CN104504937A - Aircraft track predication method of air traffic control system - Google Patents
Aircraft track predication method of air traffic control system Download PDFInfo
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- CN104504937A CN104504937A CN201510007935.7A CN201510007935A CN104504937A CN 104504937 A CN104504937 A CN 104504937A CN 201510007935 A CN201510007935 A CN 201510007935A CN 104504937 A CN104504937 A CN 104504937A
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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
-
- 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]
- G08G5/0073—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
Abstract
The invention relates to an aircraft track predication method of 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; the monitoring data fusion module is used for fusing air control radar monitoring data and automatic related monitoring data so as to provide real-time track information to the control terminal module; the control terminal module comprises a preflight conflict-free 4D track generation sub-module and an in-flight short-period 4D track generation sub-module. The aircraft track predication method of the system has the advantages that the flight plan data are processed through the control terminal module, and a 4D track is generated through a hidden markov model, so as to analyze the potential traffic conflict of the air traffic condition, and as a result, the safety of air traffic can be improved.
Description
Technical field
The present invention relates to a kind of air traffic control system and method, particularly relate to a kind of method that air traffic control system run based on 4D flight path is predicted aircraft track.
Background technology
Along with fast-developing the becoming increasingly conspicuous with spatial domain resource-constrained contradiction of World Airways forwarding business, the complicated spatial domain that traffic flow is aloft intensive, still the air traffic control mode adopting flight planning to allocate in conjunction with interval demonstrates its lag gradually, be in particular in: (1) flight planning is not for aircraft configures accurate blank pipe interval, easily cause traffic flow tactics manage in crowded, reduce spatial domain security; (2) air traffic control automation system centered by flight planning, to the reckoning of flight profile, mission profile and Trajectory Prediction low precision, causes conflict dissolution ability; (3) job of air traffic control still lays particular emphasis on the personal distance kept between single aircraft, is difficult to rise to and carries out strategic Management to traffic flow.Prediction for aircraft track seems particularly important.
4D flight path is with room and time form, to each point locus (longitude, latitude and height) in a certain aircraft flight path and the accurate description of time, operation based on flight path refers to that use on the way point of 4D flight path " controls time of arrival ", namely controls " time window " of aircraft by specific way point.In high density spatial domain using the operation (Trajectory based Operation) based on 4D flight path as one of basic operating mechanism, it is following a kind of effective means spatial domain under large discharge, high density, closely-spaced condition being implemented to management, the uncertainty of aircraft flight path can be reduced significantly, improve security and the utilization factor of spatial domain and Airport Resources.
The air traffic method of operation run based on flight path needs calculate single aircraft flight path and optimize on strategic level, implements collaborative and adjustment to the traffic flow that many aircrafts are formed; By revising the flight path of indivedual aircraft in traffic flow to solve congestion problems on pre-tactical level, and ensure the operational efficiency of all aircrafts in this traffic flow; And prediction conflicts and optimization solution off-square case on tactical level, then depend on very much and can exactly the track of aircraft be predicted, all can not accurately in real time the track of aircraft 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 overcome the deficiencies in the prior art, provides a kind of aircraft trajectory predictions method of the air traffic control system based on the operation of 4D flight path, can predict the track of aircraft effectively, accurately and real-time.
The technical scheme realizing the object of the invention is to provide a kind of aircraft trajectory predictions method of air traffic control system, and described air traffic control system comprises Airborne Terminal module, data communication module, monitoring data Fusion Module and control terminal module; Monitoring data Fusion Module for realizing the fusion of air traffic control radar monitoring data and automatic dependent surveillance data, for control terminal module provides real-time flight path information;
Described control terminal module comprises following submodule:
Lothrus apterus 4D flight path generation module before flight, according to the forecast data of flight planning and world area forecast system, set up aircraft kinetic model, then set up flight path conflict according to flight collision Coupling point and allocate theoretical model in advance, generate aircraft Lothrus apterus 4D flight path;
Short-term 4D flight path generation module in-flight, the real-time flight path information provided according to monitoring data Fusion Module, utilizes Hidden Markov Model (HMM), infers the aircraft 4D track in following certain hour window;
The aircraft trajectory predictions method of described air traffic control system comprises following several step:
Before steps A, flight, Lothrus apterus 4D flight path generation module is according to the forecast data of flight planning and world area forecast system, set up aircraft kinetic model, and set up flight path conflict according to flight collision Coupling point and allocate theoretical model in advance, generate aircraft Lothrus apterus 4D flight path;
Air traffic control radar monitoring data and automatic dependent surveillance data merge by step B, monitoring data Fusion Module, generate the real-time flight path information of aircraft and are supplied to control terminal module; The flight path of short-term 4D in-flight generation module in control terminal module infers the aircraft 4D track in following certain hour window according to aircraft real-time flight path information and history flight path information; Described foundation aircraft real-time flight path information and history flight path information infer that the specific implementation process of the aircraft 4D track in following certain hour window is as follows:
Step B6, to the pre-service of aircraft track data, according to the aircraft original discrete two-dimensional position sequence x=[x that obtains
1, x
2..., x
n] and y=[y
1, y
2..., y
n], adopt first order difference method to carry out processing new aircraft discrete location sequence △ x=[the △ x of acquisition to it
1, △ x
2..., △ x
n-1] and △ y=[△ y
1, △ y
2..., △ y
n-1], wherein △ x
b=x
b+1-x
b, △ y
b=y
b+1-y
b(b=1,2 ..., n-1);
Step B7, to aircraft track data cluster, to new aircraft discrete two-dimensional position sequence △ x and △ y after process, by setting cluster number M', K-means clustering algorithm is adopted to carry out cluster to it respectively;
Step B8, Hidden Markov Model (HMM) is utilized to carry out parameter training to the aircraft track data after cluster, by the aircraft running orbit data △ x after process and △ y being considered as the aobvious observed reading of hidden Markov models, upgrade period ζ ' by setting hidden state number N' and parameter, according to nearest T' position detection value and adopt B-W algorithm roll the up-to-date Hidden Markov Model (HMM) parameter lambda of acquisition ';
Step B9, foundation Hidden Markov Model (HMM) parameter, adopt the hidden state q corresponding to Viterbi algorithm acquisition current time observed reading;
Step B10, by setting prediction time domain h', based on the hidden state q of aircraft current time, obtain the position prediction value O of future time period aircraft.
Further, 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 upgrades period ζ ', and T' is 10, and prediction time domain h' is 300 seconds.
Further, the B8 of step B specifically refers to: because obtained flight path sequence data length is dynamic change, in order to the state of real-time follow-up aircraft flight path changes, be necessary initial flight path Hidden Markov Model (HMM) parameter lambda '=(π, A, B) basis is readjusted it, to infer the position of aircraft in certain moment following more accurately; Every period ζ ', according to T' observed reading (o of up-to-date acquisition
1, o
2..., o
t') to flight path Hidden Markov Model (HMM) parameter lambda '=(π, A, B) reappraise;
The B10 of step B specifically refers to: every the period
according to the Hidden Markov Model (HMM) parameter lambda of up-to-date acquisition '=(π, A, B) and nearest H conception of history measured value (o
1, o
2..., o
h), based on the hidden state q of aircraft current time, by setting prediction time domain h', obtain the position prediction value O of aircraft at future time period h' at moment t.
Further, the period
it is 4 seconds.
Further, the aircraft Lothrus apterus 4D flight path of described steps A generates in accordance with the following methods:
Steps A 1, carry out aircraft states transfer modeling, according to the flying height section of aircraft in flight planning, set up the Petri network model that single aircraft shifts in different leg: E=(g, G, Pre, Post, m) be aircraft stage metastasis model, wherein g represents flight leg, and G represents the transfer point of flight status parameter in vertical section, Pre and Post represents that the front and back of leg and way point are to annexation respectively
represent the mission phase residing for aircraft;
Steps A 2, to set up aircraft full flight profile, mission profile hybrid model as follows,
v
H＝κ(v
CAS,Mach,h
p,t
LOC)，
v
GS＝μ(v
CAS,Mach,h
p,t
LOC,v
WS,α)，
Wherein v
cASfor calibrated airspeed, Mach is Mach number, h
pfor barometer altitude, α is the angle in wind direction forecast and air route, v
wSfor wind speed predicted value, t
lOCfor temperature forecast value, v
hfor altitude rate, v
gSfor ground velocity;
Steps A 3, the mode of hybrid system emulation is adopted to infer and solve flight path: adopt the method for time subdivision, utilization state continually varying characteristic Recursive Solution any time aircraft is in the voyage of a certain mission phase apart from reference point
And height
Wherein J
0for initial time aircraft is apart from the voyage of reference point, △ τ is the numerical value of time window, and J (τ) is the voyage of τ moment aircraft distance reference point, h
0for initial time aircraft is apart from the height of reference point, h (τ), for τ moment aircraft is apart from the height of reference point, can infer the 4D flight path obtaining single aircraft thus;
Steps A 4, to many aircrafts coupling model implement Lothrus apterus allotment: the time reaching point of crossing according to two aircrafts in advance, according to air traffic control principle, quadratic programming is carried out to the aircraft 4D flight path not meeting space requirement near point of crossing, obtains Lothrus apterus 4D flight path.
Further, in described step B, air traffic control radar monitoring data and automatic dependent surveillance data merge by monitoring data Fusion Module, generate the real-time flight path information of aircraft, specifically in accordance with the following methods:
Step B1, by coordinate unit and time unification;
Step B2, adopt most proximity data association algorithm to be associated by the point belonging to same target, extract targetpath; Step B3, the track data that will extract from automatic dependent surveillance system and air traffic control radar are respectively joined from different space-time
Examine coordinate system conversion, be registered to the unified space-time reference coordinate system of control terminal;
The related coefficient of step B4, calculating two flight paths, if related coefficient is less than a certain predetermined threshold value, then thinks that two flight paths are uncorrelated; Otherwise these two flight paths are correlated with, and can merge;
Step B5, relevant flight path to be merged.
Further, in described step B5, relevant flight path is merged, adopt the Weighted Average Algorithm based on the sampling period, its weighting coefficient was determined according to sampling period and precision of information, and associated automatic dependent surveillance flight path and air traffic control radar Track Fusion are system flight path by recycling Weighted Average Algorithm.
The present invention has positive effect: the aircraft trajectory predictions method of (1) air traffic control system of the present invention is in aircraft real-time track supposition process, incorporate the impact of enchancement factor, the rolling track adopted infers that scheme can extract the changing condition of extraneous enchancement factor in time, improves the accuracy that aircraft track is inferred.
(2) air traffic control system of the present invention aircraft trajectory predictions method to the reckoning of flight profile, mission profile and Trajectory Prediction precision high, and then conflict dissolution ability and automatization level are improved, reduce the working load of controller.
Accompanying drawing explanation
Fig. 1 is Lothrus apterus 4D flight path generation method flow schematic diagram before flight;
Fig. 2 is short-term 4D flying track conjecture method flow schematic diagram in-flight.
Embodiment
(embodiment 1)
The air traffic control system run based on 4D flight path of the present embodiment, comprises Airborne Terminal module 101, data communication module 102, monitoring data Fusion Module 103 and control terminal module 104.Below the embodiment of each several part is described in detail respectively.
1. Airborne Terminal module
Airborne Terminal module 101 is that pilot obtains ground control order, reference 4D flight path, and the interface of input flight intent, 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: aircraft position vector, velocity vector that (1) ADS-B information acquisition unit 201 is gathered by Airborne GPS, and the catchword of this aircraft, pass to airborne data communication module 102 by information and data after coding; (2) aircraft driver needs the flight intent inconsistent with ground control order, and by man-machine inputting interface, and the form that the ground controller of agreement can identify passes to airborne data communication module 102 by information and data.Airborne Terminal module 101 realizes following information output in addition: (1), by terminal display, receives and show the air traffic control instruction that pilot can identify; (2) the Lothrus apterus 4D flight path with the explicitly front generation of facial canal terminal flight is received, and when the optimum of calculating 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, realizes downlink transfer and the ground control command unit 203 of airborne real time position data and flight intent data cell 202, and the uplink of reference 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 positional information, and other additional datas, if the information transmission such as flight intent, flying speed, meteorology are to ground secondary radar (SSR), secondary radar is resolved data message after receiving, and be transferred to central data processing components 301 and decode, be transferred to control terminal 104 by instruction track data interface; Upstream data communication: ground control terminal 104 by instruction track data interface, after central data processing components 301 is encoded, the inquisitor of ground secondary radar just ground control order or be presented at Airborne Terminal 101 with reference to 4D flight path information transmission.
3. monitoring data Fusion Module
Monitoring data Fusion Module 103 realizes air traffic control radar and monitors the fusion with automatic dependent surveillance ADS-B data, provides real-time flight path information for the flight path of short-term 4D in-flight in control terminal module 104 generates submodule and real-time flight conflict monitoring and alarm submodule.
Its specific embodiments is as follows:
(1) at pretreatment stage by coordinate unit and time unification, suppose that the data extracted from ADS-B and air traffic control radar are respectively the coordinate (as longitude, latitude, sea level elevation) of series of discrete point, the corresponding acquisition time of each point; (2) adopt most proximity data association algorithm to be associated by the point belonging to same target, extract targetpath; (3) track data extracted from ADS-B and air traffic control radar is respectively converted from different space-time reference coordinate system, is registered to the unified space-time reference coordinate system of control terminal; (4) calculate the related coefficient of two flight paths, if related coefficient is less than a certain predetermined threshold value, then think that two flight paths are uncorrelated, otherwise these two flight paths are correlated with, and can merge; (5) relevant flight path is merged.Because ADS-B is different with the sampling period with the precision of air traffic control radar, native system adopts the Weighted Average Algorithm based on the sampling period, its weighting coefficient was determined according to sampling period and precision of information, and associated ADS-B flight path and air traffic control radar Track Fusion are system flight path by recycling Weighted Average Algorithm.
4. control terminal module
Before control terminal module 104 comprises flight, the generation of Lothrus apterus 4D flight path, in-flight short-term 4D flight path generate this 2 submodules.
(1) before flight, Lothrus apterus 4D flight path generates
The wind that the flight planning obtained according to Flight Data Processing System (FDP) and world area forecast system (WAFS) are issued, the GRIB lattice point forecast data of temperature, Air Traffic System is set up to the hybrid model of stratification, by the evolution of system at safe condition, describe the time locus of state evolution, generate aircraft flight path.
As shown in Figure 1, its specific implementation process is as follows:
First, aircraft states transfer modeling is carried out.Aircraft shows as switching at runtime process between leg along the process of track flight, according to the flying height section of aircraft in flight planning, set up the Petri network model that single aircraft shifts in different leg: E=(g, G, Pre, Post, m) be aircraft stage metastasis model, wherein g represents flight leg, G represent flight status parameter in vertical section (comprise air speed, highly, configuration) transfer point, Pre and Post represents that the front and back of leg and way point are to annexation respectively
represent the mission phase residing for aircraft.
Secondly, the full flight profile, mission profile hybrid model of aircraft is set up.The flight of aircraft in single leg is considered as continuous process, according to particle energy model, derivation aircraft in the different operation phase with the aircraft kinetics equation under meteorological condition, v
h=κ (v
cAS, Mach, h
p, t
lOC), v
gS=μ (v
cAS, Mach, h
p, t
lOC, v
wS, α), wherein v
cASfor calibrated airspeed, Mach is Mach number, h
pfor barometer altitude, α is the angle in wind direction forecast and air route, v
wSfor wind speed predicted value, t
lOCfor temperature forecast value, v
hfor altitude rate, v
gSfor ground velocity.
Then, adopt the mode of hybrid system emulation to infer and solve flight path.Adopt the method for time subdivision, utilization state continually varying characteristic Recursive Solution any time aircraft is in the voyage of a certain mission phase apart from reference point
And height
Wherein J
0for initial time aircraft is apart from the voyage of reference point, △ τ is the numerical value of time window, and J (τ) is the voyage of τ moment aircraft distance reference point, h
0for initial time aircraft is apart from the height of reference point, h (τ), for τ moment aircraft is apart from the height of reference point, can infer the 4D flight path obtaining single aircraft thus.
Finally, Lothrus apterus allotment is implemented to many aircrafts coupling model.Reach the time of point of crossing according to two aircrafts in advance, according to air traffic control principle, quadratic programming is carried out to the aircraft 4D flight path not meeting space requirement near point of crossing, obtains Lothrus apterus 4D flight path.
(2) short-term 4D flight path generates in-flight
Implement to merge the real-time track data of rear acquisition aircraft according to control radar and automatic dependent surveillance system ADS-B, utilize Hidden Markov Model (HMM), infer the aircraft 4D track in following 5 minutes windows.
As shown in Figure 2, its specific implementation process is as follows:
First, to the pre-service of aircraft track data, according to the aircraft original discrete two-dimensional position sequence x=[x obtained
1, x
2..., x
n] and y=[y
1, y
2..., y
n], adopt first order difference method to carry out processing new aircraft discrete location sequence △ x=[the △ x of acquisition to it
1, △ x
2..., △ x
n-1] and △ y=[△ y
1, △ y
2..., △ y
n-1], wherein △ x
b=x
b+1-x
b, △ y
b=y
b+1-y
b(b=1,2 ..., n-1).
Secondly, to aircraft track data cluster.To new aircraft discrete two-dimensional position sequence △ x and △ y after process, by setting cluster number M', K-means clustering algorithm is adopted to carry out cluster to it respectively.
Then, Hidden Markov Model (HMM) is utilized to carry out parameter training to the aircraft track data after cluster.By the aircraft running orbit data △ x after process and △ y being considered as the aobvious observed reading of hidden Markov models, by setting hidden state number N' and parameter renewal period ζ ', according to nearest T' position detection value and adopt B-W algorithm roll obtain up-to-date Hidden Markov Model (HMM) parameter lambda ': because obtained flight path sequence data length is dynamic change, in order to the state of real-time follow-up aircraft flight path changes, be necessary initial flight path Hidden Markov Model (HMM) parameter lambda '=(π, A, B) basis is readjusted it, to infer the position of aircraft in certain moment following more accurately.Every period ζ ', according to T' observed reading (o of up-to-date acquisition
1, o
2..., o
t') to flight path Hidden Markov Model (HMM) parameter lambda '=(π, A, B) reappraise.
Again and, according to Hidden Markov Model (HMM) parameter, adopt Viterbi algorithm to obtain hidden state q corresponding to current time observed reading.
Finally, every the period
according to the Hidden Markov Model (HMM) parameter lambda of up-to-date acquisition '=(π, A, B) and nearest H conception of history measured value (o
1, o
2..., o
h), based on the hidden state q of aircraft current time, by setting prediction time domain h', obtain the position prediction value O of aircraft at future time period h' at moment t.
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 upgrades period ζ ', and T' is 10, and prediction time domain h' is 300 seconds, the period
it is 4 seconds.
Obviously, above-described embodiment is only for example of the present invention is clearly described, and is not the restriction to embodiments of the present invention.For those of ordinary skill in the field, can also make other changes in different forms on the basis of the above description.Here exhaustive without the need to also giving all embodiments.And these belong to spirit institute's apparent change of extending out of the present invention or change and are still among protection scope of the present invention.
Claims (7)
1. an aircraft trajectory predictions method for air traffic control system, described air traffic control system comprises Airborne Terminal module, data communication module, monitoring data Fusion Module and control terminal module; Monitoring data Fusion Module for realizing the fusion of air traffic control radar monitoring data and automatic dependent surveillance data, for control terminal module provides real-time flight path information; It is characterized in that:
Described control terminal module comprises following submodule:
Lothrus apterus 4D flight path generation module before flight, according to the forecast data of flight planning and world area forecast system, set up aircraft kinetic model, then set up flight path conflict according to flight collision Coupling point and allocate theoretical model in advance, generate aircraft Lothrus apterus 4D flight path;
Short-term 4D flight path generation module in-flight, the real-time flight path information provided according to monitoring data Fusion Module, utilizes Hidden Markov Model (HMM), infers the aircraft 4D track in following certain hour window;
The aircraft trajectory predictions method of described air traffic control system comprises following several step:
Before steps A, flight, Lothrus apterus 4D flight path generation module is according to the forecast data of flight planning and world area forecast system, set up aircraft kinetic model, and set up flight path conflict according to flight collision Coupling point and allocate theoretical model in advance, generate aircraft Lothrus apterus 4D flight path;
Air traffic control radar monitoring data and automatic dependent surveillance data merge by step B, monitoring data Fusion Module, generate the real-time flight path information of aircraft and are supplied to control terminal module; The flight path of short-term 4D in-flight generation module in control terminal module infers the aircraft 4D track in following certain hour window according to aircraft real-time flight path information and history flight path information; Described foundation aircraft real-time flight path information and history flight path information infer that the specific implementation process of the aircraft 4D track in following certain hour window is as follows:
Step B6, to the pre-service of aircraft track data, according to the aircraft original discrete two-dimensional position sequence x=[x that obtains
1, x
2..., x
n] and y=[y
1, y
2..., y
n], adopt first order difference method to carry out processing new aircraft discrete location sequence △ x=[the △ x of acquisition to it
1, △ x
2..., △ x
n-1] and △ y=[△ y
1, △ y
2..., △ y
n-1], wherein △ x
b=x
b+1-x
b, △ y
b=y
b+1-y
b(b=1,2 ..., n-1);
Step B7, to aircraft track data cluster, to new aircraft discrete two-dimensional position sequence △ x and △ y after process, by setting cluster number M', K-means clustering algorithm is adopted to carry out cluster to it respectively;
Step B8, Hidden Markov Model (HMM) is utilized to carry out parameter training to the aircraft track data after cluster, by the aircraft running orbit data △ x after process and △ y being considered as the aobvious observed reading of hidden Markov models, upgrade period ζ ' by setting hidden state number N' and parameter, according to nearest T' position detection value and adopt B-W algorithm roll the up-to-date Hidden Markov Model (HMM) parameter lambda of acquisition ';
Step B9, foundation Hidden Markov Model (HMM) parameter, adopt the hidden state q corresponding to Viterbi algorithm acquisition current time observed reading;
Step B10, by setting prediction time domain h', based on the hidden state q of aircraft current time, obtain the position prediction value O of future time period aircraft.
2. the aircraft trajectory predictions method of air traffic control system according to claim 1, is characterized in that: in step B, and the value of described cluster number M' is 4, the value of hidden state number N' is 3, it is 30 seconds that parameter upgrades period ζ ', and T' is 10, and prediction time domain h' is 300 seconds.
3. the aircraft trajectory predictions method of air traffic control system according to claim 1 and 2, it is characterized in that: the B8 of step B specifically refers to: because obtained flight path sequence data length is dynamic change, in order to the state of real-time follow-up aircraft flight path changes, be necessary initial flight path Hidden Markov Model (HMM) parameter lambda '=(π, A, B) basis is readjusted it, to infer the position of aircraft in certain moment following more accurately; Every period ζ ', according to T' observed reading (o of up-to-date acquisition
1, o
2..., o
t') to flight path Hidden Markov Model (HMM) parameter lambda '=(π, A, B) reappraise;
The B10 of step B specifically refers to: every the period
, according to the Hidden Markov Model (HMM) parameter lambda of up-to-date acquisition '=(π, A, B) and nearest H conception of history measured value (o
1, o
2..., o
h), based on the hidden state q of aircraft current time, by setting prediction time domain h', obtain the position prediction value O of aircraft at future time period h' at moment t.
4., according to the aircraft trajectory predictions method of the air traffic control system one of claims 1 to 3 Suo Shu, it is characterized in that: the period
it is 4 seconds.
5. according to the aircraft trajectory predictions method of the air traffic control system one of Claims 1-4 Suo Shu, it is characterized in that: the aircraft Lothrus apterus 4D flight path of described steps A generates in accordance with the following methods:
Steps A 1, carry out aircraft states transfer modeling, according to the flying height section of aircraft in flight planning, set up the Petri network model that single aircraft shifts in different leg: E=(g, G, Pre, Post, m) be aircraft stage metastasis model, wherein g represents flight leg, and G represents the transfer point of flight status parameter in vertical section, Pre and Post represents that the front and back of leg and way point are to annexation respectively
represent the mission phase residing for aircraft;
Steps A 2, to set up aircraft full flight profile, mission profile hybrid model as follows,
v
H＝κ(v
CAS,Mach,h
p,t
LOC)，
v
GS＝μ(v
CAS,Mach,h
p,t
LOC,v
WS,α)，
Wherein v
cASfor calibrated airspeed, Mach is Mach number, h
pfor barometer altitude, α is the angle in wind direction forecast and air route, v
wSfor wind speed predicted value, t
lOCfor temperature forecast value, v
hfor altitude rate, v
gSfor ground velocity;
Steps A 3, the mode of hybrid system emulation is adopted to infer and solve flight path: adopt the method for time subdivision, utilization state continually varying characteristic Recursive Solution any time aircraft is in the voyage of a certain mission phase apart from reference point
And height
Wherein J
0for initial time aircraft is apart from the voyage of reference point, △ τ is the numerical value of time window, and J (τ) is the voyage of τ moment aircraft distance reference point, h
0for initial time aircraft is apart from the height of reference point, h (τ), for τ moment aircraft is apart from the height of reference point, can infer the 4D flight path obtaining single aircraft thus;
Steps A 4, to many aircrafts coupling model implement Lothrus apterus allotment: the time reaching point of crossing according to two aircrafts in advance, according to air traffic control principle, quadratic programming is carried out to the aircraft 4D flight path not meeting space requirement near point of crossing, obtains Lothrus apterus 4D flight path.
6. according to the aircraft trajectory predictions method of the air traffic control system one of claim 1 to 5 Suo Shu, it is characterized in that: in described step B, air traffic control radar monitoring data and automatic dependent surveillance data merge by monitoring data Fusion Module, generate the real-time flight path information of aircraft, specifically in accordance with the following methods:
Step B1, by coordinate unit and time unification;
Step B2, adopt most proximity data association algorithm to be associated by the point belonging to same target, extract targetpath; Step B3, the track data that will extract from automatic dependent surveillance system and air traffic control radar respectively from the conversion of different space-time reference coordinate system, be registered to the unified space-time reference coordinate system of control terminal;
The related coefficient of step B4, calculating two flight paths, if related coefficient is less than a certain predetermined threshold value, then thinks that two flight paths are uncorrelated; Otherwise these two flight paths are correlated with, and can merge;
Step B5, relevant flight path to be merged.
7. according to the aircraft trajectory predictions method of the air traffic control system one of claim 1 to 6 Suo Shu, it is characterized in that: in described step B5, relevant flight path is merged, adopt the Weighted Average Algorithm based on the sampling period, its weighting coefficient was determined according to sampling period and precision of information, and associated automatic dependent surveillance flight path and air traffic control radar Track Fusion are system flight path by recycling Weighted Average Algorithm.
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510007935.7A CN104504937B (en) | 2015-01-07 | 2015-01-07 | The airborne vehicle trajectory predictions method of air traffic control system |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN112017480A (en) * | 2020-08-20 | 2020-12-01 | 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 |
| CN114842678A (en) * | 2022-03-28 | 2022-08-02 | 中国民用航空中南地区空中交通管理局广西分局 | Similar daily measurement system for civil aviation control operation site |
| 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 |
Families Citing this family (7)
| 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 |
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Citations (5)
| 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 |
| EP1995706A2 (en) * | 2007-05-15 | 2008-11-26 | 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)
| 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 |
-
2015
- 2015-01-07 CN CN201610871468.7A patent/CN106251709B/en active Active
- 2015-01-07 CN CN201610871406.6A patent/CN106205221A/en active Pending
- 2015-01-07 CN CN201510007935.7A patent/CN104504937B/en active Active
- 2015-01-07 CN CN201610871349.1A patent/CN106409013A/en active Pending
Patent Citations (5)
| 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 |
| EP1995706A2 (en) * | 2007-05-15 | 2008-11-26 | 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)
| Title |
|---|
| 韩云祥: "飞行冲突解决机制研究", 《桂林航天工业高等专科学校校报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN112017480A (en) * | 2020-08-20 | 2020-12-01 | 南京航空航天大学 | Dynamic memory planning method for green cruise track of aircraft |
| 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 |
| CN114842678A (en) * | 2022-03-28 | 2022-08-02 | 中国民用航空中南地区空中交通管理局广西分局 | Similar daily measurement system for civil aviation control operation site |
| 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 |
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| CN106251709A (en) | 2016-12-21 |
| CN106409013A (en) | 2017-02-15 |
| CN104504937B (en) | 2017-01-04 |
| CN106205221A (en) | 2016-12-07 |
| CN106251709B (en) | 2018-11-23 |
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