CN106297419B - A kind of aircraft trajectory predictions method based on 4D - Google Patents
A kind of aircraft trajectory predictions method based on 4D Download PDFInfo
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- CN106297419B CN106297419B CN201610913526.8A CN201610913526A CN106297419B CN 106297419 B CN106297419 B CN 106297419B CN 201610913526 A CN201610913526 A CN 201610913526A CN 106297419 B CN106297419 B CN 106297419B
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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]
- G08G5/0043—Traffic management of multiple aircrafts from the ground
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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]
- G08G5/003—Flight plan management
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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]
- G08G5/0095—Aspects of air-traffic control not provided for in the other subgroups of this main group
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Abstract
The aircraft trajectory predictions method based on 4D that the present invention relates to a kind of, the air traffic control system includes data communication module, monitoring data fusion module, Airborne Terminal module, control terminal module, wherein monitoring data fusion module merging for realizing air traffic control radar monitoring data and automatic dependent surveillance data, provides real-time track information for control terminal module；Control terminal module includes that Lothrus apterus 4D track generates, flight middle or short term 4D track generates this 2 submodules before flying；The aircraft trajectory predictions method of above system handles flight plan data and generates 4D track using Hidden Markov Model, realize the analysis of the potential traffic conflict of airspace traffic condition by control terminal module.The present invention can effectively improve the safety of air traffic.
Description
The application is application No. is 201510008041.X, and invention and created name is a kind of " air traffic control system
Aircraft trajectory predictions method ", the applying date are as follows: the divisional application of the application for a patent for invention on January 7th, 2015.
Technical field
The present invention relates to a kind of air traffic control system and method more particularly to it is a kind of based on 4D track operation it is aerial
The method that traffic control system predicts aircraft track.
Background technique
With the fast development of World Airways transport service and airspace resource it is limited it is contradictory become increasingly conspicuous, traffic flow is close in the sky
The complicated airspace of collection still combines the air traffic control mode of interval allotment gradually to show its backwardness using flight plan
Property, be in particular in: (1) flight plan is not that aircraft configures accurate blank pipe interval, be easy to cause traffic flow tactics pipe
It is crowded in reason, reduce airspace safety；(2) reckoning of the air traffic control automation system centered on flight plan to flight profile, mission profile
With Trajectory Prediction low precision, cause conflict dissolution ability poor；(3) job of air traffic control, which is still laid particular emphasis on, keeps single aviation
Personal distance between device is difficult to rise to traffic flow progress strategic Management.Seem outstanding for the prediction of aircraft track
It is important.
4D track be in the form of room and time, in a certain aircraft track each point spatial position (longitude, latitude and
Highly) and the accurate description of time, the operation based on track refer to use " the control arrival time " on the way point of 4D track,
It controls " time window " that aircraft passes through specific way point.In high density airspace the operation based on 4D track
(Trajectory based Operation) one of as basic operating mechanism, be it is following to big flow, it is high density, closely-spaced
Under the conditions of airspace implement a kind of effective means of management, can reduce the uncertainty of aircraft track significantly, improve airspace
With the safety and utilization rate of Airport Resources.
The air traffic method of operation based on track operation needs to carry out single aircraft track on strategic level
It calculates and optimizes, collaboration and adjustment are implemented to the traffic flow that more aircrafts are constituted；Pass through amendment traffic flow on pre- tactical level
In individual aircrafts track to solve congestion problems, and guarantee the operational efficiency of all aircrafts in the traffic flow；And it is fighting
Scheme is freed in prediction conflict and optimization in art level, then can be highly dependent on accurately predict the track of aircraft,
Accurately the track of aircraft cannot be predicted in real time at present, the difference especially done in real-time.
Summary of the invention
It is a kind of based on the operation of 4D track the technical problem to be solved by the present invention is to be to overcome the deficiencies of the prior art and provide
Air traffic control system aircraft trajectory predictions method, can effectively, accurately and real-time predict the track of aircraft.
Realize that the technical solution of the object of the invention is to provide a kind of aircraft trajectory predictions method based on 4D, by handing in the air
Logical control system is implemented, and the air traffic control system includes Airborne Terminal module, data communication module, monitoring data fusion
Module and control terminal module；Monitor data fusion module for realizing air traffic control radar monitoring data and automatic dependent surveillance number
According to fusion, provide real-time track information for control terminal module；
The control terminal module includes following submodule:
Lothrus apterus 4D track generation module before flying, according to the forecast data of flight plan and world area forecast system,
Aircraft kinetic model is established, then the pre- allotment theoretical model of track conflict is established according to flight collision Coupling point, generates boat
Pocket Lothrus apterus 4D track；
Flight middle or short term 4D track generation module is utilized according to the real-time track information that monitoring data fusion module provides
Hidden Markov Model, thus it is speculated that the track aircraft 4D in the following certain time window；
The aircraft trajectory predictions method based on 4D comprises the following steps:
Step A, before flight Lothrus apterus 4D track generation module according to the forecast of flight plan and world area forecast system
Data establish aircraft kinetic model, and establish the pre- allotment theoretical model of track conflict according to flight collision Coupling point, generate
Aircraft Lothrus apterus 4D track；
Step B, monitoring data fusion module merges air traffic control radar monitoring data with automatic dependent surveillance data, raw
At the real-time track information of aircraft and it is supplied to control terminal module；Flight middle or short term 4D track in control terminal module generates
Module speculates the track aircraft 4D in the following certain time window according to the real-time track information of aircraft and history track information；Institute
State the tool that the track aircraft 4D in the following certain time window is speculated according to the real-time track information of aircraft and history track information
Body implementation process is as follows:
Step B6, aircraft track data is pre-processed, according to the acquired original discrete two-dimensional position sequence x of aircraft
=[x1,x2,...,xn] and y=[y1,y2,...,yn], processing is carried out to it using first-order difference method and obtains new aircraft
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, aircraft track data is clustered, to aircraft discrete two-dimensional position sequence Δ x and Δ new after processing
Y is clustered number M' by setting, is clustered respectively to it using genetic algorithm for clustering；
Step B8, to the aircraft track data after cluster using Hidden Markov Model carry out parameter training, pass through by
Treated, and aircraft running track data Δ x and Δ y are considered as the aobvious observation of hidden Markov models, by setting hidden state
Number N ' and parameter update period ζ ', the newest hidden horse of acquisition is rolled according to T' nearest position detection value and using B-W algorithm
Er Kefu model parameter λ '；
Step B9, it according to Hidden Markov Model parameter, is obtained corresponding to current time observation using Viterbi algorithm
Hidden state q；
Step B10, by setting prediction time domain h', the hidden state q based on aircraft current time, future time period boat is obtained
The position prediction value O of pocket.
Further, in step B, the value of the cluster number M' is 4, and the value of hidden state number N' is 3, when parameter updates
Section ζ ' is 30 seconds, T' 10, and prediction time domain h' is 300 seconds.
Further, the B8 of step B is specifically referred to: since track sequence data length obtained is dynamic change,
For the state change of real-time tracking aircraft track, it is necessary to initial track Hidden Markov Model parameter lambda '=(π, A,
B it is readjusted on the basis of), more accurately to speculate aircraft in the position at certain following moment；Every period ζ ', according to
According to T' observation (o of newest acquisition1,o2,...,oT') to track Hidden Markov Model parameter lambda '=(π, A, B) carry out weight
New estimation.
The B10 of step B is specifically referred to: every the periodAccording to the Hidden Markov Model parameter lambda of newest acquisition '=(π,
A, B) and nearest H history observation (o1,o2,...,oH), the hidden state q based on aircraft current time is predicted by setting
Time domain h' obtains aircraft in the position prediction value O of future time period h' in moment t.
Further, the periodIt is 4 seconds.
Further, the aircraft Lothrus apterus 4D track of the step A generates in accordance with the following methods:
Step A1, aircraft states transfer modeling is carried out to be established according to the flying height section of aircraft in flight plan
The Petri net model that single aircraft is shifted in different segments: E=(g, G, Pre, Post, m) is to shift mould in the aircraft stage
Type, wherein g indicates that flight leg, G indicate the transfer point of flight status parameter in vertical section, and Pre and Post respectively indicate boat
The front and back of section and way point to connection relationship,Indicate mission phase locating for aircraft；
Step A2, it is as follows to establish the full flight profile, mission profile hybrid model of aircraft,
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, solution track is speculated by the way of hybrid system emulation: using by the method for time subdivision, utilizing shape
The characteristic Recursive Solution any time aircraft of state consecutive variations is in voyage of a certain mission phase away from reference pointAnd heightWherein J0It is initial time aircraft away from reference point
Voyage, Δ τ are the numerical value of time window, and J (τ) is voyage of the τ moment aircraft away from reference point, h0It is initial time aircraft away from ginseng
The height of examination point, h (τ) are height of the τ moment aircraft away from reference point, thereby it is assumed that the 4D track for obtaining single aircraft；
Step A4, Lothrus apterus allotment is implemented to more aircraft coupling models: reaches the time in crosspoint in advance according to two aircrafts,
According to air traffic control principle, the aircraft 4D track that space requirement is nearby unsatisfactory for crosspoint carries out quadratic programming, obtains
To Lothrus apterus 4D track.
Further, monitor that air traffic control radar is monitored data and automatic dependent surveillance by data fusion module in the step B
Data are merged, and the real-time track information of aircraft is generated, specifically in accordance with the following methods:
Step B1, by coordinate unit and time unification；
Step B2, the point for belonging to the same target is associated using closest data association algorithm, extracts targetpath；
Step B3, will join respectively from the track data that automatic dependent surveillance system and air traffic control radar are extracted from different space-time
It examines coordinate system transformation, be registered to the unified space-time reference coordinate system of control terminal；
Step B4, the related coefficient of two tracks is calculated, if related coefficient is less than a certain preset threshold, then it is assumed that two boats
Mark is uncorrelated；Otherwise two track correlations, can be merged；
Step B5, relevant track is merged.
Further, relevant track is merged in the step B5, it is flat using the weighting based on the sampling period
Equal algorithm, weighting coefficient determine according to sampling period and precision of information, recycle Weighted Average Algorithm by it is associated from
Dynamic dependent surveillance track and air traffic control radar Track Fusion are system track.
The present invention has the effect of positive: (1) a kind of aircraft trajectory predictions method based on 4D of the invention is in aviation
During device real-time track speculates, the influence of enchancement factor is incorporated, used rolling track speculates that scheme can mention in time
The changing condition for taking extraneous enchancement factor improves the accuracy of aircraft track supposition.
(2) a kind of aircraft trajectory predictions method based on 4D of the invention is to the reckoning of flight profile, mission profile and Trajectory Prediction essence
Degree is high, so that conflict dissolution ability and automatization level improve, reduces the workload of controller.
Detailed description of the invention
Fig. 1 is Lothrus apterus 4D track generation method flow diagram before flying；
Fig. 2 is flight middle or short term 4D flying track conjecture method flow schematic diagram.
Specific embodiment
(embodiment 1)
The air traffic control system based on the operation of 4D track of the present embodiment, including Airborne Terminal module 101, data are logical
Believe module 102, monitoring data fusion module 103 and control terminal module 104.Below to the specific embodiment of each section point
It is not described in detail.
1. Airborne Terminal module
Airborne Terminal module 101 is that pilot obtains ground control order, with reference to 4D track, and inputs flight intent
Interface, while still acquiring the interface of current aerospace device position data.
Its specific embodiment is as follows:
Airborne Terminal module 101 receives following information input: (1) ADS-B information acquisition unit 201 passes through Airborne GPS
The catchword of the aircraft position vector of acquisition, velocity vector and this aircraft passes to machine by information and data after coding
Carry data communication module 102；(2) aircraft driver needs the flight intent inconsistent with ground control order passing through people
Machine input interface, and the form that can identify of ground controller of agreement pass to airborne data communication by information and data
Module 102.In addition Airborne Terminal module 101 realizes following information output: (1) by terminal display, receiving and show
The air traffic control instruction that pilot can identify；(2) receive and show ground line terminal flight previous existence at Lothrus apterus 4D navigate
Mark, and when ground line end-probing optimal frees 4D track to what is calculated after conflict.
2. data communication module
Data communication module 102 can realize vacant lot bidirectional data communication, realize airborne real time position data and flight intent
The downlink transfer and ground control command unit 203 of data cell 202, and the uplink with reference to 4D track unit 204.
Its specific embodiment is as follows:
Downlink data communication: Airborne Terminal 101 passes through airborne secondary radar answering machine for aircraft identification mark and 4D
Confidence breath and other additional datas, such as flight intent, flying speed, meteorology information are transferred to ground secondary radar
(SSR), secondary radar parses data message after receiving, and is transferred to the decoding of central data processing component 301, passes through finger
Track data interface is enabled to be transferred to control terminal 104；Upstream data communication: ground control terminal 104 passes through instruction track data
Interface, after the coding of central data processing component 301, the inquisitor of ground secondary radar will be by ground control order or with reference to 4D
Track information is transmitted and is shown in Airborne Terminal 101.
3. monitoring data fusion module
The realization air traffic control radar monitoring of monitoring data fusion module 103 is merged with automatic dependent surveillance ADS-B data, for pipe
Flight middle or short term 4D track in terminal module 104 processed generates submodule and real-time flight conflict monitoring and provides with alarm submodule
Real-time track information.
Its specific embodiment is as follows:
(1) in pretreatment stage by coordinate unit and time unification, it is assumed that extracted from ADS-B and air traffic control radar respectively
Data are that coordinate (such as longitude, latitude, height above sea level), each point of series of discrete point correspond to acquisition time；(2) using closest
The point for belonging to the same target is associated by data association algorithm, extracts targetpath；It (3) will be respectively from ADS-B and blank pipe thunder
Up to the track data of middle extraction from different space-time reference coordinate system transformation, the unified space-time of control terminal is registered to reference to seat
Mark system；(4) related coefficient of two tracks is calculated, if related coefficient is less than a certain preset threshold, then it is assumed that two tracks are not
Correlation, otherwise two track correlations, can be merged；(5) relevant track is merged.Due to ADS-B and blank pipe
The precision of radar and sampling period are different, and this system uses the Weighted Average Algorithm based on the sampling period, weighting coefficient according to
Sampling period and precision of information determine, recycle Weighted Average Algorithm by associated ADS-B track and air traffic control radar track
It is fused to system track.
4. control terminal module
Control terminal module 104 includes that Lothrus apterus 4D track generates, flight middle or short term 4D track generates this 2 sons before flying
Module.
(1) Lothrus apterus 4D track generates before flying
Flight plan and world area forecast system (WAFS) publication obtained according to Flight Data Processing System (FDP)
The GRIB lattice point forecast data of wind, temperature, the hybrid model of stratification is established to Air Traffic System, is being pacified by system
The evolution of total state describes the time locus of state evolution, generates aircraft track.
As shown in Figure 1, its specific implementation process is as follows:
Firstly, carrying out aircraft states transfer modeling.Aircraft shows as moving between segment along the process of track flight
State handoff procedure establishes what single aircraft was shifted in different segments according to the flying height section of aircraft in flight plan
Petri net model: E=(g, G, Pre, Post, m) is aircraft stage metastasis model, and wherein g indicates that flight leg, G indicate to hang down
The transfer point of flight status parameter (including air speed, height, configuration) in straight section, Pre and Post respectively indicate segment and air route
The front and back of point to connection relationship,Indicate mission phase locating for aircraft.
Secondly, establishing the full flight profile, mission profile hybrid model of aircraft.Flight of the aircraft in single segment is considered as company
Continuous process derives aircraft dynamics of the aircraft in the case where the different operation phase is with meteorological condition according to particle energy model
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, tLOCIt is pre- for temperature
Report value, vHFor altitude rate, vGSFor ground velocity.
Then, solution track is speculated by the way of hybrid system emulation.Using by the method for time subdivision, utilize
The characteristic Recursive Solution any time aircraft of state consecutive variations is in voyage of a certain mission phase away from reference pointAnd heightWherein J0It is initial time aircraft away from reference point
Voyage, Δ τ are the numerical value of time window, and J (τ) is voyage of the τ moment aircraft away from reference point, h0It is initial time aircraft away from ginseng
The height of examination point, h (τ) are height of the τ moment aircraft away from reference point, thereby it is assumed that the 4D track for obtaining single aircraft.
Finally, implementing Lothrus apterus allotment to more aircraft coupling models.It the time for reaching crosspoint in advance according to two aircrafts, presses
According to air traffic control principle, the aircraft 4D track that space requirement is nearby unsatisfactory for crosspoint carries out quadratic programming, obtains
Lothrus apterus 4D track.
(2) flight middle or short term 4D track generates
The real-time track data of aircraft is obtained after implementing fusion according to control radar and automatic dependent surveillance system ADS-B,
Utilize Hidden Markov Model, thus it is speculated that the track aircraft 4D in the following 5 minutes windows.
As shown in Fig. 2, its specific implementation process is as follows:
Firstly, being pre-processed to aircraft track data, according to the acquired original discrete two-dimensional position sequence x=of aircraft
[x1,x2,...,xn] and y=[y1,y2,...,yn], it is carried out using first-order difference method processing obtain new aircraft from
Dissipate 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, being clustered to aircraft track data.To aircraft discrete two-dimensional position sequence Δ x and Δ y new after processing,
Number M' is clustered by setting, it is clustered respectively using genetic algorithm for clustering.
Then, parameter training is carried out using Hidden Markov Model to the aircraft track data after cluster.By that will locate
Aircraft running track data Δ x and Δ y after reason are considered as the aobvious observation of hidden Markov models, by setting hidden status number
Mesh N' and parameter update period ζ ', roll the newest hidden Ma Er of acquisition according to T' nearest position detection value and using B-W algorithm
Section husband model parameter λ ': since track sequence data length obtained is dynamic change, for real-time tracking aircraft boat
The state change of mark, it is necessary to initial track Hidden Markov Model parameter lambda '=(π, A, B) on the basis of it is adjusted again
It is whole, more accurately to speculate aircraft in the position at certain following moment.The T' observation every period ζ ', according to newest acquisition
It is worth (o1,o2,...,oT') to track Hidden Markov Model parameter lambda '=(π, A, B) reevaluated.
And, according to Hidden Markov Model parameter, obtained corresponding to current time observation using Viterbi algorithm again
Hidden state q.
Finally, every the periodAccording to the Hidden Markov Model parameter lambda of newest acquisition '=(π, A, B) and nearest H
History observation (o1,o2,...,oH), the hidden state q based on aircraft current time predicts time domain h' by setting, at the moment
T obtains aircraft in the position prediction value O of future time period h'.
The value of the cluster number M' is 4, and the value of hidden state number N' is 3, and it is 30 seconds that parameter, which updates period ζ ', and T' is
10, prediction time domain h' is 300 seconds, the periodIt is 4 seconds.
Obviously, the above embodiment is merely an example for clearly illustrating the present invention, and is not to of the invention
The restriction of embodiment.For those of ordinary skill in the art, it can also be made on the basis of the above description
Its various forms of variation or variation.There is no necessity and possibility to exhaust all the enbodiments.And these belong to this hair
The obvious changes or variations that bright spirit is extended out are still in the protection scope of this invention.
Claims (1)
1. a kind of aircraft trajectory predictions method based on 4D, is implemented, the air traffic control by air traffic control system
System includes Airborne Terminal module, data communication module, monitoring data fusion module and control terminal module；Monitoring data are melted
Molding block merges for realizing air traffic control radar monitoring data and automatic dependent surveillance data, provides in real time for control terminal module
Track information；It is characterized by:
The control terminal module includes following submodule:
Lothrus apterus 4D track generation module before flying is established according to the forecast data of flight plan and world area forecast system
Then aircraft kinetic model establishes the pre- allotment theoretical model of track conflict according to flight collision Coupling point, generates aircraft
Lothrus apterus 4D track；
Flight middle or short term 4D track generation module utilizes hidden horse according to the real-time track information that monitoring data fusion module provides
Er Kefu model, thus it is speculated that the track aircraft 4D in the following certain time window；
The aircraft trajectory predictions method based on 4D comprises the following steps:
Step A, before flight Lothrus apterus 4D track generation module according to the forecast data of flight plan and world area forecast system,
Aircraft kinetic model is established, and establishes the pre- allotment theoretical model of track conflict according to flight collision Coupling point, generates aviation
Device Lothrus apterus 4D track；
Step B, monitoring data fusion module merges air traffic control radar monitoring data with automatic dependent surveillance data, generates boat
The real-time track information of pocket is simultaneously supplied to control terminal module；Flight middle or short term 4D track generation module in control terminal module
The track aircraft 4D in the following certain time window is speculated according to the real-time track information of aircraft and history track information；It is described according to
The specific reality of the track aircraft 4D in the following certain time window is speculated according to the real-time track information of aircraft and history track information
It is as follows to apply process:
Step B6, aircraft track data is pre-processed, according to the acquired original discrete two-dimensional position sequence x=of aircraft
[x1,x2,...,xn] and y=[y1,y2,...,yn], it is carried out using first-order difference method processing obtain new aircraft from
Dissipate position sequence △ x=[△ x1,△x2,...,△xn-1] and △ y=[△ y1,△y2,...,△yn-1], wherein △ xb=xb+1-
xb,△yb=yb+1-yb(b=1,2 ..., n-1)；
Step B7, aircraft track data is clustered, to aircraft discrete two-dimensional position sequence △ x and △ y new after processing, is led to
Setting cluster number M' is crossed, it is clustered respectively using genetic algorithm for clustering；
Step B8, parameter training is carried out using Hidden Markov Model to the aircraft track data after cluster, by that will handle
Aircraft running track data △ x and △ y afterwards is considered as the aobvious observation of hidden Markov models, by setting hidden state number
N' and parameter update period ζ ', roll the newest hidden Ma Erke of acquisition according to T' nearest position detection value and using B-W algorithm
Husband's model parameter λ '；
Step B9, it according to Hidden Markov Model parameter, is obtained using Viterbi algorithm hidden corresponding to current time observation
State q；
Step B10, by setting prediction time domain h', the hidden state q based on aircraft current time, future time period aircraft is obtained
Position prediction value O；
The aircraft Lothrus apterus 4D track of the step A generates in accordance with the following methods:
Step A1, aircraft states transfer modeling is carried out, according to the flying height section of aircraft in flight plan, is established single
The Petri net model that aircraft is shifted in different segments: E=(g, G, Pre, Post, m) is aircraft stage metastasis model,
Middle g indicates that flight leg, G indicate the transfer point of flight status parameter in vertical section, and Pre and Post respectively indicate segment and boat
The front and back of waypoint is to connection relationship, m:Indicate mission phase locating for aircraft；
Step A2, it is as follows to establish the full flight profile, mission profile hybrid model of aircraft,
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, solution track is speculated by the way of hybrid system emulation: continuous using state using by the method for time subdivision
The characteristic Recursive Solution any time aircraft of variation is in voyage of a certain mission phase away from reference point
And heightWherein J0Voyage for initial time aircraft away from reference point, △ τ are the number of time window
Value, J (τ) are voyage of the τ moment aircraft away from reference point, h0Height for initial time aircraft away from reference point, when h (τ) is τ
Height of the aircraft away from reference point is carved, thereby it is assumed that the 4D track for obtaining single aircraft；
Step A4, Lothrus apterus allotment is implemented to more aircraft coupling models: reaches the time in crosspoint in advance according to two aircrafts, according to
Air traffic control principle, the aircraft 4D track that space requirement is nearby unsatisfactory for crosspoint carry out quadratic programming, obtain nothing
Conflict 4D track.
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| CN106571068A (en) * | 2015-01-07 | 2017-04-19 | 江苏理工学院 | Method for predicting trajectory of aircraft based on 4D flight path operation |
| CN105225541A (en) * | 2015-10-29 | 2016-01-06 | 中国民航大学 | Based on the method for Trajectory Prediction in short-term that blank pipe historical data is excavated |
| 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 |
| CN108764560B (en) * | 2018-05-22 | 2022-03-15 | 电子科技大学 | Aircraft scene trajectory prediction method based on long-short term memory neural network |
| CN110276993A (en) * | 2019-07-26 | 2019-09-24 | 郭静方 | A kind of air traffic control method and system based on 4D track |
| CN110910337A (en) * | 2019-11-29 | 2020-03-24 | 珠海云航智能技术有限公司 | Ship track fusion method and device and terminal equipment |
| CN111968414B (en) * | 2020-08-26 | 2022-08-05 | 成都民航空管科技发展有限公司 | 4D trajectory prediction method and device based on big data and AI and electronic equipment |
| CN114664120B (en) * | 2022-03-15 | 2023-03-24 | 南京航空航天大学 | ADS-B-based aircraft autonomous interval control method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070032940A1 (en) * | 2003-05-14 | 2007-02-08 | Jacques Villiers | Device and method for providing automatic assistance to air traffic controllers |
| CN101630446A (en) * | 2009-07-21 | 2010-01-20 | 民航数据通信有限责任公司 | Method for evaluating aircraft state based on broadcast type automatic correlative monitoring data and system thereof |
| CN101692315A (en) * | 2009-09-25 | 2010-04-07 | 民航总局空管局技术中心 | Method for analyzing high precision 4D flight trajectory of airplane based on real-time radar data |
| CN102013175A (en) * | 2010-12-16 | 2011-04-13 | 四川川大智胜软件股份有限公司 | Mid-term air traffic conflict detection method based on 4D flight path and radar data |
| 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 |
| EP1995706A3 (en) * | 2007-05-15 | 2012-11-14 | The Boeing Company | Systems and methods for real-time conflict-checked, operationally preferred flight trajectory revision recommendations |
| CN103824477A (en) * | 2014-02-21 | 2014-05-28 | 南京莱斯信息技术股份有限公司 | Blank pipe radar false target automatic recognition method |
| US20140188377A1 (en) * | 2012-12-29 | 2014-07-03 | Google Inc. | Methods and Systems for Determining Fleet Trajectories to Satisfy a Sequence of Coverage Requirements |
| CN104240541A (en) * | 2014-09-09 | 2014-12-24 | 中国电子科技集团公司第二十八研究所 | 4D track generating method |
Family Cites Families (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6873903B2 (en) * | 2001-09-07 | 2005-03-29 | R. Michael Baiada | Method and system for tracking and prediction of aircraft trajectories |
| US8463461B2 (en) * | 2005-03-30 | 2013-06-11 | The Boeing Company | Trajectory prediction based on state transitions and lantencies |
| EP2040137B1 (en) * | 2007-09-21 | 2012-05-16 | The Boeing Company | Predicting aircraft trajectory |
| CN101577058B (en) * | 2009-05-26 | 2011-05-18 | 民航数据通信有限责任公司 | Data processing method capable of supporting broadcast-type traffic information service |
| CN102073175B (en) * | 2009-11-25 | 2014-08-13 | 上海天马微电子有限公司 | Liquid crystal display panel and liquid crystal display device |
| CN101984560A (en) * | 2010-04-27 | 2011-03-09 | 中国人民解放军海军航空工程学院 | Centralized multi-source combined Viterbi data interconnection tracker |
| ITRM20110651A1 (en) * | 2010-12-20 | 2012-06-21 | Selex Sistemi Integrati Spa | METHOD OF QUICK PREDICTION OF THE VERTICAL PROFILE OF THE TRAJECTORY FOR THE MANAGEMENT OF AIR TRAFFIC, AND ITS RELATED ATM SYSTEM. |
| US8606491B2 (en) * | 2011-02-22 | 2013-12-10 | General Electric Company | Methods and systems for managing air traffic |
| CN103336863B (en) * | 2013-06-24 | 2016-06-01 | 北京航空航天大学 | The flight intent recognition methods of flight path observed data of flying based on radar |
| CN103593680B (en) * | 2013-11-19 | 2016-09-14 | 南京大学 | A kind of dynamic gesture identification method based on the study of HMM independent increment |
| CN104050506A (en) * | 2014-06-24 | 2014-09-17 | 电子科技大学 | Aircraft conflict detection method based on Spiking neural network |
| CN106571068A (en) * | 2015-01-07 | 2017-04-19 | 江苏理工学院 | Method for predicting trajectory of aircraft based on 4D flight path operation |
-
2015
- 2015-01-07 CN CN201610913768.7A patent/CN106571068A/en active Pending
- 2015-01-07 CN CN201510008041.XA patent/CN104504939B/en active Active
- 2015-01-07 CN CN201610913526.8A patent/CN106297419B/en active Active
- 2015-01-07 CN CN201610913767.2A patent/CN106297420A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070032940A1 (en) * | 2003-05-14 | 2007-02-08 | Jacques Villiers | Device and method for providing automatic assistance to air traffic controllers |
| EP1995706A3 (en) * | 2007-05-15 | 2012-11-14 | The Boeing Company | Systems and methods for real-time conflict-checked, operationally preferred flight trajectory revision recommendations |
| CN101630446A (en) * | 2009-07-21 | 2010-01-20 | 民航数据通信有限责任公司 | Method for evaluating aircraft state based on broadcast type automatic correlative monitoring data and system thereof |
| CN101692315A (en) * | 2009-09-25 | 2010-04-07 | 民航总局空管局技术中心 | Method for analyzing high precision 4D flight trajectory of airplane based on real-time radar data |
| CN102013175A (en) * | 2010-12-16 | 2011-04-13 | 四川川大智胜软件股份有限公司 | Mid-term air traffic conflict detection method based on 4D flight path and radar data |
| 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 |
| US20140188377A1 (en) * | 2012-12-29 | 2014-07-03 | Google Inc. | Methods and Systems for Determining Fleet Trajectories to Satisfy a Sequence of Coverage Requirements |
| CN103824477A (en) * | 2014-02-21 | 2014-05-28 | 南京莱斯信息技术股份有限公司 | Blank pipe radar false target automatic recognition method |
| CN104240541A (en) * | 2014-09-09 | 2014-12-24 | 中国电子科技集团公司第二十八研究所 | 4D track generating method |
Non-Patent Citations (1)
| Title |
|---|
| 飞行冲突解决机制研究;韩云祥等;《桂林航天工业高等专科学校学报》;20091231(第3期);317-319 * |
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| CN104504939A (en) | 2015-04-08 |
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