as follows:" line-of-sight course " matching process of the supersparsity radar data of proposition, it is highly practical, It is high with accuracy, efficiently solve and lack the association matching of extraterrestrial target multi-turn supersparsity radar data in the case of prior information and ask Topic, has filled up blank of the China in terms of supersparsity radar multi-turn observes data correlation matching.
Embodiment
With reference to embodiment, the present invention is further described, and the present invention includes but are not limited to following embodiments.
The present invention divides data using the thought and " line-of-sight course " matching principle of " first matching again orbit determination to match again " Class, first orbit determination, " line-of-sight course " Orbit Matching, the potential incidence set of step-sizing target, to achieve the purpose that data autocorrelation matching.
The present invention specifically comprises the following steps:
Step 1:Classification and matching is carried out to data, establishes data category set.
2 points of observation data of same target have for the relation of a certain observation station:1) two point datas are same segmental arc, It is defined as first kind associated data;2) it is all rail lift after multi-turn (being more than an orbital period) is spaced between 2 points or drops rail mode Observation data, be defined as the second class associated data;3) multi-turn (being more than an orbital period) is spaced between 2 points is respectively afterwards One rise and one drop or the observation data of one liter of mode of drop, are defined as three classes associated data.
With reference to three classes data correlation characteristic, classify according to following principle to data:
Arbitrary Digit strong point D is selected in not associated data point complete or collected works { A }k(corresponding observation time is t (Dk), space bit Put r (Dk)), with any one data point D in { A }j(corresponding observation time is t (Dj), locus r (Dj)) carry out potential pass Connection classification.Δh、Δt、Respectively height, the time, angle screening thresholding (thresholding size according to track motion characteristic, meter Calculate the combined factors such as precision, measurement error to consider to set).
If 1) meet formula (1) screening conditions, DjFor DkThe potential associated data of the first kind, add potential incidence setIn.
Wherein,For r (Dk)、r(Dj) angle:
tΔFor from point r (Dk) arrive r (Dj) required theoretical time under first kind mode:
(3) in formula, μ is Gravitational coefficient of the Earth, ReFor earth radius, B is observation station latitude,
N is the flat movement velocity of track:
The latitude argument u and i of orbit inclination angle is then calculated by formula (4), (5), (6):
R(Rx,Ry,Rz) it is r (Dk)、r(Dj) where orbital plane unit normal direction, and
Cosi=Rz (6)
If 2) meet formula (7) screening conditions, for DkThe potential associated data of the second class, add potential incidence setIn.
t′ΔFor from point r (Dk) arrive r (Dj) required theoretical time under the second class mode:
t′Δ=sT (8)
Wherein,(s is integer).
If 3) meet formula (9) screening conditions, for DkThe potential associated data of three classes, add potential incidence setIn.
U is calculated by (2) formula, (4) formula respectively.
Step 2:Benchmark track radical determines
1) first orbit determination
Respectively by DkWithIn each potential relating dot carry out just orbit determination, draw opposite The track σ answered1k(k=1,2 ..., m), wherein, σ1k=(a1k,e1k,i1k1k1k,M1k)T(be track semi-major axis, Eccentricity, inclination angle, the red footpath of ascending node, argument of perigee, mean anomaly).
Respectively by DkWithIn each potential relating dot carry out just orbit determination (due to time span It is long, consider Perturbation Effect in orbit determination), draw orbital tracking σ3k(k=1,2 ..., l), wherein,
σ3k=(a3k,e3k,i3k3k3k,M3k)T
2) benchmark track radical selects
If m ≠ 0, that is, gatherNon-NULL, because the potential associated data of the first kind concentrates member It is plain minimum, respectively with σ1k(k=1,2 ..., m) on the basis of, screen σ3k(k=1,2 ..., l).It is as follows to screen principle:
Δ a, Δ i, Δ Ω are respectively semi-major axis of orbit, orbit inclination angle, screening thresholding (the big rootlet of thresholding in the red footpath of ascending node Consider to set according to combined factors such as track motion characteristic, orbit determination accuracies).
Because of σ3k(k=1,2 ..., n) used by orbital tracking the span data time is grown, therefore precision is higher than σ1k(k=1, 2,...,m).Select σ3kIn with σ1kThe orbital tracking to match is as benchmark track radical
If m=0, that is, gatherFor sky, then σ3kInstitute's rail in (k=1,2 ..., l) Road radical is benchmark track radical,
Step 3:The foundation of the potential incidence set in the second level.
The data point that all participation benchmark track radicals determine is established into setI.e. second The potential incidence set of level.
Step 4:" line-of-sight course " Orbit Matching.
" line-of-sight course " refers to according to track motion characteristic, if three points (one liter of two drop or two liter one there are different segmental arcs Drop), and two groups one rise and one drop data point orbit determination (since time span is grown, considering Perturbation Effect in orbit determination) result satisfaction With principle, then it is relating dot to define three points.
The step is mainly the orbital characteristics according to potential associated 2 points, in the potential incidence set of the second classIn, lookup is associated thirdly.
1) " thirdly " calculating of track
(taken a little according to each corresponding data point of benchmark track radical successivelyWherein), respectively WillWith Pd2(Dk) in all the points carry out orbit determination (due to time span grow, consider Perturbation Effect in orbit determination), obtain errant root Number σ2k(k=1,2 ..., l),
Wherein, σ2k=(a2k,e2k,i2k2k2k,M2k)T
2) Orbit extrapolation
Due to σ2kWithTrack epoch time is different, can not compare.Quickly extrapolated principle according to track, it is main to consider ground The aspherical gravitation J of ball2The influence of item, by σ2k(corresponding epoch time t2k) be extrapolated to it is matchedCorresponding epoch time t3kRadicalPrinciple of extrapolating is as follows:
Wherein, aeFor earth radius, C20=-J2=0.0010826
3) calculating of track true anomaly
E-e sinE=M (13)
Wherein, M is track mean anomaly, and E is eccentric anomaly, and f is true anomaly, and e is eccentricity.
4) " line-of-sight course " Orbit Matching
" 3 points " in " line-of-sight course " are respectively from three data in the first kind, the second class, the potential incidence set of three classes Point, calculates, track more thanIt is by the track that two data points determine in the second class, the potential incidence set of three classes, rail RoadIt is by the track that two data points determine in the first kind, the potential incidence set of three classes, and the potential incidence number strong point of three classes For same data point.Matched by 3 points of two groups determined tracks, matching principle is as follows:
Δ a, Δ i, Δ Ω, Δ f are respectively semi-major axis of orbit, orbit inclination angle, the red footpath of ascending node, the screening door of true anomaly Limit (thresholding size considers to set according to combined factors such as track motion characteristic, orbit determination accuracies).
Step 5:The foundation of final association matched data collection.
1) according to step 4, by all orbital trackings for meeting matching principleWithIn used data point, establish The potential incidence set P of the third leveld123(Dk)={ D1,D2…,Dx, then Pd123For DkFinal association matched data collection { P (Dk)}。
2) { A }={ A }-{ P (D are madek), repeat step one, carries out the association matching of other data points, if data in { A } Element number is less than 3, then matching terminates.
With the data instance of a certain observation station:
The position of a certain observation station is set, the instantaneous orbit data of more than 5000 a extraterrestrial targets is given, utilizes Precise Orbit Extrapolating model carries out extrapolation 2 days, and each segmental arc of observing obtains two point datas, and two point data time intervals are 20 seconds or so, generation Measurement data (ranging, azimuth, pitch angle) under survey station coordinate system, adds certain random difference and System level gray correlation (ranging:At random Poor 50m, System level gray correlation 20m;Azimuth and pitch angle:0.01 ° of random difference, 0.01 ° of System level gray correlation).
Observe station location such as table 1:
The earth coordinates position of 1 survey station of table
Geodetic longitude (degree) Geodetic latitude (degree) Geodetic altitude (rice)
107.5 25.0 500.0
Simulation observation data are as follows:
Data always points 65312, and all data are ranked up according to time point.
Wherein, numbering is all data points such as table 2 in 2 days of 1584 targets, and with first point (sequence number 1) of 1584 targets For reference point, to search coupling number strong point associated with it.
The data point in target 2 days that the numbering of table 2 is 1584
Target designation Sequence number Time Ranging (rice) Azimuth (degree) Pitch angle (degree)
1584 1 2010-12-6 6:24:59.562 885619.304 97.432 58.355
1584 2 2010-12-6 6:25:17.698 871125.335 80.337 60.261
1584 3 2010-12-6 18:42:7.487 1127873.662 84.039 9.098
1584 4 2010-12-6 18:42:25.461 1109476.951 92.902 40.097
1584 5 2010-12-7 7:27:19.123 1453176.950 269.509 26.594
1584 6 2010-12-7 7:27:37.266 1473215.146 275.407 25.997
1584 7 2010-12-7 19:44:26.659 1131971.385 275.980 38.880
1585 8 2010-12-7 19:44:44.625 1155733.717 267.562 37.644
It is as follows to associate matching primitives step:
1) according to step 1, all data are carried out with thick matching classification.
In order to improve classification speed, the class condition first by all data according to the first kind, carries out same segmental arc and confirms.If DkThe data at=1,584 first time point
Classification results are as follows:
First kind Pd1(Dk) potential incidence set be:
The potential incidence set of the target first kind that the numbering of table 3 is 1584
Second class Pd2(Dk) potential incidence set be:
The potential incidence set of the second class of target that the numbering of table 4 is 1584
Three classes Pd3(Dk) potential incidence set be:
The potential incidence set of target three classes that the numbering of table 5 is 1584
2) determined according to step 2, benchmark track:
First kind Pd1(Dk) just orbit determination result be:
The first orbit determination result of the target first kind that the numbering of table 6 is 1584
Epoch time (during Beijing) A (rice) e i(°) Ω(°) ω(°) M(°)
2010-12-6 6:24:59.56201 7142267.300 0.00080492664 98.574 286.536 276.490 108.032
In view of orbit determination accuracy and arithmetic speed is improved, in Pd3(Dk) in only calculate data point of the time span in 1 day Orbit determination, according to Dk=1584 (1) and Pd3(Dk) orbit determination result and Pd1(Dk) matching, show that 9 benchmark track roots are not as follows:
The target fiducials track that the numbering of table 7 is 1584
3) according to step 3, the foundation of the potential incidence set in the second level
According to data point () used by benchmark track in table 7, the potential incidence set P in the second level is established at 18 pointsd13(Dk)
4) according to step 4, Orbit Matching is carried out according to " line-of-sight course "
Respectively according to benchmark track in Pd2(Dk) in find thirdly, calculating is learnt, in table 7 only have the 9th group find matching Thirdly, i.e., the data that target designation is 1584 in table 4.Orbit Matching result is as follows:
Table 8 " line-of-sight course " Orbit Matching result
Again using second group of track in table 8 as benchmark track, in Pd3(Dk) in more than searching the 3rd in the data of 1 day Point, finds another group of data point, i.e., data time point is 2010-12-7 19 in table 5:44:26.660 1584 data.
5) according to step 4 as a result, obtaining 1584 Data Matching result such as table 9:
Table 9 associates matching result
Target designation Sequence number Time Ranging (rice) Azimuth (degree) Pitch angle (degree)
1584 1 2010-12-6 6:24:59.562 885619.304 97.432 58.355
1584 2 2010-12-6 6:25:17.698 871125.335 80.337 60.261
1584 3 2010-12-6 18:42:7.487 1127873.662 84.039 9.098
1584 4 2010-12-6 18:42:25.461 1109476.951 92.902 40.097
1584 5 2010-12-7 7:27:19.123 1453176.950 269.509 26.594
1584 6 2010-12-7 7:27:37.266 1473215.146 275.407 25.997
1584 7 2010-12-7 19:44:26.659 1131971.385 275.980 38.880
1584 8 2010-12-7 19:44:44.625 1155733.717 267.562 37.644
It can be seen from the above that table 9 and 2 result of table are completely the same, matching accuracy is 100%.
For the practicality of verification method, and further verified by measured data.Obtained using some observation station in 2 days The part measurement data taken, 2 point datas (including 10 degree of ranging, angle measurement and position angle left sides are selected in each observation segmental arc It is right), the data mixing that will be singled out is together.Calculating process is same to emulate data.
Total statistical result such as table 10:
The association matching statistical result of table 10
Data type Total points Total segmental arc number Association matches positive exact figures Association matching accuracy
Emulate data 65312 32656 62702 96.0%
Measured data 1532 766 1397 91.2%

Claims (1)

1. a kind of supersparsity radar data associates matching process, it is characterised in that comprises the following steps:
Step 1: select Arbitrary Digit strong point D in not associated data point complete or collected works { A }k, with any one data point D in { A }jCarry out Potential associative classification;DkCorresponding observation time is t (Dk), locus is r (Dk), DjCorresponding observation time is t (Dj), it is empty Meta is set to r (Dj);Δh、Δt、Height, time, the angle respectively set screens thresholding;
If 1)K ≠ j, then DjFor DkThe potential associated data of the first kind, add potential incidence setIn, wherein,For r (Dk)、r(Dj) angle,tΔFor DjAnd DkWhen relatively a certain observation station is located at same segmental arc, from r (Dk) arrive r (Dj) required theoretical time,B is observation station latitude, the flat movement velocity of trackμ draws for the earth The i of force constant, latitude argument u and orbit inclination angle meetsR(Rx,Ry,Rz) it is r (Dk)、r(Dj) institute it is in-orbit The unit normal direction in road face, andCosi=Rz
If 2)K ≠ j, then DjFor DkThe potential associated data of the second class, add potential incidence setIn, wherein, t 'ΔFor DjAnd DkIt is all rail lift or drop rail after relatively a certain observation station interval multi-turn During mode, from r (Dk) arrive r (Dj) required theoretical time, t 'Δ=sT, Round numbers afterwards;
If 3)Then DjFor DkThe potential associated data of three classes, add potential incidence setIn;
Step 2: respectively by DkWithIn each potential relating dot carry out just orbit determination, draw Corresponding orbital tracking σ1m, wherein, σ1m=(a1m,e1m,i1m1m1m,M1m)T, be the semi-major axis of track, eccentricity, Inclination angle, the red footpath of ascending node, argument of perigee, mean anomaly;
Respectively by DkWithIn each potential relating dot carry out just orbit determination, draw orbital tracking σ3n, Wherein, σ3n=(a3n,e3n,i3n3n3n,M3n)T, be track the red footpath of semi-major axis, eccentricity, inclination angle, ascending node, near Place argument, mean anomaly;
If setNon-NULL, respectively with σ1mOn the basis of screen σ3n, it is as follows to screen principle:
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mo>|</mo> <msub> <mi>a</mi> <mrow> <mn>1</mn> <mi>m</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mn>3</mn> <mi>n</mi> </mrow> </msub> <mo>|</mo> <mo>&amp;le;</mo> <mi>&amp;Delta;</mi> <mi>a</mi> </mtd> </mtr> <mtr> <mtd> <mo>|</mo> <msub> <mi>i</mi> <mrow> <mn>1</mn> <mi>m</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>i</mi> <mrow> <mn>3</mn> <mi>n</mi> </mrow> </msub> <mo>|</mo> <mo>&amp;le;</mo> <mi>&amp;Delta;</mi> <mi>i</mi> </mtd> </mtr> <mtr> <mtd> <mo>|</mo> <msub> <mi>&amp;Omega;</mi> <mrow> <mn>1</mn> <mi>m</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>&amp;Omega;</mi> <mrow> <mn>3</mn> <mi>n</mi> </mrow> </msub> <mo>|</mo> <mo>&amp;le;</mo> <mi>&amp;Delta;</mi> <mi>&amp;Omega;</mi> </mtd> </mtr> </mtable> </mfenced>
Δ a, Δ i, Δ Ω are respectively the red footpath screening thresholding of semi-major axis of orbit, orbit inclination angle, the ascending node set;
Select σ3nIn with σ1mThe orbital tracking to match is as benchmark track radical
If setFor sky, then σ3nIn all orbital trackings be benchmark track radical,
Step 3: the data point that all participation benchmark track radicals are determined establishes set That is the potential incidence set in the second level;
Step 4: according to potential associated 2 points of orbital characteristics, in the potential incidence set of the second class In, lookup is associated thirdly:
1) successively according to each corresponding data point of benchmark track radical Respectively willWith Pd2(Dk) Middle all the points carry out orbit determination, draw orbital tracking σ2l, σ2l=(a2l,e2l,i2l2l2l,M2l)T, be track half length Axis, eccentricity, inclination angle, the red footpath of ascending node, argument of perigee, mean anomaly;
2) by σ2lIt is extrapolated to matchedCorresponding radicalPrinciple of extrapolating is as follows:
Wherein, aeFor earth radius, C20=0.0010826;
3) by E-esinE=M andThe f of the true anomaly of track is calculated, wherein, M is the flat near point of track Angle, E are eccentric anomaly, and e is eccentricity;
4) using line-of-sight course to determining respectively from three data points in the first kind, the second class, the potential incidence set of three classes Two groups of tracks are matched, and matching principle is as follows:
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mo>|</mo> <msubsup> <mi>a</mi> <mrow> <mn>2</mn> <mi>l</mi> </mrow> <mo>*</mo> </msubsup> <mo>-</mo> <msubsup> <mi>a</mi> <mrow> <mn>3</mn> <mi>n</mi> </mrow> <mo>*</mo> </msubsup> <mo>|</mo> <mo>&amp;le;</mo> <mi>&amp;Delta;</mi> <mi>a</mi> </mtd> </mtr> <mtr> <mtd> <mo>|</mo> <msubsup> <mi>i</mi> <mrow> <mn>2</mn> <mi>l</mi> </mrow> <mo>*</mo> </msubsup> <mo>-</mo> <msubsup> <mi>i</mi> <mrow> <mn>3</mn> <mi>n</mi> </mrow> <mo>*</mo> </msubsup> <mo>|</mo> <mo>&amp;le;</mo> <mi>&amp;Delta;</mi> <mi>i</mi> </mtd> </mtr> <mtr> <mtd> <mo>|</mo> <msubsup> <mi>&amp;Omega;</mi> <mrow> <mn>2</mn> <mi>l</mi> </mrow> <mo>*</mo> </msubsup> <mo>-</mo> <msubsup> <mi>&amp;Omega;</mi> <mrow> <mn>3</mn> <mi>n</mi> </mrow> <mo>*</mo> </msubsup> <mo>|</mo> <mo>&amp;le;</mo> <mi>&amp;Delta;</mi> <mi>&amp;Omega;</mi> </mtd> </mtr> <mtr> <mtd> <mo>|</mo> <msubsup> <mi>&amp;omega;</mi> <mrow> <mn>2</mn> <mi>l</mi> </mrow> <mo>*</mo> </msubsup> <mo>+</mo> <msubsup> <mi>f</mi> <mrow> <mn>2</mn> <mi>l</mi> </mrow> <mo>*</mo> </msubsup> <mo>-</mo> <msubsup> <mi>&amp;omega;</mi> <mrow> <mn>3</mn> <mi>n</mi> </mrow> <mo>*</mo> </msubsup> <mo>-</mo> <msubsup> <mi>f</mi> <mrow> <mn>3</mn> <mi>n</mi> </mrow> <mo>*</mo> </msubsup> <mo>|</mo> <mo>&amp;le;</mo> <mi>&amp;Delta;</mi> <mi>f</mi> </mtd> </mtr> </mtable> </mfenced>
Δ a, Δ i, Δ Ω, Δ f are respectively semi-major axis of orbit, orbit inclination angle, the red footpath of ascending node, the true anomaly screening door set Limit;
Step 5: by all orbital trackings for meeting matching principleWithIn used data point, establish the potential pass of the third level Connection collection Pd123(Dk)={ D1, D2..., Dz, then Pd123For DkFinal association matched data collection { P (Dk)};Make { A }={ A }-{ P (Dk), return to step one, carries out the association matching of other data points, if data element number is less than 3 in { A }, matching knot Beam.
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