CN116660951A - Track association method based on motion trend - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
Abstract
The invention relates to a track association method based on a motion trend, and belongs to the technical field of passive positioning and tracking. The satellite-borne passive positioning system has lower positioning precision on the target, and when different satellite platforms carry out track association on maneuvering targets (such as aircrafts), the track feature extraction faces new challenges. Aiming at the problem, the invention takes the whole track of the maneuvering target as a research object, extracts the motion trend of the track of the maneuvering target as a track feature through empirical mode decomposition, and the whole motion trend can embody the purpose of the motion of the section no matter how the target maneuvers, so the motion trend of the track of the maneuvering target embodies the key feature of the track of the maneuvering target; and for the target tracks acquired by different satellite platforms, under the condition that track points are not aligned due to different sampling rates and sampling moments, synchronous processing is carried out among different aircraft track sequences by using a dynamic time warping method.
Description
Technical Field
The invention relates to a track association method based on a motion trend, belongs to the technical field of passive positioning and tracking, and particularly relates to a heterogeneous track association method.
Background
In passive positioning and tracking application, the positioning accuracy of the target is low, so that the motion characteristics of the target tracks are submerged by random errors in positioning, for example, in a satellite-borne passive positioning system, when the positioning accuracy reaches kilometer level, and an observation object is a target with complex maneuver such as an airplane, the target tracks have the characteristic of Brownian motion tracks, if the two tracks are directly associated, whether the two tracks are tracks of the same target is difficult to judge, and therefore, a higher technical challenge is provided for track association.
In order to solve the technical challenges, literature (Guo Wenyan, han Chongzhao, ramin. Track association method based on EMD and ash association technology [ J ]. Control and decision, 2008 (07): 803-807.DOI:10.13195/j.cd.2008.07.85. Guowy.016.) uses empirical mode decomposition (Empirical Mode Decomposition, EMD) method to decompose singular values of IMF component matrix obtained by track decomposition, and finally calculates ash association degree with singular values as eigenvectors. However, the gray correlation of the method needs to set a proper threshold value; all IMFs are used as inputs of motion characteristics, and for tracks with poor positioning accuracy, positioning errors are also used as inputs of characteristic extraction. EMD-based feature extraction methods, literature (Li Zhenxing. Vibration signal trend term extraction method combining empirical mode decomposition [ J ]. Aircraft measurement and control theory, 2011,30 (01): 56-60.): and decomposing the flight path into a plurality of Intrinsic Mode Function (IMF) components and margins by using an EMD method, and performing least square fitting on the IMF components belonging to the trend item according to the zero crossing characteristic of the vibration signal to obtain the final trend item. The method only aims at extracting and eliminating the motion trend in the vibration signal preprocessing, and is not applied to the motion trend and solving the problem of track association.
Disclosure of Invention
The technical solution of the invention is as follows: the method for track association based on the motion trend aims at solving the problems that satellite-borne passive positioning accuracy is low and tracks are difficult to associate.
The technical scheme of the invention is as follows:
a trajectory correlation method based on motion trend, the steps of the method comprising:
step one, acquiring e aircraft tracks through a satellite platform 1, respectively performing EMD decomposition on the acquired e aircraft tracks to obtain movement trends of the corresponding e aircraft, acquiring f aircraft tracks through a satellite platform 2, respectively performing EMD decomposition on the acquired f aircraft tracks to obtain movement trends of the corresponding f aircraft; the movement trends of E airplanes are E1, E2 and …, and the movement trends of E and F airplanes are F1, F2, … and Ff respectively;
step two, performing track point number alignment on any one of the movement trends of the e airplanes obtained in the step one and the movement trends of the f airplanes obtained in the step 2 respectively to obtain movement trends of the e×f aligned airplanes corresponding to the movement trends of the e airplanes on the satellite platform 1 and movement trends of the e×f aligned airplanes corresponding to the movement trends of the f airplanes on the satellite platform 2; namely, E1 and F1, F2 and …, and Ff are respectively aligned with track points, E2 and F1, F2 and …, and Ff are respectively aligned with track points, …, ee and F1, F2 and …, and Ff are respectively aligned with track points;
step three, extracting feature sets between the motion trends of the corresponding exf aligned airplanes on the satellite platform 1 and the satellite platform 2 in the step two, and setting the feature set between the motion trends of the first aligned airplane as H 1 And so on, the feature set between the motion trends of the second aligned airplane is H 2 …, the feature set between the motion trends of the e×f aircraft after alignment is H e×f ；
Step four, carrying out association matching on a plurality of features in each feature set obtained in the step three, and calculating a cost function for associated tracks; for uncorrelated tracks, directly giving 1000 to a cost function, then performing global optimal allocation on a cost function matrix by using a Hungary algorithm to complete track correlation based on motion trend, namely, taking H as a feature set 1 The multiple features in the model are associated and matched, and the feature set is H 2 The multiple characteristics in the model are associated and matched, …, and the characteristic set is H e×f And performing association matching on the plurality of features in the model.
In the first step, the specific method for performing EMD decomposition on the acquired aircraft track is as follows:
performing EMD (empirical mode decomposition) on the aircraft track to obtain a plurality of IMF components and an EMD allowance, and summing the IMF components and the EMD allowance, which have a period smaller than the total running time of the track, to obtain a Motion Trend (MT) of the aircraft, wherein the formula is as follows:
wherein n is the IMF number of the track,for IMF components with a period less than the total time of the track's operation, +.>Is EMD allowance, < >>Representing track component +.>T represents the total time of operation of the track;
the method for acquiring the IMF component with the period smaller than the total running time of the flight path comprises the following steps: performing spectrum analysis on each obtained IMF component to obtain a period of each IMF component, and selecting the IMF component with the period smaller than the total running time of the flight path;
in the second step, the method for aligning the track points of the motion trend of the airplane comprises the following steps: a dynamic time regularization method;
in the third step, the features in the feature set comprise a first-order similarity vector difference average value, a second-order similarity vector difference average value, a Hausdorff distance and a motion trend difference;
the first order similarity vector difference is:
wherein m is the number of points of the track sequence of the motion trend of the aircraft after alignment,to pair(s)J-th speed difference information in x direction between the two aligned movement trends, +.>For the j-th speed difference information in y-direction between the two aligned motion trends, +.>The j-th speed difference information in the z direction between the two aligned movement trends; j=1, 2,3, …, m-1;
the second order similarity vector difference is:
wherein ,for the j-th acceleration difference information in the x-direction between the two aligned motion trends, +.>For the j-th acceleration difference information in y-direction between the two aligned motion trends, +.>The j-th acceleration difference information in the z direction between the two aligned motion trends;
the motion trend difference is the coefficient difference after the direct-proportion function fitting of the two motion trends after alignment;
in the fourth step, when the plurality of features are associated and matched, when the absolute values of the four features in the feature set are simultaneously smaller than the respective threshold values, the two motion trend association is judged; when any one of the absolute values of the four features in the feature set is not smaller than the threshold value, judging that the two motion trends are not associated;
setting a threshold value of a first-order similarity vector difference mean value as a threshold value of a Hausdorff distance as c, and setting the running time of two known related motion trends as t; the second-order similarity vector difference average value is b, and the motion trend difference threshold value is d; the maximum speed of the known two associated motion trends is v;
a=c/t, b=v/t, d=c/t, c being the known maximum hausdorff distance of the two phases of motion trend;
in the fourth step, the cost function is:
wherein ,motion trend difference in x direction for two motion trends, +.>Motion trend difference in y direction for two motion trends, +.>Is the motion trend difference of the two motion trends in the z direction; h is the hausdorff distance for both motion trends.
Advantageous effects
(1) According to the method, the whole track of the aircraft is taken as a research object, the motion trend of the aircraft track is decomposed and extracted by using an empirical mode, the motion trend is taken as a characteristic track, the characteristic track can reflect the purpose of the motion of the section no matter how the aircraft moves, namely the motion is regular, and therefore, the motion trend of the aircraft track reflects the most essential characteristic of the aircraft track;
(2) According to the method, under the condition that the track points of the aircraft obtained by different satellite platforms are not aligned due to unequal sampling moments, the dynamic time warping method is utilized to carry out one-to-one corresponding synchronous processing on different aircraft track sequences, namely the aircraft track points are aligned. The method does not change the trend characteristics of the aircraft tracks, reflects the similarity between two aircraft tracks to the maximum, and does not miss the association as much as possible in the track association judgment;
(3) The method extracts four characteristics of the aligned motion trend, including a first-order similarity vector difference average value, a second-order similarity vector difference average value, a Hausdorff distance and a motion trend difference. The first-order similarity vector difference represents the speed difference information between the two aligned aircraft motion trends in a physical sense; the second-order similarity vector difference represents the acceleration difference information between the two aligned aircraft motion trends in a physical sense; if the two aircraft tracks have the same movement trend speed difference information and acceleration difference information, but are far away from each other, namely the two different aircraft tracks, the error correlation of the situation can be avoided by using the Haosdorf distance; the two aircraft tracks with opposite movement directions may have the same speed difference and acceleration difference, but the two aircraft tracks are actually different tracks, and the movement trend difference can avoid the false association of the situation;
(4) The method sets four thresholds including a first-order similarity vector difference threshold, a second-order similarity vector difference threshold, a Haosdorf distance threshold and a motion trend difference threshold. For the aircraft flight path obtained by the satellite platform, the errors of the positioning results obtained by different use environments and different satellite-borne electronic reconnaissance are different. The four thresholds can lead the invention to achieve an optimal result when judging the association and the non-association between tracks.
(5) The invention relates to a track association method based on a motion trend, and belongs to the technical field of passive positioning and tracking. The satellite-borne passive positioning system has lower positioning precision on the target, and when different satellite platforms carry out track association on maneuvering targets (such as aircrafts), the track feature extraction faces new challenges. Aiming at the problem, the invention takes the whole track of the maneuvering target as a research object, extracts the motion trend of the track of the maneuvering target as a track feature through empirical mode decomposition, and the whole motion trend can embody the purpose of the motion of the section no matter how the target maneuvers, so the motion trend of the track of the maneuvering target embodies the key feature of the track of the maneuvering target; and for the target tracks acquired by different satellite platforms, under the condition that track points are not aligned due to different sampling rates and sampling moments, synchronous processing is carried out among different aircraft track sequences by using a dynamic time warping method. According to the invention, key characteristics of the maneuvering target track are extracted, the influence caused by low positioning precision of a satellite-borne platform and maneuvering of the target can be overcome, and effective association of the track is realized.
Detailed Description
Embodiments of the method of the present invention will be described in detail with reference to examples.
Examples
A track association method based on motion trend comprises the following specific steps:
step one, assuming that positioning errors of aircraft tracks observed by two satellite platforms in the directions X, Y, Z are 3000m, acquiring 10 aircraft tracks through the satellite platform 1, respectively performing EMD decomposition on the acquired 10 aircraft tracks to obtain corresponding 10 aircraft motion trends E1, E2, … and E10, respectively acquiring 10 aircraft tracks through the satellite platform 2, respectively performing EMD decomposition on the acquired F aircraft tracks to obtain corresponding 10 aircraft motion trends F1, F2, … and F10;
step two, aligning the track points of any one of the motion trends of the 10 aircrafts obtained by the satellite platform 1 and the motion trends of the 10 aircrafts obtained by the satellite platform 2 respectively to obtain 100 aligned motion trends of the aircrafts corresponding to the motion trends of the 10 aircrafts on the satellite platform 1, wherein the motion trends are M respectively 1,1 ，M 1,2 ，…，M g,h (g=1, 2, …,10; h=1, 2, …, 10), resulting in a movement trend of 100 aligned aircraft corresponding to the movement trend of 10 aircraft on the satellite platform 2, N respectively 1,1 ，N 2,1 ，…，N g,h ，(g=1，2,…，10；h=1，2，…，10)；
Step three, extracting the movement trend of the corresponding 100 aligned planes on the satellite platform 1 and the satellite platform 2 in the step twoSet the feature set between the motion trends of the first aligned airplane as H 1 Tendency M of movement of aircraft 1,1 And N 1,1 The feature set between is H 1 And so on, the feature set between the motion trends of the second aligned airplane is H 2 Tendency M of movement of aircraft 1,2 And N 2,1 The feature set between is H 2 …, the feature set between the motion trends of the 100 th aligned aircraft is H 100 Tendency M of movement of aircraft 10,10 And N 10,10 The feature set between is H 100 ；
Step four, according to prior information in the environment where the two satellite platforms are located and an aircraft motion equation, obtaining thresholds corresponding to four features: the threshold value a of the first-order similarity vector difference mean value is 24.9, the threshold value b of the second-order similarity vector difference mean value is 2.17, the threshold value c of the Haosdorf distance is 5600, and the threshold value d of the motion trend difference is 24.9. Performing association matching on a plurality of features in each feature set obtained in the step three, and calculating a cost function for associated tracks; for uncorrelated tracks, the cost function is directly given 1000, and the cost function matrix is as follows:
performing global optimal allocation by using a Hungary algorithm to obtain matching pairs of input sequence numbers of aircraft tracks of the satellite platform 1 and the satellite platform 2; 1-1,2-2,3-3,4-4,5-5,6-6,7-7,8-8,9-9, 10-10. From the matching pair, the aircraft track association accuracy is 100% in this example.
While embodiments of this invention have been described in connection with examples thereof, it will be apparent to those skilled in the art that numerous modifications can be made without departing from the principles of this invention, and these should also be considered to be within the scope of this invention.
Claims (9)
1. The track association method based on the motion trend is characterized by comprising the following steps:
step one, acquiring e aircraft tracks through a satellite platform 1, respectively performing EMD decomposition on the acquired e aircraft tracks to obtain movement trends of the corresponding e aircraft, acquiring f aircraft tracks through a satellite platform 2, respectively performing EMD decomposition on the acquired f aircraft tracks to obtain movement trends of the corresponding f aircraft;
step two, performing track point number alignment on any one of the movement trends of the e airplanes obtained in the step one and the movement trends of the f airplanes obtained in the step 2 respectively to obtain movement trends of the e×f aligned airplanes corresponding to the movement trends of the e airplanes on the satellite platform 1 and movement trends of the e×f aligned airplanes corresponding to the movement trends of the f airplanes on the satellite platform 2;
step three, extracting a corresponding feature set between the motion trend of the e×f aligned airplanes on the satellite platform 1 in the step two and the motion trend of the e×f aligned airplanes on the satellite platform 2;
and step four, performing association matching on the plurality of features in each feature set obtained in the step three to complete track association based on motion trend.
2. The motion trend based trajectory correlation method of claim 1, wherein:
in the fourth step, when the multiple features in each feature set are associated and matched, for the associated tracks, the cost function value is calculated, for the unassociated tracks, the cost function value is directly given 1000, the cost function values of all tracks are formed into a cost function matrix, and then the cost function matrix is subjected to global optimal allocation by using a Hungary algorithm.
3. A motion trend based trajectory correlation method according to claim 1 or 2, characterized in that:
in the first step, the specific method for performing EMD decomposition on the acquired aircraft track is as follows:
performing EMD (empirical mode decomposition) on the aircraft track to obtain a plurality of IMF components and an EMD allowance, and summing the IMF components and the EMD allowance, which have a period smaller than the total running time of the track, to obtain the motion trend of the aircraft, wherein the formula is as follows:
wherein n is the IMF number of the track,for IMF components with a period less than the total time of the track's operation, +.>Is EMD allowance, < >>Representing track component +.>T represents the total time of flight path operation.
4. A motion trend based trajectory correlation method according to claim 3, wherein:
the method for acquiring the IMF component with the period smaller than the total running time of the flight path comprises the following steps: and carrying out frequency spectrum analysis on each obtained IMF component to obtain a period of each IMF component, selecting the IMF component with the period smaller than the total running time of the flight path, and obtaining the IMF component with the period smaller than the total running time of the flight path.
5. The motion trend based trajectory correlation method of claim 1, wherein:
in the second step, the method for aligning the track points of the motion trend of the airplane comprises the following steps: dynamic time alignment method.
6. The motion trend based trajectory correlation method of claim 1, wherein:
in the third step, the features in the feature set comprise a first-order similarity vector difference average value, a second-order similarity vector difference average value, a Hausdorff distance and a motion trend difference;
the first order similarity vector difference is:
wherein m is the number of points of the track sequence of the motion trend of the aircraft after alignment,for the j-th speed difference information in the x-direction between the two aligned movement tendencies,/and->For the j-th speed difference information in y-direction between the two aligned motion trends, +.>The j-th speed difference information in the z direction between the two aligned movement trends; j=1, 2,3, …, m-1;
the second order similarity vector difference is:
wherein ,for the j-th acceleration difference information in the x-direction between the two aligned motion trends, +.>For the j-th acceleration difference information in y-direction between the two aligned motion trends, +.>The j-th acceleration difference information in the z direction between the two aligned motion trends;
the motion trend difference is the coefficient difference of the aligned two motion trends after the direct proportional function fitting.
7. The motion trend based trajectory correlation method of claim 1, wherein:
in the fourth step, when the plurality of features are associated and matched, when the absolute values of the four features in the feature set are simultaneously smaller than the respective threshold values, the two motion trend association is judged; and when any one of the absolute values of the four features in the feature set is not smaller than the threshold value, judging that the two motion trends are not associated.
8. The motion trend based trajectory correlation method of claim 7, wherein:
setting a threshold value of a first-order similarity vector difference mean value as a threshold value of a Hausdorff distance as c, and setting the running time of two known related motion trends as t; the second-order similarity vector difference average value is b, and the motion trend difference threshold value is d; the maximum speed of the known two associated motion trends is v;
a=c/t, b=v/t, d=c/t, c being the known maximum hausdorff distance of the two phases of motion trend.
9. The motion trend based trajectory correlation method of claim 1, wherein:
in the fourth step, the cost function is:
wherein ,motion trend difference in x direction for two motion trends, +.>Motion trend difference in y direction for two motion trends, +.>Is the motion trend difference of the two motion trends in the z direction; h is the hausdorff distance for both motion trends.
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