CN110135451B - Flight path clustering method based on distance from point to line segment set - Google Patents

Flight path clustering method based on distance from point to line segment set Download PDF

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CN110135451B
CN110135451B CN201910235711.XA CN201910235711A CN110135451B CN 110135451 B CN110135451 B CN 110135451B CN 201910235711 A CN201910235711 A CN 201910235711A CN 110135451 B CN110135451 B CN 110135451B
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肖英超
续龙飞
马园园
丁辉
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China Electric Rice Information System Co ltd
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Abstract

The invention discloses a flight path clustering method based on a point-to-line segment set distance, which measures the similarity degree between flight paths by using a user-defined distance; for each pair of tracks in all the track data, calculating the distance between the tracks, mainly comprising: calculating the distance from each track point to the other track, synthesizing the distances corresponding to all the track points to obtain the one-way distance from one track to the other track, and synthesizing the one-way distances of the two tracks to calculate the two-way distance between the tracks; calculating to obtain a similarity matrix according to the two-way distance matrix; clustering all tracks according to the similarity matrix; and obtaining the clustering label of each flight path. The invention can realize automatic clustering of the flight paths, the measurement of the flight path distance is more reasonable, and the clustering result is more in line with the actual situation. The method has wide application prospect in the fields of air traffic management and airspace planning.

Description

Flight path clustering method based on distance from point to line segment set
Technical Field
The invention relates to a flight path clustering method, in particular to a flight path clustering method based on a point-to-line segment set distance.
Background
In recent years, economic development promotes rapid increase of travel demands of people, so that traffic flow is more complex and congestion is increasingly serious. To solve this problem, in the intelligent transportation system, a large number of various positioning devices are put into use, such as sensor networks, GPS, mobile phones, etc., which makes it more and more convenient to acquire real-time position information of a mobile object. For air traffic, a large number of surveillance devices, such as radar, ADS-B, etc., provide track data for the aircraft's motion. In the air traffic management, track clustering analysis is carried out on track data describing the actual geographic position of the flight, valuable flight modes are mined, the airspace capacity is improved, and the number of flights is increased on the premise of ensuring the safety.
In the problem of track clustering, the measurement of the distance between tracks is a key point, and is the basis of a clustering method. The track data comprises a series of chronologically arranged points. A simple distance metric is to calculate the sum of the euclidean distances between all pairs of track points. Although the euclidean distance is simple to calculate, it requires that the two tracks have the same length. The requirement of the euclidean distance is difficult to meet because the tracks are usually not fixed in length, i.e. the number of tracks of two tracks will be different. The Hausdorff distance treats tracks as a set of track points, and converts the distance between tracks into the distance between the sets of points, but it does not consider the time sequence of the track points. Dynamic Time Warping (DTW) repeats track points by warping time to expand the track, thereby calculating the distance of two tracks. The repetition of this distance on track points destroys the geometric distribution of the track to some extent. The longest common subsequence (LCSS) distance measures the distance between two waypoints by finding their longest common part, but it requires a parameter to determine whether the two waypoints match, and the setting of the parameter is not simple and requires repeated debugging. For the above reasons, these track distances do not perform well in track clustering.
Disclosure of Invention
The invention aims to provide a track clustering method based on a point-to-line set distance, which fully considers the geometric relationship between a track point and a track line segment, thereby better measuring the track distance.
The technical solution for realizing the purpose of the invention is as follows: a flight path clustering method based on a point-to-line segment set distance comprises the following steps:
step 1, acquiring a track data set TR (T) to be clusterediI-1, …, N }, from which a pair of tracks, i.e. the ith track T, is selectediAnd jth track TjAnd i is not equal to j, and N takes the value of a natural number;
step 2, respectively integrating to obtain the ith track TiSet of line segments of (1) and the jth track TjA set of line segments of (a);
step 3, calculating the ith track TiFrom each track point to the jth track TjThe distance of the line segment set of (1) is calculated to the jth track TjFrom each track point to the ith track TiThe distance of the line segment set of (a);
step 4, calculating to obtain the ith track T according to the distance obtained in the step 3iTo jth track TjAnd the jth track TjTo ith track TiThe unidirectional distance of (a);
step 5, calculating the ith track T according to the one-way distanceiAnd jth track TjOf (2) a bidirectional distance DHseg(Ti,Tj)；
Step 6, obtaining a distance matrix D according to the two-way distance;
step 7, transforming the matrix D to obtain a similarity matrix S;
and 8, obtaining the clustering label of each track according to the similarity matrix S.
In the invention, the step 2 comprises the following steps:
for ith track TiThe set of track points contained in
Figure BDA0002008119520000021
The line segment set is obtained by integration
Figure BDA0002008119520000022
Line segment set
Figure BDA0002008119520000023
The k line segment of
Figure BDA0002008119520000024
With the ith track TiThe kth course point of (1)
Figure BDA0002008119520000025
And the (k + 1) th track point
Figure BDA0002008119520000026
As an endpoint;
for jth track TjThe set of track points contained in
Figure BDA0002008119520000027
The line segment set is obtained by integration
Figure BDA0002008119520000028
Line segment set
Figure BDA0002008119520000029
The r-th line segment of
Figure BDA00020081195200000210
With jth track TjThe r-th track point in (1)
Figure BDA00020081195200000211
And the r +1 th track point
Figure BDA00020081195200000212
As endpoints.
In the present invention, step 3 comprises:
for jth track TjEach track point of
Figure BDA00020081195200000213
Calculating the r track point
Figure BDA00020081195200000214
To ith track TiSet of line segments
Figure BDA00020081195200000215
The distance between
Figure BDA00020081195200000216
Using the same method, for the ith track TiEach track point of
Figure BDA00020081195200000217
Calculating the kth track point
Figure BDA00020081195200000218
To jth track TjSet of line segments
Figure BDA00020081195200000219
Is a distance of
Figure BDA00020081195200000220
In step 3 of the invention, the jth track TjMiddle r track point
Figure BDA00020081195200000221
To ith track TiSet of line segments
Figure BDA00020081195200000222
The distance between
Figure BDA00020081195200000223
And ith track TiMiddle kth track point
Figure BDA00020081195200000224
To jth track TjSet of line segments
Figure BDA00020081195200000225
Is a distance of
Figure BDA00020081195200000226
The calculation process comprises the following steps:
step 3-1, calculating the jth track TjMiddle r track point
Figure BDA0002008119520000031
To ith track TiIs the k-th line segment in the line segment set
Figure BDA0002008119520000032
In a vertical projection of
Figure BDA0002008119520000033
Setting the jth track TjMiddle r track point
Figure BDA0002008119520000034
Has coordinates of (x, y), line segment
Figure BDA0002008119520000035
The coordinates of the two end points are respectively (x)1,y1) And (x)2,y2)，
Figure BDA0002008119520000036
Has the coordinates of (x)p,yp) Then, there are:
Figure BDA0002008119520000037
step 3-2, calculating the jth track TjMiddle r track point
Figure BDA0002008119520000038
To line segment
Figure BDA0002008119520000039
Is a distance of
Figure BDA00020081195200000310
Figure BDA00020081195200000311
Step 3-3, according to the vertical projection
Figure BDA00020081195200000312
Whether on line segment
Figure BDA00020081195200000313
Above, two sets are defined, if at line segment
Figure BDA00020081195200000314
Above, is defined as
Figure BDA00020081195200000315
Otherwise defined as a set
Figure BDA00020081195200000316
Figure BDA00020081195200000317
Step 3-4, calculating the jth track TjMiddle r track point
Figure BDA00020081195200000318
To ith track TiSet of line segments
Figure BDA00020081195200000319
The distance between
Figure BDA00020081195200000320
Figure BDA00020081195200000321
Step 3-5, calculating to obtain the ith track T by adopting the methods of the step 3-1 to the step 3-4iMiddle kth track point
Figure BDA00020081195200000322
To jth track TjSet of line segments
Figure BDA00020081195200000323
Is a distance of
Figure BDA00020081195200000324
In the invention, the step 4 comprises the following steps: according to distance
Figure BDA00020081195200000325
Calculate the ith TiTo jth TjOne-way distance D ofhseg(Ti,Tj) According to distance
Figure BDA00020081195200000326
Calculate the jth TjTo the ith TiOne-way distance D ofhseg(Tj,Ti)：
Figure BDA00020081195200000327
Figure BDA00020081195200000328
In step 5 of the present invention, the bidirectional distance D is calculated according to the following formulaHseg(Ti,Tj)：
DHseg(Ti,Tj)＝max{Dhseg(Ti,Tj),Dhseg(Tj,Ti)}。
In step 6 of the present invention, the element D in the ith row and the jth column of the distance matrix DijIs set to DHseg(Ti,Tj)。
In step 7 of the invention, the ith row and jth column elements S of the similarity matrix S are calculated according to the following formulaij：
Sij＝exp(-Dij/std(D))，
Where std (D) represents the standard deviation of all elements in matrix D.
In step 8 of the invention, a spectrum clustering algorithm is adopted for the similarity matrix S to obtain clustering labels of each flight path, and the specific steps comprise:
step 8-1, decomposing the S characteristic of the similarity matrix, wherein S is U Λ U-1Wherein U is a matrix composed of eigenvectors, Λ is a diagonal matrix composed of eigenvalues (ref: department of mathematics of university of the same society. linear algebra. fifth edition. Beijing: higher education Press, 2007: 117-;
step 8-2, k eigenvectors corresponding to the minimum k eigenvalues are taken from the matrix U to form a matrix U with N rows and k columnsmWhere k is the number of clusters;
step 8-3, adding UmRegarding the vectors as N k-dimensional vectors, a k-means clustering algorithm is adopted for the N k-dimensional vectors (reference: Zhou Shi machine learning. Beijing: Qing Hua university Press, 2016: 202-;
and 8-4, obtaining clustering labels of the N tracks.
Compared with the prior art, the invention has the following remarkable advantages: the distance between the flight path point and the flight path segment is fully considered, the distance between the point and the segment is expanded, the distance between the point on one flight path and the other flight path is measured, the distance between the flight paths is further calculated, and the geometric characteristics of the flight paths can be better reflected.
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The foregoing and other advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
FIG. 1 is a flow chart of the method for clustering tracks based on the distance from a point to a set of line segments according to the present invention.
FIG. 2 is a diagram of radar track data according to an embodiment of the present invention.
FIG. 3a shows the first type of result of the inventive method for track clustering.
FIG. 3b shows the second result of the inventive method for track clustering.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
With reference to fig. 1, the flight path clustering method based on the distance from a point to a line segment set of the present invention includes the following steps:
step 1: for a track data set TR to be clustered, TiI 1, …, N, from which a pair of tracks T is selectediAnd TjAnd ensure the track TiAnd TjWhere i ≠ j.
Step 2: for track TiThe set of track points contained in
Figure BDA0002008119520000051
The line segment set is obtained by integration
Figure BDA0002008119520000052
For track point
Figure BDA0002008119520000053
And
Figure BDA0002008119520000054
Figure BDA0002008119520000055
to be composed of
Figure BDA0002008119520000056
And
Figure BDA0002008119520000057
segment as end point, for track TjThe same operation is done.
And step 3: for track TjEach track point of
Figure BDA0002008119520000058
Calculating points
Figure BDA0002008119520000059
To TiLine segment set
Figure BDA00020081195200000510
The distance between
Figure BDA00020081195200000511
Distance between two adjacent plates
Figure BDA00020081195200000512
The calculation process of (2) is as follows:
(1) calculating points
Figure BDA00020081195200000513
To line segment
Figure BDA00020081195200000514
In a vertical projection of
Figure BDA00020081195200000515
Suppose that
Figure BDA00020081195200000516
Has the coordinates of (x, y),
Figure BDA00020081195200000517
the coordinates of the two end points are respectively (x)1,y1) And (x)2,y2)，
Figure BDA00020081195200000518
Has the coordinates of (x)p,yp) Then there is
Figure BDA00020081195200000519
(2) Calculating points
Figure BDA00020081195200000520
To line segment
Figure BDA00020081195200000521
Is a distance of
Figure BDA00020081195200000522
(3) According to
Figure BDA00020081195200000523
Whether or not to be at
Figure BDA00020081195200000524
Define two sets
Figure BDA00020081195200000525
Figure BDA00020081195200000526
(4) Dot
Figure BDA00020081195200000527
To track TiIs a distance of
Figure BDA00020081195200000528
Is calculated as follows
Figure BDA00020081195200000529
Similarly, for track TiEach track point of
Figure BDA0002008119520000061
Calculate to TjDistance of line segment set
Figure BDA0002008119520000062
Step 4：According to
Figure BDA0002008119520000063
Calculating TiTo TjOne-way distance D ofhseg(Ti,Tj) According to
Figure BDA0002008119520000064
Calculating TjTo TiOne-way distance D ofhseg(Tj,Ti)，Dhseg(Tj,Ti) Is calculated as follows
Figure BDA0002008119520000065
And 5: calculating T from the one-way distanceiAnd TjOf (2) a bidirectional distance DHseg(Ti,Tj)
DHseg(Ti,Tj)＝max{Dhseg(Ti,Tj),Dhseg(Tj,Ti)}
Step 6: obtaining a distance matrix D according to the two-way distance;
and 7: transforming the matrix D to obtain a similarity matrix S;
and 8: and (3) obtaining the clustering label of each flight path by adopting a spectrum clustering algorithm for the matrix S, wherein the spectrum clustering algorithm comprises the following specific steps:
(1) decomposing the characteristic of the matrix S, wherein S is U Λ U-1；
(2) Taking the least k (k is the number of clustering clusters) of eigenvectors corresponding to the eigenvalues,
Figure BDA0002008119520000066
(3) will UmConsidering N k-dimensional vectors, and adopting a k-means clustering algorithm for the N k-dimensional vectors;
(4) and obtaining clustering labels of the N tracks.
The present invention will be further explained by embodiments of simulation experiments and evaluation of effects thereof, with reference to fig. 2 to 3a and 3b (in the figures, positive directions of X and Y axes represent east and north, respectively, and distances are in meters).
In the present embodiment, as each curve corresponds to a flight path in fig. 2, the distribution of the flight path data represents two routes, one is straight flight and the other is broken flight. The experimental objective is to cluster the track data into two categories by a clustering method. Fig. 3a and 3b show the result of the flight path clustering of the inventive method, and the experimental data are clustered into two types, which are respectively plotted in fig. 3a and 3b, and the result shows that the two types of flight paths obtained by the inventive method are identical to the two routes of the flight paths, and the inventive method has good performance. In air traffic management, flight radar data between the same city pair sometimes show tracks as shown in fig. 2, however, actual operation data does not provide a clustering result of flight routes. Therefore, by adopting the flight path clustering method provided by the invention, flights flying in straight line and broken line can be distinguished in the post analysis, and the conditions of flight 'getting-off-bend and straight flight' are further analyzed by combining factors such as weather, control and the like at the time, so that the configuration of airspace resources is optimized or a more efficient flight directing strategy is excavated.
The present invention provides a flight path clustering method based on the distance from a point to a line segment set, and a plurality of methods and approaches for implementing the technical scheme are provided, the above description is only a preferred embodiment of the present invention, it should be noted that, for those skilled in the art, a plurality of improvements and embellishments can be made without departing from the principle of the present invention, and these improvements and embellishments should also be regarded as the protection scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.

Claims (5)

1. A flight path clustering method based on a point-to-line segment set distance is characterized by comprising the following steps:
step 1, acquiring a track data set TR (T) to be clusterediI-1, …, N }, from which a pair of tracks, i.e. the ith track T, is selectediAnd jth track TjAnd i is not equal to j, and N takes the value of a natural number;
step 2, respectively integrating to obtain the ith track TiSet of line segments of (1) and the jth track TjA set of line segments of (a);
step 3, calculating the ith track TiFrom each track point to the jth track TjThe distance of the line segment set of (1) is calculated to the jth track TjFrom each track point to the ith track TiThe distance of the line segment set of (a);
step 4, calculating to obtain the ith track T according to the distance obtained in the step 3iTo jth track TjAnd the jth track TjTo ith track TiThe unidirectional distance of (a);
step 5, calculating the ith track T according to the one-way distanceiAnd jth track TjOf (2) a bidirectional distance DHseg(Ti,Tj)；
Step 6, obtaining a distance matrix D according to the two-way distance;
step 7, transforming the matrix D to obtain a similarity matrix S;
step 8, obtaining clustering labels of each track according to the similarity matrix S;
the step 2 comprises the following steps:
for ith track TiThe set of track points contained in
Figure FDA0002457698330000011
The line segment set is obtained by integration
Figure FDA0002457698330000012
Line segment set
Figure FDA0002457698330000013
The k line segment of
Figure FDA0002457698330000014
With the ith track TiThe kth course point of (1)
Figure FDA0002457698330000015
And the (k + 1) th track point
Figure FDA0002457698330000016
As an endpoint;
for jth track TjThe set of track points contained in
Figure FDA0002457698330000017
The line segment set is obtained by integration
Figure FDA0002457698330000018
Line segment set
Figure FDA0002457698330000019
The r-th line segment of
Figure FDA00024576983300000110
With jth track TjThe r-th track point in (1)
Figure FDA00024576983300000111
And the r +1 th track point
Figure FDA00024576983300000112
As an endpoint;
the step 3 comprises the following steps:
for jth track TjEach track point of
Figure FDA00024576983300000113
Calculating the r track point
Figure FDA00024576983300000114
To ith track TiSet of line segments
Figure FDA00024576983300000115
The distance between
Figure FDA00024576983300000116
Using the same method, for the ith track TiEach track point of
Figure FDA00024576983300000117
Calculating the kth track point
Figure FDA00024576983300000118
To jth track TjSet of line segments
Figure FDA00024576983300000119
Is a distance of
Figure FDA00024576983300000120
In step 3, the jth track TjMiddle r track point
Figure FDA00024576983300000121
To ith track TiSet of line segments
Figure FDA0002457698330000021
The distance between
Figure FDA0002457698330000022
And ith track TiMiddle kth track point
Figure FDA0002457698330000023
To jth track TjSet of line segments
Figure FDA0002457698330000024
Is a distance of
Figure FDA0002457698330000025
The calculation process comprises the following steps:
step 3-1, calculating the jth track TjMiddle r track point
Figure FDA0002457698330000026
To ith track TiIs the k-th line segment in the line segment set
Figure FDA0002457698330000027
In a vertical projection of
Figure FDA0002457698330000028
Setting the jth track TjMiddle r track point
Figure FDA0002457698330000029
Has coordinates of (x, y), line segment
Figure FDA00024576983300000210
The coordinates of the two end points are respectively (x)1,y1) And (x)2,y2)，
Figure FDA00024576983300000211
Has the coordinates of (x)p,yp) Then, there are:
Figure FDA00024576983300000212
step 3-2, calculating the jth track TjMiddle r track point
Figure FDA00024576983300000213
To line segment
Figure FDA00024576983300000214
Is a distance of
Figure FDA00024576983300000215
Figure FDA00024576983300000216
Step 3-3, according to the vertical projection
Figure FDA00024576983300000217
Whether on line segment
Figure FDA00024576983300000218
Above, two sets are defined, if at line segment
Figure FDA00024576983300000219
Above, is defined as
Figure FDA00024576983300000220
Otherwise defined as a set
Figure FDA00024576983300000221
Figure FDA00024576983300000222
Step 3-4, calculating the jth track TjMiddle r track point
Figure FDA00024576983300000223
To ith track TiSet of line segments
Figure FDA00024576983300000224
The distance between
Figure FDA00024576983300000225
Figure FDA00024576983300000226
Step 3-5, calculating to obtain the ith track T by adopting the methods of the step 3-1 to the step 3-4iMiddle kth track point
Figure FDA00024576983300000227
To jth track TjSet of line segments
Figure FDA00024576983300000228
Is a distance of
Figure FDA00024576983300000229
Step 4 comprises the following steps: according to distance
Figure FDA00024576983300000230
Calculate the ith TiTo jth TjOne-way distance D ofhseg(Ti,Tj) According to distance
Figure FDA00024576983300000231
Calculate the jth TjTo the ith TiOne-way distance D ofhseg(Tj,Ti)：
Figure FDA0002457698330000031
Figure FDA0002457698330000032
2. The method of claim 1, wherein in step 5, the two-way distance D is calculated according to the following formulaHseg(Ti,Tj)：
DHseg(Ti,Tj)＝max{Dhseg(Ti,Tj),Dhseg(Tj,Ti)}。
3. The method according to claim 2, wherein in step 6, the element D in the ith row and the jth column of the distance matrix DijIs set to DHseg(Ti,Tj)。
4. The method according to claim 3, wherein in step 7, the i row and j column elements S of the similarity matrix S are calculated according to the following formulaij：
Sij＝exp(-Dij/std(D))，
Where std (D) represents the standard deviation of all elements in matrix D.
5. The method according to claim 4, wherein in step 8, a spectral clustering algorithm is applied to the similarity matrix S to obtain a clustering label of each track, and the specific steps include:
step 8-1, decomposing the characteristics of the similarity matrix S, wherein S is U Λ U-1Wherein U is a matrix composed of eigenvectors, and Λ is a diagonal matrix composed of eigenvalues;
step 8-2, k eigenvectors corresponding to the minimum k eigenvalues are taken from the matrix U to form a matrix U with N rows and k columnsmWhere k is the number of clusters;
step 8-3, adding UmRegarding the N k-dimensional vectors, and adopting a k-means clustering algorithm for the N k-dimensional vectors;
and 8-4, obtaining clustering labels of the N tracks.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9060018B1 (en) * 2014-02-05 2015-06-16 Pivotal Software, Inc. Finding command and control center computers by communication link tracking
CN104898104A (en) * 2015-06-10 2015-09-09 中国西安卫星测控中心 Target combined positioning method based on Euler's distance means clustering
CN105893621B (en) * 2016-04-29 2019-11-05 中国人民解放军海军航空大学 Goal behavior law mining method based on multidimensional track cluster
CN106646450B (en) * 2016-12-29 2019-04-12 中国人民解放军海军航空大学 Radar track robust correlating method based on distance substep cluster

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108256285A (en) * 2018-01-09 2018-07-06 上海交通大学 Flight path exception detecting method and system based on density peaks fast search

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