CN106297420A - Airborne vehicle trajectory predictions method based on 4D - Google Patents

Airborne vehicle trajectory predictions method based on 4D Download PDF

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Publication number
CN106297420A
CN106297420A CN201610913767.2A CN201610913767A CN106297420A CN 106297420 A CN106297420 A CN 106297420A CN 201610913767 A CN201610913767 A CN 201610913767A CN 106297420 A CN106297420 A CN 106297420A
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airborne vehicle
flight path
data
flight
module
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韩云祥
赵景波
李广军
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Jiangsu University of Technology
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Jiangsu University of Technology
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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
    • 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]
    • G08G5/0043—Traffic management of multiple aircrafts from the ground

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  • 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 a kind of airborne vehicle trajectory predictions method based on 4D, described air traffic control system includes data communication module, monitors data fusion module, Airborne Terminal module, control terminal module, wherein monitor that data fusion module monitors the fusion of data and automatic dependent surveillance data for realizing air traffic control radar, provide real-time flight path information for control terminal module；Control terminal module include flying front Lothrus apterus 4D flight path generate, in-flight short-term 4D flight path generate this 2 submodules；The airborne vehicle trajectory predictions method of said system, relies on control terminal module, processes flight plan data and utilizes HMM to generate 4D flight path, it is achieved the analysis of the traffic conflict that spatial domain traffic is potential.The present invention can be effectively improved the safety of air traffic.

Description

Airborne vehicle trajectory predictions method based on 4D
The application is Application No.: 201510008041.X, and invention and created name is " a kind of air traffic control system Airborne vehicle trajectory predictions method ", filing date: 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, particularly relate to a kind of based on the operation of 4D flight path aerial The method that airborne vehicle track is predicted by traffic control system.
Background technology
Along with fast-developing the becoming increasingly conspicuous with spatial domain resource-constrained contradiction of World Airways transport service, aloft traffic flow is close The complicated spatial domain of collection, the air traffic control mode still using flight plan to combine interval allotment gradually demonstrates that it falls behind Property, it is in particular in: (1) flight plan does not configure accurate blank pipe interval for airborne vehicle, easily causes traffic flow tactics pipe Crowded in reason, reduces spatial domain safety；(2) reckoning to flight profile, mission profile of the air traffic control automation system centered by 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 traffic flow is carried out strategic Management.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) describing with the accurate of time, operation based on flight path refers to use " controlling the time of advent " on the way point of 4D flight path, I.e. control the airborne vehicle " time window " by specific way point.In high density spatial domain 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.
The air traffic method of operation run based on flight path needs to carry out single aircraft flight path on strategic level Calculating and optimize, the traffic flow constituting many airborne vehicles is implemented collaborative and adjusts；By revising traffic flow on pre-tactical level In the flight path of indivedual airborne vehicles 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 be predicted the track of airborne vehicle exactly, All can not the most in real time the track of airborne vehicle be predicted at present, the difference particularly that real-time is done.
Summary of the invention
The technical problem to be solved in the present invention is to be to overcome the deficiencies in the prior art, it is provided that a kind of based on the operation of 4D flight path The airborne vehicle trajectory predictions method of air traffic control system, the track of airborne vehicle can be predicted effectively, accurately and real-time.
The technical scheme realizing the object of the invention is to provide a kind of airborne vehicle trajectory predictions method based on 4D, by handing in the air Logical control system is implemented, and 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 air traffic control radar and monitors data and automatic dependent surveillance number According to fusion, for control terminal module provide real-time flight path information；
Described control terminal module includes following submodule:
Lothrus apterus 4D flight path generation module before flight, according to flight plan and the forecast data of world area forecast system, Set up airborne vehicle kinetic model, then set up flight path conflict according to flight collision Coupling point and allocate theoretical model in advance, generate boat Pocket Lothrus apterus 4D flight path；
Short-term 4D flight path generation module in-flight, according to the real-time flight path information monitoring that data fusion module provides, utilizes HMM, thus it is speculated that the airborne vehicle 4D track in following certain time window；
Described airborne vehicle trajectory predictions method based on 4D includes following several step:
Before step A, flight, Lothrus apterus 4D flight path generation module is according to flight plan and the forecast of world area forecast system Data, set up airborne vehicle kinetic model, and foundation flight collision Coupling point is set up flight path conflict and allocated theoretical model in advance, generates Airborne vehicle Lothrus apterus 4D flight path；
Air traffic control radar is monitored that data merge with automatic dependent surveillance data by step B, supervision data fusion module, raw Become airborne vehicle real-time flight path information and be supplied to control terminal module；The flight path of short-term 4D in-flight 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 according to the airborne vehicle 4D track in airborne vehicle real-time flight path information and the following certain time window of history flight path information supposition 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], use first-order difference method carry out processing to it obtain new airborne vehicle 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)；
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', uses genetic algorithm for clustering to cluster it respectively；
Step B8, to cluster after airborne vehicle track data utilize HMM to carry out parameter training, 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 update period ζ ', according to T' nearest position detection value and use B-W algorithm roll acquisition up-to-date hidden horse Er Kefu model parameter λ '；
Step B9, foundation HMM parameter, use Viterbi algorithm to obtain corresponding to current time observation Hidden state q；
Step B10, prediction time domain h' of passing through to set, hidden state q based on airborne vehicle current time, acquisition 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, it was predicted that time domain h' is 300 seconds.
Further, the B8 of step B specifically refers to: the flight path sequence data length owing to being obtained is dynamically 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, in order to speculate the airborne vehicle position in certain moment following more accurately；Every period ζ ', depend on T' the 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 conception of history measured value (o1,o2,…,oH), hidden state q based on airborne vehicle current time, predicted by setting Time domain h', obtains airborne vehicle position prediction value O at future time period h' at moment t.
Further, the periodIt it is 4 seconds.
Further, the airborne vehicle Lothrus apterus 4D flight path of described step A generates in accordance with the following methods:
Step A1, the aircraft states that carries out shift and model, and according to the flying height section of airborne vehicle in flight plan, set up At the Petri network model of different legs transfer: E=, (g, G, Pre, Post are m) the airborne vehicle stage to shift mould to single airborne vehicle Type, wherein g represents that flight leg, G represent the transfer point of flight status parameter in vertical section, Pre and Post represents boat respectively To annexation before and after section and way point,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 predicted value, tLOCFor temperature forecast value, vHFor altitude rate, vGSFor ground velocity；
Step A3, the mode of hybrid system emulation is used to speculate and solve flight path: to use the method for time subdivision, utilize shape State continually varying characteristic Recursive Solution any time airborne vehicle is in a certain mission phase voyage away from reference pointAnd heightWherein J0For initial time airborne vehicle away from reference point Voyage, Δ τ is the numerical value of time window, and J (τ) is the τ moment airborne vehicle voyage away from reference point, h0For initial time airborne vehicle away from ginseng The height of examination point, h (τ) is the τ moment airborne vehicle height away from reference point, 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 in advance according to two airborne vehicles, According to air traffic control principle, the airborne vehicle 4D flight path being unsatisfactory for space requirement near cross point is carried out quadratic programming, To Lothrus apterus 4D flight path.
Further, described step B monitoring, air traffic control radar is monitored data and automatic dependent surveillance by data fusion module Data merge, and generate airborne vehicle real-time flight path information, the most in accordance with the following methods:
Step B1, by coordinate unit and time unification；
Step B2, use closest data association algorithm to be associated by the point belonging to same target, extract targetpath； Step B3, by the track data that extracts from automatic dependent surveillance system and air traffic control radar respectively from different space-time ginsengs
Examine coordinate system conversion, be registered to the space-time reference coordinate system that control terminal is unified；
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 boats Mark is uncorrelated；Otherwise these two flight paths are correlated with, and can merge；
Step B5, relevant flight path is merged.
Further, relevant flight path is merged by described step B5, use weighting based on the sampling period to put down All algorithms, its weight coefficient determines according to sampling period and precision of information, recycling 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: a kind of based on 4D airborne vehicle trajectory predictions method of (1) present invention is in aviation During device real-time track speculates, having incorporated the impact of random factor, the rolling track used speculates that scheme can carry in time Take the changing condition of extraneous random factor, improve the accuracy that airborne vehicle track speculates.
(2) a kind of based on 4D airborne vehicle trajectory predictions method of the present invention is smart to reckoning and the Trajectory Prediction of flight profile, mission profile Degree height, and then conflict dissolution ability and automatization level are improved, reduce the workload of controller.
Accompanying drawing explanation
Fig. 1 generates method flow schematic diagram for Lothrus apterus 4D flight path before flight；
Fig. 2 is short-term 4D flying track conjecture method flow schematic diagram in-flight.
Detailed description of the invention
(embodiment 1)
The air traffic control system run based on 4D flight path of the present embodiment, leads to including Airborne Terminal module 101, data Letter module 102, supervision data fusion module 103 and control terminal module 104.Hereinafter the detailed description of the invention 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, the most still gathers the interface of current aerospace device position data.
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 gathered is vectorial, velocity vector, and the catchword of this airborne vehicle, passes to machine by information and data after coding Carry data communication module 102；(2) airborne vehicle driver needs the flight intent inconsistent with ground control order, passes through people Machine inputting interface, and agreement ground controller can with identify form pass to airborne data communication by information and data Module 102.Additionally Airborne Terminal module 101 realizes following information output: (1) passes through terminal display, receives and shows The air traffic control instruction that pilot can identify；(2) the Lothrus apterus 4D boat that generate front with the flight of explicitly facial canal terminal is received Mark, and when the optimum calculated after ground line end-probing to conflict frees 4D flight path.
2. data communication module
Data communication module 102 can realize vacant lot bidirectional data communication, it is achieved 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, as the information such as flight intent, flight speed, meteorology are transferred to ground secondary radar (SSR), secondary radar receive after data message is resolved, and be transferred to central data process assembly 301 decode, by refer to Track data interface is made to be transferred to control terminal 104；Upstream data communication: ground control terminal 104 is by instruction track data Interface, after central data processes assembly 301 coding, the inquisitor of ground secondary radar just ground control order or reference 4D Flight path information is transmitted and shows at Airborne Terminal 101.
3. monitor data fusion module
Monitor that data fusion module 103 realizes air traffic control radar and monitors the fusion with automatic dependent surveillance ADS-B data, for pipe The flight path of short-term 4D in-flight in terminal module 104 processed generates submodule and real-time flight conflict monitoring provides with alarm submodule Flight path information in real time.
Its specific embodiments is as follows:
(1) at pretreatment stage by coordinate unit and time unification, it is assumed that extract from ADS-B and air traffic control radar respectively Data are the coordinate (such as longitude, latitude, height above sea level) of series of discrete point, each point correspondence acquisition time；(2) use 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 from different space-time reference coordinate system, is registered to the unified space-time of control terminal with reference to sitting Mark system；(4) correlation coefficient of two flight paths is calculated, if correlation coefficient is less than a certain predetermined threshold value, then it is assumed that two flight paths are not Relevant, otherwise these two flight paths are correlated with, and can merge；(5) relevant flight path is merged.Due to ADS-B and blank pipe The precision of radar is different with the sampling period, native system use Weighted Average Algorithm based on the sampling period, its weight coefficient according to Sampling period and precision of information determine, recycling Weighted Average Algorithm is 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 include flying front Lothrus apterus 4D flight path generate, in-flight short-term 4D flight path generate this 2 sons Module.
(1) before flight, Lothrus apterus 4D flight path generates
The flight plan 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, describes the time locus of state evolution, generates airborne vehicle flight path.
As it is shown in figure 1, its specific implementation process is as follows:
First, aircraft states transfer modeling is carried out.Airborne vehicle shows as between leg dynamic along the process of track flight State handoff procedure, according to the flying height section of airborne vehicle in flight plan, sets up single airborne vehicle in the transfer of different legs (g, G, Pre, Post, m) be airborne vehicle stage metastasis model to Petri network model: E=, and wherein g represents that flight leg, G represent vertical Flight status parameter in straight section (include air speed, highly, configuration) transfer point, Pre and Post represents leg and air route respectively To annexation before and after Dian,Represent the mission phase residing for airborne vehicle.
Secondly, airborne vehicle full flight profile, mission profile hybrid model is set up.Airborne vehicle flight in single leg is considered as even Continuous process, according to particle energy model, derivation airborne vehicle the different operation phase with meteorological condition under airborne vehicle kinetics 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 predicted value, tLOCPre-for temperature Report value, vHFor altitude rate, vGSFor ground velocity.
Then, the mode using hybrid system emulation speculates and solves flight path.Use the method for time subdivision, utilization state Continually varying characteristic Recursive Solution any time airborne vehicle is in a certain mission phase voyage away from reference pointAnd heightWherein J0For initial time airborne vehicle away from reference point Voyage, Δ τ is the numerical value of time window, and J (τ) is the τ moment airborne vehicle voyage away from reference point, h0For initial time airborne vehicle away from ginseng The height of examination point, h (τ) is the τ moment airborne vehicle height away from reference point, thereby it is assumed that the 4D flight path obtaining single airborne vehicle.
Finally, many airborne vehicles coupling model is implemented Lothrus apterus allotment.Reach the time in cross point according to two airborne vehicles in advance, press According to air traffic control principle, the airborne vehicle 4D flight path being unsatisfactory for space requirement is carried out quadratic programming, obtain near cross point Lothrus apterus 4D flight path.
(2) short-term 4D flight path generates in-flight
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, Utilize HMM, thus it is speculated that the airborne vehicle 4D track in following 5 minutes windows.
As in figure 2 it is shown, 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], use first-order difference method discrete to its airborne vehicle carrying out processing acquisition new 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 airborne vehicle discrete two-dimensional position sequence Δ x and Δ y new after processing, By setting cluster number M', genetic algorithm for clustering is used respectively it to be clustered.
Then, HMM is utilized to carry out parameter training the airborne vehicle track data after cluster.At inciting somebody to action 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 ', according to T' nearest position detection value and use B-W algorithm to roll the up-to-date hidden Ma Er of acquisition Section's husband's model parameter λ ': the flight path sequence data length owing to being obtained is dynamically change, in order to real-time tracking airborne vehicle navigates Mark state change, it is necessary to initial flight path HMM parameter lambda '=(π, A, B) on the basis of it is adjusted again Whole, in order to speculate the airborne vehicle position in certain moment following more accurately.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) reappraise.
Again and, according to HMM parameter, use Viterbi algorithm to obtain corresponding to current time observation Hidden state q.
Finally, every the periodHMM parameter lambda according to up-to-date acquisition '=(π, A, B) and nearest H Conception of history measured value (o1,o2,…,oH), hidden state q based on airborne vehicle current time, by setting prediction time domain h', at moment t Obtain airborne vehicle position prediction value O at future time period h'.
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, it was predicted that time domain h' is 300 seconds, the periodIt it is 4 seconds.
Obviously, above-described embodiment is only for clearly demonstrating example of the present invention, and 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.Here without also cannot all of embodiment be given exhaustive.And these belong to this What bright spirit was extended out obviously changes or changes among still in protection scope of the present invention.

Claims (3)

1. an airborne vehicle trajectory predictions method based on 4D, is implemented by air traffic control system, described air traffic control System includes Airborne Terminal module, data communication module, supervision data fusion module and control terminal module；Supervision data are melted Compound module monitors the fusion of data and automatic dependent surveillance data for realizing air traffic control radar, provides in real time for control terminal module Flight path information；It is characterized in that:
Described control terminal module includes following submodule:
Lothrus apterus 4D flight path generation module before flight, according to flight plan and the forecast data of world area forecast system, sets up Airborne vehicle kinetic model, then sets up flight path conflict according to flight collision Coupling point and allocates theoretical model in advance, generate airborne vehicle Lothrus apterus 4D flight path；
Short-term 4D flight path generation module in-flight, according to the real-time flight path information monitoring that data fusion module provides, utilizes hidden horse Er Kefu model, thus it is speculated that the airborne vehicle 4D track in following certain time window；
Described airborne vehicle trajectory predictions method based on 4D includes following several step:
Before step A, flight, Lothrus apterus 4D flight path generation module is according to flight plan and the forecast data of world area forecast system, Set up airborne vehicle kinetic model, and foundation flight collision Coupling point is set up flight path conflict and allocated theoretical model in advance, generates aviation Device Lothrus apterus 4D flight path；
Air traffic control radar is monitored that data merge with automatic dependent surveillance data by step B, supervision data fusion module, generates boat Pocket real-time flight path information is also supplied to control terminal module；The flight path generation module of short-term 4D in-flight in control terminal module The airborne vehicle 4D track in following certain time window is speculated according to airborne vehicle real-time flight path information and history flight path information；Described depend on Concrete reality according to the airborne vehicle 4D track in airborne vehicle real-time flight path information and the following certain time window of history flight path information supposition Execute process 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], use first-order difference method discrete to its airborne vehicle carrying out processing acquisition new 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, logical Cross setting cluster number M', use genetic algorithm for clustering respectively it to be clustered；
Step B8, to cluster after airborne vehicle track data utilize HMM to carry out parameter training, by will process After airborne vehicle running orbit data Δ x and Δ y be considered as the aobvious observation of hidden Markov models, by setting hidden state number N' and parameter update period ζ ', according to T' nearest position detection value and use B-W algorithm to roll the up-to-date hidden Ma Erke of acquisition Husband's model parameter λ '；
Step B9, according to HMM parameter, use that Viterbi algorithm obtains corresponding to current time observation is hidden State q；
Step B10, by set prediction time domain h', hidden state q based on airborne vehicle current time, obtain future time period airborne vehicle Position prediction value O；
In step B, 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 ', T' It is 10, it was predicted that time domain h' is 300 seconds.
Airborne vehicle trajectory predictions method based on 4D the most according to claim 1, it is characterised in that: the B8 of step B is concrete Refer to: the flight path sequence data length owing to being obtained is dynamically change, in order to the state of real-time tracking airborne vehicle flight path becomes Change, it is necessary to initial flight path HMM parameter lambda '=(π, A, B) on the basis of it is readjusted, in order to more smart Really speculate the airborne vehicle position in certain moment following；Every period ζ ', according to T' observation (o of up-to-date acquisition1,o2,…, oT') to flight path HMM parameter lambda '=(π, A, B) reappraise；
The B10 of step B specifically refers to: every the periodHMM parameter lambda according to up-to-date acquisition '=(π, A, B) With nearest H conception of history measured value (o1,o2,…,oH), hidden state q based on airborne vehicle current time, by setting prediction time domain H', obtains airborne vehicle position prediction value O at future time period h' at moment t.
Airborne vehicle trajectory predictions method based on 4D the most according to claim 2, it is characterised in that: the periodIt it is 4 seconds.
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