CN104485025B - Flight conflict early warning method of air traffic control system - Google Patents
Flight conflict early warning method of air traffic control system Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/04—Anti-collision systems
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0017—Arrangements for implementing traffic-related aircraft activities, e.g. arrangements for generating, displaying, acquiring or managing traffic information
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Abstract
The invention relates to a flight conflict early warning method of an air traffic control system, wherein the air traffic control system comprises a data communication module, a monitoring data fusion module, an airborne terminal module and a control terminal module, wherein the monitoring data fusion module is used for realizing the fusion of monitoring data of an air traffic control radar and automatic related monitoring data and providing real-time flight path information for the control terminal module; the control terminal module comprises 3 submodules of conflict-free 4D track generation before flight, short-term and medium-term 4D track generation in flight, real-time flight conflict monitoring and warning; the flight conflict early warning method of the system processes flight plan data and generates a 4D track by using a hidden Markov model by depending on the control terminal module, so that the analysis of potential traffic conflicts in airspace traffic conditions is realized. The invention can effectively early warn flight conflict and improve the safety of air traffic.
Description
Technical field
The present invention relates to a kind of air traffic control system and method, more particularly, to a kind of aerial based on the operation of 4D flight path
Traffic control system is predicted to airborne vehicle track and the method to flight collision early warning.
Background technology
With fast-developing the becoming increasingly conspicuous with spatial domain resource-constrained contradiction of World Airways transport service, traffic flow is close in the air
The complicated spatial domain of collection, still gradually shows its backwardness using the air traffic control mode that flight plan combines interval allotment
Property, it is in particular in：(1) flight plan is not airborne vehicle configuration accurate blank pipe interval, easily causes traffic flow tactics pipe
Crowded in reason, reduce spatial domain safety；(2) reckoning to flight profile, mission profile for the air traffic control automation system centered on flight plan
With Trajectory Prediction low precision, cause conflict dissolution ability；(3) job of air traffic control still lays particular emphasis on the single aviation of holding
Personal distance between device, is difficult to rise to and carries out strategic Management to traffic flow.For airborne vehicle track prediction and thus
And flight collision early warning is particularly important.
4D flight path is with room and time form, in a certain airborne vehicle flight path each point locus (longitude, latitude and
Highly) and the time accurate description, refer to use " control the time of advent " on the way point of 4D flight path based on the operation of flight path,
Airborne vehicle is controlled to pass through " time window " of specific way point.In high density spatial domain the operation based on 4D flight path
(Trajectory based Operation) as one of basic operating mechanism, be following to big flow, high density, closely-spaced
Under the conditions of spatial domain implement a kind of effective means of management, can significantly decrease the uncertainty of airborne vehicle flight path, improve spatial domain
Safety with Airport Resources and utilization rate.
Need on strategic level, single aircraft flight path to be carried out based on the air traffic method of operation that flight path runs
Calculate and optimize, the traffic flow that many airborne vehicles are constituted is implemented collaborative and adjusts；Pre- tactical level passes through revise traffic flow
In indivedual airborne vehicles flight path to solve congestion problems, and ensure the operational efficiency of all airborne vehicles in this traffic flow；And in war
Can in art aspect, scheme be freed in prediction conflict and optimization, then be highly dependent on and exactly the track of airborne vehicle be predicted simultaneously
Early warning is carried out to flight collision, all can not accurately in real time the track of airborne vehicle be predicted at present, what real-time was done is outstanding
For difference.
Content of the invention
The technical problem to be solved in the present invention is to be to overcome the deficiencies in the prior art, provides a kind of 4D flight path that is based on to run
Air traffic control system flight collision method for early warning, can effectively, accurately and real-time predict the track of airborne vehicle and predict
Flight collision.
The technical scheme realizing the object of the invention is to provide a kind of flight collision method for early warning of air traffic control system,
Described air traffic control system includes Airborne Terminal module, data communication module, monitors data fusion module and control eventually
End module；Monitor that data fusion module is used for realizing the fusion that air traffic control radar monitors data and automatic dependent surveillance data, for pipe
Terminal module processed provides real-time flight path information；
Described control terminal module includes following submodule：
Lothrus apterus 4D flight path generation module before flight, according to the forecast data of flight plan and world area forecast system,
Set up airborne vehicle kinetic model, then set up flight path conflict pre- allotment theoretical model according to flight collision Coupling point, generate boat
Pocket Lothrus apterus 4D flight path；
Flight middle or short term 4D flight path generation module, according to the real-time flight path information monitoring that data fusion module provides, utilizes
HMM is thus it is speculated that airborne vehicle 4D track in following certain time window；
Real-time flight conflict monitoring and alarm module, for set up from airborne vehicle continuously dynamically to discrete conflict logic
Observer, the conflict situation that the continuous dynamic mapping of Air Traffic System is expressed for discrete observation value；When system is possible to disobey
During anti-air traffic control rules, Hybrid dynamics behavior implementing monitoring to air traffic hybrid system, provide for controller and
When warning information；
The flight collision method for early warning of described air traffic control system includes several steps as follows：
Before step A, flight, Lothrus apterus 4D flight path generation module is according to the forecast of flight plan and world area forecast system
Data, sets up airborne vehicle kinetic model, and sets up flight path conflict pre- allotment theoretical model according to flight collision Coupling point, generates
Airborne vehicle Lothrus apterus 4D flight path；
Air traffic control radar is monitored that data and automatic dependent surveillance data are merged by step B, supervision data fusion module, raw
Become airborne vehicle real-time flight path information and be supplied to control terminal module；Flight middle or short term 4D flight path in control terminal module generates
Module speculates the airborne vehicle 4D track in following certain time window according to airborne vehicle real-time flight path information and history flight path information；Institute
State the tool of the airborne vehicle 4D track speculating in following certain time window according to airborne vehicle real-time flight path information and history flight path information
Body implementation process is as follows：
Step B6, to airborne vehicle track data pretreatment, according to acquired airborne vehicle original discrete two-dimensional position sequence x
=[x1,x2,...,xn] and y=[y1,y2,...,yn], it is carried out process the new airborne vehicle of acquisition using first-order difference method
Discrete location sequence Δ x=[Δ x1,Δx2,...,Δxn-1] and Δ y=[Δ y1,Δy2,...,Δyn-1], wherein Δ xb=
xb+1-xb,Δyb=yb+1-yb(b=1,2 ..., n-1)；
Step B7, to airborne vehicle track data cluster, to process after new airborne vehicle discrete two-dimensional position sequence Δ x and Δ
Y, by setting cluster number M', is clustered to it respectively using K-means clustering algorithm；
Step B8, to cluster after airborne vehicle track data carry out parameter training using HMM, by will
Airborne vehicle running orbit data Δ x and Δ y after process is considered as the aobvious observation of hidden Markov models, by setting hidden state
Number N ' and parameter renewal period ζ ', rolled according to T' nearest position detection value and using B-W algorithm and obtain up-to-date hidden horse
Er Kefu model parameter λ '；
Step B9, foundation HMM parameter, are obtained corresponding to current time observation using Viterbi algorithm
Hidden state q；
Step B10, by setting prediction time domain h', based on hidden state q of airborne vehicle current time, obtain future time period boat
Position prediction value O of pocket；
Step C, real-time flight conflict monitoring and alarm module set up from airborne vehicle continuously dynamically to discrete conflict logic
Observer, the conflict situation that the continuous dynamic mapping of Air Traffic System is expressed for discrete observation value；When system is possible to
When violating air traffic control rules, the Hybrid dynamics behavior implementing monitoring to air traffic hybrid system, provide for controller
Timely warning information.
Further, in step B, the value of described cluster number M' is 4, and the value of hidden state number N' is 3, when parameter updates
Section ζ ' is 30 seconds, and T' is 10, and prediction time domain h' is 300 seconds.
Further, the B8 of step B specifically refers to：Flight path sequence data length by being obtained is dynamic change,
In order to real-time tracking airborne vehicle flight path state change it is necessary to initial flight path HMM parameter lambda '=(π, A,
B on the basis of), it is readjusted, more accurately to speculate the position in certain moment following for the airborne vehicle；Every period ζ ', according to
The T' observation (o according to up-to-date acquisition1,o2,...,oT') to flight path HMM parameter lambda '=(π, A, B) carry out weight
New estimation；
The B10 of step B specifically refers to：Every the periodHMM parameter lambda according to up-to-date acquisition '=(π,
A, B) and nearest H history observation (o1,o2,...,oH), based on hidden state q of airborne vehicle current time, predicted by setting
Time domain h', obtains position prediction value O in future time period h' for the airborne vehicle in moment t.
Further, the periodFor 4 seconds.
Further, the airborne vehicle Lothrus apterus 4D flight path of described step A generates in accordance with the following methods：
Step A1, carry out aircraft states transfer modeling, according to the flying height section of airborne vehicle in flight plan, set up
The Petri net model that single airborne vehicle shifts in different legs：(g, G, Pre, Post m) shift mould for the airborne vehicle stage to E=
Type, wherein g represent flight leg, and G represents the transfer point of flight status parameter in vertical section, Pre and Post represents boat respectively
Section and way point before and after to annexation,Represent the mission phase residing for airborne vehicle；
Step A2, to set up airborne vehicle full flight profile, mission profile hybrid model as follows,
vH=κ (vCAS,Mach,hp,tLOC),
vGS=μ (vCAS,Mach,hp,tLOC,vWS, α),
Wherein vCASFor calibrated airspeed, Mach is Mach number, hpFor pressure altitude, α is the angle of wind direction forecast and air route,
vWSFor wind speed forecasting value, tLOCFor temperature forecast value, vHFor altitude rate, vGSFor ground velocity；
Step A3, using hybrid system emulation by the way of speculate solution flight path：Using by the method for time subdivision, using shape
State continually varying characteristic Recursive Solution any time airborne vehicle voyage away from reference point in a certain mission phase
And heightWherein J0For the voyage away from reference point for the initial time airborne vehicle, Δ τ is the number of time window
Value, J (τ) is the voyage away from reference point for the τ moment airborne vehicle, h0For the height away from reference point for the initial time airborne vehicle, when h (τ) is τ
Carve the height away from reference point for the airborne vehicle, thereby it is assumed that the 4D flight path obtaining single airborne vehicle；
Step A4, to many airborne vehicles coupling model implement Lothrus apterus allotment：Reach the time in cross point according to two airborne vehicles in advance,
According to air traffic control principle, quadratic programming is carried out to the airborne vehicle 4D flight path being unsatisfactory for space requirement near cross point, obtains
To Lothrus apterus 4D flight path.
Further, monitor in described step B that air traffic control radar is monitored data and automatic dependent surveillance by data fusion module
Data is merged, and generates airborne vehicle real-time flight path information, specifically in accordance with the following methods：
Step B1, by coordinate unit and time unification；
Step B2, using closest data association algorithm, the point belonging to same target is associated, extracts targetpath；
Step B3, the track data extracting from automatic dependent surveillance system and air traffic control radar respectively is joined from different space-time
Examine coordinate system conversion, be registered to the unified space-time reference coordinate system of control terminal；
Step B4, the correlation coefficient of two flight paths of calculating, if correlation coefficient is less than a certain predetermined threshold value then it is assumed that two are navigated
Mark is uncorrelated；Otherwise this two flight path correlations, can be merged；
Step B5, related flight path is merged.
Further, in described step B5, related flight path is merged, put down using the weighting based on the sampling period
All algorithms, its weight coefficient determined according to sampling period and precision of information, recycle Weighted Average Algorithm by associated from
Dynamic dependent surveillance flight path and air traffic control radar Track Fusion are system flight path.
Further, the specific implementation process of described step C is as follows：
Step C1, the conflict hypersurface collection of functions based on regulation rule for the construction：Set up hypersurface collection of functions in order to reflect system
The contention situation of system, wherein, the continuous function related to single airborne vehicle in conflict hypersurfaceSuper bent for I type
Face, the continuous function related to two frame airborne vehiclesFor Type-II hypersurface；
Step C2, set up by airborne vehicle continuous state to discrete conflict situation observer：Need to be built according to control specification
Vertical observer, the collision event that observation system system is passed through hypersurface and produced, so that corresponding control decision made by controller
Instruction；Observer ξ is used for the consecutive variations of aircraft position in observation system and produces collision event, claimsFor I
Type observer,For Type-II observer；
, from the discrete watch-dog to conflict Resolution means that conflicts, this discrete watch-dog can be described as function for step C3, designWherein S is the space that observer observation vector transforms into, and D is the space that all decision vector d transform into；Work as observer
Discrete observation vector when showing that a certain unexpected state occurs, send corresponding alarm at once.
The present invention has positive effect：(1) the flight collision method for early warning of the air traffic control system of the present invention exists
During airborne vehicle real-time track speculates, incorporated the impact of random factor, the rolling track that adopted speculate scheme can and
When extract the changing condition of extraneous random factor, improve the accuracy of airborne vehicle track supposition.
(2) the flight collision method for early warning of the air traffic control system of the present invention is to the early warning effect of flight collision relatively
Good, can effectively, accurately and real-time predict the track of airborne vehicle and predict flight collision.
(3) the flight collision method for early warning of the air traffic control system of the present invention is pre- to the reckoning of flight profile, mission profile and flight path
Survey high precision, and then conflict dissolution ability and automatization level are improved, reduce the workload of controller.
Brief description
Fig. 1 is Lothrus apterus 4D flight path generation method schematic flow sheet before flight；
Fig. 2 is flight middle or short term 4D flying track conjecture method flow schematic diagram；
Fig. 3 is airborne vehicle flight path conflict monitoring and alarm method schematic flow sheet.
Specific embodiment
(embodiment 1)
The air traffic control system run based on 4D flight path of the present embodiment, is led to including Airborne Terminal module 101, data
Letter module 102, supervision data fusion module 103 and control terminal module 104.Hereinafter the specific embodiment of each several part is divided
It is not described in detail.
1. Airborne Terminal module
Airborne Terminal module 101 is that pilot obtains ground control order, reference 4D flight path, and input flight intent
Interface, still gathers the interface of current aerospace device position data simultaneously.
Its specific embodiments is as follows：
Airborne Terminal module 101 receives following information input：(1) ADS-B information acquisition unit 201 passes through Airborne GPS
The aircraft position vector of collection, velocity vector, and the catchword of this airborne vehicle, pass through information and data transfer to machine after coding
Carry data communication module 102；(2) airborne vehicle driver needs the flight intent inconsistent with ground control order, by people
Machine inputting interface, and the ground controller of agreement can in the form of identifying by information and data transfer to airborne data communication
Module 102.In addition Airborne Terminal module 101 realizes following information output：(1) pass through terminal display, receive and show
The air traffic control instruction that pilot can identify；(2) receive and show the front Lothrus apterus 4D boat generating of ground line terminal flight
Mark, and the optimum of calculating frees 4D flight path after ground line end-probing is to conflict.
2. data communication module
Data communication module 102 can achieve vacant lot bidirectional data communication, realizes airborne real time position data and flight intent
The downlink transfer of data cell 202 and ground control command unit 203, and the uplink with reference to 4D flight path unit 204.
Its specific embodiments is as follows：
Downlink data communication：Airborne Terminal 101 passes through airborne secondary radar answering machine by aircraft identification mark and 4D position
Confidence ceases, and other additional datas, and the such as information transfer such as flight intent, flight speed, meteorology is to ground secondary radar
(SSR), after secondary radar reception, data message is parsed, and be transferred to central data process assembly 301 and decode, by referring to
Track data interface is made to be transferred to control terminal 104；Upstream data communication：Ground control terminal 104 is passed through to instruct track data
Interface, after central data process assembly 301 coding, the inquisitor just ground control order of ground secondary radar or with reference to 4D
Flight path information transmission is simultaneously shown in Airborne Terminal 101.
3. monitor data fusion module
Monitor that data fusion module 103 realizes the fusion of air traffic control radar supervision and automatic dependent surveillance ADS-B data, for pipe
Flight middle or short term 4D flight path in terminal module 104 processed is generated submodule and real-time flight conflict monitoring and is provided with alarm submodule
Flight path information in real time.
Its specific embodiments is as follows：
(1) in pretreatment stage by coordinate unit and time unification it is assumed that respectively from ADS-B and air traffic control radar extract
Data is the coordinate (as longitude, latitude, height above sea level) of series of discrete point, each point correspondence acquisition time；(2) using closest
The point belonging to same target is associated by data association algorithm, extracts targetpath；(3) will be respectively from ADS-B and blank pipe thunder
The track data reaching middle extraction converts, is registered to the unified space-time of control terminal with reference to seat from different space-time reference coordinate system
Mark system；(4) calculate two flight paths correlation coefficient, if correlation coefficient be less than a certain predetermined threshold value then it is assumed that two flight paths not
Correlation, otherwise this two flight path correlations, can be merged；(5) related flight path is merged.Due to ADS-B and blank pipe
The precision of radar is different with the sampling period, the system using Weighted Average Algorithm based on the sampling period, its weight coefficient according to
Sampling period and precision of information determine, recycle Weighted Average Algorithm by associated ADS-B flight path and air traffic control radar flight path
It is fused to system flight path.
4. control terminal module
Control terminal module 104 includes flying, and front Lothrus apterus 4D flight path generates, flight middle or short term 4D flight path generates, flies in real time
Row conflict monitoring and this 3 submodules of alarm.
(1) before flying, Lothrus apterus 4D flight path generates
The flight plan being obtained according to Flight Data Processing System (FDP) and world area forecast system (WAFS) are issued
Wind, the GRIB lattice point forecast data of temperature, set up the hybrid model of stratification to Air Traffic System, by system in peace
The evolution of total state, the times locus of description state evolution, generate airborne vehicle flight path.
As shown in figure 1, its specific implementation process is as follows：
First, carry out aircraft states transfer modeling.Airborne vehicle shows as moving between leg along the process of track flight
State handoff procedure, according to the flying height section of airborne vehicle in flight plan, sets up what single airborne vehicle shifted in different legs
Petri net model：(g, G, Pre, Post are m) airborne vehicle stage metastasis model, wherein g represents flight leg, and G represents vertical to E=
Flight status parameter in straight section (include air speed, highly, configuration) transfer point, Pre and Post represent leg and air route respectively
To annexation before and after point,Represent the mission phase residing for airborne vehicle.
Secondly, set up airborne vehicle full flight profile, mission profile hybrid model.Flight in single leg for the airborne vehicle is considered as even
Continuous process, according to particle energy model, airborne vehicle kinetics under the different operation phase is with meteorological condition for the derivation airborne vehicle
Equation, vH=κ (vCAS,Mach,hp,tLOC), vGS=μ (vCAS,Mach,hp,tLOC,vWS, α), wherein vCASFor calibrated airspeed,
Mach is Mach number, hpFor pressure altitude, α is the angle of wind direction forecast and air route, vWSFor wind speed forecasting value, tLOCPre- for temperature
Report value, vHFor altitude rate, vGSFor ground velocity.
Then, speculate solution flight path by the way of hybrid system emulation.Using by the method for time subdivision, utilization state
Continually varying characteristic Recursive Solution any time airborne vehicle voyage away from reference point in a certain mission phase
And heightWherein J0For the voyage away from reference point for the initial time airborne vehicle, Δ τ is time window
Numerical value, J (τ) is the voyage away from reference point for the τ moment airborne vehicle, h0For the height away from reference point for the initial time airborne vehicle, h (τ) is τ
The height away from reference point for the moment airborne vehicle, thereby it is assumed that the 4D flight path obtaining single airborne vehicle.
Finally, many airborne vehicles coupling model is implemented with Lothrus apterus allotment.Reach the time in cross point according to two airborne vehicles in advance, press
According to air traffic control principle, quadratic programming is carried out to the airborne vehicle 4D flight path being unsatisfactory for space requirement near cross point, obtains
Lothrus apterus 4D flight path.
(2) flight middle or short term 4D flight path generates
The real-time track data of airborne vehicle is obtained after implementing to merge according to control radar and automatic dependent surveillance system ADS-B,
Using HMM thus it is speculated that airborne vehicle 4D track in following 5 minutes windows.
As shown in Fig. 2 its specific implementation process is as follows：
First, to airborne vehicle track data pretreatment, according to acquired airborne vehicle original discrete two-dimensional position sequence x=
[x1,x2,...,xn] and y=[y1,y2,...,yn], it is carried out process using first-order difference method obtain new airborne vehicle from
Scattered position sequence Δ x=[Δ x1,Δx2,...,Δxn-1] and Δ y=[Δ y1,Δy2,...,Δyn-1], wherein Δ xb=xb+1-
xb,Δyb=yb+1-yb(b=1,2 ..., n-1).
Secondly, airborne vehicle track data is clustered.To new airborne vehicle discrete two-dimensional position sequence Δ x and Δ y after processing,
By setting cluster number M', respectively it is clustered using K-means clustering algorithm.
Then, using HMM, parameter training is carried out to the airborne vehicle track data after cluster.By locating
Airborne vehicle running orbit data Δ x and Δ y after reason is considered as the aobvious observation of hidden Markov models, by setting hidden status number
Mesh N' and parameter update period ζ ', rolled according to T' nearest position detection value and using B-W algorithm and obtain up-to-date hidden Ma Er
Section's husband's model parameter λ '：Flight path sequence data length by being obtained is dynamic change, for real-time tracking airborne vehicle boat
The state change of mark it is necessary to initial flight path HMM parameter lambda '=(π, A, B) on the basis of it is adjusted again
Whole, more accurately to speculate the position in certain moment following for the airborne vehicle.Every period ζ ', according to T' observation of up-to-date acquisition
Value (o1,o2,...,oT') to flight path HMM parameter lambda '=(π, A, B) reevaluated.
Again and, according to HMM parameter, obtained corresponding to current time observation using Viterbi algorithm
Hidden state q.
Finally, every the periodHMM parameter lambda according to up-to-date acquisition '=(π, A, B) and nearest H go through
History observation (o1,o2,...,oH), based on hidden state q of airborne vehicle current time, by setting prediction time domain h', in moment t
Obtain position prediction value O in future time period h' for the airborne vehicle.
The value of described cluster number M' is 4, and the value of hidden state number N' is 3, and it is 30 seconds that parameter updates period ζ ', and T' is
10, prediction time domain h' is 300 seconds, the periodFor 4 seconds.
(3) real-time flight conflict monitoring and alarm
When system is possible to the state occurring violating safe condition collection, condition monitoring is implemented by controller, to aviation
Effective measure of control implemented by device, it is to avoid the generation of flight collision.
As shown in figure 3, its specific implementation process is as follows：
First, the conflict hypersurface collection of functions based on regulation rule for the construction.The violation of air traffic control constraint can
It is considered as controlled device (the multi rack airborne vehicle of the control zone flight) event that composition system is passed through hypersurface and produced, set up super bent
Surface function collection is in order to reflect the contention situation of system.Wherein, related to single airborne vehicle continuous function in conflict hypersurfaceFor I type hypersurface, and by the continuous function related to two frame airborne vehiclesSurpass for Type-II
Curved surface.
Then, set up by the observer of airborne vehicle continuous state to discrete conflict situation.Need to be set up according to control specification
Observer, the collision event that observation system system is passed through hypersurface and produced, so that controller is made corresponding control decision and is referred to
Order.Observer ξ is used for the consecutive variations of aircraft position in observation system and produces collision event, claimsFor I type
Observer,For Type-II observer.
Finally, design is from the discrete watch-dog of conflict to conflict Resolution means.When the discrete observation vector of observer shows
When a certain unexpected state occurs, send corresponding alarm at once.This discrete watch-dog can be described as functionIts
Middle S is the space that observer observation vector transforms into, and D is the space that all decision vector d transform into.
Obviously, above-described embodiment is only intended to clearly illustrate example of the present invention, and is not to the present invention
The restriction of embodiment.For those of ordinary skill in the field, can also be made it on the basis of the above description
The change of its multi-form or variation.There is no need to be exhaustive to all of embodiment.And these belong to this
Obvious change that bright spirit is extended out or change among still in protection scope of the present invention.
Claims (1)
1. the flight collision method for early warning of a kind of air traffic control system, described air traffic control system includes Airborne Terminal
Module, data communication module, supervision data fusion module and control terminal module；Monitor that data fusion module is used for realizing sky
Pipe radar surveillance data and the fusion of automatic dependent surveillance data, provide real-time flight path information for control terminal module；Its feature
It is：
Described control terminal module includes following submodule：
Lothrus apterus 4D flight path generation module before flight, according to the forecast data of flight plan and world area forecast system, sets up
Airborne vehicle kinetic model, then sets up flight path conflict pre- allotment theoretical model according to flight collision Coupling point, generates airborne vehicle
Lothrus apterus 4D flight path；
Flight middle or short term 4D flight path generation module, according to the real-time flight path information monitoring that data fusion module provides, using hidden horse
Er Kefu model is thus it is speculated that airborne vehicle 4D track in following certain time window；
Real-time flight conflict monitoring and alarm module, for set up from airborne vehicle continuously dynamically to the observation of discrete conflict logic
Device, the conflict situation that the continuous dynamic mapping of Air Traffic System is expressed for discrete observation value；When system is possible to violate sky
During middle traffic control rule, the Hybrid dynamics behavior implementing monitoring to air traffic hybrid system, provide timely for controller
Warning information；The flight collision method for early warning of described air traffic control system includes several steps as follows：
Before step A, flight, Lothrus apterus 4D flight path generation module is according to the forecast data of flight plan and world area forecast system,
Set up airborne vehicle kinetic model, and set up flight path conflict pre- allotment theoretical model according to flight collision Coupling point, generate aviation
Device Lothrus apterus 4D flight path；
Air traffic control radar is monitored that data and automatic dependent surveillance data are merged by step B, supervision data fusion module, generates boat
Pocket real-time flight path information is simultaneously supplied to control terminal module；Flight middle or short term 4D flight path generation module in control terminal module
Speculate the airborne vehicle 4D track in following certain time window according to airborne vehicle real-time flight path information and history flight path information；Described according to
Speculate the concrete reality of the airborne vehicle 4D track in following certain time window according to airborne vehicle real-time flight path information and history flight path information
Apply process as follows：
Step B6, to airborne vehicle track data pretreatment, according to acquired airborne vehicle original discrete two-dimensional position sequenceWith, it is carried out process using first-order difference method obtain new airborne vehicle from
Scattered position sequenceWith, wherein,；
Step B7, airborne vehicle track data is clustered, to new airborne vehicle discrete two-dimensional position sequence after processingWith, lead to
Cross setting cluster number, respectively it is clustered using K-means clustering algorithm；
Step B8, to cluster after airborne vehicle track data carry out parameter training using HMM, by processing
Airborne vehicle running orbit data afterwardsWithIt is considered as the aobvious observation of hidden Markov models, by setting hidden state numberUpdate the period with parameter, according to nearestIndividual position detection value simultaneously rolls the up-to-date hidden Ma Er of acquisition using B-W algorithm
Section's husband's model parameter；
Step B9, foundation HMM parameter, are obtained hidden corresponding to current time observation using Viterbi algorithm
State；
Step B10, by set prediction time domain, hidden state based on airborne vehicle current time, obtain future time period airborne vehicle
Position prediction value；
Step C, real-time flight conflict monitoring and alarm module set up from airborne vehicle continuously dynamically to the sight of discrete conflict logic
Survey device, the conflict situation that the continuous dynamic mapping of Air Traffic System is expressed for discrete observation value；When system is possible to violate
During air traffic control rules, Hybrid dynamics behavior implementing monitoring to air traffic hybrid system, provide in time for controller
Warning information.
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| CN201710189245.7A CN106846925A (en) | 2015-01-07 | 2015-01-07 | Air traffic control system for early warning of flight conflict |
| CN201510007997.8A CN104485025B (en) | 2015-01-07 | 2015-01-07 | Flight conflict early warning method of air traffic control system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105489068B (en) * | 2015-12-14 | 2018-04-13 | 青岛民航空管实业发展有限公司 | A kind of control order error correction method |
| CN106205219B (en) * | 2016-08-31 | 2019-03-05 | 北京招通致晟科技有限公司 | Aircraft monitoring method and device based on fusion of multiple radar information |
| US10332409B2 (en) * | 2016-09-27 | 2019-06-25 | Rockwell Collins, Inc. | Midair collision threat detection and assessment using visual information |
| CN107656266B (en) * | 2017-08-29 | 2020-03-17 | 安徽四创电子股份有限公司 | S-mode secondary radar information fusion system based on ADS-B and fusion method thereof |
| CN107564340A (en) * | 2017-09-12 | 2018-01-09 | 周俊杰 | Unattended aircraft information gathering transmission surface process and device |
| CN109903591B (en) * | 2017-12-11 | 2023-01-06 | 上海航空电器有限公司 | Aircraft automatic near-earth collision assessment method and system based on expert rules |
| CN109191921B (en) * | 2018-08-31 | 2020-09-18 | 中国电子科技集团公司第二十八研究所 | Air traffic control monitoring data simulation method based on 4D trajectory prediction |
| CN109493644B (en) * | 2018-11-16 | 2020-08-14 | 四川大学 | Four-dimensional track conjecture method based on historical track data mining |
| CN110060513A (en) * | 2019-01-24 | 2019-07-26 | 中国民用航空飞行学院 | Workload for air traffic controllers appraisal procedure based on historical trajectory data |
| CN110689763A (en) * | 2019-09-20 | 2020-01-14 | 中国飞行试验研究院 | Airborne auxiliary navigation method, device and system based on wireless reception |
| CN110853412B (en) * | 2019-11-12 | 2021-06-01 | 上海眼控科技股份有限公司 | Method and device for identifying abnormal track point |
| CN112330982B (en) * | 2020-10-15 | 2024-06-21 | 中国民用航空中南地区空中交通管理局 | Mid-term conflict early warning method, device and storage medium applied to terminal area |
| CN112258898B (en) * | 2020-10-16 | 2021-08-24 | 中国民用航空华东地区空中交通管理局 | Air traffic control method, system, electronic device and storage medium based on digital twin technology |
| CN112596538B (en) * | 2020-11-26 | 2023-06-16 | 中国电子科技集团公司第十五研究所 | Large unmanned aerial vehicle conflict detection and avoidance decision device and use method |
| CN114440891B (en) * | 2022-01-25 | 2023-08-25 | 深圳技术大学 | Four-dimensional flight path planning method, system and equipment for air traffic management |
| CN115482688B (en) * | 2022-08-30 | 2024-05-03 | 南京航空航天大学 | Aircraft traffic conflict resolution method and system based on multiple clusters |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5961568A (en) * | 1997-07-01 | 1999-10-05 | Farahat; Ayman | Cooperative resolution of air traffic conflicts |
| CN102509475A (en) * | 2011-10-26 | 2012-06-20 | 南京航空航天大学 | Air traffic control system and method for four-dimensional (4D)-trajectory-based operation |
| CN103336863A (en) * | 2013-06-24 | 2013-10-02 | 北京航空航天大学 | Radar flight path observation data-based flight intention recognition method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7876258B2 (en) * | 2006-03-13 | 2011-01-25 | The Boeing Company | Aircraft collision sense and avoidance system and method |
| CN103050024B (en) * | 2013-01-09 | 2014-09-10 | 成都民航空管科技发展有限公司 | System and method for rapid real-time detection of air traffic service safety |
| CN103226899B (en) * | 2013-03-19 | 2015-12-23 | 北京工业大学 | Based on the space domain sector method for dynamically partitioning of air traffic feature |
-
2015
- 2015-01-07 CN CN201710189244.2A patent/CN106875757A/en active Pending
- 2015-01-07 CN CN201510007997.8A patent/CN104485025B/en active Active
- 2015-01-07 CN CN201710189245.7A patent/CN106846925A/en active Pending
- 2015-01-07 CN CN201710189243.8A patent/CN106846924A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5961568A (en) * | 1997-07-01 | 1999-10-05 | Farahat; Ayman | Cooperative resolution of air traffic conflicts |
| EP0960413B1 (en) * | 1997-07-01 | 2005-04-20 | Raytheon Company | Cooperative resolution of air traffic conflicts |
| CN102509475A (en) * | 2011-10-26 | 2012-06-20 | 南京航空航天大学 | Air traffic control system and method for four-dimensional (4D)-trajectory-based operation |
| CN103336863A (en) * | 2013-06-24 | 2013-10-02 | 北京航空航天大学 | Radar flight path observation data-based flight intention recognition method |
Non-Patent Citations (2)
| Title |
|---|
| A METHODOLOGY FOR AUTOMATED TRAJECTORY PREDICTION ANALYSIS;Chester Gong 等;《AIAA Guidance, Navigation, and Control Conference and Exhibit》;20040819;第1-14页 * |
| 基于混杂系统模型的航空器4D航迹推测;汤新民 等;《南京航空航天大学学报》;20120229;第44卷(第1期);第105-112页 * |
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