A method for constructing a double-tunnel air safety flight corridor model

Abstract

The application discloses a double-tunnel air safety flight corridor model construction method. The air safety flight corridor constructed by the method is composed of an outer tunnel and an inner tunnel. The outer tunnel model is constructed based on flight plans and RNP information of each flight stage, and the inner tunnel model is constructed based on real-time predicted flight track information and ANP evaluation results. The construction method of the outer tunnel model is as follows: according to flight plan information and RNP specifications of each flight stage, the outer tunnel model is determined with the flight plan track as the center and 2 times RNP as the boundary; the construction method of the inner tunnel model is as follows: according to flight data of the current time and historical times of the aircraft, the flight track of the subsequent time of the aircraft is predicted, and the inner tunnel model is determined with the predicted track as the center and the ANP evaluation result as the boundary. The double-tunnel air safety flight corridor model provided by the application can improve the judgment of the pilot on the flight state of the aircraft and effectively guarantee the flight safety.

Description

Technical Field

[0001] This invention pertains to a flight performance monitoring technology, specifically relating to a method for constructing a dual-tunnel air safety flight corridor model. Background Technology

[0002] With the increasing air traffic volume, severe aviation emissions pollution, and limited airspace resources, the International Civil Aviation Organization (ICAO) has proposed the concept of Communication Navigation Surveillance / Air Traffic Management (CNS / ATM), which requires aircraft to have accurate, repeatable, and predictable navigation performance. The Required Navigation Performance (RNP) specification uses the concept of a tunnel to quantitatively describe the specific requirements for all flight phases from takeoff, climb, cruise, descent, approach, and landing. Each flight phase has a hypothetical tunnel that defines the perimeter of the flight path for that phase. During RNP operation, the Actual Navigation Performance (ANP) of large passenger aircraft is a crucial basis for the crew to determine whether RNP requirements are met during flight. Therefore, this paper proposes a dual-tunnel air safety corridor model construction method based on RNP and ANP. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for constructing a dual-tunnel aerial safety flight corridor model, so as to improve the pilot's judgment of the flight status and ensure flight safety.

[0004] To address the aforementioned technical problems, this invention proposes a method for constructing a dual-tunnel air safety flight corridor model, wherein the air safety flight corridor constructed by this method consists of an outer tunnel and an inner tunnel.

[0005] Preferably, based on flight plan information and RNP specifications for each flight phase, an outer tunnel model is determined with the flight plan track as the center and 2 times the RNP as the boundary.

[0006] Preferably, based on the flight data of the aircraft at the current time and historical time, the flight trajectory of the aircraft at subsequent time is predicted, and an inner tunnel model is determined with the predicted trajectory as the center and the ANP evaluation result as the boundary.

[0007] Accordingly, the construction of the tunnel model for the dual-tunnel air safety flight corridor includes the following steps:

[0008] Step S1: Based on the real-time flight information of the aircraft, a trajectory prediction method based on Kalman filtering is used to predict the flight trajectory of the aircraft in real time.

[0009] Preferably, in step S1, the real-time flight trajectory prediction method based on Kalman filtering is as follows:

[0010] A constant acceleration model is adopted as the kinematic model of the aircraft. A coordinate system is established with the aircraft's takeoff point as the origin, the x-axis pointing east, the y-axis pointing north, and the z-axis perpendicular to the local horizontal plane and pointing upwards. The state parameters are position, velocity, and acceleration in the x, y, and z directions. The state vector is:

[0011] X(t)=(xyz vx vy vz ax ay az) T (1)

[0012] In the formula, (x, y, z) is the position of the aircraft, (vx, vy, vz) is the velocity of the aircraft, and (ax, ay, az) is the acceleration.

[0013] The state equation and observation equation of the real-time flight trajectory prediction system based on Kalman filtering are as follows:

[0014]

[0015]

[0016] In the formula, F(t) is the state transition matrix, G(t) is the process noise matrix, H(t) is the measurement matrix, w(t-1) is the system noise vector, and v(t) is the observation noise vector.

[0017] Considering only acceleration noise, the expression for the system noise vector w(t) is:

[0018] w(t)=(w x (t)w y (t)w z (t)) T (4)

[0019] In the formula, w x (t), w y (t), w z (t) represents the acceleration noise in the three directions.

[0020] Using the constant acceleration model, the expression for the state transition matrix F(t) is:

[0021]

[0022] In the formula, ΔT is the time interval from t-1 to t.

[0023] Using the constant acceleration model, the expression for the process noise transfer matrix G(t) is:

[0024]

[0025] Using the aircraft's output position and velocity as observations, the expression for the measurement matrix H(t) is:

[0026]

[0027] In the formula, I 3×3 It is a 3x3 identity matrix.

[0028] Step S2: For the flight trajectory predicted in Step S1, determine the uncertainty of the aircraft's position at each epoch.

[0029] Preferably, the method for calculating the aircraft position uncertainty at each epoch in step S2 is as follows:

[0030] The 3D positioning error is decomposed into horizontal and vertical directions. The uncertainty H of the horizontal position error is... PL The calculation formula is:

[0031] H PL =K H σ m (8)

[0032] In the formula, K H σ is the amplification factor for the horizontal positioning error. m Let σ be the semi-major axis of the ellipse representing the 1σ horizontal positioning error.

[0033] The formula for calculating the uncertainty of the vertical position error is:

[0034] V PL =K v σ z (9)

[0035] In the formula, K V σ is the coefficient of thermal expansion in the vertical direction. z This represents the standard deviation of the position error in the vertical z-direction.

[0036] Step S3: Construct an inner tunnel model centered on the predicted trajectory and with the uncertainty of the aircraft's position at each epoch as the boundary.

[0037] The beneficial effects of this invention are as follows: It proposes a method for constructing a dual-tunnel air safety flight corridor model, uses dual tunnels to regulate flight boundaries, and improves the pilot's judgment of the aircraft's flight status by predictive warning assisting piloting, thereby effectively ensuring flight safety. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the construction of the dual-tunnel aerial safety flight corridor model of the present invention;

[0039] Figure 2 This is a rendering of the outer tunnel of the dual-tunnel aerial safety flight corridor model designed for this invention.

[0040] Figure 3 This is a rendering of the tunnel within the model of the dual-tunnel aerial safety flight corridor designed for this invention.

[0041] Figure 4 The deviations in the x, y, and z directions of the trajectory prediction for T=1s, 5s, and 10s in this invention;

[0042] Figure 5 This is a diagram showing the distribution of horizontal error ellipses in the reference coordinate system and the transformed coordinate system. Detailed Implementation

[0043] like Figure 1 As shown, a method for constructing a dual-tunnel air safety flight corridor model is presented. The air safety flight corridor constructed by this method consists of an outer tunnel and an inner tunnel.

[0044] Specifically, the method for constructing the outer tunnel model is as follows: based on flight plan information and the RNP specifications for each flight phase, an outer tunnel model is determined, centered on the flight plan track and bounded by twice the RNP. For example... Figure 2 The image shown is a rendering of the actual external tunnel model.

[0045] Specifically, the method for constructing the inner tunnel model is as follows: based on the aircraft's current and historical flight data, the flight trajectory of the aircraft at subsequent times is predicted, and an inner tunnel model is determined with the predicted trajectory as the center and the ANP evaluation result as the boundary. For example... Figure 3 The image shown is a rendering of the actual internal tunnel model.

[0046] Specifically, the construction of the inner tunnel includes the following steps:

[0047] Step S1: Based on the real-time flight information of the aircraft, a trajectory prediction method based on Kalman filtering is used to predict the flight trajectory of the aircraft in real time.

[0048] Specifically, the real-time flight trajectory prediction method based on Kalman filtering is as follows:

[0049] A constant acceleration model is adopted as the kinematic model of the aircraft. A coordinate system is established with the aircraft's takeoff point as the origin, the x-axis pointing east, the y-axis pointing north, and the z-axis perpendicular to the local horizontal plane and pointing upwards. The state parameters are position, velocity, and acceleration in the x, y, and z directions. The state vector is:

[0050] X(t)=(xyz vx vy vz ax ay az) T (1)

[0051] In the formula, (x, y, z) is the position of the aircraft, (vx, vy, vz) is the velocity of the aircraft, and (ax, ay, az) is the acceleration.

[0052] The state equation and observation equation of the real-time flight trajectory prediction system based on Kalman filtering are as follows:

[0053]

[0054]

[0055] In the formula, F(t) is the state transition matrix, G(t) is the process noise matrix, H(t) is the measurement matrix, w(t-1) is the system noise vector, and v(t) is the observation noise vector.

[0056] Considering only acceleration noise, the expression for the system noise vector w(t) is:

[0057] w(t)=(w x (t) w y (t)w z (t)) T (4)

[0058] In the formula, w x (t), w y (t), w z (t) represents the acceleration noise in the three directions.

[0059] Using the constant acceleration model, the expression for the state transition matrix F(t) is:

[0060]

[0061] In the formula, ΔT is the time interval from t-1 to t.

[0062] Using the constant acceleration model, the expression for the process noise transfer matrix G(t) is:

[0063]

[0064] Using the aircraft's output position and velocity as observations, the expression for the measurement matrix H(t) is:

[0065]

[0066] In the formula, I 3×3 It is a 3x3 identity matrix.

[0067] like Figure 3 The figure shows the prediction trajectory error for flight trajectory prediction based on Kalman filtering, with prediction durations of 1s, 5s, and 10s.

[0068] Step S2: For the flight trajectory predicted in Step S1, determine the uncertainty of the aircraft's position at each epoch.

[0069] Specifically, the 3D positioning error is decomposed into horizontal and vertical directions. The method for determining the uncertainty of the horizontal error is as follows: the positioning error (x, y) follows a two-dimensional normal distribution in the XY horizontal plane, and its variance is...

[0070]

[0071] In the formula, P pos The estimated covariance matrix can be obtained from the Kalman filter trajectory predictor. These correspond to the variance of the horizontal x-direction position error, the variance of the horizontal y-direction position error, and the covariance between the x and y-direction position errors, respectively. The projection of the equal probability error curve of the positioning error onto the horizontal plane is elliptical, as shown below. Figure 5 As shown. Since the covariance matrix contains off-diagonal elements, its diagonal elements cannot characterize the 1σ error ellipse; therefore, the projected ellipse needs to be rotated. The major and minor semi-axes of the rotated ellipse are...

[0072]

[0073]

[0074]

[0075] In the formula, σ m and σ n Let be the major and minor semi-axes of the 1σ error ellipse, respectively. After coordinate rotation transformation, the probability density function of the horizontal position error distribution is:

[0076]

[0077] To determine the uncertainty of the horizontal error, a circular integration region is defined centered on the estimated position. The uncertainty is the radius of this circular integration region.

[0078] H PL =K H σ m (13)

[0079] In the formula, K is the magnification factor, and its approximate calculation formula is:

[0080]

[0081] f ratio =σ m / σ n (15)

[0082] The formula for calculating the uncertainty of the vertical position error is:

[0083] V PL =K v σ z (16)

[0084] In the formula, K V =5.33 is the expansion coefficient in the vertical direction, σ z This represents the standard deviation of the position error in the vertical z-direction.

[0085] Step S3: Construct an inner tunnel model centered on the predicted trajectory and with the uncertainty of the aircraft's position at each epoch as the boundary.

Claims

1. A method for constructing a dual-tunnel aerial safety flight corridor model, characterized in that, The air safety corridor constructed using this method consists of an outer tunnel and an inner tunnel; The method for constructing the external tunnel model is as follows: based on the flight plan information and the RNP specifications for each flight phase, determine the external tunnel model with the flight plan track as the center and 2 times the RNP as the boundary. The method for constructing the inner tunnel model is as follows: based on the flight data of the aircraft at the current time and historical time, the flight trajectory of the aircraft at subsequent time is predicted, and an inner tunnel model is determined with the predicted trajectory as the center and the ANP evaluation result as the boundary. The construction of the internal tunnel model includes the following steps: Step S1: Based on the real-time flight information of the aircraft, a trajectory prediction method based on Kalman filtering is used to predict the flight trajectory of the aircraft in real time. Step S2: For the flight trajectory predicted in Step S1, determine the uncertainty of the aircraft's position at each epoch. Step S3: Construct an inner tunnel model centered on the predicted trajectory and with the uncertainty of the aircraft's position at each epoch as the boundary.

2. The method for constructing a dual-tunnel aerial safe flight corridor model according to claim 1, characterized in that... In step S1, the real-time flight trajectory prediction method based on Kalman filtering is as follows: A constant acceleration model is adopted as the kinematic model of the aircraft. A coordinate system is established with the aircraft's takeoff point as the origin, the x-axis pointing east, the y-axis pointing north, and the z-axis perpendicular to the local horizontal plane and pointing upwards. The state parameters are position, velocity, and acceleration in the x, y, and z directions. The state vector is: X(t)=(xyz vx vv vz ax ay z) T (1)? In the formula, (x, y, z) is the position of the aircraft, (vx, vy, vz) is the velocity of the aircraft, and (ax, ay, az) is the acceleration. The state equation and observation equation of the real-time flight trajectory prediction system based on Kalman filtering are as follows: ； In the formula, F(t) is the state transition matrix, G(t) is the process noise matrix, H(t) is the measurement matrix, w(t-1) is the system noise vector, and v(t) is the observation noise vector; Considering only acceleration noise, the expression for the system noise vector w(t) is: w(t)=(w x (t) w y (t) w z (t)) T (4); In the formula, w x (t), w y (t), w z (t) represents the acceleration noise in three directions; Using the constant acceleration model, the expression for the state transition matrix t is: ； In the formula, ΔT is the time interval from t-1 to t; Using the constant acceleration model, the expression for the process noise transfer matrix G(t) is: ； Using the aircraft's output position and velocity as observations, the expression for the measurement matrix H(t) is: ； In the formula, I 3×3 It is a 3x3 identity matrix.

3. The method for constructing a dual-tunnel aerial safety flight corridor model according to claim 1, characterized in that... In step S2, the method for calculating the aircraft position uncertainty at each epoch is as follows: The 3D positioning error is decomposed into horizontal and vertical directions. The uncertainty of the horizontal position error, HPL, is calculated using the following formula: H PL =K H s m (8); In the formula, K H σ is the amplification factor for the horizontal positioning error. m Let be the semi-major axis of the ellipse representing the 1σ horizontal positioning error; The formula for calculating the uncertainty of the vertical position error is: V PL =K v s z (9); In the formula, K V σ is the coefficient of thermal expansion in the vertical direction. z This represents the standard deviation of the position error in the vertical z-direction.

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