CN106409013A - Aircraft trajectory prediction method - Google Patents

Aircraft trajectory prediction method Download PDF

Info

Publication number
CN106409013A
CN106409013A CN201610871349.1A CN201610871349A CN106409013A CN 106409013 A CN106409013 A CN 106409013A CN 201610871349 A CN201610871349 A CN 201610871349A CN 106409013 A CN106409013 A CN 106409013A
Authority
CN
China
Prior art keywords
airborne vehicle
flight
flight path
data
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201610871349.1A
Other languages
Chinese (zh)
Inventor
韩云祥
赵景波
李广军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu University of Technology
Original Assignee
Jiangsu University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangsu University of Technology filed Critical Jiangsu University of Technology
Priority to CN201610871349.1A priority Critical patent/CN106409013A/en
Publication of CN106409013A publication Critical patent/CN106409013A/en
Pending legal-status Critical Current

Links

Classifications

    • 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
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0073—Surveillance aids
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The present invention relates to an aircraft trajectory prediction method. The aircraft trajectory prediction method is implemented by an air traffic control system. 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 air control radar monitoring data and automatic correlation monitoring data and providing real-time flight trajectory information for the control terminal module, and the control terminal module includes a pre-flight conflict-free 4D flight trajectory generation sub-module and an in-flight medium-short-term 4D flight trajectory generation sub-module. According to the aircraft trajectory prediction method of the system, flight plan data are processed by means of the control terminal module, and hidden Markov model is adopted to generate 4D flight trajectories, and therefore, analysis on potential traffic conflicts in an air traffic condition can be realized. With the method of the invention adopted, the safety of air traffic can be effectively improved.

Description

A kind of airborne vehicle trajectory predictions method
The application is Application No.：201510007935.7, invention and created name is《The aviation of air traffic control system Device trajectory predictions method》, the applying date is：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 a kind of aerial based on the operation of 4D flight path The method that traffic control system is predicted to airborne vehicle track.
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 security；(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.Prediction for airborne vehicle track seems outstanding For 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 arrival time " 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 Security 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, All can not accurately in real time the track of airborne vehicle be predicted at present, the difference particularly that real-time is done.
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 airborne vehicle trajectory predictions method, can effectively, accurately and real-time predict the track of airborne vehicle.
The technical scheme realizing the object of the invention is to provide a kind of airborne vehicle trajectory predictions method by air traffic control system System implement, described air traffic control system include Airborne Terminal module, data communication module, monitor data fusion module and Control terminal module；Monitor that data fusion module is used for realizing air traffic control radar monitoring melting of data and automatic dependent surveillance data Close, provide real-time flight path information for control terminal module；
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；
The airborne vehicle trajectory predictions method 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 pre-process, 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 △ 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, airborne vehicle track data is clustered, to new airborne vehicle discrete two-dimensional position sequence △ x and △ after processing 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 △ x and △ 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.
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 phaseAnd heightWherein J0For initial time airborne vehicle away from reference point Voyage, △ τ is the numerical value of time window, and J (τ) is the voyage away from reference point for the τ moment airborne vehicle, h0For initial time airborne vehicle away from ginseng The height of examination point, h (τ) is the height away from reference point for the τ moment 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 crosspoint 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 crosspoint, 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 coefficient correlation of two flight paths of calculating, if coefficient correlation 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.
The present invention has positive effect：(1) the airborne vehicle trajectory predictions method of the air traffic control system of the present invention During airborne vehicle real-time track speculates, incorporate the impact of enchancement factor, the rolling track being adopted speculates that scheme can Extract the changing condition of extraneous enchancement factor in time, improve the accuracy of airborne vehicle track supposition.
(2) the airborne vehicle trajectory predictions method of the air traffic control system of the present invention is to the reckoning of flight profile, mission profile and flight path Precision of prediction is high, and then conflict dissolution ability and automatization level are improved, and reduces the live load 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.
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, flying 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 coefficient correlation, if coefficient correlation 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 this 2 sons Module.
(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 time 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 dynamics 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 phaseAnd heightWherein J0For initial time airborne vehicle away from reference point Voyage, △ τ is the numerical value of time window, and J (τ) is the voyage away from reference point for the τ moment airborne vehicle, h0For initial time airborne vehicle away from ginseng The height of examination point, h (τ) is the height away from reference point for the τ 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 crosspoint 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 crosspoint, 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, airborne vehicle track data is pre-processed, according to acquired airborne vehicle original discrete two-dimensional position sequence x= [x1,x2,…,xn] and y=[y1,y2,...,yn], the new airborne vehicle of process acquisition is carried out using first-order difference method to it discrete 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).
Secondly, airborne vehicle track data is clustered.To new airborne vehicle discrete two-dimensional position sequence △ x and △ 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 △ x and △ 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 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.
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. a kind of airborne vehicle trajectory predictions method is implemented by air traffic control system, and described air traffic control system includes machine Mounted terminal module, data communication module, supervision data fusion module and control terminal module；Monitor that data fusion module is used for Realize the fusion that air traffic control radar monitors data and automatic dependent surveillance data, provide real-time flight path information for control terminal module； It is characterized in that：
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；
The airborne vehicle trajectory predictions method 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 pre-process, 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；
Step B7, airborne vehicle track data is clustered, to new airborne vehicle discrete two-dimensional position sequence Δ x and Δ y after processing, lead to Cross setting cluster number M', 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 Δ x and Δ y afterwards is considered as the aobvious observation of hidden Markov models, by setting hidden state number 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 Erke Husband's model parameter λ '；
Step B9, foundation HMM parameter, are obtained hidden corresponding to current time observation using Viterbi algorithm State q；
Step B10, by setting prediction time domain h', based on hidden state q of airborne vehicle current time, obtain future time period airborne vehicle Position prediction value O；
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 single The Petri net model that airborne vehicle shifts in different legs：E=(g, G, Pre, Post, m) are airborne vehicle stage metastasis model, its Middle g represents flight leg, and G represents the transfer point of flight status parameter in vertical section, Pre and Post represents leg and boat respectively To annexation before and after waypoint,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, utilization state continuously becomes The characteristic Recursive Solution any time airborne vehicle changed 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 crosspoint according to two airborne vehicles in advance, according to Air traffic control principle, carries out quadratic programming to the airborne vehicle 4D flight path being unsatisfactory for space requirement near crosspoint, obtains no Conflict 4D flight path.
CN201610871349.1A 2015-01-07 2015-01-07 Aircraft trajectory prediction method Pending CN106409013A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610871349.1A CN106409013A (en) 2015-01-07 2015-01-07 Aircraft trajectory prediction method

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610871349.1A CN106409013A (en) 2015-01-07 2015-01-07 Aircraft trajectory prediction method
CN201510007935.7A CN104504937B (en) 2015-01-07 2015-01-07 The airborne vehicle trajectory predictions method of air traffic control system

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
CN201510007935.7A Division CN104504937B (en) 2015-01-07 2015-01-07 The airborne vehicle trajectory predictions method of air traffic control system

Publications (1)

Publication Number Publication Date
CN106409013A true CN106409013A (en) 2017-02-15

Family

ID=52946553

Family Applications (4)

Application Number Title Priority Date Filing Date
CN201610871468.7A Active CN106251709B (en) 2015-01-07 2015-01-07 Aircraft trajectory predictions method
CN201610871349.1A Pending CN106409013A (en) 2015-01-07 2015-01-07 Aircraft trajectory prediction method
CN201510007935.7A Active CN104504937B (en) 2015-01-07 2015-01-07 The airborne vehicle trajectory predictions method of air traffic control system
CN201610871406.6A Pending CN106205221A (en) 2015-01-07 2015-01-07 The airborne vehicle trajectory predictions method run based on 4D flight path

Family Applications Before (1)

Application Number Title Priority Date Filing Date
CN201610871468.7A Active CN106251709B (en) 2015-01-07 2015-01-07 Aircraft trajectory predictions method

Family Applications After (2)

Application Number Title Priority Date Filing Date
CN201510007935.7A Active CN104504937B (en) 2015-01-07 2015-01-07 The airborne vehicle trajectory predictions method of air traffic control system
CN201610871406.6A Pending CN106205221A (en) 2015-01-07 2015-01-07 The airborne vehicle trajectory predictions method run based on 4D flight path

Country Status (1)

Country Link
CN (4) CN106251709B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109143850A (en) * 2018-07-05 2019-01-04 江苏理工学院 Rail vehicle suspension system method for parameter estimation based on strong tracking filter
CN113284369A (en) * 2021-05-14 2021-08-20 中国民航大学 Prediction method for actual measurement airway data based on ADS-B

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108257421A (en) * 2017-12-22 2018-07-06 广州市中南民航空管通信网络科技有限公司 A kind of flight Trajectory Prediction method, electronic equipment and storage medium
CN110648561A (en) * 2019-11-04 2020-01-03 中国民航大学 Air traffic situation risk measurement method based on double-layer multi-level network model
SG10202001670UA (en) * 2020-02-25 2021-09-29 Thales Solutions Asia Pte Ltd Computer-implemented method and non-transitory computer-readable memory for air traffic control performance monitoring
CN111627258A (en) * 2020-04-16 2020-09-04 中国航空无线电电子研究所 Method for generating air traffic operation simulation data
CN115956226A (en) * 2020-08-18 2023-04-11 西门子工业软件有限公司 Method and data processing system for multi-state simulation for verifying industrial scene security
CN112017480B (en) * 2020-08-20 2021-07-13 南京航空航天大学 Dynamic memory planning method for green cruise track of aircraft
CN113611158A (en) * 2021-06-30 2021-11-05 四川大学 Aircraft trajectory prediction and altitude deployment method based on airspace situation
CN114842678B (en) * 2022-03-28 2024-04-26 中国民用航空中南地区空中交通管理局广西分局 Civil aviation control operation site similarity daily measurement system
CN115035747A (en) * 2022-06-07 2022-09-09 成都民航空管科技发展有限公司 Flow overrun warning method and system
CN116824925B (en) * 2023-08-31 2023-11-03 四川九洲空管科技有限责任公司 Method for improving TCAS target track quality based on mixed monitoring

Citations (5)

* Cited by examiner, † Cited by third party
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
US20080288164A1 (en) * 2007-05-15 2008-11-20 The Boeing Company Systems and Methods for Real-Time Conflict-Checked, Operationally Preferred Flight Trajectory Revision Recommendations
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
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 (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8560148B2 (en) * 2010-11-09 2013-10-15 Lockheed Martin Corporation Method and apparatus for air traffic trajectory synchronization
US9014880B2 (en) * 2010-12-21 2015-04-21 General Electric Company Trajectory based sense and avoid
US8433506B2 (en) * 2011-06-30 2013-04-30 General Electric Company Weather data selection relative to an aircraft trajectory
US8798898B2 (en) * 2011-10-31 2014-08-05 General Electric Company Methods and systems for inferring aircraft parameters
CN102436764A (en) * 2011-11-21 2012-05-02 南京莱斯信息技术股份有限公司 Method for mining flight number regulatory factors through historical data
CN102496313B (en) * 2011-12-31 2013-10-23 南京莱斯信息技术股份有限公司 Correction method of aircraft plan prediction locus by using supervision data
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

Patent Citations (5)

* Cited by examiner, † Cited by third party
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
US20080288164A1 (en) * 2007-05-15 2008-11-20 The Boeing Company Systems and Methods for Real-Time Conflict-Checked, Operationally Preferred Flight Trajectory Revision Recommendations
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
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)

* Cited by examiner, † Cited by third party
Title
潘奇明 等: "基于隐马尔可夫模型的运动目标轨迹识别", 《计算机应用研究》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109143850A (en) * 2018-07-05 2019-01-04 江苏理工学院 Rail vehicle suspension system method for parameter estimation based on strong tracking filter
CN113284369A (en) * 2021-05-14 2021-08-20 中国民航大学 Prediction method for actual measurement airway data based on ADS-B
CN113284369B (en) * 2021-05-14 2022-07-01 中国民航大学 Prediction method for actually measured airway data based on ADS-B

Also Published As

Publication number Publication date
CN104504937B (en) 2017-01-04
CN106205221A (en) 2016-12-07
CN106251709A (en) 2016-12-21
CN104504937A (en) 2015-04-08
CN106251709B (en) 2018-11-23

Similar Documents

Publication Publication Date Title
CN104485025B (en) The flight collision method for early warning of air traffic control system
CN106157700B (en) Air traffic control method based on the operation of 4D flight paths
CN106340208B (en) The working method of air traffic control system based on the operation of 4D track
CN104504937B (en) The airborne vehicle trajectory predictions method of air traffic control system
CN104504939B (en) A kind of airborne vehicle trajectory predictions method of air traffic control system
CN104504942B (en) A kind of flight collision method for early warning of air traffic control system
CN102509475B (en) Air traffic control system and method for four-dimensional (4D)-trajectory-based operation
CN107085978B (en) Control aid decision instruction generation method based on required arrival time
Ramasamy et al. Next generation flight management system for real-time trajectory based operations
CN104504941B (en) A kind of Solving Flight Conflicts method of air traffic control system
CN105894862A (en) Intelligent command system for air traffic control
CN104077928A (en) Position information updating method suitable for irregular-flying navigation through points out of airways
Thipphavong Reducing aircraft climb trajectory prediction errors with top-of-climb data
Jiang et al. Study on 4D track prediction and optimization of aircraft climbing phase
Chu et al. An Adaptive Capacity Estimation Method for Terminal Airspace Operation
Ma et al. Analysis of key technologies for UAV conflict-free four-dimensional trajectory planning in controlled airspace
Liu et al. 4D Trajectory and Controller Command Generation Based on Schedule Time of Arrival

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20170215