CN105911524B  Supersparsity radar data associates matching process  Google Patents
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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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Classifications

 G—PHYSICS
 G01—MEASURING; TESTING
 G01S—RADIO DIRECTIONFINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCEDETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
 G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
 G01S7/02—Details 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 " lineofsight course " matching principle of " first matching again orbit determination to match again ", classified to data, first orbit determination, " lineofsight course " Orbit Matching, the potential incidence set of stepsizing target, to achieve the purpose that data autocorrelation matching." lineofsight 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 multiturn supersparsity radar data in the case of prior information is efficiently solved, has filled up blank of the China in terms of supersparsity radar multiturn observes data correlation matching.
Description
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 oc 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 multirevolution 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 multirevolution 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；D_{k}Corresponding observation time is t (D_{k}), locus is r (D_{k}), D_{j}Corresponding observation time is t
(D_{j}), locus is r (D_{j})；Δh、Δt、Height, time, the angle respectively set screens thresholding；
If 1)K ≠ j, then D_{j}For D_{k}The potential associated data of the first kind, add potential pass
Connection collectionIn, wherein,For r (D_{k})、r(D_{j}) angle,
t_{Δ}For D_{j}And D_{k}When relatively a certain observation station is located at same segmental arc, from r (D_{k}) arrive r (D_{j}) 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(R_{x},R_{y},R_{z}) it is r (D_{k})、r(D_{j}) institute it is inorbit
The unit normal direction in road face, and
If 2)K ≠ j, then D_{j}For D_{k}The potential associated data of the second class, add potential association
CollectionIn, wherein, t '_{Δ}For D_{j}And D_{k}It is all rail lift or drop rail after relatively a certain observation station interval multiturn
During mode, from r (D_{k}) arrive r (D_{j}) required theoretical time, t '_{Δ}=sT,
Round numbers afterwards；
If 3)Then D_{j}For D_{k}The potential associated data of three classes, add potential incidence setIn；
Step 2: respectively by D_{k}WithIn each potential relating dot carry out just orbit determination,
Draw corresponding track σ_{1m}, wherein, σ_{1m}=(a_{1m},e_{1m},i_{1m},Ω_{1m},ω_{1m},M_{1m})^{T}, be the semimajor axis of track, eccentricity,
Inclination angle, the red footpath of ascending node, argument of perigee, mean anomaly；
Respectively by D_{k}WithIn each potential relating dot carry out just orbit determination, draw orbital tracking
σ_{3n}, wherein, σ_{3n}=(a_{3n},e_{3n},i_{3n},Ω_{3n},ω_{3n},M_{3n})^{T}, be track the red footpath of semimajor axis, eccentricity, inclination angle, ascending node,
Argument of perigee, mean anomaly；
If setNonNULL, respectively with σ_{1m}On the basis of screen σ_{3n}, screening principle is such as
Under：
Δ a, Δ i, Δ Ω are respectively the red footpath screening thresholding of semimajor axis of orbit, orbit inclination angle, the ascending node set；
Select σ_{3n}In with σ_{1m}The orbital tracking to match is as benchmark track radical
If setFor sky, then σ_{3n}In 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 P_{d2}
(D_{k}) in all the points carry out orbit determination, draw orbital tracking σ_{2l}, σ_{2l}=(a_{2l},e_{2l},i_{2l},Ω_{2l},ω_{2l},M_{2l})^{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 σ_{2l}It is extrapolated to matchedCorresponding radicalPrinciple of extrapolating is as follows：
Wherein, a_{e}For earth radius, C_{20}=0.0010826；
3) by EesinE=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 lineofsight 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 semimajor 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 P_{d123}(D_{k})={ D_{1},D_{2}…,D_{x}, then P_{d123}For D_{k}Final association matched data collection { P (D_{k})}；Make { A }=
{A}{P(D_{k}), 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：" lineofsight 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 multiturn supersparsity radar data in the case of prior information and ask
Topic, has filled up blank of the China in terms of supersparsity radar multiturn 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 " lineofsight course " matching principle of " first matching again orbit determination to match again "
Class, first orbit determination, " lineofsight course " Orbit Matching, the potential incidence set of stepsizing 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 multiturn (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) multiturn (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 (D_{k}), space bit
Put r (D_{k})), with any one data point D in { A }_{j}(corresponding observation time is t (D_{j}), locus r (D_{j})) 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, D_{j}For D_{k}The potential associated data of the first kind, add potential incidence setIn.
Wherein,For r (D_{k})、r(D_{j}) angle：
t_{Δ}For from point r (D_{k}) arrive r (D_{j}) required theoretical time under first kind mode：
(3) in formula, μ is Gravitational coefficient of the Earth, R_{e}For 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(R_{x},R_{y},R_{z}) it is r (D_{k})、r(D_{j}) where orbital plane unit normal direction, and
Cosi=R_{z} (6)
If 2) meet formula (7) screening conditions, for D_{k}The potential associated data of the second class, add potential incidence setIn.
t′_{Δ}For from point r (D_{k}) arrive r (D_{j}) required theoretical time under the second class mode：
t′_{Δ}=sT (8)
Wherein,(s is integer).
If 3) meet formula (9) screening conditions, for D_{k}The 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 D_{k}WithIn each potential relating dot carry out just orbit determination, draw opposite
The track σ answered_{1k}(k=1,2 ..., m), wherein, σ_{1k}=(a_{1k},e_{1k},i_{1k},Ω_{1k},ω_{1k},M_{1k})^{T}(be track semimajor axis,
Eccentricity, inclination angle, the red footpath of ascending node, argument of perigee, mean anomaly).
Respectively by D_{k}WithIn 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 σ_{3k}(k=1,2 ..., l), wherein,
σ_{3k}=(a_{3k},e_{3k},i_{3k},Ω_{3k},ω_{3k},M_{3k})^{T}。
2) benchmark track radical selects
If m ≠ 0, that is, gatherNonNULL, because the potential associated data of the first kind concentrates member
It is plain minimum, respectively with σ_{1k}(k=1,2 ..., m) on the basis of, screen σ_{3k}(k=1,2 ..., l).It is as follows to screen principle：
Δ a, Δ i, Δ Ω are respectively semimajor 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 σ_{3k}(k=1,2 ..., n) used by orbital tracking the span data time is grown, therefore precision is higher than σ_{1k}(k=1,
2,...,m).Select σ_{3k}In with σ_{1k}The orbital tracking to match is as benchmark track radical
If m=0, that is, gatherFor sky, then σ_{3k}Institute'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：" lineofsight course " Orbit Matching.
" lineofsight 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 P_{d2}(D_{k}) in all the points carry out orbit determination (due to time span grow, consider Perturbation Effect in orbit determination), obtain errant root
Number σ_{2k}(k=1,2 ..., l),
Wherein, σ_{2k}=(a_{2k},e_{2k},i_{2k},Ω_{2k},ω_{2k},M_{2k})^{T}。
2) Orbit extrapolation
Due to σ_{2k}WithTrack epoch time is different, can not compare.Quickly extrapolated principle according to track, it is main to consider ground
The aspherical gravitation J of ball_{2}The influence of item, by σ_{2k}(corresponding epoch time t_{2k}) be extrapolated to it is matchedCorresponding epoch time
t_{3k}RadicalPrinciple of extrapolating is as follows：
Wherein, a_{e}For earth radius, C_{20}=J_{2}=0.0010826
3) calculating of track true anomaly
Ee sinE=M (13)
Wherein, M is track mean anomaly, and E is eccentric anomaly, and f is true anomaly, and e is eccentricity.
4) " lineofsight course " Orbit Matching
" 3 points " in " lineofsight 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 semimajor 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 level_{d123}(D_{k})={ D_{1},D_{2}…,D_{x}, then P_{d123}For D_{k}Final association matched data collection { P (D_{k})}。
2) { A }={ A }{ P (D are made_{k}), 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  2010126 6:24:59.562  885619.304  97.432  58.355 
1584  2  2010126 6:25:17.698  871125.335  80.337  60.261 
1584  3  2010126 18:42:7.487  1127873.662  84.039  9.098 
1584  4  2010126 18:42:25.461  1109476.951  92.902  40.097 
1584  5  2010127 7:27:19.123  1453176.950  269.509  26.594 
1584  6  2010127 7:27:37.266  1473215.146  275.407  25.997 
1584  7  2010127 19:44:26.659  1131971.385  275.980  38.880 
1585  8  2010127 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
D_{k}The data at=1,584 first time point
Classification results are as follows：
First kind P_{d1}(D_{k}) potential incidence set be：
The potential incidence set of the target first kind that the numbering of table 3 is 1584
Second class P_{d2}(D_{k}) potential incidence set be：
The potential incidence set of the second class of target that the numbering of table 4 is 1584
Three classes P_{d3}(D_{k}) 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 P_{d1}(D_{k}) 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(°) 
2010126 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 P_{d3}(D_{k}) in only calculate data point of the time span in 1 day
Orbit determination, according to D_{k}=1584 (1) and P_{d3}(D_{k}) orbit determination result and P_{d1}(D_{k}) 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 points_{d13}(D_{k})
4) according to step 4, Orbit Matching is carried out according to " lineofsight course "
Respectively according to benchmark track in P_{d2}(D_{k}) 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 " lineofsight course " Orbit Matching result
Again using second group of track in table 8 as benchmark track, in P_{d3}(D_{k}) 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 2010127 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  2010126 6:24:59.562  885619.304  97.432  58.355 
1584  2  2010126 6:25:17.698  871125.335  80.337  60.261 
1584  3  2010126 18:42:7.487  1127873.662  84.039  9.098 
1584  4  2010126 18:42:25.461  1109476.951  92.902  40.097 
1584  5  2010127 7:27:19.123  1453176.950  269.509  26.594 
1584  6  2010127 7:27:37.266  1473215.146  275.407  25.997 
1584  7  2010127 19:44:26.659  1131971.385  275.980  38.880 
1584  8  2010127 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 }_{j}Carry out
Potential associative classification；D_{k}Corresponding observation time is t (D_{k}), locus is r (D_{k}), D_{j}Corresponding observation time is t (D_{j}), it is empty
Meta is set to r (D_{j})；Δh、Δt、Height, time, the angle respectively set screens thresholding；
If 1)K ≠ j, then D_{j}For D_{k}The potential associated data of the first kind, add potential incidence setIn, wherein,For r (D_{k})、r(D_{j}) angle,t_{Δ}For
D_{j}And D_{k}When relatively a certain observation station is located at same segmental arc, from r (D_{k}) arrive r (D_{j}) 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(R_{x},R_{y},R_{z}) it is r (D_{k})、r(D_{j}) institute it is inorbit
The unit normal direction in road face, andCosi=R_{z}；
If 2)K ≠ j, then D_{j}For D_{k}The potential associated data of the second class, add potential incidence setIn, wherein, t '_{Δ}For D_{j}And D_{k}It is all rail lift or drop rail after relatively a certain observation station interval multiturn
During mode, from r (D_{k}) arrive r (D_{j}) required theoretical time, t '_{Δ}=sT,
Round numbers afterwards；
If 3)Then D_{j}For D_{k}The potential associated data of three classes, add potential incidence setIn；
Step 2: respectively by D_{k}WithIn each potential relating dot carry out just orbit determination, draw
Corresponding orbital tracking σ_{1m}, wherein, σ_{1m}=(a_{1m},e_{1m},i_{1m},Ω_{1m},ω_{1m},M_{1m})^{T}, be the semimajor axis of track, eccentricity,
Inclination angle, the red footpath of ascending node, argument of perigee, mean anomaly；
Respectively by D_{k}WithIn each potential relating dot carry out just orbit determination, draw orbital tracking σ_{3n},
Wherein, σ_{3n}=(a_{3n},e_{3n},i_{3n},Ω_{3n},ω_{3n},M_{3n})^{T}, be track the red footpath of semimajor axis, eccentricity, inclination angle, ascending node, near
Place argument, mean anomaly；
If setNonNULL, respectively with σ_{1m}On the basis of screen σ_{3n}, it is as follows to screen principle：
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Δ a, Δ i, Δ Ω are respectively the red footpath screening thresholding of semimajor axis of orbit, orbit inclination angle, the ascending node set；
Select σ_{3n}In with σ_{1m}The orbital tracking to match is as benchmark track radical
If setFor sky, then σ_{3n}In 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 P_{d2}(D_{k})
Middle all the points carry out orbit determination, draw orbital tracking σ_{2l}, σ_{2l}=(a_{2l},e_{2l},i_{2l},Ω_{2l},ω_{2l},M_{2l})^{T}, be track half length
Axis, eccentricity, inclination angle, the red footpath of ascending node, argument of perigee, mean anomaly；
2) by σ_{2l}It is extrapolated to matchedCorresponding radicalPrinciple of extrapolating is as follows：
Wherein, a_{e}For earth radius, C_{20}=0.0010826；
3) by EesinE=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 lineofsight 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：
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Δ a, Δ i, Δ Ω, Δ f are respectively semimajor 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 P_{d123}(D_{k})={ D_{1}, D_{2}..., D_{z}, then P_{d123}For D_{k}Final association matched data collection { P (D_{k})}；Make { A }={ A }{ P
(D_{k}), 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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