CN106251709B - aircraft trajectory prediction method - Google Patents

Abstract

The invention relates to an aircraft trajectory prediction method implemented by an air traffic control system. The air traffic control system includes 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 to realize air traffic control. The fusion of radar surveillance data and automatic dependent surveillance data provides real-time track information for the control terminal module; the control terminal module includes two sub-modules: generation of conflict-free 4D track before flight and short-term 4D track generation during flight; the above-mentioned system The aircraft trajectory prediction method relies on the control terminal module to process the flight plan data and use the hidden Markov model to generate 4D trajectory, so as to realize the analysis of potential traffic conflicts in airspace traffic conditions. The invention can effectively improve the safety of air traffic.

Description

Aircraft Trajectory Prediction Method

This application is a divisional application of the invention patent application with the application number: 201510007935.7, the invention name is "Aircraft Trajectory Prediction Method for Air Traffic Control System", and the application date is: January 7, 2015.

technical field

The invention relates to an air traffic control system and method, in particular to a method for predicting aircraft trajectories by an air traffic control system based on 4D track operation.

Background technique

With the rapid development of the global air transport industry and the increasingly prominent contradiction between the limited airspace resources, in the complex airspace with dense air traffic flow, the air traffic management method that still adopts flight planning combined with interval allocation gradually shows its backwardness, as shown in: ( 1) The flight plan does not configure accurate air traffic control intervals for aircraft, which may easily cause congestion in the tactical management of traffic flow and reduce airspace security; (3) Air traffic control still focuses on maintaining a safe separation between individual aircraft, and it is difficult to upgrade to strategic management of traffic flow. The prediction of aircraft trajectory is particularly important.

4D track is a precise description of the spatial position (longitude, latitude and altitude) and time of each point in an aircraft track in the form of space and time. Track-based operation refers to the waypoints on the 4D track. Use "Controlled Time of Arrival", which controls the "window of time" in which the aircraft passes through a particular waypoint. Taking 4D trajectory-based operation (Trajectory based Operation) as one of the basic operating mechanisms in high-density airspace is an effective means to manage airspace under the conditions of large flow, high density and small separation in the future, which can significantly reduce the number of aircraft Uncertainty of flight path improves the safety and utilization of airspace and airport resources.

The air traffic operation mode based on trajectory operation needs to calculate and optimize the flight path of a single aircraft at the strategic level, coordinate and adjust the traffic flow composed of multiple aircraft; trajectory to solve the congestion problem and ensure the operational efficiency of all aircraft in the traffic flow; while predicting conflicts and optimizing relief solutions at the tactical level are very dependent on the ability to accurately predict aircraft trajectories, which are currently not accurate Predicting the trajectory of the aircraft in real time is particularly poor in real time.

Contents of the invention

The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide an aircraft track prediction method based on 4D track operation air traffic control system, which can effectively, accurately and real-time predict the track of the aircraft.

The technical solution for realizing the purpose of the present invention is to provide a kind of aircraft trajectory prediction method to be implemented by the air traffic control system, and the air traffic control system includes an airborne terminal module, a data communication module, a monitoring data fusion module and a control terminal module; the monitoring data fusion The module is used to realize the fusion of air traffic control radar surveillance data and automatic dependent surveillance data, and provide real-time track information for the control terminal module;

The control terminal module includes the following submodules:

Conflict-free 4D trajectory generation module before flight, based on the flight plan and the forecast data of the world area forecast system, establishes the aircraft dynamics model, and then establishes the theoretical model of trajectory conflict pre-allocation according to the flight conflict coupling point, and generates the aircraft conflict-free 4D trajectory ;

The mid- and short-term 4D trajectory generation module uses the hidden Markov model to predict the 4D trajectory of the aircraft within a certain time window in the future based on the real-time trajectory information provided by the monitoring data fusion module;

The aircraft trajectory prediction method of the air traffic control system comprises the following steps:

Step A, pre-flight conflict-free 4D track generation module, based on the flight plan and the forecast data of the world area forecast system, establishes the aircraft dynamics model, and establishes the theoretical model of track conflict pre-allocation according to the flight conflict coupling point, and generates the aircraft conflict-free 4D track;

Step B, the monitoring data fusion module fuses the air traffic control radar monitoring data and the automatic correlation monitoring data, generates the real-time track information of the aircraft and provides it to the control terminal module; Track information and historical track information infer the aircraft 4D trajectory in a certain time window in the future; the specific implementation process of predicting the aircraft 4D trajectory in a certain time window in the future according to the real-time track information and historical track information of the aircraft is as follows:

Step B6, preprocessing the aircraft trajectory data, based on the acquired original discrete two-dimensional position sequence x=[x ₁ ,x ₂ ,...,x _n ] and y=[y ₁ ,y ₂ ,... ,y _n ], using the first-order difference method to process it to obtain a new aircraft discrete position sequence △x=[△x ₁ ,△x ₂ ,...,△x _n-1 ] and △y=[△y ₁ ,△y ₂ ,...,△y _n-1 ], where △x _b ＝x _b+1 -x _b ,△y _b ＝y _b+1 -y _b (b＝1,2,.. .,n-1);

Step B7, clustering the aircraft trajectory data, and clustering the new processed aircraft discrete two-dimensional position sequences △x and △y by setting the number of clusters M', using the K-means clustering algorithm to cluster them respectively ;

Step B8. Use the hidden Markov model to perform parameter training on the clustered aircraft trajectory data. By treating the processed aircraft trajectory data △x and △y as the obvious observations of the hidden Markov process, by setting Hidden state number N' and parameter update period ζ', based on the latest T' location observations and using the B-W algorithm to scroll to obtain the latest hidden Markov model parameter λ';

Step B9, according to the Hidden Markov Model parameters, use the Viterbi algorithm to obtain the hidden state q corresponding to the observed value at the current moment;

Step B10, by setting the prediction time domain h', based on the hidden state q of the aircraft at the current moment, obtain the predicted position value O of the aircraft in the future period.

Further, in step B, the value of the number of clusters M' is 4, the value of the number of hidden states N' is 3, the parameter update period ζ' is 30 seconds, T' is 10, and the prediction time domain h' is 300 seconds.

Further, B8 of Step B specifically refers to: Since the length of the obtained track sequence data is dynamically changing, in order to track the state change of the aircraft track in real time, it is necessary to set the initial track hidden Markov model parameter λ'= Based on (π,A,B), it is readjusted in order to more accurately predict the position of the aircraft at a certain time in the future; every period ζ', based on the latest T' observations (o ₁ ,o ₂ , ..., o _T' ) re-estimate the track hidden Markov model parameter λ'=(π, A, B);

B10 of step B specifically refers to: every time period According to the latest hidden Markov model parameters λ'=(π,A,B) and the latest H historical observations (o ₁ ,o ₂ ,...,o _H ), based on the hidden state q of the aircraft at the current moment , by setting the prediction time domain h', the predicted position value O of the aircraft in the future period h' is obtained at time t.

Furthermore, the time period for 4 seconds.

Further, the conflict-free 4D track of the aircraft in step A is generated according to the following method:

Step A1, carry out aircraft state transfer modeling, according to the flight altitude profile of the aircraft in the flight plan, establish the Petri net model that single aircraft transfers in different flight segments: E=(g, G, Pre, Post, m) is the aircraft stage transfer Model, where g represents the flight segment, G represents the transition point of the flight state parameters in the vertical section, Pre and Post represent the forward and backward connection relationship between the flight segment and the waypoint, respectively, Indicates the phase of flight the aircraft is in;

Step A2, establishing the hybrid system model of the full flight profile of the aircraft is 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),

Where v _CAS is the calibrated airspeed, Mach is the Mach number, h _p is the pressure altitude, α is the angle between the wind direction forecast and the route, v _WS is the wind speed forecast value, t _LOC is the temperature forecast value, v _H is the altitude change rate, v _GS is ground speed;

Step A3. Use the hybrid system simulation method to estimate and solve the flight path: use the method of subdividing time, and use the characteristics of continuous state changes to recursively solve the flight distance of the aircraft at a certain flight stage from the reference point at any time and height where J ₀ is the flight distance from the aircraft to the reference point at the initial moment, △τ is the value of the time window, J(τ) is the flight distance from the aircraft to the reference point at the time τ, h ₀ is the height of the aircraft from the reference point at the initial moment, h(τ ) is the altitude of the aircraft from the reference point at time τ, from which the 4D track of a single aircraft can be inferred;

Step A4, implement conflict-free deployment on the multi-aircraft coupling model: according to the time when the two aircraft arrive at the intersection, according to the air traffic control principle, carry out secondary planning on the 4D track of the aircraft that does not meet the separation requirements near the intersection, and obtain a conflict-free 4D track.

Further, in the step B, the monitoring data fusion module fuses the air traffic control radar monitoring data and the automatic correlation monitoring data to generate real-time track information of the aircraft, specifically according to the following methods:

Step B1, unify the coordinate unit and time;

Step B2, using the nearest neighbor data association algorithm to correlate the points belonging to the same target to extract the target track; Step B3, extracting the track data from the automatic dependent surveillance system and the air traffic control radar respectively

Coordinate system transformation and alignment to the unified space-time reference coordinate system of the control terminal;

Step B4, calculating the correlation coefficient of the two tracks, if the correlation coefficient is less than a certain preset threshold, the two tracks are considered irrelevant; otherwise, the two tracks are related and can be fused;

Step B5, fusing related tracks.

Furthermore, in the step B5, the relevant tracks are fused, and a weighted average algorithm based on the sampling period is adopted, and its weighting coefficient is determined according to the sampling period and information accuracy, and then the weighted average algorithm is used to correlate the relevant automatic correlation monitoring The track and the ATC radar track are fused into the system track.

The present invention has positive effects: (1) the aircraft trajectory prediction method of the air traffic control system of the present invention incorporates the influence of random factors in the aircraft real-time trajectory estimation process, and the rolling trajectory estimation scheme adopted can extract external random factors in time The change status of the aircraft improves the accuracy of the aircraft trajectory estimation.

(2) The aircraft trajectory prediction method of the air traffic control system of the present invention has high precision in calculating the flight profile and trajectory prediction, thereby improving the conflict resolution capability and automation level, and reducing the controller's workload.

Description of drawings

Fig. 1 is a schematic flow chart of a conflict-free 4D track generation method before flight;

Fig. 2 is a schematic diagram of the flow chart of the short-term 4D flight path estimation method in flight.

Detailed ways

(Example 1)

The air traffic control system based on 4D track operation in this embodiment includes an airborne terminal module, a data communication module, a monitoring data fusion module and a control terminal module. The specific implementation of each part will be described in detail below.

1. Airborne terminal module

The airborne terminal module is an interface for pilots to obtain ground control instructions, refer to 4D track, and input flight intentions, and is also an interface for collecting current aircraft position data.

Its concrete implementation scheme is as follows:

The airborne terminal module receives the following information input: (1) The aircraft position vector, velocity vector, and the call sign of the aircraft collected by the ADS-B information collection unit 201 through the airborne GPS, after encoding, pass information and data to the airborne data Communication module; (2) The pilot of the aircraft needs to transmit the flight intention inconsistent with the ground control instructions to the airborne data communication module through the man-machine input interface and the agreed form that the ground controller can recognize. In addition, the airborne terminal module realizes the following information output: (1) through the terminal display screen, receive and display the flight control instructions that the pilot can recognize; (2) receive and display the non-conflict 4D track generated by the ground control terminal before the flight, and The optimal release 4D track calculated when the ground control terminal detects the conflict.

2. Data communication module

The data communication module can realize air-ground two-way data communication, realize the downlink transmission of the airborne real-time position data and flight intention data unit and the ground control command unit, and the uplink transmission of the reference 4D track unit.

Its concrete implementation scheme is as follows:

Downlink data communication: The airborne terminal transmits the aircraft identification mark and 4D position information, as well as other additional data, such as flight intention, flight speed, weather and other information to the ground secondary radar (SSR) through the airborne secondary radar transponder. After the secondary radar receives the data message, it analyzes the data message and transmits it to the central data processing component 301 for decoding, and transmits it to the control terminal through the command track data interface; uplink data communication: the ground control terminal passes the command track data interface, and the central data processing After component 301 is coded, the interrogator of the ground secondary radar will transmit and display the ground control command or reference 4D track information on the airborne terminal.

3. Monitoring data fusion module

The monitoring data fusion module realizes the fusion of air traffic control radar monitoring and automatic dependent surveillance ADS-B data, and provides real-time track information for the short-term flight 4D track generation sub-module and the real-time flight conflict monitoring and warning sub-module in the control terminal module.

Its concrete implementation scheme is as follows:

(1) Unify the coordinate unit and time in the preprocessing stage, assuming that the data extracted from ADS-B and air traffic control radar are the coordinates of a series of discrete points (such as longitude, latitude, altitude), and the corresponding collection time of each point ; (2) Use the nearest neighbor data association algorithm to correlate the points belonging to the same target to extract the target track; (3) extract the track data from ADS-B and air traffic control radar from different space-time reference coordinates The system transforms and aligns to the unified space-time reference coordinate system of the control terminal; (4) calculates the correlation coefficient of the two tracks, if the correlation coefficient is less than a preset threshold, the two tracks are considered irrelevant, otherwise the two tracks Tracks are related and can be fused; (5) Fusion of related tracks. Since the accuracy and sampling period of ADS-B and air traffic control radar are different, this system adopts a weighted average algorithm based on the sampling period, and its weighting coefficient is determined according to the sampling period and information accuracy, and then the ADS-B The track and the ATC radar track are fused into the system track.

4. Control terminal module

The control terminal module includes two sub-modules: pre-flight non-conflict 4D trajectory generation and short-term and mid-flight 4D trajectory generation.

(1) Conflict-free 4D track generation before flight

According to the flight plan obtained by the flight data processing system (FDP) and the GRIB grid point forecast data of wind and temperature released by the world area forecast system (WAFS), a hierarchical hybrid system model is established for the air traffic system, and the system is in a safe state. Evolution, which describes the time trajectory of state evolution and generates aircraft tracks.

As shown in Figure 1, the specific implementation process is as follows:

Firstly, model the aircraft state transition. The process of aircraft flying along the track is a dynamic switching process between flight segments. According to the flight altitude profile of the aircraft in the flight plan, a Petri net model for the transfer of a single aircraft in different flight segments is established: E=(g,G,Pre, Post, m) is the aircraft stage transfer model, where g represents the flight segment, G represents the transition point of the flight state parameters (including airspeed, altitude, configuration) in the vertical profile, Pre and Post represent the flight segment and waypoint respectively forward and backward connections, Indicates the phase of flight the aircraft is in.

Secondly, a hybrid system model of the aircraft's full flight profile is established. The flight of an aircraft in a single flight segment is regarded as a continuous process. According to the particle energy model, the aircraft dynamics equation of the aircraft in different operating stages and under the same meteorological conditions is deduced, v _H = κ(v _CAS ,Mach,h _p ,t _LOC ), v _GS ＝μ(v _CAS ,Mach,h _p ,t _LOC ,v _WS ,α), where v _CAS is the calibrated airspeed, Mach is the Mach number, h _p is the pressure altitude, α is the wind direction forecast and the route The included angle, v _WS is the wind speed forecast value, t _LOC is the temperature forecast value, v _H is the altitude change rate, and v _GS is the ground speed.

Then, the hybrid system simulation method is used to infer and solve the track. Using the method of subdividing time, using the characteristics of continuous state changes to recursively solve the flight distance of the aircraft at a certain flight stage from the reference point at any time and height where J ₀ is the flight distance from the aircraft to the reference point at the initial moment, △τ is the value of the time window, J(τ) is the flight distance from the aircraft to the reference point at the time τ, h ₀ is the height of the aircraft from the reference point at the initial moment, h(τ ) is the height of the aircraft from the reference point at time τ, from which the 4D track of a single aircraft can be inferred.

Finally, a conflict-free deployment is implemented for the multi-aircraft coupling model. According to the time when the two aircrafts arrive at the intersection and according to the air traffic control principle, the 4D trajectory of the aircraft that does not meet the separation requirement near the intersection is re-planned to obtain a non-conflicting 4D trajectory.

(2) Short-term 4D track generation during flight

According to the real-time trajectory data of the aircraft obtained after the fusion of the control radar and the automatic dependent surveillance system ADS-B, the hidden Markov model is used to predict the 4D trajectory of the aircraft in the next 5-minute time window.

As shown in Figure 2, the specific implementation process is as follows:

First, the aircraft trajectory data is preprocessed, based on the acquired original discrete two-dimensional position sequence x=[x ₁ ,x ₂ ,...,x _n ] and y=[y ₁ ,y ₂ ,..., y _n ], using the first-order difference method to process it to obtain a new aircraft discrete position sequence △x=[△x ₁ ,△x ₂ ,...,△x _n-1 ] and △y=[△y ₁ ,△y ₂ ,...,△y _n-1 ], where △x _b ＝x _b+1 -x _b , △y _b ＝y _b+1 -y _b (b=1,2,... ,n-1).

Second, cluster the aircraft trajectory data. For the new aircraft discrete two-dimensional position sequence △x and △y after processing, by setting the number of clusters M', K-means clustering algorithm is used to cluster them respectively.

Then, the hidden Markov model is used for parameter training on the clustered aircraft trajectory data. By treating the processed aircraft trajectory data △x and △y as the obvious observations of the hidden Markov process, by setting the number of hidden states N' and the parameter update period ζ', according to the latest T' position observations And use the BW algorithm to scroll to obtain the latest hidden Markov model parameter λ': Since the length of the obtained track sequence data is dynamically changing, in order to track the state changes of the aircraft track in real time, it is necessary to Based on the model parameter λ'=(π,A,B), it is readjusted in order to more accurately predict the position of the aircraft at a certain moment in the future. At intervals ζ', according to the latest T' observations (o ₁ ,o ₂ ,...,o _T' ), the parameters of the track hidden Markov model λ'=(π,A,B) are calculated Re-estimate.

Furthermore, according to the hidden Markov model parameters, the Viterbi algorithm is used to obtain the hidden state q corresponding to the observed value at the current moment.

Finally, every time period According to the latest hidden Markov model parameters λ'=(π,A,B) and the latest H historical observations (o ₁ ,o ₂ ,...,o _H ), based on the hidden state q of the aircraft at the current moment , by setting the prediction time domain h', the predicted position value O of the aircraft in the future period h' is obtained at time t.

The value of the number of clusters M' is 4, the value of the number of hidden states N' is 3, the parameter update period ζ' is 30 seconds, T' is 10, the prediction time domain h' is 300 seconds, and the period for 4 seconds.

Apparently, the above-mentioned embodiments are only examples for clearly illustrating the present invention, rather than limiting the implementation of the present invention. For those of ordinary skill in the art, on the basis of the above description, other changes or changes in different forms can also be made. It is not necessary and impossible to exhaustively list all the implementation manners here. And these obvious changes or modifications derived from the spirit of the present invention are still within the protection scope of the present invention.

Claims (2)

1. a kind of aircraft trajectory predictions method, this method are implemented by air traffic control system, the air traffic control system System includes Airborne Terminal module, data communication module, monitoring data fusion module and control terminal module；Monitor data fusion Module merges for realizing air traffic control radar monitoring data and automatic dependent surveillance data, provides real-time boat for control terminal module Mark information；It is characterized in that：

The control terminal module includes following submodule：

Lothrus apterus 4D track generation module before flying is established according to the forecast data of flight plan and world area forecast system Then aircraft kinetic model establishes the pre- allotment theoretical model of track conflict according to flight collision Coupling point, generates aircraft Lothrus apterus 4D track；

Flight middle or short term 4D track generation module utilizes hidden horse according to the real-time track information that monitoring data fusion module provides Er Kefu model, thus it is speculated that the track aircraft 4D in the following certain time window；

The aircraft trajectory predictions method of the air traffic control system comprises the following steps：

Step A, before flight Lothrus apterus 4D track generation module according to the forecast data of flight plan and world area forecast system, Aircraft kinetic model is established, and establishes the pre- allotment theoretical model of track conflict according to flight collision Coupling point, generates aviation Device Lothrus apterus 4D track；

Step B, monitoring data fusion module merges air traffic control radar monitoring data with automatic dependent surveillance data, generates boat The real-time track information of pocket is simultaneously supplied to control terminal module；Flight middle or short term 4D track generation module in control terminal module The track aircraft 4D in the following certain time window is speculated according to the real-time track information of aircraft and history track information；It is described according to The specific reality of the track aircraft 4D in the following certain time window is speculated according to the real-time track information of aircraft and history track information It is as follows to apply process：

Step B6, aircraft track data is pre-processed, according to the acquired original discrete two-dimensional position sequence x=of aircraft [x₁,x₂,...,x_n] and y=[y₁,y₂,...,y_n], it is carried out using first-order difference method processing obtain new aircraft from Dissipate position sequence △ x=[△ x₁,△x₂,...,△x_n-1] and △ y=[△ y₁,△y₂,...,△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, aircraft track data is clustered, to aircraft discrete two-dimensional position sequence △ x and △ y new after processing, is led to Setting cluster number M' is crossed, it is clustered respectively using K-means clustering algorithm；

Step B8, parameter training is carried out using Hidden Markov Model to the aircraft track data after cluster, by that will handle Aircraft running track data △ x and △ y afterwards is considered as the aobvious observation of hidden Markov models, by setting hidden state number N' and parameter update period ζ ', roll the newest hidden Ma Erke of acquisition according to T' nearest position detection value and using B-W algorithm Husband's model parameter λ '；

Step B9, it according to Hidden Markov Model parameter, is obtained using Viterbi algorithm hidden corresponding to current time observation State q；

Step B10, by setting prediction time domain h', the hidden state q based on aircraft current time, future time period aircraft is obtained Position prediction value O；

In the step B, the value of the cluster number M' is 4, and the value of hidden state number N' is 3, and it is 30 that parameter, which updates period ζ ', Second, T' 10, prediction time domain h' is 300 seconds.

2. aircraft trajectory predictions method according to claim 1, it is characterised in that：The B8 of step B is specifically referred to：Due to Track sequence data length obtained is dynamic change, for the state change of real-time tracking aircraft track, it is necessary to Initial track Hidden Markov Model parameter lambda '=(π, A, B) on the basis of it is readjusted, more accurately to speculate Aircraft is in the position at certain following moment；The T' observation (o every period ζ ', according to newest acquisition₁,o₂,...,o_T') right Track Hidden Markov Model parameter lambda '=(π, A, B) reevaluated；

The B10 of step B is specifically referred to：Every the periodAccording to the Hidden Markov Model parameter lambda of newest acquisition '=(π, A, B) With nearest H history observation (o₁,o₂,...,o_H), the hidden state q based on aircraft current time predicts time domain by setting H' obtains aircraft in the position prediction value O of future time period h' in moment t.